From 66f63a449bfcec4768f17585e14dffe001204893 Mon Sep 17 00:00:00 2001 From: Nikita Malinin Date: Wed, 20 Nov 2024 12:22:44 +0100 Subject: [PATCH] Release v2.14.0 of NNCF to master --- .github/workflows/api_set_label.yml | 2 +- .github/workflows/build_html_doc.yml | 4 + .github/workflows/build_schema_page.yml | 5 + .github/workflows/examples.yml | 66 + .github/workflows/model_hub.yml | 2 +- .github/workflows/mypy.yml | 8 +- .github/workflows/nightly.yml | 3 +- .github/workflows/post_pr_merge.yml | 83 - .github/workflows/pre-commit-linters.yml | 5 +- .github/workflows/precommit.yml | 149 +- .github/workflows/python-publish.yml | 5 +- .../workflows/upload_coverage_for_develop.yml | 44 - .isort.cfg | 6 - .mypy.ini | 9 - .pre-commit-config.yaml | 1 - MANIFEST.in | 1 + Makefile | 8 +- README.md | 57 +- ReleaseNotes.md | 52 + codecov.yml | 94 - constraints.txt | 11 +- custom_version.py | 114 + docs/Algorithms.md | 4 +- docs/Installation.md | 3 +- docs/ModelZoo.md | 6 +- 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8 +- tests/torch/test_model_transformer.py | 2 +- tests/torch/test_models/synthetic.py | 87 + tests/torch/test_pattern_manager.py | 4 +- tests/torch/test_pytorch_patch.py | 7 +- tests/torch/test_sanity_sample.py | 12 +- tests/torch/test_sota_checkpoints.py | 10 +- tests/torch/test_telemetry.py | 2 +- tests/torch/test_tensor.py | 2 +- tests/torch2/__init__.py | 10 + tests/torch2/conftest.py | 107 + .../to_pydot_style_disable.dot | 18 + .../to_pydot_style_full.dot | 18 + .../to_pydot_style_short.dot | 18 + .../inner_functions_BatchNormModel.dot | 16 + .../inner_functions_MultiHeadAttention.dot | 72 + .../inner_functions_ReluModel.dot | 14 + .../nncf_graph/convert_to_nncf_graph.dot | 17 + .../convert_to_nncf_graph_multi_edges.dot | 7 + .../nncf_graph/model_graph_convnext_small.dot | 1421 ++ .../nncf_graph/model_graph_densenet121.dot | 2614 ++ .../model_graph_efficientnet_b0.dot | 1132 + .../nncf_graph/model_graph_inception_v3.dot | 1612 ++ .../nncf_graph/model_graph_mobilenet_v2.dot | 845 + .../model_graph_mobilenet_v3_small.dot | 754 + .../nncf_graph/model_graph_resnet18.dot | 355 + .../model_graph_resnext50_32x4d.dot | 905 + .../model_graph_shufflenet_v2_x0_5.dot | 1075 + .../nncf_graph/model_graph_squeezenet1_0.dot | 249 + .../nncf_graph/model_graph_swin_v2_b.dot | 3722 +++ .../nncf_graph/model_graph_vgg16.dot | 149 + .../graph/test_build_graph_mode.py | 263 + .../graph/test_graph_visualisation.py | 33 + tests/torch2/function_hook/helpers.py | 105 + .../nncf_graph/test_nncf_graph.py | 148 + .../function_hook/test_function_hook_mode.py | 116 + .../test_handle_inner_functions.py | 169 + .../torch2/function_hook/test_hook_storage.py | 97 + tests/torch2/function_hook/test_train.py | 82 + tests/torch2/function_hook/test_weak_map.py | 52 + tests/torch2/function_hook/test_wrapper.py | 176 + tests/torch2/pytest.ini | 5 + tests/torch2/requirements.txt | 11 + tests/torch2/utils.py | 39 + tools/README.md | 45 + tools/visualize_compression_results.py | 180 + 521 files changed, 133740 insertions(+), 58668 deletions(-) create mode 100644 .github/workflows/examples.yml delete mode 100644 .github/workflows/post_pr_merge.yml delete mode 100644 .github/workflows/upload_coverage_for_develop.yml delete mode 100644 .isort.cfg delete mode 100644 .mypy.ini delete mode 100644 codecov.yml create mode 100644 custom_version.py create mode 100644 docs/usage/post_training_compression/weights_compression/llama2_asym.png create mode 100644 docs/usage/post_training_compression/weights_compression/phi3_asym.png create mode 100644 examples/llm_compression/openvino/tiny_llama_synthetic_data/README.md create mode 100644 examples/llm_compression/openvino/tiny_llama_synthetic_data/main.py create mode 100644 examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt create mode 100644 examples/post_training_quantization/torch_fx/resnet18/README.md create mode 100644 examples/post_training_quantization/torch_fx/resnet18/main.py create mode 100644 examples/post_training_quantization/torch_fx/resnet18/requirements.txt delete mode 100644 md_dead_link_check.toml create mode 100644 nncf/common/tensor_statistics/statistics_serializer.py create mode 100644 nncf/common/tensor_statistics/statistics_validator.py create mode 100644 nncf/data/generators.py create mode 100644 nncf/experimental/torch/fx/constant_folding.py create mode 100644 nncf/experimental/torch/fx/groups.py create mode 100644 nncf/experimental/torch/fx/model_utils.py create mode 100644 nncf/experimental/torch/fx/quantization/backend_parameters.py rename {tests/shared => nncf/experimental/torch2}/__init__.py (100%) create mode 100644 nncf/experimental/torch2/function_hook/__init__.py rename {tests/shared/test_templates => nncf/experimental/torch2/function_hook/graph}/__init__.py (100%) create mode 100644 nncf/experimental/torch2/function_hook/graph/build_graph_mode.py create mode 100644 nncf/experimental/torch2/function_hook/graph/graph_utils.py create mode 100644 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tests/torch2/function_hook/test_wrapper.py create mode 100644 tests/torch2/pytest.ini create mode 100644 tests/torch2/requirements.txt create mode 100644 tests/torch2/utils.py create mode 100644 tools/visualize_compression_results.py diff --git a/.github/workflows/api_set_label.yml b/.github/workflows/api_set_label.yml index 020226cbcfe..2510012ce73 100644 --- a/.github/workflows/api_set_label.yml +++ b/.github/workflows/api_set_label.yml @@ -57,5 +57,5 @@ jobs: issue_number: ${{ steps.status.outputs.pr_number }}, owner: context.repo.owner, repo: context.repo.repo, - labels: "API" + name: "API" }) diff --git a/.github/workflows/build_html_doc.yml b/.github/workflows/build_html_doc.yml index 41d43a18b51..21400ba741c 100644 --- a/.github/workflows/build_html_doc.yml +++ b/.github/workflows/build_html_doc.yml @@ -15,6 +15,10 @@ jobs: uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 with: ref: ${{ inputs.ref }} + - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 + with: + python-version: 3.10.14 + cache: pip - name: Install NNCF and doc requirements run: | pip install . diff --git a/.github/workflows/build_schema_page.yml b/.github/workflows/build_schema_page.yml index 05045b9d5c5..1fada3af3fc 100644 --- a/.github/workflows/build_schema_page.yml +++ b/.github/workflows/build_schema_page.yml @@ -11,6 +11,11 @@ jobs: - name: Checkout uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 + - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 + with: + python-version: 3.10.14 + cache: pip + - name: Install and Build run: | pip install json-schema-for-humans diff --git a/.github/workflows/examples.yml b/.github/workflows/examples.yml new file mode 100644 index 00000000000..150ec493839 --- /dev/null +++ b/.github/workflows/examples.yml @@ -0,0 +1,66 @@ +name: Test examples +permissions: read-all + +on: + schedule: + - cron: '0 0 * * *' + workflow_dispatch: + inputs: + pull_request_number: + description: 'The pull request number' + default: '' + pytest_args: + description: 'Pytest arguments' + default: '' + +jobs: + examples-cpu: + name: Test exmaples CPU [${{ matrix.group }}/4] + runs-on: ubuntu-22.04-16-cores + strategy: + fail-fast: false + matrix: + group: [1, 2, 3, 4] + defaults: + run: + shell: bash + steps: + - uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 + with: + lfs: true + fetch-depth: 0 # Fetch full history to allow checking out any branch or PR + - name: Fetch and Checkout the Pull Request Branch + if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.pull_request_number != '' }} + run: | + git fetch origin pull/${{ github.event.inputs.pull_request_number }}/head:pr-${{ github.event.inputs.pull_request_number }} + git checkout pr-${{ github.event.inputs.pull_request_number }} + - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 + with: + python-version: 3.10.14 + cache: pip + - name: cpuinfo + run: cat /proc/cpuinfo + - name: Install NNCF and test requirements + run: | + pip install -e . + pip install -r tests/cross_fw/examples/requirements.txt + - name: Print installed modules + run: pip list + - name: Run examples test scope + run: | + python -m pytest -ras tests/cross_fw/examples \ + --junit-xml=pytest-results-${{ matrix.group }}.xml \ + --durations-path=tests/cross_fw/examples/.test_durations \ + --splitting-algorithm=least_duration \ + --splits 4 \ + --group ${{ matrix.group }} \ + ${{ github.event.inputs.pytest_args || '' }} + env: + TQDM_DISABLE: 1 + - name: Upload artifact + uses: actions/upload-artifact@v4 + if: ${{ !cancelled() }} + with: + name: pytest-results-${{ matrix.group }} + path: pytest-results-${{ matrix.group }}.xml + overwrite: True diff --git a/.github/workflows/model_hub.yml b/.github/workflows/model_hub.yml index d3ce5ecbd3a..3f78e9ad250 100644 --- a/.github/workflows/model_hub.yml +++ b/.github/workflows/model_hub.yml @@ -14,7 +14,7 @@ jobs: - uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 - name: Install NNCF and test requirements run: make install-models-hub-torch diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index af15b0236b6..88aaeaf6ce7 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -15,10 +15,12 @@ jobs: - uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 - name: Install NNCF - run: pip install -e . + run: | + pip install -e . + pip install torch -c constraints.txt - name: Install mypy run: pip install mypy==1.8.0 - name: Run mypy - run: mypy --install-types --config-file=.mypy.ini --non-interactive + run: mypy --install-types --non-interactive diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml index 068e3fdd0a3..9ecb8082e87 100644 --- a/.github/workflows/nightly.yml +++ b/.github/workflows/nightly.yml @@ -12,5 +12,4 @@ jobs: steps: - uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 - uses: AlexanderDokuchaev/md-dead-link-check@cc3ed55268899a1a6d5fd7068abbc4591eab1f74 # v0.9 - with: - config: md_dead_link_check.toml + diff --git a/.github/workflows/post_pr_merge.yml b/.github/workflows/post_pr_merge.yml deleted file mode 100644 index 09c23143f7a..00000000000 --- a/.github/workflows/post_pr_merge.yml +++ /dev/null @@ -1,83 +0,0 @@ -# With the squash-and-merge strategy that we are employing, the final commit that ends up in the develop branch -# after the PR merge has a brand-new commit SHA that is not equal to the SHA of the last commit in the PR. -# This means that the coverage reports uploaded to Codecov in the course of the (yet unmerged) PR checks will not -# be translated to the ultimate commit in the develop and Codecov will show "missing base report" errors in the UI. -# We don't want to re-run the precommits after PR merge via a separate 'on: push' action on the develop branch, so -# instead will grab the latest coverage report artifact from the just-merged PR and upload it as the report for the -# new commit on develop. Note that this will break if the PR is merged before the coverage artifact for the latest -# PR commit is generated. - -name: Post-PR merge actions -permissions: read-all - -on: - pull_request: - branches: - - develop - types: - - closed - paths-ignore: - - '**/*.md' - - 'docs/**/*' - -jobs: - upload-coverage-common: - if: github.event.pull_request.merged == true - uses: ./.github/workflows/upload_coverage_for_develop.yml - with: - merge_commit_sha: ${{ github.event.pull_request.merge_commit_sha }} - last_sha_in_pr: ${{ github.event.pull_request.head.sha }} - coverage_artifact_name_in_pr: coverage_common - coverage_flags: COMMON - secrets: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} - upload-coverage-onnx: - if: github.event.pull_request.merged == true - uses: ./.github/workflows/upload_coverage_for_develop.yml - with: - merge_commit_sha: ${{ github.event.pull_request.merge_commit_sha }} - last_sha_in_pr: ${{ github.event.pull_request.head.sha }} - coverage_artifact_name_in_pr: coverage_onnx - coverage_flags: ONNX - secrets: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} - upload-coverage-openvino: - if: github.event.pull_request.merged == true - uses: ./.github/workflows/upload_coverage_for_develop.yml - with: - merge_commit_sha: ${{ github.event.pull_request.merge_commit_sha }} - last_sha_in_pr: ${{ github.event.pull_request.head.sha }} - coverage_artifact_name_in_pr: coverage_openvino - coverage_flags: OPENVINO - secrets: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} - upload-coverage-torch-cpu: - if: github.event.pull_request.merged == true - uses: ./.github/workflows/upload_coverage_for_develop.yml - with: - merge_commit_sha: ${{ github.event.pull_request.merge_commit_sha }} - last_sha_in_pr: ${{ github.event.pull_request.head.sha }} - coverage_artifact_name_in_pr: coverage_pt_cpu - coverage_flags: TORCH - secrets: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} - upload-coverage-torch-cuda: - if: github.event.pull_request.merged == true - uses: ./.github/workflows/upload_coverage_for_develop.yml - with: - merge_commit_sha: ${{ github.event.pull_request.merge_commit_sha }} - last_sha_in_pr: ${{ github.event.pull_request.head.sha }} - coverage_artifact_name_in_pr: coverage_pt_cuda - coverage_flags: TORCH - secrets: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} - upload-coverage-tensorflow: - if: github.event.pull_request.merged == true - uses: ./.github/workflows/upload_coverage_for_develop.yml - with: - merge_commit_sha: ${{ github.event.pull_request.merge_commit_sha }} - last_sha_in_pr: ${{ github.event.pull_request.head.sha }} - coverage_artifact_name_in_pr: coverage_tensorflow - coverage_flags: TENSORFLOW - secrets: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/pre-commit-linters.yml b/.github/workflows/pre-commit-linters.yml index d24b1592d41..0b1d530f08e 100644 --- a/.github/workflows/pre-commit-linters.yml +++ b/.github/workflows/pre-commit-linters.yml @@ -15,7 +15,7 @@ jobs: - uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 - name: Install pre-commit package run: make install-pre-commit - name: Run pre-commit linter suite @@ -25,5 +25,4 @@ jobs: steps: - uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 - uses: AlexanderDokuchaev/md-dead-link-check@cc3ed55268899a1a6d5fd7068abbc4591eab1f74 # v0.9 - with: - config: md_dead_link_check.toml + diff --git a/.github/workflows/precommit.yml b/.github/workflows/precommit.yml index 0af09cbeae7..6dd6bff293d 100644 --- a/.github/workflows/precommit.yml +++ b/.github/workflows/precommit.yml @@ -10,7 +10,10 @@ on: paths-ignore: - '**/*.md' - 'docs/**/*' - + - 'tests/post_training/*' # post_training tests runs on Jenkins + - 'tests/torch/sota_checkpoints_eval.json' # reference for PT e2e + - 'tests/tensorflow/sota_checkpoints_eval.json' # reference for TF e2e + - 'tests/cross_fw/examples/*' # examples tests runs in separate workflow jobs: common: runs-on: ubuntu-20.04 @@ -20,26 +23,17 @@ jobs: lfs: true - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 cache: pip - name: Install NNCF and test requirements run: make install-common-test + - name: Print installed modules + run: pip list - name: Run common precommit test scope run: make test-common env: - NNCF_COVERAGE: 1 NUM_WORKERS: 2 - - name: Upload coverage report as artifact - uses: actions/upload-artifact@65462800fd760344b1a7b4382951275a0abb4808 # v4.3.3 - with: - name: coverage_common - path: ./coverage.xml - - name: Upload coverage report to codecov - uses: codecov/codecov-action@125fc84a9a348dbcf27191600683ec096ec9021c # v4.4.1 - with: - token: ${{ secrets.CODECOV_TOKEN }} - name: coverage_common - flags: COMMON + onnx: runs-on: ubuntu-20.04-8-cores steps: @@ -48,25 +42,16 @@ jobs: lfs: true - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 - name: Install NNCF and test requirements run: make install-onnx-test + - name: Print installed modules + run: pip list - name: Run ONNX precommit test scope run: make test-onnx env: - NNCF_COVERAGE: 1 NUM_WORKERS: 4 - - name: Upload coverage report as artifact - uses: actions/upload-artifact@65462800fd760344b1a7b4382951275a0abb4808 # v4.3.3 - with: - name: coverage_onnx - path: ./coverage.xml - - name: Upload coverage report to codecov - uses: codecov/codecov-action@125fc84a9a348dbcf27191600683ec096ec9021c # v4.4.1 - with: - token: ${{ secrets.CODECOV_TOKEN }} - name: coverage_onnx - flags: ONNX + openvino: runs-on: ubuntu-20.04-8-cores steps: @@ -75,26 +60,17 @@ jobs: lfs: true - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 cache: pip - name: Install NNCF and test requirements run: make install-openvino-test + - name: Print installed modules + run: pip list - name: Run OV precommit test scope run: make test-openvino env: - NNCF_COVERAGE: 1 NUM_WORKERS: 4 - - name: Upload coverage report as artifact - uses: actions/upload-artifact@65462800fd760344b1a7b4382951275a0abb4808 # v4.3.3 - with: - name: coverage_openvino - path: ./coverage.xml - - name: Upload coverage report to codecov - uses: codecov/codecov-action@125fc84a9a348dbcf27191600683ec096ec9021c # v4.4.1 - with: - token: ${{ secrets.CODECOV_TOKEN }} - name: coverage_openvino - flags: OPENVINO + pytorch-cpu: timeout-minutes: 40 defaults: @@ -113,7 +89,7 @@ jobs: lfs: true - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 cache: pip - name: Runner info continue-on-error: true @@ -122,23 +98,14 @@ jobs: cat /proc/cpuinfo - name: Install NNCF and test requirements run: make install-torch-test + - name: Print installed modules + run: pip list - name: Run PyTorch precommit test scope run: | make test-torch-cpu env: - NNCF_COVERAGE: 1 NUM_WORKERS: 4 - - name: Upload coverage report as artifact - uses: actions/upload-artifact@65462800fd760344b1a7b4382951275a0abb4808 # v4.3.3 - with: - name: coverage_pt_cpu - path: ./coverage.xml - - name: Upload coverage report to codecov - uses: codecov/codecov-action@125fc84a9a348dbcf27191600683ec096ec9021c # v4.4.1 - with: - token: ${{ secrets.CODECOV_TOKEN }} - name: coverage_pt_cpu - flags: TORCH + pytorch-cuda: timeout-minutes: 40 defaults: @@ -154,13 +121,13 @@ jobs: sudo apt-get --assume-yes install build-essential ninja-build libgl1-mesa-dev libglib2.0-0 wget make - name: Download CUDA run: | - wget -q https://developer.download.nvidia.com/compute/cuda/12.1.1/local_installers/cuda_12.1.1_530.30.02_linux.run - sudo sh cuda_12.1.1_530.30.02_linux.run --toolkit --silent + wget -q https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_550.54.14_linux.run + sudo sh cuda_12.4.0_550.54.14_linux.run --toolkit --silent - name: Runner info continue-on-error: true run: | - export PATH=/usr/local/cuda-12.1/bin${PATH:+:${PATH}} - export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} + export PATH=/usr/local/cuda-12.4/bin${PATH:+:${PATH}} + export LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} nvidia-smi cat /proc/cpuinfo nvcc --version @@ -169,31 +136,21 @@ jobs: lfs: true - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 cache: pip - name: Install NNCF and test requirements run: make install-torch-test + - name: Print installed modules + run: pip list - name: Check CUDA run: | python -c "import torch; print(torch.cuda.is_available())" - name: Run PyTorch precommit test scope run: | - export PATH=/usr/local/cuda-12.1/bin${PATH:+:${PATH}} - export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} + export PATH=/usr/local/cuda-12.4/bin${PATH:+:${PATH}} + export LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} make test-torch-cuda - env: - NNCF_COVERAGE: 1 - - name: Upload coverage report as artifact - uses: actions/upload-artifact@65462800fd760344b1a7b4382951275a0abb4808 # v4.3.3 - with: - name: coverage_pt_cuda - path: ./coverage.xml - - name: Upload coverage report to codecov - uses: codecov/codecov-action@125fc84a9a348dbcf27191600683ec096ec9021c # v4.4.1 - with: - token: ${{ secrets.CODECOV_TOKEN }} - name: coverage_pt_cuda - flags: TORCH + tensorflow: timeout-minutes: 40 defaults: @@ -208,27 +165,17 @@ jobs: lfs: true - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.9.19 + python-version: 3.10.14 cache: pip - name: Install NNCF and test requirements run: make install-tensorflow-test + - name: Print installed modules + run: pip list - name: Run TensorFlow precommit test scope - run: | - make test-tensorflow + run: make test-tensorflow env: - NNCF_COVERAGE: 1 NUM_WORKERS: 6 - - name: Upload coverage report as artifact - uses: actions/upload-artifact@65462800fd760344b1a7b4382951275a0abb4808 # v4.3.3 - with: - name: coverage_tensorflow - path: ./coverage.xml - - name: Upload coverage report to codecov - uses: codecov/codecov-action@125fc84a9a348dbcf27191600683ec096ec9021c # v4.4.1 - with: - token: ${{ secrets.CODECOV_TOKEN }} - name: coverage_tensorflow - flags: TENSORFLOW + tools: runs-on: ubuntu-20.04 steps: @@ -237,10 +184,30 @@ jobs: lfs: true - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: 3.8.18 + python-version: 3.10.14 cache: pip - name: Install NNCF and test requirements - run: | - pip install -r tests/tools/requirements.txt + run: pip install -r tests/tools/requirements.txt + - name: Print installed modules + run: pip list - name: Run tools precommit test scope run: PYTHONPATH=./ pytest -ra tests/tools + + pytorch2-cpu: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 + with: + lfs: true + - uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 + with: + python-version: 3.10.14 + cache: pip + - name: Install NNCF and test requirements + run: | + pip install -e . + pip install -r tests/torch2/requirements.txt + - name: Print installed modules + run: pip list + - name: Run torch2 precommit test scope + run: pytest -ra tests/torch2 -m "not cuda" diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index b8d9f5c4f70..abcd7394638 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -30,7 +30,7 @@ jobs: - name: Set up Python uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0 with: - python-version: '3.x' + python-version: 3.10.14 - name: Install dependencies run: | python -m pip install --upgrade pip @@ -39,8 +39,9 @@ jobs: env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + NNCF_RELEASE_BUILD: "1" run: | - python -m build -C--global-option=--release + python -m build # Disallow uploading dev packages for file in dist/*; do diff --git a/.github/workflows/upload_coverage_for_develop.yml b/.github/workflows/upload_coverage_for_develop.yml deleted file mode 100644 index b2fc87246bb..00000000000 --- a/.github/workflows/upload_coverage_for_develop.yml +++ /dev/null @@ -1,44 +0,0 @@ -name: Upload coverage from an artifact of a merged PR -permissions: read-all - -on: - workflow_call: - inputs: - merge_commit_sha: - required: true - type: string - last_sha_in_pr: - required: true - type: string - coverage_artifact_name_in_pr: - required: true - type: string - coverage_flags: - required: true - type: string - secrets: - CODECOV_TOKEN: - required: true - -jobs: - upload-coverage: - if: github.event.pull_request.merged == true - runs-on: ubuntu-20.04 - steps: - - uses: actions/checkout@a5ac7e51b41094c92402da3b24376905380afc29 # v4.1.6 # codecov uploader demands that the scanned files be present when uploading - with: - ref: ${{ inputs.merge_commit_sha }} - - uses: dawidd6/action-download-artifact@268677152d06ba59fcec7a7f0b5d961b6ccd7e1e # v2.28.0 - with: - workflow: precommit.yml - check_artifacts: true - commit: ${{ inputs.last_sha_in_pr }} # this is the latest commit in the PR - name: ${{ inputs.coverage_artifact_name_in_pr }} - - name: Upload coverage report to Codecov - run: | - curl -Os https://uploader.codecov.io/latest/linux/codecov - chmod +x codecov - - # github.event.pull_request.merge_commit_sha is the fresh commit in the develop, - # provided that github.event.pull_request.merged == true - ./codecov -f ./coverage.xml -t ${{ secrets.CODECOV_TOKEN }} -F ${{ inputs.coverage_flags }} -C ${{ inputs.merge_commit_sha }} -B develop -n "${{ inputs.coverage_artifact_name_in_pr }}" diff --git a/.isort.cfg b/.isort.cfg deleted file mode 100644 index f9b4e5d5a32..00000000000 --- a/.isort.cfg +++ /dev/null @@ -1,6 +0,0 @@ -[settings] -line_length = 120 -force_single_line = true -profile = black -single_line_exclusions = typing -skip_glob=examples/post_training_quantization/torch/ssd300_vgg16/main.py diff --git a/.mypy.ini b/.mypy.ini deleted file mode 100644 index 994dc9f2cad..00000000000 --- a/.mypy.ini +++ /dev/null @@ -1,9 +0,0 @@ -[mypy] -files = nncf/common/sparsity, nncf/common/graph, nncf/common/accuracy_aware_training/, nncf/common/utils/ -follow_imports = silent -strict = True - -# should be removed later -# mypy recommends the following tool as an autofix: -# https://github.com/hauntsaninja/no_implicit_optional -implicit_optional = True diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 34a33e90ad7..46f56b504fc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,6 @@ repos: hooks: - id: black files: '^.*\.py' - args: ["--line-length", "120"] - repo: https://github.com/pycqa/isort rev: 5.12.0 diff --git a/MANIFEST.in b/MANIFEST.in index e4af5d0cef4..51186a59c13 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -3,3 +3,4 @@ graft nncf/common/hardware/configs include LICENSE include licensing/third-party-programs.txt include docs/PyPiPublishing.md +include custom_version.py diff --git a/Makefile b/Makefile index db7a70cada0..4be051099e9 100644 --- a/Makefile +++ b/Makefile @@ -35,7 +35,7 @@ install-pre-commit: install-onnx-test: pip install -U pip pip install -e . - pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=accuracy_checker&subdirectory=tools/accuracy_checker" + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@e7df86da686d2e1600282422e54f66c2fecea160#egg=accuracy_checker&subdirectory=tools/accuracy_checker" pip install -r tests/onnx/requirements.txt pip install -r tests/cross_fw/install/requirements.txt pip install -r tests/cross_fw/examples/requirements.txt @@ -63,7 +63,7 @@ test-examples-onnx: install-openvino-test: pip install -U pip pip install -e . - pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=accuracy_checker&subdirectory=tools/accuracy_checker" + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@e7df86da686d2e1600282422e54f66c2fecea160#egg=accuracy_checker&subdirectory=tools/accuracy_checker" pip install -r tests/openvino/requirements.txt pip install -r tests/cross_fw/install/requirements.txt pip install -r tests/cross_fw/examples/requirements.txt @@ -93,7 +93,7 @@ test-examples-openvino: install-tensorflow-test: pip install -U pip pip install -e . - pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=accuracy_checker&subdirectory=tools/accuracy_checker" + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@e7df86da686d2e1600282422e54f66c2fecea160#egg=accuracy_checker&subdirectory=tools/accuracy_checker" pip install -r tests/tensorflow/requirements.txt pip install -r tests/cross_fw/install/requirements.txt pip install -r tests/cross_fw/examples/requirements.txt @@ -123,7 +123,7 @@ test-examples-tensorflow: install-torch-test: pip install -U pip pip install -e . - pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@37f60eb#egg=accuracy_checker&subdirectory=tools/accuracy_checker" + pip install "git+https://github.com/openvinotoolkit/open_model_zoo.git@e7df86da686d2e1600282422e54f66c2fecea160#egg=accuracy_checker&subdirectory=tools/accuracy_checker" pip install -r tests/torch/requirements.txt pip install -r tests/cross_fw/install/requirements.txt pip install -r tests/cross_fw/examples/requirements.txt diff --git a/README.md b/README.md index b3dbbae07c9..6c263b4c34a 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in [OpenVINO™](https://docs.openvino.ai) with a minimal accuracy drop. -NNCF is designed to work with models from [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), [ONNX](https://onnx.ai/) and [OpenVINO™](https://docs.openvino.ai). +NNCF is designed to work with models from [PyTorch](https://pytorch.org/), [TorchFX](https://pytorch.org/docs/stable/fx.html), [TensorFlow](https://www.tensorflow.org/), [ONNX](https://onnx.ai/) and [OpenVINO™](https://docs.openvino.ai). NNCF provides [samples](#demos-tutorials-and-samples) that demonstrate the usage of compression algorithms for different use cases and models. See compression results achievable with the NNCF-powered samples on the [NNCF Model Zoo page](./docs/ModelZoo.md). @@ -33,11 +33,11 @@ learning frameworks. ### Post-Training Compression Algorithms -| Compression algorithm | OpenVINO | PyTorch | TensorFlow | ONNX | -| :------------------------------------------------------------------------------------------------------- | :-------: | :-------: | :-----------: | :-----------: | -| [Post-Training Quantization](./docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Supported | Supported | -| [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md) | Supported | Supported | Not supported | Not supported | -| [Activation Sparsity](./nncf/experimental/torch/sparsify_activations/ActivationSparsity.md) | Not supported | Experimental |Not supported| Not supported | +| Compression algorithm | OpenVINO | PyTorch | TorchFX | TensorFlow | ONNX | +| :------------------------------------------------------------------------------------------------------- | :-------: | :-------: | :-----------: | :-----------: | :-----------: | +| [Post-Training Quantization](./docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Experimental | Supported | Supported | +| [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md) | Supported | Supported | Experimental | Not supported | Not supported | +| [Activation Sparsity](./nncf/experimental/torch/sparsify_activations/ActivationSparsity.md) | Not supported | Experimental | Not supported| Not supported| Not supported | ### Training-Time Compression Algorithms @@ -138,6 +138,42 @@ quantized_model = nncf.quantize(model, calibration_dataset) +
TorchFX + +```python +import nncf +import torch.fx +from torchvision import datasets, models +from nncf.torch import disable_patching + +# Instantiate your uncompressed model +model = models.mobilenet_v2() + +# Provide validation part of the dataset to collect statistics needed for the compression algorithm +val_dataset = datasets.ImageFolder("/path", transform=transforms.Compose([transforms.ToTensor()])) +dataset_loader = torch.utils.data.DataLoader(val_dataset) + +# Step 1: Initialize the transformation function +def transform_fn(data_item): + images, _ = data_item + return images + +# Step 2: Initialize NNCF Dataset +calibration_dataset = nncf.Dataset(dataset_loader, transform_fn) + +# Step 3: Export model to TorchFX +input_shape = (1, 3, 224, 224) +with nncf.torch.disable_patching(): + fx_model = torch.export.export_for_training(model, args=(ex_input,)).module() + # or + # fx_model = torch.export.export(model, args=(ex_input,)).module() + + # Step 4: Run the quantization pipeline + quantized_fx_model = nncf.quantize(fx_model, calibration_dataset) + + ``` + +
TensorFlow ```python @@ -384,6 +420,7 @@ Compact scripts demonstrating quantization and corresponding inference speed boo | [OpenVINO Anomaly Classification](./examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/README.md) | Post-Training Quantization with Accuracy Control | OpenVINO | Anomaly Classification | | [PyTorch MobileNetV2](./examples/post_training_quantization/torch/mobilenet_v2/README.md) | Post-Training Quantization | PyTorch | Image Classification | | [PyTorch SSD](./examples/post_training_quantization/torch/ssd300_vgg16/README.md) | Post-Training Quantization | PyTorch | Object Detection | +| [TorchFX Resnet18](./examples/post_training_quantization/torch_fx/resnet18/README.md) | Post-Training Quantization | TorchFX | Image Classification | | [TensorFlow MobileNetV2](./examples/post_training_quantization/tensorflow/mobilenet_v2/README.md) | Post-Training Quantization | TensorFlow | Image Classification | | [ONNX MobileNetV2](./examples/post_training_quantization/onnx/mobilenet_v2/README.md) | Post-Training Quantization | ONNX | Image Classification | @@ -437,14 +474,14 @@ conda install -c conda-forge nncf ### System requirements - Ubuntu\* 18.04 or later (64-bit) -- Python\* 3.8 or later +- Python\* 3.9 or later - Supported frameworks: - - PyTorch\* >=2.3, <2.5 + - PyTorch\* >=2.4, <2.6 - TensorFlow\* >=2.8.4, <=2.15.1 - - ONNX\* ==1.16.0 + - ONNX\* ==1.17.0 - OpenVINO\* >=2022.3.0 -This repository is tested on Python* 3.8.10, PyTorch* 2.4.0 (NVidia CUDA\* Toolkit 12.1) and TensorFlow* 2.12.1 (NVidia CUDA\* Toolkit 11.8). +This repository is tested on Python* 3.10.14, PyTorch* 2.5.0 (NVidia CUDA\* Toolkit 12.4) and TensorFlow* 2.12.1 (NVidia CUDA\* Toolkit 11.8). ## NNCF Compressed NNCF Model Zoo diff --git a/ReleaseNotes.md b/ReleaseNotes.md index 391d8e115a5..46b27138a16 100644 --- a/ReleaseNotes.md +++ b/ReleaseNotes.md @@ -1,5 +1,57 @@ # Release Notes +## New in Release 2.14.0 + +Post-training Quantization: + +- Features: + - Introduced `backup_mode` optional parameter in `nncf.compress_weights()` to specify the data type for embeddings, convolutions and last linear layers during 4-bit weights compression. Available options are INT8_ASYM by default, INT8_SYM, and NONE which retains the original floating-point precision of the model weights. + - Added the `quantizer_propagation_rule` parameter, providing fine-grained control over quantizer propagation. This advanced option is designed to improve accuracy for models where quantizers with different granularity could be merged to per-tensor, potentially affecting model accuracy. + - Introduced `nncf.data.generate_text_data` API method that utilizes LLM to generate data for further data-aware optimization. See the [example](examples/llm_compression/openvino/tiny_llama_synthetic_data/) for details. + - (OpenVINO) Extended support of data-free and data-aware weight compression methods for `nncf.compress_weights()` with NF4 per-channel quantization, which makes compressed LLMs more accurate and faster on NPU. + - (OpenVINO) Introduced a new option `statistics_path` to cache and reuse statistics for `nncf.compress_weights()`, reducing the time required to find optimal compression configurations. See the [TinyLlama example](https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama_find_hyperparams) for details. + - (TorchFX, Experimental) Added support for quantization and weight compression of [Torch FX](https://pytorch.org/docs/stable/fx.html) models. The compressed models can be directly executed via `torch.compile(compressed_model, backend="openvino")` (see details [here](https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html)). Added [INT8 quantization example](https://github.com/openvinotoolkit/nncf/tree/develop/examples/post_training_quantization/torch_fx/resnet18). The list of supported features: + - INT8 quantization with SmoothQuant, MinMax, FastBiasCorrection, and BiasCorrection algorithms via `nncf.quantize()`. + - Data-free INT8, INT4, and mixed-precision weights compression with `nncf.compress_weights()`. + - (PyTorch, Experimental) Added model tracing and execution pre-post hooks based on TorchFunctionMode. +- Fixes: + - Resolved an issue with redundant quantizer insertion before elementwise operations, reducing noise introduced by quantization. + - Fixed type mismatch issue for `nncf.quantize_with_accuracy_control()`. + - Fixed BiasCorrection algorithm for specific branching cases. + - (OpenVINO) Fixed GPTQ weight compression method for Stable Diffusion models. + - (OpenVINO) Fixed issue with the variational statistics processing for `nncf.compress_weights()`. + - (PyTorch, ONNX) Scaled dot product attention pattern quantization setup is aligned with OpenVINO. +- Improvements: + - Reduction in peak memory by 30-50% for data-aware `nncf.compress_weights()` with AWQ, Scale Estimation, LoRA and mixed-precision algorithms. + - Reduction in compression time by 10-20% for `nncf.compress_weights()` with AWQ algorithm. + - Aligned behavior for ignored subgraph between different `networkx` versions. + - Extended ignored patterns with RoPE block for `nncf.ModelType.TRANSFORMER` scheme. + - (OpenVINO) Extended to the ignored scope for `nncf.ModelType.TRANSFORMER` scheme with GroupNorm metatype. + - (ONNX) SE-block ignored pattern variant for `torchvision` mobilenet_v3 has been extended. +- Tutorials: + - [Post-Training Optimization of Llama-3.2-11B-Vision Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/mllama-3.2/mllama-3.2.ipynb) + - [Post-Training Optimization of YOLOv11 Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov11-optimization/yolov11-object-detection.ipynb) + - [Post-Training Optimization of Whisper in Automatic speech recognition with OpenVINO Generate API](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/whisper-asr-genai/whisper-asr-genai.ipynb) + - [Post-Training Optimization of Pixtral Model](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/pixtral/pixtral.ipynb) + - [Post-Training Optimization of LLM ReAct Agent Model](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-agent-react/llm-agent-react.ipynb) + - [Post-Training Optimization of CatVTON Model](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/catvton/catvton.ipynb) + - [Post-Training Optimization of Stable Diffusion v3 Model in Torch FX Representation](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/stable-diffusion-v3/stable-diffusion-v3-torch-fx.ipynb) +- Known issues: + - (ONNX) `nncf.quantize()` method can generate inaccurate INT8 results for MobileNet models with the BiasCorrection algorithm. + +Deprecations/Removals: + +- Migrated from using `setup.py` to `pyproject.toml` for the build and package configuration. It is aligned with Python packaging standards as outlined in PEP 517 and PEP 518. The installation through `setup.py` does not work anymore. No impact on the installation from PyPI and Conda. +- Removed support for Python 3.8. +- (PyTorch) `nncf.torch.create_compressed_model()` function has been deprecated. + +Requirements: + +- Updated ONNX (1.17.0) and ONNXRuntime (1.19.2) versions. +- Updated PyTorch (2.5.1) and Torchvision (0.20.1) versions. +- Updated NumPy (<2.2.0) version support. +- Updated Ultralytics (8.3.22) version. + ## New in Release 2.13.0 Post-training Quantization: diff --git a/codecov.yml b/codecov.yml deleted file mode 100644 index 62c50309c6b..00000000000 --- a/codecov.yml +++ /dev/null @@ -1,94 +0,0 @@ -ignore: - - "examples" - - "tests" - - "tools" - -codecov: - notify: - wait_for_ci: true - max_report_age: off - -coverage: - status: - project: - default: - branches: - - develop - target: 90% - informational: true - only_pulls: true - paths: - - "nncf" - patch: - default: - branches: - - develop - target: 90% - informational: true - only_pulls: true - paths: - - "nncf" - -comment: - layout: "reach, diff, files, flags, components" - require_changes: false - - require_head: false - require_base: false - -flag_management: - # Flag coverage percentage seems to show the "percentage of lines under the flag path covered as reported ONLY - # by the upload with the corresponding flag", so e.g. for COMMON the flag coverage percentage will report the - # percentage of common code tested ONLY by the common tests, and e.g. not by backend-specific precommit parts - # (which also run common code and are therefore indirectly providing coverage). Ideally each flag-specific path - # would be described below with the corresponding flag and provide valuable information on whether the test code base - # is written efficiently, e.g. that the backend-specific tests predominantly validate backend-specific code and the - # common tests completely cover the common code on their own. However, if we set all flags with paths here, then the - # total repo coverage percentage will sink, because codecov currently reports the overall coverage based on the union - # of the "flag" coverages - not the "component" coverages (see below) - and currently NNCF's precommit tests are - # biased toward validating common code via backend-specific tests. In the future the tests will be gradually - # refactored to have more "locality" in what each precommit section tests. - individual_flags: - - name: COMMON - paths: - - nncf/common - - nncf/quantization - -component_management: - # In contrast to the "flag" coverage above, the "component" display seems to calculate percentage based on the - # coverage information from ALL uploads for the code in the specified path. With this, the "component" coverage - # percentage is a better representation of what sub-paths in the NNCF code base are covered with at least one test, - # without distinction whether the test was run in the - individual_components: - - component_id: common - name: common - paths: - - nncf/common - - "!nncf/**/torch_*.py" - - "!nncf/**/tensorflow_*.py" - - "!nncf/**/onnx_*.py" - - "!nncf/**/openvino_*.py" - - component_id: torch - name: torch - paths: - - nncf/torch - - nncf/**/torch_*.py - - component_id: tensorflow - name: tensorflow - paths: - - nncf/tensorflow - - nncf/**/tensorflow_*.py - - component_id: onnx - name: onnx - paths: - - nncf/onnx - - nncf/**/onnx_*.py - - component_id: openvino - name: openvino - paths: - - nncf/openvino - - nncf/**/openvino_*.py - - component_id: quantization - name: ptq - paths: - - nncf/quantization \ No newline at end of file diff --git a/constraints.txt b/constraints.txt index fe0e42aace1..30a61be45b4 100644 --- a/constraints.txt +++ b/constraints.txt @@ -1,13 +1,13 @@ # Openvino -openvino==2024.4.0 +openvino==2024.5.0 # Pytorch -torch==2.4.0 -torchvision==0.19.0 +torch==2.5.1 +torchvision==0.20.1 # ONNX -onnx==1.16.0 -onnxruntime==1.17.1 +onnx==1.17.0 +onnxruntime==1.19.2 # TensorFlow tensorflow==2.12.1; python_version < '3.9' @@ -21,3 +21,4 @@ pytest-dependency==0.6.0 pytest-ordering==0.6 pytest-xdist==3.5.0 pytest-forked==1.6.0 +pytest-split==0.9.0 diff --git a/custom_version.py b/custom_version.py new file mode 100644 index 00000000000..04d673bdefb --- /dev/null +++ b/custom_version.py @@ -0,0 +1,114 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Dynamic Version Generation for Package Builds + +This module is responsible for generating the dynamic version for the package during the build process. +It provides the `version` attribute for setuptools as specified in the `pyproject.toml` configuration file: + + [tool.setuptools.dynamic] + version = { attr = "_version_helper.version" } + +Environment Variable Instructions: +----------------------------------- +1. **NNCF_RELEASE_BUILD**: + - Set this environment variable to generate a release package using `python -m build` without including + the commit hash in the version. + - If this variable is not set, the file `nncf/version.py` will be overridden with a custom version + that includes the commit hash. Example usage: + + NNCF_RELEASE_BUILD=1 python -m build + +2. **NNCF_BUILD_SUFFIX**: + - Use this environment variable to specify a particular suffix for the build. Example usage: + + NNCF_BUILD_SUFFIX="rc1" python -m build + +Post-Build Recommendation: +--------------------------- +After generating the package, it is recommended to revert any changes to `nncf/version.py` to avoid potential conflicts. +This can be done using the following command: + + git checkout nncf/version.py + +This ensures that `nncf/version.py` remains in its original state after the dynamic versioning process. +""" + + +from __future__ import annotations + +import contextlib +import os +import re +import subprocess +from pathlib import Path + +NNCF_VERSION_FILE = "nncf/version.py" + + +def get_custom_version() -> str: + version_match = re.search( + r"^__version__ = ['\"]((\d+\.\d+\.\d+)([^'\"]*))['\"]", Path(NNCF_VERSION_FILE).read_text(), re.M + ) + if not version_match: + raise RuntimeError("Unable to find version string.") + + version_full = version_match.group(1) + version_value = version_match.group(2) + version_suffix = version_match.group(3) + + nncf_build_suffix = os.environ.get("NNCF_BUILD_SUFFIX") + if nncf_build_suffix: + # Suffix expected on build package + return f"{version_value}.{nncf_build_suffix}" + + is_building_release = "NNCF_RELEASE_BUILD" in os.environ + if is_building_release or version_suffix: + return version_full + + dev_version_id = "unknown_version" + repo_root = os.path.dirname(os.path.realpath(__file__)) + + # Get commit hash + with contextlib.suppress(subprocess.CalledProcessError): + dev_version_id = ( + subprocess.check_output(["git", "rev-parse", "--short", "HEAD"], cwd=repo_root).strip().decode() # nosec + ) + + # Detect modified files + with contextlib.suppress(subprocess.CalledProcessError): + run = subprocess.run(["git", "diff-index", "--quiet", "HEAD"], cwd=repo_root) # nosec + if run.returncode == 1: + dev_version_id += "dirty" + + return version_value + f".dev0+{dev_version_id}" + + +version: str + + +def __getattr__(name: str) -> str: + + if name == "version": + global version + version = get_custom_version() + + # Rewrite version.py to pass custom version to package + if os.environ.get("_PYPROJECT_HOOKS_BUILD_BACKEND"): + content = Path(NNCF_VERSION_FILE).read_text() + version_str = re.search(r"^__version__ = ['\"][^'\"]*['\"]", content, re.M).group(0) + content = content.replace(version_str, f'__version__ = "{version}"') + Path(NNCF_VERSION_FILE).write_text(content) + + return version + + raise AttributeError(name) diff --git a/docs/Algorithms.md b/docs/Algorithms.md index d6e23cebfdb..992f8183962 100644 --- a/docs/Algorithms.md +++ b/docs/Algorithms.md @@ -2,12 +2,12 @@ ## Post-training Compression -- [Post Training Quantization (PTQ)](./usage/post_training_compression/post_training_quantization/Usage.md) (OpenVINO, PyTorch, ONNX, TensorFlow) +- [Post Training Quantization (PTQ)](./usage/post_training_compression/post_training_quantization/Usage.md) (OpenVINO, PyTorch, TorchFX, ONNX, TensorFlow) - Symmetric and asymmetric quantization modes - Signed and unsigned - Per tensor/per channel - Each backend support export to the OpenVINO format -- [Weights compression](./usage/post_training_compression/weights_compression/Usage.md) (OpenVINO, PyTorch) +- [Weights compression](./usage/post_training_compression/weights_compression/Usage.md) (OpenVINO, PyTorch, TorchFX) - Symmetric 8 bit compression mode - Symmetric and asymmetric 4 bit compression mode - NF4 compression mode diff --git a/docs/Installation.md b/docs/Installation.md index 807ff4f793b..760bcd67d36 100644 --- a/docs/Installation.md +++ b/docs/Installation.md @@ -43,7 +43,8 @@ as well as the supported versions of Python: | NNCF | OpenVINO | PyTorch | ONNX | TensorFlow | Python | |-----------|------------|----------|----------|------------|--------| -| `develop` | `2024.4.0` | `2.4.0` | `1.16.0` | `2.15.1` | `3.8`* | +| `develop` | `2024.5.0` | `2.5.1` | `1.17.0` | `2.15.1` | `3.10` | +| `2.14.0` | `2024.5.0` | `2.5.1` | `1.17.0` | `2.15.1` | `3.10` | | `2.13.0` | `2024.4.0` | `2.4.0` | `1.16.0` | `2.15.1` | `3.8`* | | `2.12.0` | `2024.3.0` | `2.3.0` | `1.16.0` | `2.15.1` | `3.8`* | | `2.11.0` | `2024.2.0` | `2.3.0` | `1.16.0` | `2.12.0` | `3.8` | diff --git a/docs/ModelZoo.md b/docs/ModelZoo.md index 70f1cbe1d71..9dad44aaacc 100644 --- a/docs/ModelZoo.md +++ b/docs/ModelZoo.md @@ -1,8 +1,10 @@ # NNCF Compressed Model Zoo -Here we present the results achieved using our sample scripts, example patches to third-party repositories and NNCF configuration files. +Ready-to-use **Compressed LLMs** can be found on [OpenVINO Hugging Face page](https://huggingface.co/OpenVINO#models). Each model card includes NNCF parameters that were used to compress the model. -The applied quantization compression algorithms are divided into two broad categories: Quantization-Aware Training ([QAT](../README.md#training-time-compression)) and Post-Training Quantization ([PTQ](../README.md#post-training-quantization)). Here we mainly report the QAT results and the PTQ results may be found on an OpenVino Performance Benchmarks [page](https://docs.openvino.ai/2024/about-openvino/performance-benchmarks.html). +**INT8 Post-Training Quantization** ([PTQ](../README.md#post-training-quantization)) results for public Vision, NLP and GenAI models can be found on [OpenVino Performance Benchmarks page](https://docs.openvino.ai/2024/about-openvino/performance-benchmarks.html). PTQ results for ONNX models are available in the [ONNX](#onnx) section below. + +**Quantization-Aware Training** ([QAT](../README.md#training-time-compression)) results for PyTorch and TensorFlow public models can be found below. - [PyTorch](#pytorch) - [Classification](#pytorch-classification) diff --git a/docs/PyPiPublishing.md b/docs/PyPiPublishing.md index 2ac6ebfd097..1880edd4d7b 100644 --- a/docs/PyPiPublishing.md +++ b/docs/PyPiPublishing.md @@ -92,7 +92,7 @@ For detailed installation instructions, refer to the ### System Requirements - Ubuntu 18.04 or later (64-bit) -- Python 3.8 or later +- Python 3.9 or later - Supported frameworks: - PyTorch >=2.2, <2.5 - TensorFlow >=2.8.4, <=2.15.1 diff --git a/docs/api/source/conf.py b/docs/api/source/conf.py index 4637c7875d4..fe3afe0525c 100644 --- a/docs/api/source/conf.py +++ b/docs/api/source/conf.py @@ -137,7 +137,6 @@ def collect_api_entities() -> APIInfo: "openvino", "tensorflow", "keras", - "tensorflow_addons", # Need add backend implementation functions to avoid endless loops on registered functions by mock module, "nncf.tensor.functions.numpy_numeric", "nncf.tensor.functions.numpy_linalg", diff --git a/docs/usage/post_training_compression/post_training_quantization/Usage.md b/docs/usage/post_training_compression/post_training_quantization/Usage.md index 0a078a4399f..c98fe38f298 100644 --- a/docs/usage/post_training_compression/post_training_quantization/Usage.md +++ b/docs/usage/post_training_compression/post_training_quantization/Usage.md @@ -51,7 +51,7 @@ Every backend has its own return value format for the data transformation functi backend inference framework. Below are the formats of data transformation function for each supported backend. -
PyTorch, TensorFlow, OpenVINO +
PyTorch, TorchFX, TensorFlow, OpenVINO The return format of the data transformation function is directly the input tensors consumed by the model. \ _If you are not sure that your implementation of data transformation function is correct you can validate it by using the @@ -89,7 +89,7 @@ for data_item in val_loader:
NNCF provides the examples of Post-Training Quantization where you can find the implementation of data transformation -function: [PyTorch](/examples/post_training_quantization/torch/mobilenet_v2/README.md), [TensorFlow](/examples/post_training_quantization/tensorflow/mobilenet_v2/README.md), [ONNX](/examples/post_training_quantization/onnx/mobilenet_v2/README.md), and [OpenVINO](/examples/post_training_quantization/openvino/mobilenet_v2/README.md) +function: [PyTorch](/examples/post_training_quantization/torch/mobilenet_v2/README.md), [TorchFX](/examples/post_training_quantization/torch_fx/resnet18/README.md), [TensorFlow](/examples/post_training_quantization/tensorflow/mobilenet_v2/README.md), [ONNX](/examples/post_training_quantization/onnx/mobilenet_v2/README.md), and [OpenVINO](/examples/post_training_quantization/openvino/mobilenet_v2/README.md) In case the Post-Training Quantization algorithm could not reach quality requirements you can fine-tune a quantized pytorch model. Example of the Quantization-Aware training pipeline for a pytorch model could be found [here](/examples/quantization_aware_training/torch/resnet18/README.md). diff --git a/docs/usage/post_training_compression/weights_compression/Usage.md b/docs/usage/post_training_compression/weights_compression/Usage.md index 40a3749498f..6dc6edfb0aa 100644 --- a/docs/usage/post_training_compression/weights_compression/Usage.md +++ b/docs/usage/post_training_compression/weights_compression/Usage.md @@ -1,21 +1,36 @@ -## Weights Compression -[OpenVINO](https://github.com/openvinotoolkit/openvino) is the preferred backend to run Weights Compression with, and PyTorch is also supported. +- [The algorithm description](#the-algorithm-description) +- [Supported modes](#supported-modes) +- [User guide](#user-guide) + - [Data-free methods](#data-free-methods) + - [Data-aware methods](#data-aware-methods) + - [Caching Statistics](#caching-statistics) +- [Evaluation results](#evaluation-results) + - [Data-free Mixed-Precision on Lambada OpenAI dataset](#data-free-mixed-precision-on-lambada-openai-dataset) + - [Data-aware Mixed-Precision and AWQ methods on Wikitext dataset](#data-aware-mixed-precision-and-awq-methods-on-wikitext-dataset) + - [Scale Estimation and GPTQ methods on Lambada OpenAI dataset](#scale-estimation-and-gptq-methods-on-lambada-openai-dataset) + - [Accuracy/Footprint trade-off](#accuracyfootprint-trade-off) +- [Limitations](#limitations) +- [Additional resources](#additional-resources) ### The algorithm description The Weights Compression algorithm is aimed at compressing the weights of the models and can be used to optimize the model footprint and performance of large models where the size of weights is relatively larger than the size of activations, for example, Large Language Models (LLM). The algorithm compresses weights for Linear, Convolution and Embedding layers. +[OpenVINO](https://github.com/openvinotoolkit/openvino) is the preferred backend to run Weights Compression with. PyTorch and Torch FX are also supported. + ### Supported modes By default, weights are compressed asymmetrically to 8-bit integer data type - "INT8_ASYM" mode. OpenVINO backend also supports 4 modes of mixed precision weight quantization with a 4-bit data type as a primary precision - INT4_SYM, INT4_ASYM, NF4, E2M1. The primary precision in case of INT4_SYM mode is signed 4-bit integer and weights are quantized to it [symmetrically](/docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#symmetric-quantization) without zero point. In case of INT4_ASYM mode - unsigned 4-bit integer and weight are quantized to it [asymmetrically](/docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#asymmetric-quantization) with a typical non-fixed zero point. In case of NF4 mode - [nf4](https://arxiv.org/pdf/2305.14314v1.pdf) data type without zero point. In case of E2M1 mode - [e2m1](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) data type without zero point and has 8bit [E8M0](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) scale. All 4-bit modes have a grouped quantization support, when small group of weights (e.g. 128) in the channel dimension share quantization parameters (scale). -All embeddings, convolutions and last linear layers are always compressed to 8-bit integer data type. To quantize embeddings and last linear layers to 4-bit, use `all_layers=True`. -Percent of the rest layers compressed to 4-bit can be configured by "ratio" parameter. E.g. ratio=0.9 means 90% of layers compressed to the corresponding 4-bit data type and the rest to 8-bit asymmetric integer data type. +All embeddings, convolutions and last linear layers are always compressed to a backup mode, which is "INT8_ASYM", by default. To quantize embeddings and last linear layers to 4-bit, use `all_layers=True`. +Percent of the rest layers compressed to 4-bit can be configured by "ratio" parameter. E.g. ratio=0.9 means 90% of layers compressed to the corresponding 4-bit data type and the rest to a backup mode. OpenVINO backend supports 3 backup modes: INT8_SYM, INT8_ASYM, and NONE, which retains the original floating-point precision of the model weights. Backup mode is supported only for mixed-precision weight quantization. ### User guide +#### Data-free methods + - Compress weights asymmetrically to 8-bit integer data type. ```python @@ -37,6 +52,13 @@ from nncf import compress_weights, CompressWeightsMode compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_SYM) # model is openvino.Model object ``` +- Compress weights to NF4 with group size = 128, except embeddings, convolutions and last linear layers - they are remain in original floating-point precision. + +```python +from nncf import compress_weights, BackupMode, CompressWeightsMode +compressed_model = compress_weights(model, mode=CompressWeightsMode.NF4, backup_mode=BackupMode.NONE) # model is openvino.Model object +``` + - Generally, `INT4_SYM` mode is the fastest mixed-precision mode, but it may lead to a significant accuracy degradation or perplexity increase. Compressing weights asymmetrically (`INT4_ASYM` mode) is the way to increase accuracy, however in turns it slows down inference a bit. If the accuracy or perplexity is still not satisfying, there are 2 more hyper-parameters to tune: `group_size` and `ratio`. Please refer to the [example](https://github.com/openvinotoolkit/nncf/blob/develop/examples/llm_compression/openvino/tiny_llama_find_hyperparams) how to automatically tune these parameters. @@ -49,6 +71,8 @@ from nncf import compress_weights, CompressWeightsMode compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_ASYM, group_size=64, ratio=0.9) # model is openvino.Model object ``` +#### Data-aware methods + - Accuracy of the 4-bit compressed models can be improved by using data-aware mixed-precision algorithm. It is capable to find outliers in the input activations and assign different quantization precision to minimize accuracy degradation. Below is the example how to compress 80% of layers to 4-bit integer with a default data-aware mixed precision algorithm. It requires just one extra parameter - a NNCF wrapper of the dataset. Refer to the [full example](https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino) of data-aware weight compression for more details. If dataset is not specified, data-free mixed precision algorithm works based on weights only. @@ -61,59 +85,71 @@ nncf_dataset = nncf.Dataset(data_source, transform_fn) compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_SYM, ratio=0.8, dataset=nncf_dataset) # model is openvino.Model object ``` +- Additionally, it is possible to generate a synthetic dataset by the `nncf.data.generate_text_data` method to use it in the data-aware weight compression. The method takes a language model (e.g. from `optimum.intel.openvino`) and a tokenizer (e.g. from `transformers`) as input and returns the list of strings generated by the model. Note that the dataset generation takes time and depends on various conditions like the model size, requested dataset length or environment setup. Also, since the dataset is generated by the model output, it does not guarantee significant accuracy improvement after the compression. This method is recommended only in cases when a better dataset is not available. Refer to the [example](https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama_synthetic_data/) for details of the usage. + +```python +from nncf import compress_weights, CompressWeightsMode, Dataset +from nncf.data import generate_text_data +synthetic_data = generate_text_data(model, tokenizer) +nncf_dataset = nncf.Dataset(synthetic_data, transform_fn) +``` + - Accuracy of the 4-bit compressed models also can be improved by using AWQ, Scale Estimation, GPTQ or Lora Correction algorithms over data-based mixed-precision algorithm. These algorithms work by equalizing a subset of weights to minimize the difference between the original precision and the 4-bit precision. Unlike all others, the Lora Correction algorithm inserts an additional Linear layers for reducing quantization noise and further accuracy improvement. Inevitably, this approach introduces a memory and a runtime overheads, but they are negligible, since the inserted weight much smaller and can be quantized to 8-bit. The AWQ, Scale Estimation (SE) and Lora Correction (LC) algo can be used in any combination together: AWQ + SE, AWQ + LC, SE + LC, AWQ + SE + LC. The GPTQ algorithm can be combined with AWQ and Scale Estimation in any combination: AWQ + GPTQ, GPTQ + SE, AWQ + GPTQ + SE. Below are examples demonstrating how to enable the AWQ, Scale Estimation, GPTQ or Lora Correction algorithms: - Prepare the calibration dataset for data-based algorithms: +
+ Prepare the calibration dataset for data-based algorithms -```python -from datasets import load_dataset -from functools import partial -from nncf import compress_weights, CompressWeightsMode, Dataset -from optimum.intel.openvino import OVModelForCausalLM -from transformers import AutoTokenizer + ```python + from datasets import load_dataset + from functools import partial + from nncf import compress_weights, CompressWeightsMode, Dataset + from optimum.intel.openvino import OVModelForCausalLM + from transformers import AutoTokenizer -def transform_func(item, tokenizer, input_shapes): - text = item['text'] - tokens = tokenizer(text) + def transform_func(item, tokenizer, input_shapes): + text = item['text'] + tokens = tokenizer(text) - res = {'input_ids': np.expand_dims(np.array(tokens['input_ids']), 0), - 'attention_mask': np.expand_dims(np.array(tokens['attention_mask']), 0)} + res = {'input_ids': np.expand_dims(np.array(tokens['input_ids']), 0), + 'attention_mask': np.expand_dims(np.array(tokens['attention_mask']), 0)} - if 'position_ids' in input_shapes: - position_ids = np.cumsum(res['attention_mask'], axis=1) - 1 - position_ids[res['attention_mask'] == 0] = 1 - res['position_ids'] = position_ids + if 'position_ids' in input_shapes: + position_ids = np.cumsum(res['attention_mask'], axis=1) - 1 + position_ids[res['attention_mask'] == 0] = 1 + res['position_ids'] = position_ids - for name, shape in input_shapes.items(): - if name in res: - continue - res[name] = np.zeros(shape) + for name, shape in input_shapes.items(): + if name in res: + continue + res[name] = np.zeros(shape) - return res + return res -def get_input_shapes(model, batch_size = 1): - inputs = {} + def get_input_shapes(model, batch_size = 1): + inputs = {} - for val in model.model.inputs: - name = val.any_name - shape = list(val.partial_shape.get_min_shape()) - shape[0] = batch_size - inputs[name] = shape + for val in model.model.inputs: + name = val.any_name + shape = list(val.partial_shape.get_min_shape()) + shape[0] = batch_size + inputs[name] = shape - return inputs + return inputs -# load your model and tokenizer -model = OVModelForCausalLM.from_pretrained(...) -tokenizer = AutoTokenizer.from_pretrained(...) + # load your model and tokenizer + model = OVModelForCausalLM.from_pretrained(...) + tokenizer = AutoTokenizer.from_pretrained(...) -# prepare dataset for compression -dataset = load_dataset('wikitext', 'wikitext-2-v1', split='train') -dataset = dataset.filter(lambda example: len(example["text"]) > 80) -input_shapes = get_input_shapes(model) -nncf_dataset = Dataset(dataset, partial(transform_func, tokenizer=tokenizer, - input_shapes=input_shapes)) -``` + # prepare dataset for compression + dataset = load_dataset('wikitext', 'wikitext-2-v1', split='train') + dataset = dataset.filter(lambda example: len(example["text"]) > 80) + input_shapes = get_input_shapes(model) + nncf_dataset = Dataset(dataset, partial(transform_func, tokenizer=tokenizer, + input_shapes=input_shapes)) + ``` + +
- How to compress 80% of layers to 4-bit integer with a default data-based mixed precision algorithm and AWQ with Scale Estimation. It requires to set `awq` to `True` and `scale_estimation` to `True` additionally to data-based mixed-precision algorithm. @@ -164,6 +200,26 @@ from nncf import compress_weights, CompressWeightsMode compressed_model = compress_weights(model, mode=CompressWeightsMode.E2M1, group_size=32, all_layers=True) ``` +#### Caching Statistics + +To optimize compression time and reuse statistics across multiple configurations, you can use the `statistics_path` option. This feature enables caching of calculated statistics, allowing them to be loaded from a specified path rather than recalculated for each configuration. This approach can significantly reduce compression time during repeated model compression iterations, making it ideal when searching for optimal compression parameters. + +To enable statistics caching, set the `statistics_path` parameter to your chosen path. + +```python +from nncf.quantization.advanced_parameters import AdvancedCompressionParameters +from nncf import compress_weights + +compressed_model = compress_weights( + model, + advanced_parameters=AdvancedCompressionParameters(statistics_path="statistics") +) +``` + +When `statistics_path` is provided, the system first checks if the specified path exists. If it does, the statistics are loaded from this path. If the path does not exist, the statistics are computed and saved to this path for future use. + +> **NOTE**: Use the `statistics_path` option only in the secure environment to avoid any substitution of the files with statistics. + ### Evaluation results #### Data-free Mixed-Precision on Lambada OpenAI dataset @@ -553,14 +609,13 @@ Here is the perplexity and accuracy with data-free and data-aware mixed-precisio #### Accuracy/Footprint trade-off -Below are the tables showing the accuracy/footprint trade-off for `Qwen/Qwen2-7B` and +Below are the tables showing the accuracy/footprint trade-off for `meta-llama/Llama-2-7b-chat-hf` and `microsoft/Phi-3-mini-4k-instruct` compressed with different options. Compression ratio is defined as the ratio between the size of fp32 model and size of the compressed one. -Accuracy metrics are measured on 4 tasks [lambada openai](https://huggingface.co/datasets/EleutherAI/lambada_openai), [wikitext](https://arxiv.org/pdf/1609.07843.pdf), -[winogrande](https://arxiv.org/abs/1907.10641), [WWB](https://github.com/openvinotoolkit/openvino.genai/tree/master/llm_bench/python/who_what_benchmark/whowhatbench). +Accuracy metrics are measured on 3 tasks [lambada openai](https://huggingface.co/datasets/EleutherAI/lambada_openai), [wikitext](https://arxiv.org/pdf/1609.07843.pdf), [WWB](https://github.com/openvinotoolkit/openvino.genai/tree/master/tools/who_what_benchmark). The `average relative error` in the tables below is the mean of relative errors for each of four tasks with respect to -the metric value for fp32 model. All int4 models are compressed group-wise with `group_size=128` and `mode=CompressionMode.INT4_SYM` and +the metric value for fp32 model. All int4 models are compressed group-wise with `group_size=64` and `mode=CompressionMode.INT4_ASYM` and with calibration dataset based on 128 samples from `wikitext-2-v1`. Int8 model is compressed with `mode=CompressionMode.INT8_ASYM`. The following advanced parameters were used for AWQ, Scale Estimation and Lora Correction algorithms: @@ -574,233 +629,48 @@ AdvancedCompressionParameters( The tables clearly shows the followings: -- More layers in 8 bit does improve accuracy, but it increases the footprint a lot. -- Scale Estimation, AWQ, GPTQ do improve accuracy of the baseline int4 model without footprint increase. -- Lora correction algorithm improves the accuracy of int4 models further with a footprint much less compared to mixed-precision models with the same or worse accuracy. +- More layers in 8 bit does improve accuracy, but it also increases the footprint significantly. +- Scale Estimation, AWQ, GPTQ improve the accuracy of the baseline int4 model without increasing the footprint. +- The Lora Correction algorithm further improves the accuracy of int4 models with a much smaller footprint compared to mixed-precision models that have the same or worse accuracy. -Accuracy/footprint trade-off for `Qwen/Qwen2-7B`: +Accuracy/footprint trade-off for `meta-llama/Llama-2-7b-chat-hf`: -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Mode %int4%int8lora
rank		average
relative
error		compression
rate
fp320%0%0.0%1.0x
int80%100%7.9%3.9x
int4 + awq + scale estimation + lora correction100%0%25616.5%5.8x
int4 + awq + scale estimation40%60%17.1%4.7x
int4 + awq + scale estimation60%40%17.1%5.2x
int4 + awq + scale estimation + lora correction100%0%3217.4%6.5x
int4 + awq + scale estimation + lora correction100%0%817.5%6.6x
int4 + awq + scale estimation80%20%17.5%5.8x
int4 + awq + scale estimation + lora correction100%0%1618.0%6.6x
int4 + awq + scale estimation100%0%18.4%6.7x
int4 + awq + scale estimation + gptq100%0%20.2%6.7x
int4100%0%21.4%6.7x
+| mode | %int4 | %int8 | lora
rank | average
relative
error | compression
rate | +|:------------------------------------------------|:--------|:--------|:---------------|:-------------------------------|:----------------------| +| fp32 | 0% | 0% | | 0.0% | 1.0x | +| int4 + awq + scale estimation + lora correction | 100% | 0% | 256.0 | 2.5% | 6.1x | +| int4 + awq + scale estimation | 40% | 60% | | 2.5% | 4.8x | +| int4 + awq + scale estimation | 60% | 40% | | 2.7% | 5.4x | +| int4 + awq + scale estimation | 80% | 20% | | 3.5% | 6.2x | +| int4 + awq + scale estimation + lora correction | 100% | 0% | 128.0 | 3.6% | 6.6x | +| int4 + awq + scale estimation + lora correction | 100% | 0% | 32.0 | 3.9% | 7.0x | +| int4 + awq + scale estimation + gptq | 100% | 0% | | 4.1% | 7.2x | +| int4 + awq + scale estimation | 100% | 0% | | 5.3% | 7.2x | +| int4 | 100% | 0% | | 8.5% | 7.2x | + +![alt text](llama2_asym.png) Accuracy/footprint trade-off for `microsoft/Phi-3-mini-4k-instruct`: -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Mode %int4%int8lora
rank		average
relative
error		compression
rate
fp320%0%0.0%1.0x
int80%100%7.3%4.0x
int4 + scale estimation40%60%16.9%4.9x
int4 + scale estimation60%40%18.4%5.5x
int4 + scale estimation + lora correction100%0%25618.7%6.2x
int4 + scale estimation + lora correction100%0%1620.5%7.3x
int4 + scale estimation + lora correction100%0%3220.6%7.2x
int4 + scale estimation80%20%21.3%6.3x
int4 + scale estimation + gptq100%0%21.7%7.4x
int4 + scale estimation + lora correction100%0%822.1%7.3x
int4 + scale estimation100%0%24.5%7.4x
int4100%0%25.3%7.4x
+| mode | %int4 | %int8 | lora
rank | average
relative
error | compression
rate | +|:------------------------------------------|:--------|:--------|:---------------|:-------------------------------|:----------------------| +| fp32 | 0% | 0% | | 0.0% | 1.0x | +| int8 | 0% | 100% | | 1.0% | 4.0x | +| int4 + scale estimation + lora correction | 100% | 0% | 256.0 | 3.9% | 6.0x | +| int4 + scale estimation | 40% | 60% | | 4.1% | 4.8x | +| int4 + scale estimation | 60% | 40% | | 4.3% | 5.4x | +| int4 + scale estimation + lora correction | 100% | 0% | 128.0 | 4.6% | 6.5x | +| int4 + scale estimation | 80% | 20% | | 5.7% | 6.1x | +| int4 + scale estimation + lora correction | 100% | 0% | 8.0 | 5.8% | 7.1x | +| int4 + scale estimation + gptq | 100% | 0% | | 6.1% | 7.1x | +| int4 + scale estimation | 100% | 0% | | 7.5% | 7.1x | +| int4 | 100% | 0% | | 11.9% | 7.1x | + +![alt text](phi3_asym.png) ### Limitations -- The algorithm is supported for OpenVINO and PyTorch models. +- The algorithm is supported for OpenVINO, PyTorch and Torch FX models. - The compression applies in-place. - The compressed model is not trainable. - INT4_SYM, INT4_ASYM, NF4 and E2M1 modes, grouped quantization and mixed precision selection is available for OpenVINO backend only. diff --git a/docs/usage/post_training_compression/weights_compression/llama2_asym.png b/docs/usage/post_training_compression/weights_compression/llama2_asym.png new file mode 100644 index 00000000000..d99da5ddf11 --- /dev/null +++ b/docs/usage/post_training_compression/weights_compression/llama2_asym.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:041669290e99c841e29752de311e096723096ac58460b6dff9a79ddc19eab93a +size 58306 diff --git a/docs/usage/post_training_compression/weights_compression/phi3_asym.png b/docs/usage/post_training_compression/weights_compression/phi3_asym.png new file mode 100644 index 00000000000..031d136b594 --- /dev/null +++ b/docs/usage/post_training_compression/weights_compression/phi3_asym.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:41f22429de120433fdfcd27f9b392703ffc67fe1cfeda94f8b46a5f193132483 +size 57184 diff --git a/examples/llm_compression/openvino/tiny_llama/main.py b/examples/llm_compression/openvino/tiny_llama/main.py index dd03a4361c6..d6d52f0578d 100644 --- a/examples/llm_compression/openvino/tiny_llama/main.py +++ b/examples/llm_compression/openvino/tiny_llama/main.py @@ -11,9 +11,9 @@ import time from functools import partial -import datasets import numpy as np import openvino as ov +from datasets import load_dataset from optimum.intel.openvino import OVModelForCausalLM from transformers import AutoTokenizer @@ -24,7 +24,7 @@ def main(): MODEL_ID = "PY007/TinyLlama-1.1B-Chat-v0.3" OUTPUT_DIR = "tinyllama_compressed" - dataset = datasets.load_dataset("wikitext", "wikitext-2-raw-v1", split="test") + dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="test") tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) model = OVModelForCausalLM.from_pretrained(MODEL_ID, export=True, load_in_8bit=False, compile=False, stateful=False) diff --git a/examples/llm_compression/openvino/tiny_llama/requirements.txt b/examples/llm_compression/openvino/tiny_llama/requirements.txt index c3fbdb2081f..37bf1fed187 100644 --- a/examples/llm_compression/openvino/tiny_llama/requirements.txt +++ b/examples/llm_compression/openvino/tiny_llama/requirements.txt @@ -1,4 +1,4 @@ transformers datasets==2.14.7 optimum-intel[openvino] -onnx<1.16.2 +onnx==1.17.0 diff --git a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/main.py b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/main.py index 7ab0176eb85..081e99125b4 100644 --- a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/main.py +++ b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/main.py @@ -24,6 +24,7 @@ import nncf from nncf.common.logging import nncf_logger +from nncf.quantization.advanced_parameters import AdvancedCompressionParameters DataItem = TypeVar("DataItem") ModelInput = TypeVar("ModelInput") @@ -63,6 +64,7 @@ def compress_model( group_size=group_size, awq=awq, sensitivity_metric=nncf.parameters.SensitivityMetric.MAX_ACTIVATION_VARIANCE, + advanced_parameters=AdvancedCompressionParameters(statistics_path="statistics"), ) return optimized_ov_model diff --git a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt index 9c33456cb8b..e5e57118e2a 100644 --- a/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt +++ b/examples/llm_compression/openvino/tiny_llama_find_hyperparams/requirements.txt @@ -1,7 +1,8 @@ datasets whowhatbench @ git+https://github.com/andreyanufr/who_what_benchmark.git numpy>=1.23.5 -openvino==2024.4 +openvino==2024.5 optimum-intel[openvino]>=1.13.0 transformers>=4.35.2 -onnx<1.16.2 +onnx==1.17.0 +numpy<2 diff --git a/examples/llm_compression/openvino/tiny_llama_synthetic_data/README.md b/examples/llm_compression/openvino/tiny_llama_synthetic_data/README.md new file mode 100644 index 00000000000..da4556bfba3 --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama_synthetic_data/README.md @@ -0,0 +1,34 @@ +# Compress TinyLLama model using synthetic data + +This example demonstrates how to optimize Large Language Models (LLMs) using NNCF weight compression API & synthetic data for the advanced algorithms usage. The example applies 4/8-bit mixed-precision quantization & Scale Estimation algorithm to weights of Linear (Fully-connected) layers of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) model. +To evaluate the accuracy of the compressed model we measure similarity between two texts generated by the baseline and compressed models using [WhoWhatBench](https://github.com/openvinotoolkit/openvino.genai/tree/master/llm_bench/python/who_what_benchmark) library. + +The example includes the following steps: + +- Prepare `wikitext` dataset. +- Prepare `TinyLlama/TinyLlama-1.1B-Chat-v1.0` text-generation model in OpenVINO representation using [Optimum-Intel](https://huggingface.co/docs/optimum/intel/inference). +- Compress weights of the model with NNCF Weight compression algorithm with Scale Estimation & `wikitext` dataset. +- Prepare `synthetic` dataset using `nncf.data.generate_text_data` method. +- Compress weights of the model with NNCF Weight compression algorithm with Scale Estimation & `synthetic` dataset. +- Measure the similarity of the two models optimized with different datasets. + +## Install requirements + +To use this example: + +- Create a separate Python* environment and activate it: `python3 -m venv nncf_env && source nncf_env/bin/activate` +- Install dependencies: + +```bash +pip install -U pip +pip install -r requirements.txt +pip install ../../../../ +``` + +## Run Example + +The example is fully automated. Just run the following command in the prepared Python environment: + +```bash +python main.py +``` diff --git a/examples/llm_compression/openvino/tiny_llama_synthetic_data/main.py b/examples/llm_compression/openvino/tiny_llama_synthetic_data/main.py new file mode 100644 index 00000000000..86eaa04fa54 --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama_synthetic_data/main.py @@ -0,0 +1,94 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from functools import partial + +import numpy as np +import openvino as ov +import torch +from optimum.intel.openvino import OVModelForCausalLM +from transformers import AutoTokenizer + +import nncf + +SEED = 0 + + +def transform_func(text, tokenizer, ov_model): + input_dtypes = {inp.get_any_name(): inp.get_element_type() for inp in ov_model.inputs} + tokens = tokenizer(text) + input_ids = np.expand_dims(np.array(tokens["input_ids"]), 0) + attention_mask = np.expand_dims(np.array(tokens["attention_mask"]), 0) + position_ids = np.cumsum(attention_mask, axis=1) - 1 + position_ids[attention_mask == 0] = 1 + res = { + "input_ids": input_ids, + "attention_mask": attention_mask, + "position_ids": position_ids.reshape(*attention_mask.shape), + } + + def gen_pkv(num_heads, head_dim, num_layers): + res = {} + shape = (1, num_heads, 0, head_dim) + for i in range(num_layers): + key_name = f"past_key_values.{i}.key" + val_name = f"past_key_values.{i}.value" + res[key_name] = ov.Tensor(shape=shape, type=input_dtypes[key_name]) + res[val_name] = ov.Tensor(shape=shape, type=input_dtypes[val_name]) + return res + + res.update(gen_pkv(4, 64, 22)) + return res + + +def main(): + MODEL_ID = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" + + tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) + hf_model = OVModelForCausalLM.from_pretrained( + MODEL_ID, export=True, load_in_8bit=False, compile=False, stateful=False + ) + + dataset_size = 100 + + # Synthetic-based compression + saved_seed = torch.seed() + torch.manual_seed(SEED) + synthetic_dataset = nncf.data.generate_text_data(hf_model, tokenizer, dataset_size=dataset_size) + quantization_dataset = nncf.Dataset( + synthetic_dataset, partial(transform_func, tokenizer=tokenizer, ov_model=hf_model.model) + ) + hf_model.request = None + torch.manual_seed(saved_seed) + + optimized_model = nncf.compress_weights( + hf_model.model.clone(), + dataset=quantization_dataset, + mode=nncf.CompressWeightsMode.INT4_SYM, + ratio=1.0, + scale_estimation=True, + ) + + # Verify the model output in comparison to floating-point one + input_ids = tokenizer("What is Python? ", return_tensors="pt").to(device=hf_model.device) + max_new_tokens = 100 + + hf_model.model = optimized_model + hf_model.request = None + opt_output = hf_model.generate(**input_ids, max_new_tokens=max_new_tokens) + opt_output_text = tokenizer.decode(opt_output[0]) + + print(f"Optimized model output: {opt_output_text}\n") + return opt_output_text + + +if __name__ == "__main__": + main() diff --git a/examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt b/examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt new file mode 100644 index 00000000000..dfde1f7d619 --- /dev/null +++ b/examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt @@ -0,0 +1,8 @@ +torch==2.5.1 +datasets==3.0.1 +numpy>=1.23.5 +openvino==2024.5 +optimum-intel[openvino]>=1.13.0 +transformers>=4.35.2 +onnx==1.17.0 +numpy<2 diff --git a/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt b/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt index 4b37523b0c1..e9abad5edb4 100644 --- a/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt +++ b/examples/post_training_quantization/onnx/mobilenet_v2/requirements.txt @@ -2,6 +2,7 @@ torchvision tqdm scikit-learn fastdownload -onnx==1.16.0 -onnxruntime==1.17.1 -openvino==2024.4 +onnx==1.17.0 +onnxruntime==1.19.2 +openvino==2024.5 +numpy<2 diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py index 555709a103f..b1b5a7a8c9f 100644 --- a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py +++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py @@ -42,7 +42,7 @@ def validate_ov_model( ) -> Tuple[Dict, int, int]: validator.seen = 0 validator.jdict = [] - validator.stats = [] + validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[]) validator.batch_i = 1 validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) compiled_model = ov.compile_model(ov_model, device_name="CPU") diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py index 2109b750b3b..db385d520a5 100644 --- a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py +++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py @@ -38,7 +38,7 @@ def validate( ) -> Tuple[Dict, int, int]: validator.seen = 0 validator.jdict = [] - validator.stats = [] + validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[]) validator.batch_i = 1 validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) @@ -101,13 +101,12 @@ def print_statistics(stats: np.ndarray, total_images: int, total_objects: int) - def prepare_validation(model: YOLO, args: Any) -> Tuple[Validator, torch.utils.data.DataLoader]: - validator = model.smart_load("validator")(args) + validator = model.task_map[model.task]["validator"](args=args) validator.data = check_det_dataset(args.data) + validator.stride = 32 data_loader = validator.get_dataloader(f"{DATASETS_DIR}/coco128-seg", 1) - validator = model.smart_load("validator")(args) - validator.is_coco = True validator.class_map = coco80_to_coco91_class() validator.names = model.model.names @@ -146,7 +145,7 @@ def validation_ac( ) -> float: validator.seen = 0 validator.jdict = [] - validator.stats = [] + validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[]) validator.batch_i = 1 validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) @@ -190,6 +189,7 @@ def validation_ac( model, quantization_dataset, quantization_dataset, + subset_size=len(data_loader), validation_fn=validation_fn, max_drop=0.003, preset=nncf.QuantizationPreset.MIXED, diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt index 39a2975bb85..796f9bacef9 100644 --- a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt +++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt @@ -1,3 +1,3 @@ -ultralytics==8.0.170 -onnx==1.16.0 -openvino==2024.4 +ultralytics==8.3.22 +onnx==1.17.0 +openvino==2024.5 diff --git a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py index 0919f041ff3..659666962fe 100644 --- a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py +++ b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py @@ -125,7 +125,7 @@ def run_example(): image_size=(256, 256), train_batch_size=1, eval_batch_size=1, - num_workers=1, + num_workers=0, ) datamodule.setup() test_loader = datamodule.test_dataloader() @@ -159,6 +159,7 @@ def transform_fn(data_item): ov_quantized_model = nncf.quantize_with_accuracy_control( model=ov_model, calibration_dataset=calibration_dataset, + subset_size=len(anomaly_images), validation_dataset=validation_dataset, validation_fn=validation_fn, max_drop=max_accuracy_drop, diff --git a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt index 860f9fd1f83..f13627d2edb 100644 --- a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt +++ b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/requirements.txt @@ -1,2 +1,4 @@ anomalib==0.6.0 -openvino==2024.4 +openvino==2024.5 +setuptools<=72.1.0 +numpy<2 diff --git a/examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt b/examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt index b813ce41f94..42fb979ba2b 100644 --- a/examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt +++ b/examples/post_training_quantization/openvino/mobilenet_v2/requirements.txt @@ -2,4 +2,4 @@ torchvision tqdm scikit-learn fastdownload -openvino==2024.4 +openvino==2024.5 diff --git a/examples/post_training_quantization/openvino/yolov8/main.py b/examples/post_training_quantization/openvino/yolov8/main.py index 88a34d5c24c..1ec7b6b4a4d 100644 --- a/examples/post_training_quantization/openvino/yolov8/main.py +++ b/examples/post_training_quantization/openvino/yolov8/main.py @@ -36,7 +36,7 @@ def validate( ) -> Tuple[Dict, int, int]: validator.seen = 0 validator.jdict = [] - validator.stats = [] + validator.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[]) validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) model.reshape({0: [1, 3, -1, -1]}) compiled_model = ov.compile_model(model, device_name="CPU") @@ -66,8 +66,9 @@ def print_statistics(stats: np.ndarray, total_images: int, total_objects: int) - def prepare_validation(model: YOLO, args: Any) -> Tuple[Validator, torch.utils.data.DataLoader]: - validator = model.smart_load("validator")(args) + validator = model.task_map[model.task]["validator"](args=args) validator.data = check_det_dataset(args.data) + validator.stride = 32 dataset = validator.data["val"] print(f"{dataset}") @@ -120,6 +121,7 @@ def transform_fn(data_item: Dict): quantized_model = nncf.quantize( model, quantization_dataset, + subset_size=len(data_loader), preset=nncf.QuantizationPreset.MIXED, ignored_scope=nncf.IgnoredScope( types=["Multiply", "Subtract", "Sigmoid"], diff --git a/examples/post_training_quantization/openvino/yolov8/requirements.txt b/examples/post_training_quantization/openvino/yolov8/requirements.txt index d76435e18c4..796f9bacef9 100644 --- a/examples/post_training_quantization/openvino/yolov8/requirements.txt +++ b/examples/post_training_quantization/openvino/yolov8/requirements.txt @@ -1,3 +1,3 @@ -ultralytics==8.0.170 -onnx>=1.12.0,<1.16.2 -openvino==2024.4 +ultralytics==8.3.22 +onnx==1.17.0 +openvino==2024.5 diff --git a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py index 82e5941f609..abdcb982d12 100644 --- a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py +++ b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py @@ -38,7 +38,7 @@ def validate( ) -> Tuple[Dict, int, int]: validator.seen = 0 validator.jdict = [] - validator.stats = [] + validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[]) validator.batch_i = 1 validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) model.reshape({0: [1, 3, -1, -1]}) @@ -92,15 +92,14 @@ def print_statistics(stats: np.ndarray, total_images: int, total_objects: int) - def prepare_validation(model: YOLO, args: Any) -> Tuple[Validator, torch.utils.data.DataLoader]: - validator = model.smart_load("validator")(args) + validator = model.task_map[model.task]["validator"](args=args) validator.data = check_det_dataset(args.data) + validator.stride = 32 dataset = validator.data["val"] print(f"{dataset}") data_loader = validator.get_dataloader(f"{DATASETS_DIR}/coco128-seg", 1) - validator = model.smart_load("validator")(args) - validator.is_coco = True validator.class_map = coco80_to_coco91_class() validator.names = model.model.names @@ -146,7 +145,7 @@ def validation_ac( ) -> float: validator.seen = 0 validator.jdict = [] - validator.stats = [] + validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[]) validator.batch_i = 1 validator.confusion_matrix = ConfusionMatrix(nc=validator.nc) num_outputs = len(compiled_model.outputs) @@ -183,6 +182,7 @@ def validation_ac( model, quantization_dataset, quantization_dataset, + subset_size=len(data_loader), validation_fn=validation_fn, max_drop=0.003, preset=nncf.QuantizationPreset.MIXED, @@ -210,6 +210,7 @@ def main(): model = YOLO(f"{ROOT}/{MODEL_NAME}.pt") args = get_cfg(cfg=DEFAULT_CFG) args.data = "coco128-seg.yaml" + args.workers = 0 # Prepare validation dataset and helper validator, data_loader = prepare_validation(model, args) diff --git a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt index d76435e18c4..796f9bacef9 100644 --- a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt +++ b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt @@ -1,3 +1,3 @@ -ultralytics==8.0.170 -onnx>=1.12.0,<1.16.2 -openvino==2024.4 +ultralytics==8.3.22 +onnx==1.17.0 +openvino==2024.5 diff --git a/examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt b/examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt index 7f51755575c..ea116868d8e 100644 --- a/examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt +++ b/examples/post_training_quantization/tensorflow/mobilenet_v2/requirements.txt @@ -2,4 +2,4 @@ tensorflow~=2.12.0; python_version < '3.9' tensorflow~=2.15.1; python_version >= '3.9' tensorflow-datasets tqdm -openvino==2024.4 +openvino==2024.5 diff --git a/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt b/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt index 5a04422318d..ecadc5aa374 100644 --- a/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt +++ b/examples/post_training_quantization/torch/mobilenet_v2/requirements.txt @@ -1,5 +1,6 @@ fastdownload==0.0.7 -openvino==2024.4 +openvino==2024.5 scikit-learn -torch==2.4.0 -torchvision==0.19.0 +torch==2.5.1 +torchvision==0.20.1 +setuptools<=72.1.0 diff --git a/examples/post_training_quantization/torch/ssd300_vgg16/main.py b/examples/post_training_quantization/torch/ssd300_vgg16/main.py index 7eff8b6bf20..3e28861743c 100644 --- a/examples/post_training_quantization/torch/ssd300_vgg16/main.py +++ b/examples/post_training_quantization/torch/ssd300_vgg16/main.py @@ -152,7 +152,7 @@ def main(): # Quantize model calibration_dataset = nncf.Dataset(dataset, partial(transform_fn, device=device)) - quantized_model = nncf.quantize(model, calibration_dataset) + quantized_model = nncf.quantize(model, calibration_dataset, subset_size=len(dataset)) # Convert to OpenVINO dummy_input = torch.randn(1, 3, 480, 480) diff --git a/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt b/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt index 685f414a2e9..4792214d244 100644 --- a/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt +++ b/examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt @@ -1,7 +1,9 @@ fastdownload==0.0.7 -onnx==1.16.0 -openvino==2024.4 +onnx==1.17.0 +openvino==2024.5 pycocotools==2.0.7 -torch==2.4.0 +torch==2.5.1 torchmetrics==1.0.1 -torchvision==0.19.0 +torchvision==0.20.1 +numpy<2 +setuptools<=72.1.0 diff --git a/examples/post_training_quantization/torch_fx/resnet18/README.md b/examples/post_training_quantization/torch_fx/resnet18/README.md new file mode 100644 index 00000000000..53f6a360b78 --- /dev/null +++ b/examples/post_training_quantization/torch_fx/resnet18/README.md @@ -0,0 +1,30 @@ +# Post-Training Quantization of Resnet18 PyTorch Model exported to torch.fx.GraphModule + +This example demonstrates how to use Post-Training Quantization API from Neural Network Compression Framework (NNCF) to quantize PyTorch models exported to torch.fx.GraphModule on the example of Resnet18 post-training quantization, pretrained on Tiny ImageNet-200 dataset. + +The example includes the following steps: + +- Loading the Tiny ImageNet-200 dataset (~237 Mb) and the Resnet18 PyTorch model pretrained on this dataset. +- Exporting model to torch.fx.GraphModule by torch.export.export function. +- Quantizing the model using NNCF Post-Training Quantization algorithm. +- Output of the following characteristics of the quantized model: + - Accuracy drop of the quantized model (INT8) over the pre-trained model (FP32) + - Performance speed up of the quantized model (INT8) + +## Install requirements + +At this point it is assumed that you have already installed NNCF. You can find information on installation NNCF [here](https://github.com/openvinotoolkit/nncf#user-content-installation). + +To work with the example you should install the corresponding Python package dependencies: + +```bash +pip install -r requirements.txt +``` + +## Run Example + +It's pretty simple. The example does not require additional preparation. It will do the preparation itself, such as loading the dataset and model, etc. + +```bash +python main.py +``` diff --git a/examples/post_training_quantization/torch_fx/resnet18/main.py b/examples/post_training_quantization/torch_fx/resnet18/main.py new file mode 100644 index 00000000000..eb348e9bfd7 --- /dev/null +++ b/examples/post_training_quantization/torch_fx/resnet18/main.py @@ -0,0 +1,246 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from pathlib import Path +from time import time +from typing import Tuple + +# We need to import openvino.torch for torch.compile() with openvino backend to work. +import openvino.torch # noqa +import torch +import torch.nn as nn +import torch.nn.parallel +import torch.optim +import torch.utils.data +import torch.utils.data.distributed +import torchvision.datasets as datasets +import torchvision.models as models +import torchvision.transforms as transforms +from fastdownload import FastDownload +from torch._dynamo.exc import BackendCompilerFailed + +import nncf +import nncf.torch +from nncf.common.logging.track_progress import track +from nncf.common.utils.helpers import create_table +from nncf.common.utils.os import is_windows +from nncf.torch import disable_patching + +IMAGE_SIZE = 64 + + +ROOT = Path(__file__).parent.resolve() +BEST_CKPT_NAME = "resnet18_int8_best.pt" +CHECKPOINT_URL = ( + "https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/302_resnet18_fp32_v1.pth" +) +DATASET_URL = "http://cs231n.stanford.edu/tiny-imagenet-200.zip" +DATASET_PATH = "~/.cache/nncf/datasets" + + +def download_dataset() -> Path: + downloader = FastDownload(base=DATASET_PATH, archive="downloaded", data="extracted") + return downloader.get(DATASET_URL) + + +def load_checkpoint(model: torch.nn.Module) -> torch.nn.Module: + checkpoint = torch.hub.load_state_dict_from_url(CHECKPOINT_URL, map_location=torch.device("cpu"), progress=False) + model.load_state_dict(checkpoint["state_dict"]) + return model, checkpoint["acc1"] + + +def get_resnet18_model(device: torch.device) -> torch.nn.Module: + num_classes = 200 # 200 is for Tiny ImageNet, default is 1000 for ImageNet + model = models.resnet18(weights=None) + # Update the last FC layer for Tiny ImageNet number of classes. + model.fc = nn.Linear(in_features=512, out_features=num_classes, bias=True) + model.to(device) + return model + + +def measure_latency(model, example_inputs, num_iters=2000) -> float: + with torch.no_grad(): + model(example_inputs) + total_time = 0 + for _ in range(num_iters): + start_time = time() + model(example_inputs) + total_time += time() - start_time + average_time = (total_time / num_iters) * 1000 + return average_time + + +def validate(val_loader: torch.utils.data.DataLoader, model: torch.nn.Module, device: torch.device) -> float: + top1_sum = 0.0 + + with torch.no_grad(): + for images, target in track(val_loader, total=len(val_loader), description="Validation:"): + images = images.to(device) + target = target.to(device) + + # Compute output. + output = model(images) + + # Measure accuracy and record loss. + [acc1] = accuracy(output, target, topk=(1,)) + top1_sum += acc1.item() + + num_samples = len(val_loader) + top1_avg = top1_sum / num_samples + return top1_avg + + +def accuracy(output: torch.Tensor, target: torch.tensor, topk: Tuple[int, ...] = (1,)): + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +def create_data_loaders(): + dataset_path = download_dataset() + + prepare_tiny_imagenet_200(dataset_path) + print(f"Successfully downloaded and prepared dataset at: {dataset_path}") + + val_dir = dataset_path / "val" + + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + val_dataset = datasets.ImageFolder( + val_dir, + transforms.Compose( + [ + transforms.Resize(IMAGE_SIZE), + transforms.ToTensor(), + normalize, + ] + ), + ) + + val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=True) + + calibration_dataset = torch.utils.data.DataLoader( + val_dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=True + ) + + return val_loader, calibration_dataset + + +def prepare_tiny_imagenet_200(dataset_dir: Path): + # Format validation set the same way as train set is formatted. + val_data_dir = dataset_dir / "val" + val_images_dir = val_data_dir / "images" + if not val_images_dir.exists(): + return + + val_annotations_file = val_data_dir / "val_annotations.txt" + with open(val_annotations_file, "r") as f: + val_annotation_data = map(lambda line: line.split("\t")[:2], f.readlines()) + for image_filename, image_label in val_annotation_data: + from_image_filepath = val_images_dir / image_filename + to_image_dir = val_data_dir / image_label + if not to_image_dir.exists(): + to_image_dir.mkdir() + to_image_filepath = to_image_dir / image_filename + from_image_filepath.rename(to_image_filepath) + val_annotations_file.unlink() + val_images_dir.rmdir() + + +def main(): + device = torch.device("cpu") + print(f"Using {device} device") + + ############################################################################### + # Step 1: Prepare model and dataset + print(os.linesep + "[Step 1] Prepare model and dataset") + + model = get_resnet18_model(device) + model, acc1_fp32 = load_checkpoint(model) + + print(f"Accuracy@1 of original FP32 model: {acc1_fp32:.3f}") + + val_loader, calibration_dataset = create_data_loaders() + + def transform_fn(data_item): + return data_item[0].to(device) + + quantization_dataset = nncf.Dataset(calibration_dataset, transform_fn) + + ############################################################################### + # Step 2: Quantize model + print(os.linesep + "[Step 2] Quantize model") + + input_shape = (1, 3, IMAGE_SIZE, IMAGE_SIZE) + example_input = torch.ones(*input_shape).to(device) + + with disable_patching(): + fx_model = torch.export.export_for_training(model.eval(), args=(example_input,)).module() + quantized_fx_model = nncf.quantize(fx_model, quantization_dataset) + quantized_fx_model = torch.compile(quantized_fx_model, backend="openvino") + + acc1_int8 = validate(val_loader, quantized_fx_model, device) + + print(f"Accuracy@1 of INT8 model: {acc1_int8:.3f}") + print(f"Accuracy diff FP32 - INT8: {acc1_fp32 - acc1_int8:.3f}") + + ############################################################################### + # Step 3: Run benchmarks + print(os.linesep + "[Step 3] Run benchmarks") + print("Benchmark FP32 model compiled with default backend ...") + with disable_patching(): + compiled_model = torch.compile(model) + try: + fp32_latency = measure_latency(compiled_model, example_inputs=example_input) + except BackendCompilerFailed as exp: + if not is_windows(): + raise exp + print( + "WARNING: Torch Inductor is currently unavailable on Windows. " + "For more information, visit https://github.com/pytorch/pytorch/issues/135954" + ) + fp32_latency = float("nan") + print(f"{fp32_latency:.3f} ms") + + print("Benchmark FP32 model compiled with openvino backend ...") + with disable_patching(): + compiled_model = torch.compile(model, backend="openvino") + fp32_ov_latency = measure_latency(compiled_model, example_inputs=example_input) + print(f"{fp32_ov_latency:.3f} ms") + + print("Benchmark INT8 model compiled with openvino backend ...") + with disable_patching(): + int8_latency = measure_latency(quantized_fx_model, example_inputs=example_input) + print(f"{int8_latency:.3f} ms") + + print("[Step 4] Summary:") + tabular_data = [ + ["default", "FP32", f"{fp32_latency:.3f}", ""], + ["openvino", "FP32", f"{fp32_ov_latency:.3f}", f"x{fp32_latency / fp32_ov_latency:.3f}"], + ["openvino", "INT8", f"{int8_latency:.3f}", f"x{fp32_latency / int8_latency:.3f}"], + ] + print(create_table(["Backend", "Precision", "Performance (ms)", "Speed up"], tabular_data)) + return acc1_fp32, acc1_int8, fp32_latency, fp32_ov_latency, int8_latency + + +if __name__ == "__main__": + main() diff --git a/examples/post_training_quantization/torch_fx/resnet18/requirements.txt b/examples/post_training_quantization/torch_fx/resnet18/requirements.txt new file mode 100644 index 00000000000..f1050a782d2 --- /dev/null +++ b/examples/post_training_quantization/torch_fx/resnet18/requirements.txt @@ -0,0 +1,4 @@ +fastdownload==0.0.7 +openvino==2024.5 +torch==2.5.1 +torchvision==0.20.1 diff --git a/examples/quantization_aware_training/torch/anomalib/requirements.txt b/examples/quantization_aware_training/torch/anomalib/requirements.txt index 16bfe3c7f2c..6372c123d36 100644 --- a/examples/quantization_aware_training/torch/anomalib/requirements.txt +++ b/examples/quantization_aware_training/torch/anomalib/requirements.txt @@ -1 +1,2 @@ -anomalib[core,openvino]==1.0.0 \ No newline at end of file +anomalib[core,openvino]==1.0.1 +setuptools<=72.1.0 diff --git a/examples/quantization_aware_training/torch/resnet18/requirements.txt b/examples/quantization_aware_training/torch/resnet18/requirements.txt index 040eb6d97dd..0ee68a09fd9 100644 --- a/examples/quantization_aware_training/torch/resnet18/requirements.txt +++ b/examples/quantization_aware_training/torch/resnet18/requirements.txt @@ -1,4 +1,5 @@ fastdownload==0.0.7 -openvino==2024.4 -torch==2.4.0 -torchvision==0.19.0 +openvino==2024.5 +torch==2.5.1 +torchvision==0.20.1 +setuptools<=72.1.0 diff --git a/examples/tensorflow/classification/main.py b/examples/tensorflow/classification/main.py index 1f977068b56..06a850eba44 100644 --- a/examples/tensorflow/classification/main.py +++ b/examples/tensorflow/classification/main.py @@ -14,7 +14,6 @@ from pathlib import Path import tensorflow as tf -import tensorflow_addons as tfa import nncf from examples.common.paths import configure_paths @@ -199,8 +198,8 @@ def run(config): metrics = [ tf.keras.metrics.CategoricalAccuracy(name="acc@1"), tf.keras.metrics.TopKCategoricalAccuracy(k=5, name="acc@5"), - tfa.metrics.MeanMetricWrapper(loss_obj, name="ce_loss"), - tfa.metrics.MeanMetricWrapper(compression_ctrl.loss, name="cr_loss"), + tf.keras.metrics.MeanMetricWrapper(loss_obj, name="ce_loss"), + tf.keras.metrics.MeanMetricWrapper(compression_ctrl.loss, name="cr_loss"), ] compress_model.compile( diff --git a/examples/tensorflow/common/optimizer.py b/examples/tensorflow/common/optimizer.py index 78fead279ec..a84afe13782 100644 --- a/examples/tensorflow/common/optimizer.py +++ b/examples/tensorflow/common/optimizer.py @@ -10,7 +10,6 @@ # limitations under the License. import tensorflow as tf -import tensorflow_addons as tfa from examples.tensorflow.common.logger import logger @@ -33,7 +32,7 @@ def build_optimizer(config, scheduler): weight_decay = optimizer_config.get("weight_decay", None) common_params = {"learning_rate": scheduler, "nesterov": nesterov, "momentum": momentum} if weight_decay: - optimizer = tfa.optimizers.SGDW(**common_params, weight_decay=weight_decay) + optimizer = tf.keras.optimizers.SGD(**common_params, weight_decay=weight_decay) else: optimizer = tf.keras.optimizers.SGD(**common_params) elif optimizer_type == "rmsprop": @@ -59,18 +58,10 @@ def build_optimizer(config, scheduler): "amsgrad": amsgrad, } if weight_decay: - optimizer = tfa.optimizers.AdamW(**common_params, weight_decay=weight_decay) + optimizer = tf.keras.optimizers.AdamW(**common_params, weight_decay=weight_decay) else: optimizer = tf.keras.optimizers.Adam(**common_params) else: raise ValueError("Unknown optimizer %s" % optimizer_type) - moving_average_decay = optimizer_params.get("moving_average_decay", 0.0) - if moving_average_decay > 0.0: - logger.info("Including moving average decay.") - optimizer = tfa.optimizers.MovingAverage(optimizer, average_decay=moving_average_decay, num_updates=None) - if optimizer_params.get("lookahead", None): - logger.info("Using lookahead optimizer.") - optimizer = tfa.optimizers.Lookahead(optimizer) - return optimizer diff --git a/examples/tensorflow/requirements.txt b/examples/tensorflow/requirements.txt index 2a7da4ea429..2c758123698 100644 --- a/examples/tensorflow/requirements.txt +++ b/examples/tensorflow/requirements.txt @@ -4,8 +4,6 @@ absl-py==1.0.0 tensorflow tensorflow_datasets==4.2.0 tensorflow_hub -tensorflow_addons==0.20.0; python_version < '3.9' -tensorflow_addons==0.23.0; python_version >= '3.9' tensorflow-metadata==1.13.0 opencv-python pycocotools==2.0.6 diff --git a/examples/torch/common/models/classification/mobilenet_v3_tv_092.py b/examples/torch/common/models/classification/mobilenet_v3_tv_092.py index 1ba5f4d5cb3..3cec3ec0bfd 100644 --- a/examples/torch/common/models/classification/mobilenet_v3_tv_092.py +++ b/examples/torch/common/models/classification/mobilenet_v3_tv_092.py @@ -301,7 +301,7 @@ def _mobilenet_v3_model( last_channel: int, pretrained: bool, progress: bool, - **kwargs: Any + **kwargs: Any, ): model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs) if pretrained: diff --git a/examples/torch/common/restricted_pickle_module.py b/examples/torch/common/restricted_pickle_module.py index 2589eace3d1..94add3f5344 100644 --- a/examples/torch/common/restricted_pickle_module.py +++ b/examples/torch/common/restricted_pickle_module.py @@ -38,7 +38,8 @@ class Unpickler(pickle.Unpickler): "torch.nn": {"Module"}, "torch.optim.adam": {"Adam"}, "nncf.api.compression": {"CompressionStage", "CompressionLevel"}, - "numpy.core.multiarray": {"scalar"}, + "numpy.core.multiarray": {"scalar"}, # numpy<2 + "numpy._core.multiarray": {"scalar"}, # numpy>=2 "numpy": {"dtype"}, "_codecs": {"encode"}, } diff --git a/md_dead_link_check.toml b/md_dead_link_check.toml deleted file mode 100644 index cfe1ac2823c..00000000000 --- a/md_dead_link_check.toml +++ /dev/null @@ -1,2 +0,0 @@ -[tool.md_dead_link_check] -exclude_files = ["ReleaseNotes.md"] diff --git a/nncf/__init__.py b/nncf/__init__.py index 64449545310..88ad2cfb09e 100644 --- a/nncf/__init__.py +++ b/nncf/__init__.py @@ -32,6 +32,7 @@ from nncf.errors import UnsupportedModelError as UnsupportedModelError from nncf.errors import UnsupportedVersionError as UnsupportedVersionError from nncf.errors import ValidationError as ValidationError +from nncf.parameters import BackupMode as BackupMode from nncf.parameters import CompressWeightsMode as CompressWeightsMode from nncf.parameters import DropType as DropType from nncf.parameters import ModelType as ModelType diff --git a/nncf/common/accuracy_aware_training/runner.py b/nncf/common/accuracy_aware_training/runner.py index 7eec4417907..a383a527e0a 100644 --- a/nncf/common/accuracy_aware_training/runner.py +++ b/nncf/common/accuracy_aware_training/runner.py @@ -42,7 +42,7 @@ Image = Any # Default type for Image. try: - import matplotlib.pyplot as plt # type: ignore + import matplotlib.pyplot as plt import PIL.Image from PIL.Image import Image diff --git a/nncf/common/graph/graph.py b/nncf/common/graph/graph.py index 2e442b8ba42..8766a754de7 100644 --- a/nncf/common/graph/graph.py +++ b/nncf/common/graph/graph.py @@ -11,7 +11,7 @@ import pathlib from collections import defaultdict from copy import deepcopy -from typing import Any, Callable, Dict, Generator, KeysView, List, Optional, Tuple, Type, ValuesView, cast +from typing import Any, Callable, Dict, Generator, KeysView, List, Optional, Tuple, Type, Union, ValuesView, cast import networkx as nx # type:ignore import networkx.algorithms.isomorphism as iso # type:ignore @@ -278,7 +278,7 @@ def get_all_nodes(self) -> List[NNCFNode]: def get_all_simple_paths( self, start_node_name: NNCFNodeName, end_node_name: NNCFNodeName - ) -> Generator[List[NNCFNodeName], None, None]: + ) -> Generator[List[str], None, None]: """ Generates all simple paths in the NNCFGraph from start node to end node. A simple path is a path with no repeated nodes. @@ -292,9 +292,7 @@ def get_all_simple_paths( end_node = self.get_node_by_name(end_node_name) start_node_key = self.get_node_key_by_id(start_node.node_id) end_node_key = self.get_node_key_by_id(end_node.node_id) - return cast( - Generator[List[NNCFNodeName], None, None], nx.all_simple_paths(self._nx_graph, start_node_key, end_node_key) - ) + return cast(Generator[List[str], None, None], nx.all_simple_paths(self._nx_graph, start_node_key, end_node_key)) @staticmethod def _get_edge_boundaries( @@ -541,7 +539,7 @@ def add_edge_between_nncf_nodes( self, from_node_id: int, to_node_id: int, - tensor_shape: List[int], + tensor_shape: Union[Tuple[int, ...], List[int]], input_port_id: int, output_port_id: int, dtype: Dtype, @@ -640,7 +638,7 @@ def get_graph_for_structure_analysis(self, extended: bool = False) -> nx.DiGraph if "shape" in label and len(label) == 1: attrs_edge["label"] = label["shape"] else: - attrs_edge["label"] = ", ".join((f"{k}:{v}" for k, v in label.items())) + attrs_edge["label"] = ", ".join((f"{k} {v}" for k, v in label.items())) out_graph.add_edge(u, v, **attrs_edge) return out_graph diff --git a/nncf/common/graph/patterns/patterns.py b/nncf/common/graph/patterns/patterns.py index 1acbaf4b6d8..6d5f00a8e9b 100644 --- a/nncf/common/graph/patterns/patterns.py +++ b/nncf/common/graph/patterns/patterns.py @@ -406,3 +406,4 @@ class IgnoredPatternNames(Enum): SE_BLOCK = PatternDesc("se_block") FC_BN_HSWISH_ACTIVATION = PatternDesc("fc_bn_hswish_activation") EQUAL_LOGICALNOT = PatternDesc("equal_logicalnot") + ROPE = PatternDesc("rope", model_types=[ModelType.TRANSFORMER]) diff --git a/nncf/common/insertion_point_graph.py b/nncf/common/insertion_point_graph.py index 118d079fc8e..7bb1368dadf 100644 --- a/nncf/common/insertion_point_graph.py +++ b/nncf/common/insertion_point_graph.py @@ -73,7 +73,6 @@ class InsertionPointGraph(nx.DiGraph): def __init__( self, nncf_graph: NNCFGraph, - weight_modifiable_node_names: List[NNCFNodeName] = None, allowed_pre_hook_insertion_points: List[PreHookInsertionPoint] = None, allowed_post_hook_insertion_points: List[PostHookInsertionPoint] = None, ): @@ -81,8 +80,6 @@ def __init__( Initializes the insertion point graph. :param nncf_graph: The base NNCFGraph representing the model structure. - :param weight_modifiable_node_names: Names of the nodes in `nncf_graph` that correspond to operations with - modifiable weights. :param allowed_pre_hook_insertion_points: A list of pre-hook insertion points for this graph to allow. If left unspecified, every node in `nncf_graph` will be allowed to have a separate pre-hook for each of its tensor inputs. @@ -93,10 +90,6 @@ def __init__( super().__init__() self._base_nx_graph = deepcopy(nncf_graph.get_nx_graph_copy()) - if weight_modifiable_node_names is None: - self._weight_modifiable_node_names = [] - else: - self._weight_modifiable_node_names = weight_modifiable_node_names if allowed_pre_hook_insertion_points is None: allowed_pre_hook_insertion_points = self._get_default_pre_hook_ip_list(nncf_graph) @@ -236,10 +229,6 @@ def __init__( for follower_node_key in self.successors(from_node_key): self.edges[from_node_key, follower_node_key][self.IS_INTEGER_PATH_EDGE_ATTR] = True - @property - def weight_modifiable_node_names(self) -> List[NNCFNodeName]: - return self._weight_modifiable_node_names - @staticmethod def _get_default_pre_hook_ip_list(nncf_graph: NNCFGraph) -> List[PreHookInsertionPoint]: # Pre-hook all input ports of all nodes diff --git a/nncf/common/quantization/quantizer_propagation/graph.py b/nncf/common/quantization/quantizer_propagation/graph.py index a9e488ee754..7e1edcc5f39 100644 --- a/nncf/common/quantization/quantizer_propagation/graph.py +++ b/nncf/common/quantization/quantizer_propagation/graph.py @@ -719,6 +719,7 @@ def remove_propagating_quantizer( if prop_quantizer.unified_scale_type is not None: gid = self._unified_scale_group_manager.get_group_id_by_propagating_quantizer_id(prop_quantizer.id) self._unified_scale_group_manager.remove_from_group(gid, prop_quantizer) + self._pqs_after_weight_dependent_output_quantized_nodes.pop(prop_quantizer, None) def propagate_quantizer_via_path( self, prop_quantizer: PropagatingQuantizer, path: PropagationPath diff --git a/nncf/common/quantization/quantizer_propagation/solver.py b/nncf/common/quantization/quantizer_propagation/solver.py index f16771efcf2..0008f94b780 100644 --- a/nncf/common/quantization/quantizer_propagation/solver.py +++ b/nncf/common/quantization/quantizer_propagation/solver.py @@ -34,6 +34,7 @@ from nncf.common.quantization.quantizer_propagation.structs import PropagatingQuantizer from nncf.common.quantization.quantizer_propagation.structs import PropagationPath from nncf.common.quantization.quantizer_propagation.structs import QuantizationTrait +from nncf.common.quantization.quantizer_propagation.structs import QuantizerPropagationRule from nncf.common.quantization.quantizer_propagation.structs import QuantizerPropagationStateGraphNodeType from nncf.common.quantization.quantizer_setup import DEFAULT_QUANTIZER_CONFIG from nncf.common.quantization.quantizer_setup import MultiConfigQuantizerSetup @@ -59,25 +60,6 @@ class TransitionStatus(Enum): SHOULD_WAIT_FOR_MERGE = 3 -class PropagationStrategy(Enum): - # While propagating up through a downward-branching node: - # ... do not merge at all - DO_NOT_MERGE_BRANCH_FQS = 0 - - # ... only merge for exact configuration space matches - MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME = 1 - - # ... merge common parts, and if a branch quantizer has options for - # narrowing (bitwidth/mode/per-channel/etc.) in addition to - # the common part, keep the quantizer on branch - MERGE_WITH_POTENTIAL_REQUANTIZATION = 2 - - # ... merge common config options into a single config space for the global FQ, - # do not merge if this is impossible for the current branching situation and given - # HW config file - MERGE_WITH_SINGLE_FQ_RESULT = 3 - - class FinalizedQuantizationProposal: """ Describes a version of QuantizationProposal in which a single quantizer configuration has been chosen @@ -317,7 +299,7 @@ class QuantizerPropagationSolver: QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC, signedness_to_force=None, per_channel=False) ] - DEFAULT_PROPAGATION_STRATEGY = PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT + DEFAULT_PROPAGATION_STRATEGY = QuantizerPropagationRule.MERGE_ALL_IN_ONE def __init__( self, @@ -327,7 +309,7 @@ def __init__( weight_target_scopes: List[str] = None, hw_config: HWConfig = None, default_trait_to_metatype_map: Dict[QuantizationTrait, List[OperatorMetatype]] = None, - propagation_strategy: PropagationStrategy = None, + propagation_strategy: QuantizerPropagationRule = None, default_qconfig_list: List[QuantizerConfig] = None, quantizable_layer_nodes: List[QuantizableWeightedLayerNode] = None, scope_overrides: Dict = None, @@ -468,7 +450,9 @@ def _filter_by_weight_ignored_target_scopes( nncf_logger.debug(f"Ignored adding weight quantizer for: {node_name}") return weight_quantizable_node_names_vs_qconfigs - def run_on_ip_graph(self, ip_graph: InsertionPointGraph) -> QuantizationProposal: + def run_on_ip_graph( + self, ip_graph: InsertionPointGraph, metatypes_for_filter: Optional[List[OperatorMetatype]] = None + ) -> QuantizationProposal: """ The main function to be used on an InsertionPointGraph to produce the list of insertion commands and configs corresponding to the desired quantized @@ -479,6 +463,7 @@ def run_on_ip_graph(self, ip_graph: InsertionPointGraph) -> QuantizationProposal :param ip_graph: The InsertionPointGraph, potentially with fused operations w.r.t. the original model graph. The propagating quantizers will travel along the pre- and post- hook nodes registered in this graph. + :param metatypes_for_filter: Metatypes are used for the removal criterion. :return: The intermediate propagation state in the form of QuantizationProposal, which defines unambiguously the locations of the propagating quantizers, but not the final configurations. @@ -513,6 +498,8 @@ def run_on_ip_graph(self, ip_graph: InsertionPointGraph) -> QuantizationProposal iteration_counter += 1 quant_prop_graph = self._filter_integer_input_quantizers(quant_prop_graph) + if metatypes_for_filter: + quant_prop_graph = self._filter_quantizers_by_metatypes(quant_prop_graph, metatypes_for_filter) if self._visualizer is not None: self._visualizer.visualize_quantizer_propagation(self, quant_prop_graph, "proposed") @@ -627,10 +614,7 @@ def _handle_quantizer_merge( qconfs_list = [pq.potential_quant_configs for pq in waiting_pqs_list] merged_qconf_list, branch_qconf_lists = self.get_merged_qconfigs_for_downward_branching_case(qconfs_list) - if ( - merged_qconf_list is None - and self._propagation_strategy == PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT - ): + if merged_qconf_list is None and self._propagation_strategy == QuantizerPropagationRule.MERGE_ALL_IN_ONE: all_confs = "\n".join(", ".join([f"[{str(qconf)}]" for qconf in qconfs]) for qconfs in qconfs_list) nncf_logger.debug( f"Could not merge the quantizers at branching point {branching_node_key} - " @@ -1400,10 +1384,10 @@ def get_merged_qconfigs_for_downward_branching_case( that would have to remain on the branches (if any). """ - if self._propagation_strategy == PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: + if self._propagation_strategy == QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: # Do not merge at all return None, potential_qconfigs_for_each_branch - if self._propagation_strategy == PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: + if self._propagation_strategy == QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME: # Only merge for exact matches of the qconfig lists first_pq_list = potential_qconfigs_for_each_branch[0] first_pq_list_counter = Counter(first_pq_list) @@ -1435,9 +1419,9 @@ def compatible_wo_requant(qconf: QuantizerConfig, other_qconf_list: List[Quantiz return True return False - if self._propagation_strategy == PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: + if self._propagation_strategy == QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: compatible_fn = compatible_with_requant - elif self._propagation_strategy == PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: + elif self._propagation_strategy == QuantizerPropagationRule.MERGE_ALL_IN_ONE: compatible_fn = compatible_wo_requant else: raise nncf.ValidationError(f"Unknown propagation strategy: {self._propagation_strategy}") @@ -1469,7 +1453,7 @@ def compatible_wo_requant(qconf: QuantizerConfig, other_qconf_list: List[Quantiz merged_qconfig_list_counter = Counter(merged_qconfig_list) resulting_branch_qconfig_lists = [None for _ in potential_qconfigs_for_each_branch] - if self._propagation_strategy == PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: + if self._propagation_strategy == QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: for idx, branch_qconfig_list in enumerate(potential_qconfigs_for_each_branch): if Counter(branch_qconfig_list) == merged_qconfig_list_counter: continue # This branch will have the branch quantizer removed @@ -1597,3 +1581,66 @@ def _filter_integer_input_quantizers( quant_prop_graph.remove_propagating_quantizer(integer_input_pq) return quant_prop_graph + + def _filter_quantizers_by_metatypes( + self, quant_prop_graph: QuantizerPropagationStateGraph, metatypes: List[OperatorMetatype] + ) -> QuantizerPropagationStateGraph: + """ + Removes quantizers for which _is_quantizer_to_remove returns True. + + :param quant_prop_graph: The quantizer propagation state graph. + :param metatypes: Metatypes are used for the removal criterion. + :return: Filtered quantizer propagation state graph. + """ + + def _is_quantizer_to_remove( + quant_prop_graph: QuantizerPropagationStateGraph, + quantizer: PropagatingQuantizer, + metatypes: List[OperatorMetatype], + ) -> bool: + """ + Returns True if the quantizer meets the criteria for removal. The criteria are as follows: + 1. The quantizer is generated from a node whose metatype is in the provided metatypes. + 2. The quantizer is not propagated. + 3. The quantizer has only one child. + 4. The quantized node generates only one activation quantizer. + The function relies on the fact that considered metatypes should have two inputs. + In that case, if considered node at InsertionPointGraph has only one input, + it means that the another one is a constant. + + :param quant_prop_graph: The quantizer propagation state graph holding the `quantizer`. + :param quantizer: The propagating quantizer to be currently considered. + :param metatypes: Metatypes are used for the criterion. + :return: True if quantizer satisfies the criteria, otherwise - False. + """ + quantizer_children = quantizer.quantized_input_sink_operator_nodes + quantized_node_metatype = quant_prop_graph.nodes[quantized_node_key][ + QuantizerPropagationStateGraph.OPERATOR_METATYPE_NODE_ATTR + ] + quantizers_generated_for_node = quant_prop_graph.nodes[quantized_node_key][ + quant_prop_graph.AFFECTING_PROPAGATING_QUANTIZERS_ATTR + ] + + is_one_quantizer_generated_for_node = len(quantizers_generated_for_node) == 1 + is_one_child = len(quantizer_children) == 1 + is_metatype_to_filter = quantized_node_metatype in metatypes + is_quantizer_not_propagated = len(quantizer.propagation_path) <= 1 + + return ( + is_one_child + and is_metatype_to_filter + and is_one_quantizer_generated_for_node + and is_quantizer_not_propagated + ) + + quantizers = self._finished_propagating_quantizers + to_remove_quantizers = [] + for quantizer in quantizers: + quantized_node_key = next(iter(quantizer.quantized_input_sink_operator_nodes)) + if _is_quantizer_to_remove(quant_prop_graph, quantizer, metatypes): + nncf_logger.debug(f"Quantizer generated for a node {quantized_node_key} will be removed.") + to_remove_quantizers.append(quantizer) + for quantizer in to_remove_quantizers: + quant_prop_graph.remove_propagating_quantizer(quantizer) + self._finished_propagating_quantizers.remove(quantizer) + return quant_prop_graph diff --git a/nncf/common/quantization/quantizer_propagation/structs.py b/nncf/common/quantization/quantizer_propagation/structs.py index 37444978d62..62dd6bc6a7d 100644 --- a/nncf/common/quantization/quantizer_propagation/structs.py +++ b/nncf/common/quantization/quantizer_propagation/structs.py @@ -14,6 +14,8 @@ from nncf.common.quantization.structs import QuantizerConfig from nncf.common.quantization.structs import UnifiedScaleType +from nncf.common.utils.api_marker import api +from nncf.parameters import StrEnum class QuantizationTrait(Enum): @@ -108,3 +110,23 @@ class IgnoreReason(Enum): PropagationPath = List[Tuple[str, str]] + + +@api() +class QuantizerPropagationRule(StrEnum): + # While propagating up through a downward-branching node: + # ... do not merge at all + DO_NOT_MERGE_BRANCHES = 0 + + # ... only merge for exact configuration space matches + MERGE_IF_ALL_BRANCHES_SAME = 1 + + # ... merge common parts, and if a branch quantizer has options for + # narrowing (bitwidth/mode/per-channel/etc.) in addition to + # the common part, keep the quantizer on branch + MERGE_WITH_POTENTIAL_REQUANTIZATION = 2 + + # ... merge common config options into a single config space for the global quantizer, + # do not merge if this is impossible for the current branching situation and given + # HW config file + MERGE_ALL_IN_ONE = 3 diff --git a/nncf/common/tensor_statistics/aggregator.py b/nncf/common/tensor_statistics/aggregator.py index 65b2310beb5..2b1b21aa7f3 100644 --- a/nncf/common/tensor_statistics/aggregator.py +++ b/nncf/common/tensor_statistics/aggregator.py @@ -8,20 +8,28 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + from abc import ABC from abc import abstractmethod from itertools import islice from typing import Any, Dict, Optional, TypeVar import nncf +import nncf.common.tensor_statistics.statistics_serializer as statistics_serializer +import nncf.common.tensor_statistics.statistics_validator as statistics_validator from nncf.common import factory from nncf.common.graph.graph import NNCFGraph +from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.graph.transformations.layout import TransformationLayout +from nncf.common.logging import nncf_logger from nncf.common.logging.track_progress import track from nncf.common.tensor import NNCFTensor from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer +from nncf.common.utils.backend import BackendType +from nncf.data.dataset import DataItem from nncf.data.dataset import Dataset -from nncf.tensor import Tensor +from nncf.data.dataset import ModelInput +from nncf.experimental.common.tensor_statistics.statistics import TensorStatistic TensorType = TypeVar("TensorType") TModel = TypeVar("TModel") @@ -36,7 +44,9 @@ class StatisticsAggregator(ABC): Base class for statistics collection. """ - def __init__(self, dataset: Dataset): + BACKEND: BackendType + + def __init__(self, dataset: Dataset[DataItem, ModelInput]): self.dataset = dataset self.stat_subset_size = None self.statistic_points = StatisticPointsContainer() @@ -65,12 +75,11 @@ def collect_statistics(self, model: TModel, graph: NNCFGraph) -> None: model_transformer = factory.ModelTransformerFactory.create(model) merged_statistics = self._get_merged_statistic_points(self.statistic_points, model, graph) transformation_layout = self._get_transformation_layout_extra_outputs(merged_statistics) - model_with_outputs = model_transformer.transform(transformation_layout) + model_with_outputs: TModel = model_transformer.transform(transformation_layout) engine = factory.EngineFactory.create(model_with_outputs) - iterations_number = self._get_iterations_number() - empty_statistics = True - for input_data in track( + processed_samples = 0 + for input_data in track( # type: ignore islice(self.dataset.get_inference_data(), iterations_number), total=iterations_number, description="Statistics collection", @@ -78,9 +87,64 @@ def collect_statistics(self, model: TModel, graph: NNCFGraph) -> None: outputs = engine.infer(input_data) processed_outputs = self._process_outputs(outputs) self._register_statistics(processed_outputs, merged_statistics) - empty_statistics = False - if empty_statistics: + processed_samples += 1 + if processed_samples == 0: raise nncf.ValidationError(EMPTY_DATASET_ERROR) + if self.stat_subset_size is not None and self.stat_subset_size > processed_samples: + nncf_logger.warning( + f"Dataset contains only {processed_samples} samples, " + f"smaller than the requested subset size {self.stat_subset_size}." + ) + + def load_statistics_from_dir(self, dir_path: str) -> None: + """ + Loads statistics from a directory and populates the statistic points with the loaded data. + + :param dir_path: The name of the directory from which to load the statistics. + """ + loaded_data, metadata = statistics_serializer.load_from_dir(dir_path) + statistics_validator.validate_backend(metadata, self.BACKEND) + self._load_statistics(loaded_data) + nncf_logger.info(f"Statistics were successfully loaded from a directory {dir_path}.") + + def _load_statistics(self, data: Dict[str, Any]) -> None: + """ + Loads statistics into the registered statistic points from the given data. + + :param data: A dictionary containing the statistics loaded from a file. + """ + for _, statistic_point, tensor_collector in self.statistic_points.get_tensor_collectors(): + statistics = tensor_collector.get_statistics() + statistics_key = self._get_statistics_key(statistics, statistic_point.target_point) + if statistics_key not in data: + raise nncf.ValidationError(f"Not found statistics for {statistics_key}") + statistics_container = tensor_collector.create_statistics_container(data[statistics_key]) + tensor_collector.set_cache(statistics_container) + + def dump_statistics(self, dir_path: str) -> None: + """ + Dumps the current statistics to a directory in a compressed format. + + :param dir_path: The path of the directory where the statistics will be saved. + """ + data_to_dump = self._prepare_statistics() + metadata = {"backend": self.BACKEND.value, "subset_size": self.stat_subset_size} + statistics_serializer.dump_to_dir(data_to_dump, dir_path, metadata) + nncf_logger.info(f"Statistics were successfully saved to a directory {dir_path}.") + + def _prepare_statistics(self) -> Dict[str, Any]: + """ + Prepares the statistics data for dumping into a directory. + + :return: A dictionary containing the statistics data to be dumped. + """ + data_to_dump = {} + for _, statistic_point, tensor_collector in self.statistic_points.get_tensor_collectors(): + statistics = tensor_collector.get_statistics() + statistics_key = self._get_statistics_key(statistics, statistic_point.target_point) + data = statistics.get_data() + data_to_dump[statistics_key] = data + return data_to_dump def register_statistic_points(self, statistic_points: StatisticPointsContainer) -> None: """ @@ -141,10 +205,20 @@ def _get_merged_statistic_points( @staticmethod @abstractmethod - def _process_outputs(outputs: Any) -> Dict[str, Tensor]: + def _process_outputs(outputs: Any) -> Dict[str, NNCFTensor]: """ Post-process model outputs for the further statistics collection. :param outputs: raw model outputs :return: processed model outputs in Dict[str, Tensor] format """ + + @abstractmethod + def _get_statistics_key(self, statistics: TensorStatistic, target_point: TargetPoint) -> str: + """ + Returns key of statistics. + + :param statistics: Statistics value. + :param target_point: Statistics target point. + :return: Statistics key. + """ diff --git a/nncf/common/tensor_statistics/collectors.py b/nncf/common/tensor_statistics/collectors.py index 9e4e8e63e4e..38a3d1c3f89 100644 --- a/nncf/common/tensor_statistics/collectors.py +++ b/nncf/common/tensor_statistics/collectors.py @@ -12,9 +12,10 @@ from abc import ABC from abc import abstractmethod from collections import deque -from typing import List, Optional, Tuple +from typing import Any, Deque, Dict, List, Optional, Tuple, Union, cast import numpy as np +from numpy.typing import NDArray from nncf.common.tensor import NNCFTensor from nncf.common.tensor import TensorType @@ -39,7 +40,7 @@ def __init__(self, reduction_shape: Optional[ReductionAxes] = None, num_samples: self._num_samples = num_samples @property - def num_samples(self) -> int: + def num_samples(self) -> Union[int, None]: return self._num_samples def register_input(self, x: TensorType) -> TensorType: @@ -49,38 +50,38 @@ def register_input(self, x: TensorType) -> TensorType: if self._num_samples is not None and self._collected_samples >= self._num_samples: return x if self._reduction_shape is None: - self._reduction_shape = tuple(range(len(x.shape))) + self._reduction_shape = tuple(range(len(cast(NNCFTensor, x).shape))) self._register_input(x) self._collected_samples += 1 return x @abstractmethod - def _register_input(self, x: TensorType): + def _register_input(self, x: TensorType) -> None: pass - def get_statistics(self): + def get_statistics(self) -> Any: """Returns collected statistics, if present.""" if self._collected_samples == 0: raise StatisticsNotCollectedError() return self._get_statistics() @abstractmethod - def _get_statistics(self): + def _get_statistics(self) -> Any: pass - def enable(self): + def enable(self) -> None: self._enabled = True - def disable(self): + def disable(self) -> None: self._enabled = False - def reset(self): + def reset(self) -> None: """Resets all the statistics in the collector.""" self._collected_samples = 0 self._reset() @abstractmethod - def _reset(self): + def _reset(self) -> None: pass def collected_samples(self) -> int: @@ -102,9 +103,9 @@ def __init__( self, reduction_shape: Optional[ReductionAxes] = None, num_samples: int = None, window_size: int = None ): super().__init__(reduction_shape, num_samples) - self._samples = deque(maxlen=window_size) + self._samples: Deque[int] = deque(maxlen=window_size) - def _reset(self): + def _reset(self) -> None: self._samples.clear() @@ -121,10 +122,10 @@ def __init__(self, use_abs_max: bool, reduction_shape: ReductionAxes, num_sample @staticmethod @abstractmethod - def _get_processor(): + def _get_processor() -> Any: pass - def _register_input_common(self, x: NNCFTensor): + def _register_input_common(self, x: NNCFTensor) -> None: min_reduced = self._tensor_processor.reduce_min(x, self._reduction_shape) if self._use_abs_max: x = self._tensor_processor.abs(x) @@ -140,7 +141,7 @@ def _register_input_common(self, x: NNCFTensor): else: self._max_values = self._tensor_processor.max(max_reduced, self._max_values) - def _reset(self): + def _reset(self) -> None: self._min_values = None self._max_values = None @@ -164,15 +165,15 @@ def __init__( self._use_abs_max = use_abs_max self._tensor_processor = self._get_processor() - self._all_min_values = deque(maxlen=window_size) - self._all_max_values = deque(maxlen=window_size) + self._all_min_values: Deque[int] = deque(maxlen=window_size) + self._all_max_values: Deque[int] = deque(maxlen=window_size) @staticmethod @abstractmethod - def _get_processor(): + def _get_processor() -> Any: pass - def _register_input_common(self, x: NNCFTensor): + def _register_input_common(self, x: NNCFTensor) -> None: min_reduced = self._tensor_processor.reduce_min(x, self._reduction_shape) if self._use_abs_max: x = self._tensor_processor.abs(x) @@ -186,14 +187,14 @@ def _register_input_common(self, x: NNCFTensor): self._all_max_values.append(max_reduced) @abstractmethod - def _min_aggregate(self): + def _min_aggregate(self) -> None: pass @abstractmethod - def _max_aggregate(self): + def _max_aggregate(self) -> None: pass - def _reset(self): + def _reset(self) -> None: self._all_min_values.clear() self._all_max_values.clear() @@ -217,13 +218,13 @@ def __init__( self._use_means_of_mins = use_means_of_mins self._use_means_of_maxs = use_means_of_maxs - def _min_aggregate(self): + def _min_aggregate(self) -> Any: stacked_min = self._tensor_processor.stack(self._all_min_values) if self._use_means_of_mins: return self._tensor_processor.mean(stacked_min, axis=0) return self._tensor_processor.reduce_min(stacked_min, axis=0) - def _max_aggregate(self): + def _max_aggregate(self) -> Any: stacked_max = self._tensor_processor.stack(self._all_max_values) if self._use_means_of_maxs: return self._tensor_processor.mean(stacked_max, axis=0) @@ -235,11 +236,11 @@ class MeanMinMaxStatisticCollector(MinMaxOfflineStatisticCollectorBase): Collector aggregates mean of minimum values and mean of maximum values. """ - def _min_aggregate(self): + def _min_aggregate(self) -> Any: stacked_min = self._tensor_processor.stack(self._all_min_values) return self._tensor_processor.mean(stacked_min, axis=0) - def _max_aggregate(self): + def _max_aggregate(self) -> Any: stacked_max = self._tensor_processor.stack(self._all_max_values) return self._tensor_processor.mean(stacked_max, axis=0) @@ -259,30 +260,30 @@ def __init__(self, channel_axis: int, num_samples: Optional[int] = None, window_ super().__init__(num_samples=num_samples) self._channel_axis = channel_axis self._tensor_processor = self._get_processor() - self._all_values = deque(maxlen=window_size) - self._all_shapes = deque(maxlen=window_size) + self._all_values: Deque[int] = deque(maxlen=window_size) + self._all_shapes: Deque[int] = deque(maxlen=window_size) @staticmethod @abstractmethod - def _get_processor(): + def _get_processor() -> Any: pass - def _register_input_common(self, x: NNCFTensor): + def _register_input_common(self, x: NNCFTensor) -> None: if self._channel_axis == 0: self._all_values.append(self._tensor_processor.batch_mean(x)) else: self._all_values.append(self._tensor_processor.mean_per_channel(x, self._channel_axis)) - self._all_shapes.append(x.shape) + self._all_shapes.append(cast(int, x.shape)) - def _reset(self): + def _reset(self) -> None: self._all_values.clear() self._all_shapes.clear() - def _mean_aggregate(self): + def _mean_aggregate(self) -> Any: all_values_stack = self._tensor_processor.stack(self._all_values) return self._tensor_processor.mean(all_values_stack, 0) - def _shape(self): + def _shape(self) -> Any: return self._all_shapes[0] @@ -298,17 +299,17 @@ def __init__(self, num_samples: Optional[int] = None) -> None: the number of samples that will be processed. """ super().__init__(num_samples=num_samples) - self._all_values = [] + self._all_values: List[int] = [] @staticmethod @abstractmethod - def _get_processor(): + def _get_processor() -> Any: pass - def _register_input_common(self, x: NNCFTensor): - self._all_values.append(x.tensor) + def _register_input_common(self, x: NNCFTensor) -> None: + self._all_values.append(cast(int, x.tensor)) - def _reset(self): + def _reset(self) -> None: self._all_values.clear() @@ -317,8 +318,10 @@ class MedianMADStatisticCollector(OfflineTensorStatisticCollector): Collector estimates median and median absolute deviation (MAD). """ - def _prepare_statistics(self): - per_channel_history = get_per_channel_history(self._samples, list(self._reduction_shape), discard_zeros=True) + def _prepare_statistics(self) -> Tuple[NDArray[Any], NDArray[Any]]: + per_channel_history = get_per_channel_history( + self._samples, cast(List[int], self._reduction_shape), discard_zeros=True + ) per_channel_median = [np.median(channel_hist) for channel_hist in per_channel_history] per_channel_mad = [] for idx, median in enumerate(per_channel_median): @@ -343,8 +346,8 @@ def __init__( super().__init__(reduction_shape, num_samples, window_size) self._percentiles_to_collect = percentiles_to_collect - def _prepare_statistics(self): - per_channel_history = get_per_channel_history(self._samples, list(self._reduction_shape)) + def _prepare_statistics(self) -> Dict[float, Any]: + per_channel_history = get_per_channel_history(self._samples, cast(List[int], self._reduction_shape)) percentile_vs_values_dict = {} for pc in self._percentiles_to_collect: per_channel_percentiles = [np.percentile(channel_hist, pc) for channel_hist in per_channel_history] @@ -366,10 +369,10 @@ def __init__( window_size: int = None, ): super().__init__(reduction_shape, num_samples, window_size) - self._all_pct_values = {} + self._all_pct_values: Dict[float, Any] = {} for pc in percentiles_to_collect: self._all_pct_values[pc] = deque(maxlen=window_size) - def _reset(self): + def _reset(self) -> None: for _, val in self._all_pct_values.items(): val.clear() diff --git a/nncf/common/tensor_statistics/reduction.py b/nncf/common/tensor_statistics/reduction.py index 3b90b2503ff..0c2115d29a5 100644 --- a/nncf/common/tensor_statistics/reduction.py +++ b/nncf/common/tensor_statistics/reduction.py @@ -9,10 +9,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -from collections import deque -from typing import List, Tuple +from typing import Any, Deque, List, Tuple import numpy as np +from numpy.typing import NDArray def get_channel_count_and_dim_idx(scale_shape: List[int]) -> Tuple[int, int]: @@ -25,7 +25,7 @@ def get_channel_count_and_dim_idx(scale_shape: List[int]) -> Tuple[int, int]: return channel_count, channel_dim_idx -def split_into_channels(input_: np.ndarray, scale_shape: List[int]) -> List[np.ndarray]: +def split_into_channels(input_: NDArray[Any], scale_shape: List[int]) -> List[NDArray[Any]]: channel_count, channel_dim_idx = get_channel_count_and_dim_idx(scale_shape) channel_first_tensor = np.moveaxis(input_, channel_dim_idx, 0) if channel_count == 1: @@ -37,9 +37,11 @@ def split_into_channels(input_: np.ndarray, scale_shape: List[int]) -> List[np.n return ret_list -def get_per_channel_history(raw_input_history: deque, scale_shape: List[int], discard_zeros=False) -> List: +def get_per_channel_history( + raw_input_history: Deque[Any], scale_shape: List[int], discard_zeros: bool = False +) -> List[Any]: channel_count, _ = get_channel_count_and_dim_idx(scale_shape) - per_channel_history = [None for i in range(channel_count)] + per_channel_history: List[Any] = [None for i in range(channel_count)] for _ in range(len(raw_input_history)): entry = raw_input_history.popleft() split = split_into_channels(entry, scale_shape) @@ -59,7 +61,7 @@ def get_per_channel_history(raw_input_history: deque, scale_shape: List[int], di return per_channel_history -def np_percentile_reduce_like(input_: np.array, ref_tensor_shape: Tuple[int], q: float) -> np.array: +def np_percentile_reduce_like(input_: NDArray[Any], ref_tensor_shape: Tuple[int], q: float) -> NDArray[Any]: numel = np.prod(ref_tensor_shape) if numel == 1: return np.array([np.percentile(input_, q)]) diff --git a/nncf/common/tensor_statistics/statistic_point.py b/nncf/common/tensor_statistics/statistic_point.py index 3b3b5b4932f..2e41d96261d 100644 --- a/nncf/common/tensor_statistics/statistic_point.py +++ b/nncf/common/tensor_statistics/statistic_point.py @@ -10,11 +10,10 @@ # limitations under the License. from collections import UserDict -from typing import Callable, Generator, Optional, Tuple +from typing import Any, Callable, Generator, Optional, Tuple, cast from nncf.common.graph.transformations.commands import TargetPoint -from nncf.common.tensor import TensorType -from nncf.common.tensor_statistics.collectors import TensorStatisticCollectorBase +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector class StatisticPoint: @@ -25,23 +24,19 @@ class StatisticPoint: algorithm implies on what algorithm nedeed this statistics. """ - def __init__(self, target_point: TargetPoint, tensor_collector: TensorStatisticCollectorBase, algorithm: str): + def __init__(self, target_point: TargetPoint, tensor_collector: TensorCollector, algorithm: str): self.target_point = target_point self.algorithm_to_tensor_collectors = {algorithm: [tensor_collector]} - def __eq__(self, other): - return ( + def __eq__(self, other: Any) -> bool: + return cast( + bool, self.target_point == other.target_point - and self.algorithm_to_tensor_collectors == other.self.algorithm_to_tensor_collectors + and self.algorithm_to_tensor_collectors == other.self.algorithm_to_tensor_collectors, ) - def register_tensor(self, x: TensorType): - for tensor_collectors in self.algorithm_to_tensor_collectors.values(): - for tensor_collector in tensor_collectors: - tensor_collector.register_input(x) - -class StatisticPointsContainer(UserDict): +class StatisticPointsContainer(UserDict): # type: ignore """ Container with iteration interface for handling a composition of StatisticPoint. """ @@ -52,7 +47,7 @@ def add_statistic_point(self, statistic_point: StatisticPoint) -> None: :param statistic_point: Statistic point to add. """ - target_node_name = statistic_point.target_point.target_node_name + target_node_name = statistic_point.target_point.target_node_name # type: ignore if target_node_name not in self.data: self.data[target_node_name] = [statistic_point] else: @@ -73,7 +68,7 @@ def add_statistic_point(self, statistic_point: StatisticPoint) -> None: def iter_through_statistic_points_in_target_node( self, target_node_name: str, filter_fn: Callable[[StatisticPoint], bool] - ) -> StatisticPoint: + ) -> Generator[StatisticPoint, None, None]: """ Returns iterable through all statistic points in node with target_node_name. @@ -87,7 +82,7 @@ def iter_through_statistic_points_in_target_node( def get_tensor_collectors( self, filter_fn: Optional[Callable[[StatisticPoint], bool]] = None - ) -> Generator[Tuple[str, StatisticPoint, TensorStatisticCollectorBase], None, None]: + ) -> Generator[Tuple[str, StatisticPoint, TensorCollector], None, None]: """ Returns iterable through all tensor collectors. @@ -98,7 +93,7 @@ def get_tensor_collectors( """ if filter_fn is None: - def default_filter_fn(stat_point: StatisticPoint): + def default_filter_fn(stat_point: StatisticPoint) -> bool: return True filter_fn = default_filter_fn @@ -114,7 +109,7 @@ def get_algo_statistics_for_node( target_node_name: str, filter_fn: Callable[[StatisticPoint], bool], algorithm: str, - ) -> Generator[TensorStatisticCollectorBase, None, None]: + ) -> Generator[TensorCollector, None, None]: """ Returns iterable through all statistic collectors in node with target_node_name. diff --git a/nncf/common/tensor_statistics/statistics.py b/nncf/common/tensor_statistics/statistics.py index d736989fe9a..68204a2ad94 100644 --- a/nncf/common/tensor_statistics/statistics.py +++ b/nncf/common/tensor_statistics/statistics.py @@ -12,7 +12,7 @@ from abc import ABC from abc import abstractmethod from collections import Counter -from typing import TypeVar +from typing import Any, Dict, TypeVar, cast from nncf.tensor import Tensor from nncf.tensor import functions as fns @@ -26,11 +26,11 @@ class TensorStatistic(ABC): TENSOR_STATISTIC_OUTPUT_KEY = "tensor_statistic_output" @staticmethod - def tensor_eq(tensor1: Tensor, tensor2: Tensor, rtol=1e-6) -> bool: - return fns.allclose(tensor1, tensor2) + def tensor_eq(tensor1: Tensor, tensor2: Tensor, rtol: float = 1e-6) -> bool: + return cast(bool, fns.allclose(tensor1, tensor2)) @abstractmethod - def __eq__(self, other): + def __eq__(self, other: Any) -> bool: pass @@ -38,11 +38,13 @@ class MinMaxTensorStatistic(TensorStatistic): MIN_STAT = "min_values" MAX_STAT = "max_values" - def __init__(self, min_values, max_values): + def __init__(self, min_values: Tensor, max_values: Tensor): self.min_values = min_values self.max_values = max_values - def __eq__(self, other: "MinMaxTensorStatistic") -> bool: + def __eq__(self, other: Any) -> bool: + if not isinstance(other, MinMaxTensorStatistic): + return False return self.tensor_eq(self.min_values, other.min_values) and self.tensor_eq(self.max_values, other.max_values) @@ -54,7 +56,7 @@ class MeanTensorStatistic(TensorStatistic): Base class for the statistics that collects as mean per-axis """ - def __init__(self, mean_values, shape): + def __init__(self, mean_values: Tensor, shape: Tensor): """ :param mean_values: Collected mean per-axis values. :param shape: The shape of the collected statistics. @@ -62,7 +64,9 @@ def __init__(self, mean_values, shape): self.mean_values = mean_values self.shape = shape - def __eq__(self, other: "MeanTensorStatistic") -> bool: + def __eq__(self, other: Any) -> bool: + if not isinstance(other, MeanTensorStatistic): + return False return self.tensor_eq(self.mean_values, other.mean_values) and self.tensor_eq(self.shape, other.shape) @@ -70,11 +74,13 @@ class MedianMADTensorStatistic(TensorStatistic): MEDIAN_VALUES_STAT = "median_values" MAD_VALUES_STAT = "mad_values" - def __init__(self, median_values, mad_values): + def __init__(self, median_values: Tensor, mad_values: Tensor): self.median_values = median_values self.mad_values = mad_values - def __eq__(self, other: "MedianMADTensorStatistic") -> bool: + def __eq__(self, other: Any) -> bool: + if not isinstance(other, MedianMADTensorStatistic): + return False return self.tensor_eq(self.median_values, other.median_values) and self.tensor_eq( self.mad_values, other.mad_values ) @@ -83,10 +89,12 @@ def __eq__(self, other: "MedianMADTensorStatistic") -> bool: class PercentileTensorStatistic(TensorStatistic): PERCENTILE_VS_VALUE_DICT = "percentile_vs_values_dict" - def __init__(self, percentile_vs_values_dict): + def __init__(self, percentile_vs_values_dict: Dict[float, Any]): self.percentile_vs_values_dict = percentile_vs_values_dict - def __eq__(self, other: "PercentileTensorStatistic", rtol=1e-9) -> bool: + def __eq__(self, other: Any, rtol: float = 1e-9) -> bool: + if not isinstance(other, PercentileTensorStatistic): + return False if Counter(self.percentile_vs_values_dict.keys()) != Counter(other.percentile_vs_values_dict.keys()): return False for pct in self.percentile_vs_values_dict: @@ -102,11 +110,13 @@ class RawTensorStatistic(TensorStatistic): Base class for the raw statistics, without any aggregation. """ - def __init__(self, values): + def __init__(self, values: Tensor): """ :param values: Collected raw values. """ self.values = values - def __eq__(self, other: "RawTensorStatistic") -> bool: + def __eq__(self, other: Any) -> bool: + if not isinstance(other, RawTensorStatistic): + return False return self.tensor_eq(self.values, other.values) diff --git a/nncf/common/tensor_statistics/statistics_serializer.py b/nncf/common/tensor_statistics/statistics_serializer.py new file mode 100644 index 00000000000..5f33c1e9b8c --- /dev/null +++ b/nncf/common/tensor_statistics/statistics_serializer.py @@ -0,0 +1,119 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import gzip +import json +import pickle +import re +from pathlib import Path +from typing import Any, Dict, Optional, Tuple, cast + +import nncf +from nncf.common.utils.os import fail_if_symlink +from nncf.common.utils.os import safe_open + +METADATA_FILE = "statistics_metadata.json" + + +def sanitize_filename(filename: str) -> str: + """ + Replaces any forbidden characters with an underscore. + """ + return re.sub(r'[\/:*?"<>|]', "_", filename) + + +def load_metadata(dir_path: Path) -> Dict[str, Any]: + """ + Loads the metadata, including the mapping and any other metadata information from the metadata file. + :param dir_path: The directory where the metadata file is stored. + :return: A dictionary containing the mapping and metadata. + """ + metadata_file = dir_path / METADATA_FILE + if metadata_file.exists(): + with safe_open(metadata_file, "r") as f: + return cast(Dict[str, Any], json.load(f)) + return {"mapping": {}, "metadata": {}} + + +def save_metadata(metadata: Dict[str, Any], dir_path: Path) -> None: + """ + Saves the mapping and metadata to the metadata file. + :param metadata: The dictionary containing both the mapping and other metadata. + :param dir_path: The directory where the metadata file will be stored. + """ + metadata_file = dir_path / METADATA_FILE + with safe_open(metadata_file, "w") as f: + json.dump(metadata, f, indent=4) + + +def load_from_dir(dir_path: str) -> Tuple[Dict[str, Any], Dict[str, str]]: + """ + Loads statistics from gzip-compressed files in the given directory. + :param dir_path: The path to the directory from which to load the statistics. + :return: 1) A dictionary with the original statistic names as keys and the loaded statistics as values. + 2) Metadata dictionary. + """ + statistics = {} + path = Path(dir_path) + if not path.exists(): + raise nncf.ValidationError("The provided directory path does not exist.") + metadata = load_metadata(path) + mapping = metadata.get("mapping", {}) + + for statistics_file in path.iterdir(): + if statistics_file.name == METADATA_FILE: + continue # Skip the metadata file + + try: + fail_if_symlink(statistics_file) + with gzip.open(statistics_file, "rb") as f: + sanitized_name = statistics_file.name + original_name = mapping.get(sanitized_name, sanitized_name) + statistics[original_name] = pickle.load(f) + except (pickle.UnpicklingError, IOError) as e: + raise nncf.InternalError(f"Error loading statistics from {statistics_file.name}: {e}") + return statistics, metadata.get("metadata", {}) + + +def dump_to_dir( + statistics: Dict[str, Any], dir_path: str, additional_metadata: Optional[Dict[str, Any]] = None +) -> None: + """ + Dumps statistics to gzip-compressed files in the specified directory, while maintaining a mapping file. + :param statistics: A dictionary with statistic names as keys and the statistic data as values. + :param dir_path: The path to the directory where the statistics will be dumped. + :param additional_metadata: A dictionary containing any additional metadata to be saved with the mapping. + """ + path = Path(dir_path) + path.mkdir(parents=True, exist_ok=True) + + metadata, mapping = {}, {} + + for original_name, statistics_value in statistics.items(): + sanitized_name = sanitize_filename(original_name) + file_path = path / sanitized_name + + # Update the mapping + mapping[sanitized_name] = original_name + + try: + fail_if_symlink(file_path) + with gzip.open(file_path, "wb") as f: + pickle.dump(statistics_value, f) + except (IOError, pickle.PicklingError) as e: + raise nncf.InternalError(f"Failed to write data to file {file_path}: {e}") + + # Add additional metadata if provided + if additional_metadata: + metadata["metadata"] = additional_metadata + + # Update the mapping in the metadata file + metadata["mapping"] = mapping + save_metadata(metadata, path) diff --git a/nncf/common/tensor_statistics/statistics_validator.py b/nncf/common/tensor_statistics/statistics_validator.py new file mode 100644 index 00000000000..fe926aaa3f2 --- /dev/null +++ b/nncf/common/tensor_statistics/statistics_validator.py @@ -0,0 +1,30 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Dict + +import nncf +from nncf.common.utils.backend import BackendType + + +def validate_backend(data: Dict[str, Any], backend: BackendType) -> None: + """ + Checks whether backend in loaded data is equal to a provided backend. + + :param data: Loaded statistics. + :param backend: Provided backend. + """ + if "backend" not in data: + raise nncf.ValidationError("The provided metadata has no information about backend.") + data_backend = data["backend"] + if data_backend != backend.value: + raise nncf.ValidationError( + f"Backend in loaded statistics {data_backend} does not match to an expected backend {backend.value}." + ) diff --git a/nncf/common/utils/backend.py b/nncf/common/utils/backend.py index a76c0b8670d..f3b3384375f 100644 --- a/nncf/common/utils/backend.py +++ b/nncf/common/utils/backend.py @@ -58,8 +58,8 @@ def is_torch_model(model: TModel) -> bool: :param model: A target model. :return: True if the model is an instance of torch.nn.Module and not torch.fx.GraphModule, otherwise False. """ - import torch # type: ignore - import torch.fx # type: ignore + import torch + import torch.fx return not isinstance(model, torch.fx.GraphModule) and isinstance(model, torch.nn.Module) diff --git a/nncf/data/__init__.py b/nncf/data/__init__.py index f6dde64a3e4..b6acbcef923 100644 --- a/nncf/data/__init__.py +++ b/nncf/data/__init__.py @@ -10,3 +10,4 @@ # limitations under the License. from nncf.data.dataset import Dataset as Dataset +from nncf.data.generators import generate_text_data as generate_text_data diff --git a/nncf/data/generators.py b/nncf/data/generators.py new file mode 100644 index 00000000000..b8ca0edc947 --- /dev/null +++ b/nncf/data/generators.py @@ -0,0 +1,92 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, TypeVar + +import nncf +from nncf.common.logging.track_progress import track + +BASE_VOCAB_SIZE = 12000 + +TModel = TypeVar("TModel") +TTokenizer = TypeVar("TTokenizer") + + +def generate_text_data( + model: TModel, tokenizer: TTokenizer, seq_len: int = 32, dataset_size: int = 128, unique_tokens_lower_limit: int = 5 +) -> List[str]: + """ + Generates text dataset based on the model output. + + Since the model is required to be the instance of the PreTrainedModel + and the tokenizer is required to be the instance of the PreTrainedTokenizerBase, + environment must have `transformers` & `torch` modules installed to run this method. + + :param model: Model instance. + :param tokenizer: Tokenizer instance. + :param seq_len: Sequence length for generation. + :param dataset_size: Size of the data. + :return: List of the text data ready to use. + """ + + try: + import torch + except ImportError: + raise nncf.ModuleNotFoundError("torch is required in order to generate text data: `pip install torch`.") + + try: + from transformers import PreTrainedModel + from transformers import PreTrainedTokenizerBase + from transformers.utils import logging + + logging.set_verbosity_error() + except ImportError: + raise nncf.ModuleNotFoundError( + "transformers is required in order to generate text data: `pip install transformers`." + ) + + if not isinstance(model, PreTrainedModel.__bases__): + raise nncf.ValidationError("Model should be instance of the `transformers.PreTrainedModel`.") + + if not isinstance(tokenizer, PreTrainedTokenizerBase.__bases__): + raise nncf.ValidationError("tokenizer should be instance of the `transformers.PreTrainedTokenizerBase`.") + + generated_data = [] + + vocab_size_names = ["padded_vocab_size", "vocab_size"] + vocab_size = BASE_VOCAB_SIZE + for vocab_size_name in vocab_size_names: + if hasattr(model.config, vocab_size_name): + vocab_size = getattr(model.config, vocab_size_name) + + step_num = max(1, vocab_size // dataset_size) + ids_counter = 0 + + with track(total=dataset_size, description="Generating text data") as pbar: + while len(generated_data) < dataset_size: + # Creating the input for pre-generate step + input_ids = torch.tensor([[ids_counter % vocab_size]]).to(model.device) + + # Collecting data from the pre & post generate steps + outputs_prep = model.generate(input_ids, do_sample=False, max_length=seq_len // 2) + outputs_post = model.generate(outputs_prep, do_sample=True, max_length=seq_len + seq_len // 2) + gen_text = tokenizer.batch_decode(outputs_post[:, outputs_prep.shape[1] :], skip_special_tokens=True) + + if len(set(gen_text[0])) < unique_tokens_lower_limit: + ids_counter += 1 + continue + + ids_counter += step_num + + pbar.progress.update(pbar.task, advance=1) + generated_data.extend(gen_text) + + return generated_data diff --git a/nncf/experimental/common/tensor_statistics/collectors.py b/nncf/experimental/common/tensor_statistics/collectors.py index f622ed5186a..36b3b803566 100644 --- a/nncf/experimental/common/tensor_statistics/collectors.py +++ b/nncf/experimental/common/tensor_statistics/collectors.py @@ -13,6 +13,7 @@ from abc import abstractmethod from collections import defaultdict from collections import deque +from copy import deepcopy from typing import Any, Dict, List, Optional, Set, Tuple, Type, TypeVar, Union import nncf @@ -20,11 +21,7 @@ from nncf.common.tensor import TensorType from nncf.common.tensor_statistics.collectors import ReductionAxes from nncf.experimental.common.tensor_statistics.statistical_functions import mean_per_channel -from nncf.experimental.common.tensor_statistics.statistics import MeanTensorStatistic from nncf.experimental.common.tensor_statistics.statistics import MedianMADTensorStatistic -from nncf.experimental.common.tensor_statistics.statistics import MinMaxTensorStatistic -from nncf.experimental.common.tensor_statistics.statistics import PercentileTensorStatistic -from nncf.experimental.common.tensor_statistics.statistics import RawTensorStatistic from nncf.experimental.common.tensor_statistics.statistics import TensorStatistic from nncf.quantization.advanced_parameters import AggregatorType from nncf.tensor import Tensor @@ -73,13 +70,13 @@ def _reduce_out_of_place(self, x: List[TensorType]) -> List[TensorType]: :param x: Tensor to register. """ - @abstractmethod def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]: """ Returns correspondent inplace operation builder if inplace operations are available in backend. :return: Inplace operation builder if possible else None. """ + return None def __call__(self, x: List[Tensor]): if any(t.isempty() for t in x): @@ -193,12 +190,13 @@ class TensorCollector: a dict could be collected by `get_statistics` call. """ - def __init__(self, statistic_container: Optional[TensorStatistic] = None) -> None: + def __init__(self, statistic_container: Optional[Type[TensorStatistic]] = None) -> None: self._reducers: Set[TensorReducerBase] = set() self._aggregators: Dict[Tuple[int, int, int], AggregatorBase] = {} self._stat_container_kwargs_map: Dict[str, Tuple[int, int, int]] = {} self._stat_container = statistic_container - self._enabled = True + self.enable() + self.clear_cache() @property def num_samples(self) -> Optional[int]: @@ -260,19 +258,6 @@ def register_statistic_branch( self._aggregators[key] = aggregator self._stat_container_kwargs_map[container_key] = key - def get_output_info(self, target_node_name: str, port_id: int) -> List[Tuple[int, List[str]]]: - """ - Returns list of pairs of reducers names and correspondent output names. - - :param target_node_name: Target node name to assemble output name. - :param port_id: Target node specific port id to assemble output name. - :returns: List of pairs of reducers hashes and correspondent output names. - """ - retval = [] - for reducer in self._reducers: - retval.append((hash(reducer), reducer.get_output_names(target_node_name, port_id))) - return retval - def register_inputs(self, inputs: Dict[int, List[Tensor]]) -> None: """ Registers given input in TensorCollector. @@ -280,9 +265,8 @@ def register_inputs(self, inputs: Dict[int, List[Tensor]]) -> None: :param inputs: Tensor inputs in format of dict where keys are reducer names and values are correspondent input tensors """ - if not self._enabled: + if not self.enabled: return - reduced_inputs = {} for reducer in self._reducers: reducer_hash = hash(reducer) @@ -316,43 +300,47 @@ def _aggregate(self) -> None: result[key] = val return result - def get_statistics(self) -> Union[TensorStatistic, Dict[str, Any]]: + def set_cache(self, statistics: TensorStatistic) -> None: """ - Returns aggregated values in format of a TensorStatistic instance or - a dict. - - :returns: Aggregated values. + Sets cached statistics from given config and disable TensorCollector. + :param statistics: TensorStatistic. """ + self._cached_statistics = statistics + self.reset() + self.disable() - aggregated_values = self._aggregate() - kwargs = {} - for container_key, branch_key in self._stat_container_kwargs_map.items(): - kwargs[container_key] = aggregated_values[branch_key] - - if not self._stat_container: - return kwargs - return self._build_statistic_container(self._stat_container, kwargs) + def create_statistics_container(self, config: Dict[str, Any]) -> TensorStatistic: + """ + Returns a TensorStatistic instance with aggregated values. - def get_inplace_fn_info(self) -> List[Tuple[Any, int]]: + :param config: Aggregated values. + :return: TensorStatistic instance. """ - Returns necessary information to insert inplace operation into graph. + if not self._stat_container: # TODO(kshpv): need to remove an ability to return a Dict. + return config + return self._stat_container.from_config(config) - :returns: necessary information to insert inplace operation into graph - in format of pair of reducer builder and correspondent reducer output port id. + def clear_cache(self) -> None: """ - retval = [] - for reducer in self._reducers: - if reducer.inplace: - retval.append((reducer.get_inplace_fn(), reducer.output_port_id)) - return retval + Clears the cached statistics and enables TensorCollector. + """ + self._cached_statistics = None - def any_stat_out_of_place(self) -> bool: + def get_statistics(self) -> TensorStatistic: """ - Returns True if any reducer is calculated out of place. + Returns aggregated values in format of a TensorStatistic instance or + a dict. - :returns: True if any reducer is calculated out of place. + :return: Aggregated values. """ - return any(not reducer.inplace for reducer in self._reducers) + if self._cached_statistics is not None: + return deepcopy(self._cached_statistics) + + aggregated_values = self._aggregate() + statistics_config = {} + for container_key, branch_key in self._stat_container_kwargs_map.items(): + statistics_config[container_key] = aggregated_values[branch_key] + return self.create_statistics_container(statistics_config) def replace_aggregator(self, key: Tuple[int, int, int], aggregator: AggregatorBase) -> None: """ @@ -389,39 +377,6 @@ def get_tensor_collector_inputs( target_inputs[reducer] = [outputs[name] for name in names] return target_inputs - @staticmethod - def _build_statistic_container(statistic_container_cls: Type[TensorStatistic], kwargs: Dict[Any, Any]): - if issubclass(statistic_container_cls, MinMaxTensorStatistic): - return statistic_container_cls( - min_values=kwargs[MinMaxTensorStatistic.MIN_STAT], max_values=kwargs[MinMaxTensorStatistic.MAX_STAT] - ) - if issubclass(statistic_container_cls, MeanTensorStatistic): - return statistic_container_cls( - mean_values=kwargs[MeanTensorStatistic.MEAN_STAT], shape=kwargs[MeanTensorStatistic.SHAPE_STAT] - ) - if issubclass(statistic_container_cls, RawTensorStatistic): - return statistic_container_cls(values=kwargs[RawTensorStatistic.VALUES_STATS]) - if issubclass(statistic_container_cls, MedianMADTensorStatistic): - return statistic_container_cls( - median_values=kwargs[MedianMADTensorStatistic.TENSOR_STATISTIC_OUTPUT_KEY][ - MedianMADTensorStatistic.MEDIAN_VALUES_STAT - ], - mad_values=kwargs[MedianMADTensorStatistic.TENSOR_STATISTIC_OUTPUT_KEY][ - MedianMADTensorStatistic.MAD_VALUES_STAT - ], - ) - if issubclass(statistic_container_cls, PercentileTensorStatistic): - if PercentileTensorStatistic.TENSOR_STATISTIC_OUTPUT_KEY in kwargs: - percentile_vs_values_dict = kwargs[PercentileTensorStatistic.TENSOR_STATISTIC_OUTPUT_KEY] - else: - percentile_vs_values_dict = {} - for (_, percentile), value in kwargs.items(): - percentile_vs_values_dict[percentile] = value - return statistic_container_cls(percentile_vs_values_dict=percentile_vs_values_dict) - raise nncf.InternalError( - f"Statistic collector class {statistic_container_cls} is not supported by the TensorCollector class." - ) - class MergedTensorCollector(TensorCollector): """ @@ -476,6 +431,17 @@ def __call__(self, x: List[Tensor]): return self._reduce_out_of_place(x) +class ShapeReducer(TensorReducerBase): + def __init__(self, inplace: bool = False): + super().__init__(inplace=inplace) + + def _reduce_out_of_place(self, x: List[TensorType]) -> List[TensorType]: + return [Tensor(x[0].shape)] + + def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]: + return None + + class MinReducer(TensorReducerBase): def _reduce_out_of_place(self, x: List[Tensor]) -> List[Tensor]: x = x[0] @@ -504,6 +470,21 @@ def _reduce_out_of_place(self, x: List[Tensor]) -> List[Tensor]: return [fns.mean(x, reduction_axes, keepdims=self._keepdims)] +class MeanVarianceReducer(TensorReducerBase): + def _reduce_out_of_place(self, x: List[TensorType]) -> List[TensorType]: + raise NotImplementedError() + + +class MaxVarianceReducer(TensorReducerBase): + def _reduce_out_of_place(self, x: List[TensorType]) -> List[TensorType]: + raise NotImplementedError() + + +class MeanAbsMaxReducer(TensorReducerBase): + def _reduce_out_of_place(self, x: List[TensorType]) -> List[TensorType]: + raise NotImplementedError() + + class QuantileReducerBase(TensorReducerBase): def __init__( self, @@ -814,6 +795,21 @@ def _aggregate_impl(self) -> Dict[float, Tensor]: return retval +class HAWQAggregator(AggregatorBase): + def __init__(self, num_samples: Optional[int] = None): + super().__init__(num_samples=num_samples) + self._container = Tensor(0.0) + + def _register_reduced_input_impl(self, x: TensorType) -> None: + trace = fns.sum(fns.multiply(x, x)) + # NOTE: average trace?? divide by number of diagonal elements + # TODO: revise this formula as possibly it is with an error; adopted from previous HAWQ implementation + self._container = (self._container + trace) / x.size + + def _aggregate_impl(self) -> List[TensorType]: + return [self._container * 2 / self._collected_samples] + + def _move_axes_flatten_cat( tensor_list: List[Tensor], aggregation_axes: Tuple[int, ...] ) -> Tuple[Tensor, Tuple[int, ...]]: diff --git a/nncf/experimental/common/tensor_statistics/statistics.py b/nncf/experimental/common/tensor_statistics/statistics.py index 5711ca900c7..9396674edb3 100644 --- a/nncf/experimental/common/tensor_statistics/statistics.py +++ b/nncf/experimental/common/tensor_statistics/statistics.py @@ -13,7 +13,7 @@ from collections import Counter from dataclasses import dataclass -from typing import ClassVar, Dict, Tuple +from typing import Any, ClassVar, Dict, List, Tuple from nncf.tensor import Tensor from nncf.tensor import functions as fns @@ -24,6 +24,22 @@ class TensorStatistic: TENSOR_STATISTIC_OUTPUT_KEY = "tensor_statistic_output" + def get_data(self) -> Dict[str, Any]: + return {key: getattr(self, key) for key in self.keys()} + + def load_data(self, data: Dict[str, Any]): + for key in self.keys(): + setattr(self, key, data.get(key)) + + @classmethod + def from_config(cls, config: Dict[str, Any]) -> TensorStatistic: + args = {key: config[key] for key in cls.keys()} # noqa: SIM118 + return cls(**args) + + @classmethod + def keys(cls) -> Tuple[str]: + return () + @dataclass class MinMaxTensorStatistic(TensorStatistic): @@ -33,12 +49,32 @@ class MinMaxTensorStatistic(TensorStatistic): min_values: Tensor max_values: Tensor + @classmethod + def keys(cls): + return (cls.MIN_STAT, cls.MAX_STAT) + def __eq__(self, other: TensorStatistic): if isinstance(other, MinMaxTensorStatistic): return fns.allclose(self.min_values, other.min_values) and fns.allclose(self.max_values, other.max_values) return False +@dataclass +class AbsMaxTensorStatistic(TensorStatistic): + ABS_MAX_STAT: ClassVar[str] = "abs_max" + + abs_max: Tensor + + @classmethod + def keys(cls): + return (cls.ABS_MAX_STAT,) + + def __eq__(self, other: TensorStatistic): + if isinstance(other, AbsMaxTensorStatistic): + return fns.allclose(self.abs_max, other.abs_max) + return False + + @dataclass class MeanTensorStatistic(TensorStatistic): MEAN_STAT: ClassVar[str] = "mean_values" @@ -47,6 +83,10 @@ class MeanTensorStatistic(TensorStatistic): mean_values: Tensor shape: Tuple[int, ...] + @classmethod + def keys(cls): + return (cls.MEAN_STAT, cls.SHAPE_STAT) + def __eq__(self, other: TensorStatistic): if isinstance(other, MeanTensorStatistic): return self.shape == other.shape and fns.allclose(self.mean_values, other.mean_values) @@ -61,6 +101,10 @@ class MedianMADTensorStatistic(TensorStatistic): median_values: Tensor mad_values: Tensor + @classmethod + def keys(cls): + return (cls.MEDIAN_VALUES_STAT, cls.MAD_VALUES_STAT) + def __eq__(self, other: TensorStatistic): if isinstance(other, MedianMADTensorStatistic): return fns.allclose(self.median_values, other.median_values) and fns.allclose( @@ -68,6 +112,13 @@ def __eq__(self, other: TensorStatistic): ) return False + @classmethod + def from_config(cls, config: Dict[str, Any]) -> TensorStatistic: + return cls( + median_values=config[cls.TENSOR_STATISTIC_OUTPUT_KEY][cls.MEDIAN_VALUES_STAT], + mad_values=config[cls.TENSOR_STATISTIC_OUTPUT_KEY][cls.MAD_VALUES_STAT], + ) + @dataclass class PercentileTensorStatistic(TensorStatistic): @@ -75,6 +126,10 @@ class PercentileTensorStatistic(TensorStatistic): percentile_vs_values_dict: Dict[str, Tensor] + @classmethod + def keys(cls): + return (cls.PERCENTILE_VS_VALUE_DICT,) + def __eq__(self, other: TensorStatistic): if isinstance(other, PercentileTensorStatistic): if Counter(self.percentile_vs_values_dict.keys()) != Counter(other.percentile_vs_values_dict.keys()): @@ -85,6 +140,16 @@ def __eq__(self, other: TensorStatistic): return True return False + @classmethod + def from_config(cls, config: Dict[str, Any]) -> TensorStatistic: + if cls.TENSOR_STATISTIC_OUTPUT_KEY in config: + percentile_vs_values_dict = config[cls.TENSOR_STATISTIC_OUTPUT_KEY] + else: + percentile_vs_values_dict = {} + for (_, percentile), value in config.items(): + percentile_vs_values_dict[percentile] = value + return cls(percentile_vs_values_dict=percentile_vs_values_dict) + @dataclass class RawTensorStatistic(TensorStatistic): @@ -92,7 +157,106 @@ class RawTensorStatistic(TensorStatistic): values: Tensor + @classmethod + def keys(cls): + return (cls.VALUES_STATS,) + def __eq__(self, other: RawTensorStatistic) -> bool: - if isinstance(other, PercentileTensorStatistic): + if isinstance(other, RawTensorStatistic): return fns.allclose(self.values, other.values) return False + + +@dataclass +class HessianTensorStatistic(TensorStatistic): + HESSIAN_INPUT_ACTIVATION_STATS: ClassVar[str] = "hessian" + + hessian: Tensor + + @classmethod + def keys(cls): + return (cls.HESSIAN_INPUT_ACTIVATION_STATS,) + + def __eq__(self, other: TensorStatistic): + if isinstance(other, HessianTensorStatistic): + return fns.allclose(self.hessian, other.hessian) + return False + + +@dataclass +class MeanVarianceTensorStatistic(TensorStatistic): + MEAN_VARIANCE_STAT: ClassVar[str] = "mean_variance" + + mean_variance: Tensor + + @classmethod + def keys(cls): + return (cls.MEAN_VARIANCE_STAT,) + + def __eq__(self, other: TensorStatistic): + if isinstance(other, MeanVarianceTensorStatistic): + return fns.allclose(self.mean_variance, other.mean_variance) + return False + + +@dataclass +class MaxVarianceTensorStatistic(TensorStatistic): + MAX_VARIANCE_STAT: ClassVar[str] = "max_variance" + + max_variance: Tensor + + @classmethod + def keys(cls): + return (cls.MAX_VARIANCE_STAT,) + + def __eq__(self, other: TensorStatistic): + if isinstance(other, MaxVarianceTensorStatistic): + return fns.allclose(self.max_variance, other.max_variance) + return False + + +@dataclass +class MeanMagnitudeTensorStatistic(TensorStatistic): + MEAN_MAGNITUDE_STAT: ClassVar[str] = "mean_magnitude" + + mean_magnitude: Tensor + + @classmethod + def keys(cls): + return (cls.MEAN_MAGNITUDE_STAT,) + + def __eq__(self, other: TensorStatistic): + if isinstance(other, MeanMagnitudeTensorStatistic): + return fns.allclose(self.mean_magnitude, other.mean_magnitude) + return False + + +@dataclass +class WCTensorStatistic(TensorStatistic): + MEAN_STAT = "mean_values" + SHAPE_STAT = "shape_values" + + mean_values: List[Tensor] + shape_values: List[Tuple[int, ...]] + + @classmethod + def keys(cls): + return (cls.MEAN_STAT, cls.SHAPE_STAT) + + def __eq__(self, other: Any) -> bool: + shapes_equal = all(self.shapes[i] == other.shapes[i] for i in range(len(self.mean_values))) + if not shapes_equal: + return False + mean_values_equal = all( + self.tensor_eq(self.mean_values[i], other.mean_values[i]) for i in range(len(self.mean_values)) + ) + return mean_values_equal + + @classmethod + def from_config(cls, config: Dict[str, Any]) -> TensorStatistic: + mean_values, shape_values = None, None + if cls.MEAN_STAT in config and config[cls.MEAN_STAT] is not None: + mean_values = [fns.squeeze(it) for it in config[cls.MEAN_STAT]] + if cls.SHAPE_STAT in config and config[cls.SHAPE_STAT] is not None: + shape_values = [tuple(it) for it in config[cls.SHAPE_STAT]] + return cls(mean_values=mean_values, shape_values=shape_values) diff --git a/nncf/experimental/torch/fx/constant_folding.py b/nncf/experimental/torch/fx/constant_folding.py new file mode 100644 index 00000000000..945f79b497c --- /dev/null +++ b/nncf/experimental/torch/fx/constant_folding.py @@ -0,0 +1,257 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections +from typing import Any, Callable, Dict, List, Optional + +import torch.fx +import torch.utils._pytree as pytree + +aten = torch.ops.aten + + +def _replace_node_with_constant( + gm: torch.fx.GraphModule, + node: torch.fx.Node, + value: torch.Tensor, +) -> None: + """ + Replaces given node to a constant with given value. + + :param gm: GraphModule instance. + :param node: A node to replace with a constant. + :param value: Value to use as a constant instead of the given node. + """ + g = gm.graph + + if not hasattr(gm, "_frozen_param_count"): + gm._frozen_param_count = 0 # type: ignore[assignment] + i = gm._frozen_param_count + + while True: + qualname = f"_frozen_param{i}" + if not hasattr(gm, qualname): + break + i += 1 + + gm._frozen_param_count = i + 1 + + with g.inserting_before(node): + new_input_node = g.create_node("get_attr", qualname, (), {}) + node.replace_all_uses_with(new_input_node) + new_input_node.meta.update(node.meta) + g.erase_node(node) + + # needed to suppress `does not reference an nn.Module, nn.Parameter, or buffer` warning + gm.register_buffer(qualname, value) + setattr(gm, qualname, value) + + +def _is_const_source(node: torch.fx.Node) -> bool: + """ + Return True if node is a constant node. + + :param node: Given node. + :return: True if node is a constant node. + """ + return node.op == "get_attr" + + +class ConstantFolder(torch.fx.Interpreter): + """ + Finds constant subraphs and place it in node_replacement attribute. + """ + + def __init__( + self, + gm: torch.fx.GraphModule, + ) -> None: + super().__init__(gm) + self.node_replacements: Dict[torch.fx.Node, Any] = {} + self.replaced_uses: Dict[torch.fx.Node, int] = collections.Counter() + self.unknown_value = object() + + # overwrite this to deallocate env values if their only remaining use + # is the output + self.user_to_last_uses = self.node_to_last_non_output_use() + + def _deduce_value(self, node: torch.fx.Node) -> Any: + return super().run_node(node) + + def is_impure(self, node: torch.fx.node.Node) -> bool: + def is_woq_int8_pattern(node: torch.fx.node.Node) -> bool: + return ( + node.target == torch.ops.prims.convert_element_type.default # type: ignore[return-value] + and isinstance(node.args[0], torch.fx.Node) + and "val" in node.args[0].meta + and node.args[0].meta["val"].dtype == torch.int8 # type: ignore[union-attr] + and node.args[1] == torch.bfloat16 + ) + + if ( + is_woq_int8_pattern(node) + or ( + node.target == torch.ops.aten.permute.default + and len(node.users) == 1 + and is_woq_int8_pattern(next(iter(node.users))) + ) + ) and _is_const_source(node.args[0]): + # Case 1: int8_weight -> dq -> bf16_weight + # Case 2: int8_weight -> permute -> dq -> bf16_weight + return True + + quant_registered = getattr(torch.ops.quantized_decomposed, "dequantize_per_channel", None) is not None + if quant_registered and node.target in [ + torch.ops.quantized_decomposed.dequantize_per_channel.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor, + ]: + # For the pattern fp32_weight -> q -> dq + # We only folding fp32_weight -> q + # int8_weight and leave dq in graph to be fused + return True + return False + + def node_to_last_non_output_use(self) -> Dict[torch.fx.Node, List[torch.fx.Node]]: + last_non_output_use = collections.defaultdict(list) + seen_uses = set() + output_node = next(iter(reversed(self.module.graph.nodes))) + + for node in reversed(self.module.graph.nodes): + if node.target == "output": + continue + + def add_use(inp: torch.fx.Node) -> None: + if inp in seen_uses: + return + + seen_uses.add(inp) + last_non_output_use[node].append(inp) + + # In-place is fine since we don't mutate + pytree.tree_map_only_(torch.fx.Node, add_use, (node.args, node.kwargs)) + + # if this node is only used in output, we want to gc it right away + if len(node.users) == 1 and output_node in node.users: + last_non_output_use[node].append(node) + + return last_non_output_use + + def run_node(self, node: torch.fx.Node) -> Any: + if node.target == "output": + # because we remove nodes from env on last non output use, + # re-define them now or we'll get error in interpreter + def set_env(arg: torch.fx.Node) -> None: + self.env[arg] = self.unknown_value + + # In-place is fine since we don't mutate + pytree.tree_map_only_(torch.fx.Node, set_env, node.args) + return super().run_node(node) + + args, kwargs = self.fetch_args_kwargs_from_env(node) + flattened_inputs = pytree.arg_tree_leaves(*args, **kwargs) + + # We need to do this weird thing because in cases where flattened_inputs + # contains a ScriptObject, equality checking results in a type error if + # the types are different. + if any( + type(self.unknown_value) is type(input_) and self.unknown_value == input_ for input_ in flattened_inputs + ): + return self.unknown_value + + if node.op == "call_function" and node.target == aten._efficientzerotensor.default: + return self.unknown_value + + if node.op == "call_function" and node.name == "triton_kernel_wrapper_functional_proxy": + return self.unknown_value + + # skip constructors, since inductor generates optimal code for them already + # and turning into tensor would result in an additional global memory read + if not _is_const_source(node) and not any(isinstance(e, torch.Tensor) for e in flattened_inputs): + return self.unknown_value + + # All mutations should either be removed or on inputs which we did not make constant + if isinstance(node.target, torch._ops.OpOverload) and torch.Tag.nondeterministic_seeded in node.target.tags: + return self.unknown_value + + out = self._deduce_value(node) + if out == self.unknown_value: + return self.unknown_value + + if not _is_const_source(node) and isinstance(out, torch.Tensor): + if out.device.type == "meta": + return out + + if self.is_impure(node): + return self.unknown_value + + self.add_node_replacement(node, out) + + flattened_node_inps = pytree.arg_tree_leaves(*node.args, **node.kwargs) + + for n in flattened_node_inps: + if not isinstance(n, torch.fx.Node): + continue + + self.replaced_uses[n] += 1 + + for to_delete in self.user_to_last_uses.get(node, []): + if self.replaced_uses[to_delete] == len(to_delete.users): + self.node_replacements.pop(to_delete, None) + + return out + + def add_node_replacement(self, node: torch.fx.Node, tensor: torch.Tensor) -> None: + self.node_replacements[node] = tensor + + def run(self) -> Any: # type: ignore[override] + env: Dict[torch.fx.Node, Any] = {} + self.insert_placerholder_values(env) + return super().run(initial_env=env) + + def insert_placerholder_values(self, env: Dict[torch.fx.Node, Any]) -> None: + for n in self.module.graph.find_nodes(op="placeholder"): + env[n] = self.unknown_value # type: ignore[assignment] + + +def constant_fold( + gm: torch.fx.GraphModule, + constraint_fn: Optional[Callable[[torch.fx.Node], bool]] = None, +) -> None: + """ + Calcualtes constant subgraphs values and replaces them with a constant node inplace. + + :param gm: Given graph model. + :param constraint_fn: Constraint function which takes a node and returs the constraint: + should the node be constant folded or not. + """ + with torch.utils._python_dispatch._disable_current_modes(): + cf = ConstantFolder(gm) + cf.run() + + for node, constant in cf.node_replacements.items(): + if constraint_fn is not None and not constraint_fn(node): + continue + _replace_node_with_constant(gm, node, constant) + + erased_params = [] + for node in gm.graph.find_nodes(op="get_attr"): + if len(node.users) == 0: + if hasattr(gm, node.target): + delattr(gm, node.target) + erased_params.append(node) + + for node in erased_params: + gm.graph.erase_node(node) + + gm.graph.eliminate_dead_code() + gm.graph.lint() + gm.recompile() diff --git a/nncf/experimental/torch/fx/groups.py b/nncf/experimental/torch/fx/groups.py new file mode 100644 index 00000000000..eb4b32c63df --- /dev/null +++ b/nncf/experimental/torch/fx/groups.py @@ -0,0 +1,15 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import nncf.torch.graph.operator_metatypes as om +from nncf.torch.model_graph_manager import OPERATORS_WITH_BIAS_METATYPES + +FX_OPERATORS_WITH_BIAS_METATYPES = tuple(OPERATORS_WITH_BIAS_METATYPES) + (om.PTLinearMetatype,) diff --git a/nncf/experimental/torch/fx/model_transformer.py b/nncf/experimental/torch/fx/model_transformer.py index caf90dfac6d..4e0f6f64499 100644 --- a/nncf/experimental/torch/fx/model_transformer.py +++ b/nncf/experimental/torch/fx/model_transformer.py @@ -10,11 +10,10 @@ # limitations under the License. from collections import defaultdict -from typing import List +from typing import List, Set import torch import torch.fx -from torch.fx.passes.split_utils import split_by_tags from nncf.common.graph.model_transformer import ModelTransformer from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand @@ -26,6 +25,8 @@ class FXModelTransformer(ModelTransformer): """ Applies transformations upon Torch FX model. + FXApplyTransformationCommands are made inplace, + PTModelExtractionCommands do not change the input model. """ def __init__(self, model: torch.fx.GraphModule): @@ -61,6 +62,34 @@ def transform(self, transformation_layout: PTTransformationLayout) -> torch.fx.G model.recompile() return model + @staticmethod + def _traverse_graph( + input_nodes: List[torch.fx.Node], + stop_nodes: Set[torch.fx.Node], + visited: Set[torch.fx.Node], + ) -> None: + """ + Traverses through the graph starting with the input nodes and + stopping for the stop nodes and the visited nodes. As the result, + it modifies the visited container with all nodes visited during the traverse. + + :param input_nodes: Given input nodes. + :param stop_nodes: Given stop nodes. + :param visited: Set of already visited nodes. + """ + + while input_nodes: + in_node = input_nodes.pop() + if in_node.name in visited or in_node.name in stop_nodes: + continue + + visited.add(in_node.name) + # Any constant is a stop op during the traversing procedure. + if in_node.op == "get_attr": + continue + input_nodes.extend(in_node.all_input_nodes) + input_nodes.extend(list(in_node.users)) + @staticmethod def _apply_model_extraction( model: torch.fx.GraphModule, @@ -75,46 +104,63 @@ def _apply_model_extraction( more than one element this function raises an assert. :return: Returns a submodel extracted from the given model by the given transformation. """ + transformation = transformations[-1] - assert len(transformation.input_node_names) == 1 - assert transformation.input_node_names == transformation.output_node_names - node_name = transformation.input_node_names[0] + stop_nodes = set(transformation.input_node_names + transformation.output_node_names) + visited = set() + + for node_name in transformation.input_node_names: + node = get_graph_node_by_name(model.graph, node_name) + visited.add(node.name) + target_inputs = node.all_input_nodes[1:] + if node.name not in transformation.output_node_names: + target_inputs += list(node.users) + FXModelTransformer._traverse_graph(target_inputs, stop_nodes, visited) + + for node_name in transformation.output_node_names: + node = get_graph_node_by_name(model.graph, node_name) + visited.add(node.name) + if node.name not in transformation.input_node_names: + FXModelTransformer._traverse_graph(node.all_input_nodes, stop_nodes, visited) + + extracted_graph = torch.fx.Graph() + value_remap = {} + + def remap_fn(node: torch.fx.Node): + return value_remap.get(node) # noqa F821 - tags = ["before", "extracted", "after"] - i = 0 for node in model.graph.nodes: - if node.name == node_name: - node.tag = tags[1] - weights = [node.all_input_nodes[1]] - while weights: - w_node = weights.pop() - assert w_node.tag in tags[0:2] - w_node.tag = tags[1] - weights.extend(w_node.all_input_nodes) - i = 2 + if node.name not in visited or node.op == "output": continue - node.tag = tags[i] - - # TODO(dlyakhov): reduce memory consumption by - # more optimal splitting implementation. - splitted_gm = split_by_tags(model, tags) - - extracted_model = splitted_gm.extracted - graph: torch.fx.Graph = extracted_model.graph - # Check extracted model has inputs. - # It is possible to have two constant inputs - # for the target layer, an placeholder is being - # placed to the input port. - target_node = get_graph_node_by_name(graph, node_name) - input_node = target_node.all_input_nodes[0] - if input_node.op != "placeholder": - with graph.inserting_before(target_node): - new_input_node = graph.create_node( - "placeholder", "placeholder_node", (), {}, name="placeholder_graph_node" + value_remap[node] = extracted_graph.node_copy(node, remap_fn) + del value_remap + + for input_name in transformation.input_node_names: + node_with_input = get_graph_node_by_name(extracted_graph, input_name) + with extracted_graph.inserting_before(node_with_input): + graph_input_name = input_name + "_input" + graph_input = extracted_graph.create_node( + op="placeholder", + target=graph_input_name, + name=graph_input_name, ) - target_node.replace_input_with(input_node, new_input_node) - extracted_model.graph.eliminate_dead_code() - return extracted_model + + args = list(node_with_input.args) + args[0] = graph_input + node_with_input.args = tuple(args) + + nodes_with_output = [get_graph_node_by_name(extracted_graph, name) for name in transformation.output_node_names] + last_node = list(extracted_graph.nodes)[-1] + with extracted_graph.inserting_after(last_node): + graph_output_name = "output" + extracted_graph.create_node( + "output", + graph_output_name, + (tuple(nodes_with_output),), + name=graph_output_name, + ) + + return torch.fx.GraphModule(model, extracted_graph) @staticmethod def _apply_transformation( diff --git a/nncf/experimental/torch/fx/model_utils.py b/nncf/experimental/torch/fx/model_utils.py new file mode 100644 index 00000000000..b4c2cf5e570 --- /dev/null +++ b/nncf/experimental/torch/fx/model_utils.py @@ -0,0 +1,76 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import deque + +import torch.fx + +from nncf.common.factory import ModelTransformerFactory +from nncf.common.graph.definitions import NNCFGraphNodeType +from nncf.common.graph.graph import NNCFGraph +from nncf.common.graph.transformations.commands import TargetType +from nncf.common.graph.transformations.layout import TransformationLayout +from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand +from nncf.experimental.torch.fx.transformations import node_removal_transformation_builder +from nncf.torch.graph.operator_metatypes import QUANTIZE_NODE_TYPES +from nncf.torch.graph.transformations.commands import PTTargetPoint + + +def remove_fq_from_inputs(model: torch.fx.GraphModule, graph: NNCFGraph) -> torch.fx.GraphModule: + """ + This method removes the activation Fake Quantize nodes from the model. + It's needed for the further bias shift calculation that relates on quantized weights. + + :param model: ov.Model instance. + :param graph: NNCFGraph instance. + :return: ov.Model instance without activation Fake Quantize nodes. + """ + transformation_layout = TransformationLayout() + model_transformer = ModelTransformerFactory.create(model) + + seen_nodes = [] + nodes_queue = deque(graph.get_input_nodes()) + while nodes_queue: + current_node = nodes_queue.popleft() + current_node_name = current_node.node_name + + if current_node_name in seen_nodes: + continue + + seen_nodes.append(current_node_name) + if current_node.node_type in QUANTIZE_NODE_TYPES: + transformation = node_removal_transformation_builder(current_node, input_port_id=0) + transformation_layout.register(FXApplyTransformationCommand(transformation)) + nodes_queue.extend(graph.get_next_nodes(current_node)) + + return model_transformer.transform(transformation_layout) + + +_TARGET_TYPE_TO_FX_INS_TYPE_MAP = { + TargetType.PRE_LAYER_OPERATION: TargetType.OPERATOR_PRE_HOOK, + TargetType.POST_LAYER_OPERATION: TargetType.OPERATOR_POST_HOOK, +} + + +def get_target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: + """ + Creates torch-specific target point. + + :param target_type: Target point target type. + :param target_node_name: Target node name to use in the target point. + :param port_id: Target port id. + :return: Torch-specific target point. + """ + if NNCFGraphNodeType.INPUT_NODE in target_node_name or target_type == TargetType.POST_LAYER_OPERATION: + port_id = None + if target_type in _TARGET_TYPE_TO_FX_INS_TYPE_MAP: + target_type = _TARGET_TYPE_TO_FX_INS_TYPE_MAP[target_type] + return PTTargetPoint(target_type, target_node_name, input_port_id=port_id) diff --git a/nncf/experimental/torch/fx/nncf_graph_builder.py b/nncf/experimental/torch/fx/nncf_graph_builder.py index 946ac27ce84..c005073b941 100644 --- a/nncf/experimental/torch/fx/nncf_graph_builder.py +++ b/nncf/experimental/torch/fx/nncf_graph_builder.py @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +from collections import Counter from typing import Tuple import torch.fx @@ -64,6 +65,22 @@ def _get_layer_attributes( ) return None + def _map_fx_unique_metatypes(node: torch.fx.Node, metatype: om.OperatorMetatype) -> om.OperatorMetatype: + """ + Attempts to retrieve correct subtype for the given node. + + :param node: Given node. + :param metatype: Given node metatype. + :param model: Target GraphModule instance. + :return: Correct FX metatype of the given node if it is exist or the original node metatype otherwise. + """ + if metatype in [om.PTEmbeddingMetatype]: + weight_node = node.args[0] + if weight_node.op == "get_attr": + return om.PTAtenEmbeddingMetatype + + return metatype + @staticmethod def _get_node_type_and_metatype( node: torch.fx.Node, model: torch.fx.GraphModule @@ -115,16 +132,18 @@ def create_nncf_graph(model: torch.fx.GraphModule) -> PTNNCFGraph: :param model: torch fx GraphModule. :return: NNCFGraph. """ - nncf_graph = PTNNCFGraph() + const_targets_counter = Counter([node.target for node in model.graph.nodes if node.op == "get_attr"]) for source_node in model.graph.nodes: node_type, node_metatype = GraphConverter._get_node_type_and_metatype(source_node, model) + node_metatype = GraphConverter._map_fx_unique_metatypes(source_node, node_metatype) + is_shared_node = source_node.op in ("get_attr",) and ( + const_targets_counter[source_node.target] > 1 or len(source_node.users) > 1 + ) nncf_graph.add_nncf_node( - node_name=source_node.name, - node_type=node_type, - node_metatype=node_metatype, + node_name=source_node.name, node_type=node_type, node_metatype=node_metatype, is_shared=is_shared_node ) for source_node in model.graph.nodes: @@ -134,7 +153,6 @@ def create_nncf_graph(model: torch.fx.GraphModule) -> PTNNCFGraph: input_port_id, output_port_id, tensor_shape = GraphConverter.get_edge_params( model, source_node, source_nncf_node, dist_node, idx ) - nncf_graph.add_edge_between_nncf_nodes( source_nncf_node.node_id, dist_node_id, @@ -160,16 +178,18 @@ def get_edge_params( :param source_node: Source node in format of torch.fx.Node. :param source_nncf_node: Source node in format of NNCFNode. :param dist_node: Distance node in format of torch.fx.Node. - :param output_idx: Output indes of the source_node. + :param output_idx: Output index of the source_node. :return: Tuple of edge parameters: edge input port id, edge output port id and edge tensor shape. """ output_port_id = 0 tensor_shape = None if source_node.op in ("get_attr",): - tensor_shape = tuple(getattr(model, source_node.target).shape) + tensor_shape = tuple(get_tensor_constant_from_node(source_node, model).shape) elif "val" in source_node.meta: - if source_nncf_node.metatype is om.PTBatchNormMetatype: + if source_nncf_node.metatype is om.PTBatchNormMetatype and isinstance( + source_node.meta["val"], (tuple, list) + ): tensor = source_node.meta["val"][0] elif source_nncf_node.metatype in [om.PTSplitMetatype, om.PTMaxMetatype, om.PTMinMetatype]: tensor = source_node.meta["val"][output_idx] diff --git a/nncf/experimental/torch/fx/node_utils.py b/nncf/experimental/torch/fx/node_utils.py index a0757ea027c..76e807e4b5f 100644 --- a/nncf/experimental/torch/fx/node_utils.py +++ b/nncf/experimental/torch/fx/node_utils.py @@ -11,6 +11,12 @@ import torch.fx +import nncf.torch.graph.operator_metatypes as om +from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFNode +from nncf.experimental.torch.fx.groups import FX_OPERATORS_WITH_BIAS_METATYPES +from nncf.tensor import Tensor + # TODO(dlyakhov): Use torch.fx.graph.find_nodes method instead after # torch version update (>= 2.4) @@ -49,3 +55,34 @@ def get_tensor_constant_from_node(constant_node: torch.fx.Node, model: torch.fx. raise RuntimeError(f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}") attr_itr = getattr(attr_itr, atom) return attr_itr + + +def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + """ + Returns True if the node has a bias, False otherwise. + + :param node: Target node. + :param nncf_graph: Target nncf_graph. + :return: True if the node has a bias, False otherwise. + """ + # Assumes that all biases were unfused + if node.metatype in FX_OPERATORS_WITH_BIAS_METATYPES: + next_nodes = nncf_graph.get_next_nodes(node) + if len(next_nodes) != 1: + return False + return next_nodes[0].metatype in (om.PTAddMetatype,) + + +def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: torch.fx.GraphModule) -> Tensor: + """ + Retrieves the bias value from the given node. + + :param node: Target node. + :param nncf_graph: Target nncf_graph. + :param model: Target GraphModule. + :return: Bias value of the given node. + """ + bias_node = nncf_graph.get_next_nodes(node)[0] + # TODO(dlyakhov): make a node_name_vs_node map to speed up the process + graph_bias_node = get_graph_node_by_name(model.graph, bias_node.node_name) + return Tensor(get_tensor_constant_from_node(graph_bias_node.all_input_nodes[1], model)) diff --git a/nncf/experimental/torch/fx/quantization/backend_parameters.py b/nncf/experimental/torch/fx/quantization/backend_parameters.py new file mode 100644 index 00000000000..401c781b341 --- /dev/null +++ b/nncf/experimental/torch/fx/quantization/backend_parameters.py @@ -0,0 +1,31 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters + + +class FXBackendParameters: + COMPRESS_WEIGHTS = "compress_weights" + + +def is_weight_compression_needed(advanced_parameters: Optional[AdvancedQuantizationParameters]) -> bool: + """ + Determines whether weight compression is needed based on the provided + advanced quantization parameters. + + :param advanced_parameters: Advanced quantization parameters. + :return: True if weight compression is needed, False otherwise. + """ + if advanced_parameters is not None and advanced_parameters.backend_params is not None: + return advanced_parameters.backend_params.get(FXBackendParameters.COMPRESS_WEIGHTS, True) + return True diff --git a/nncf/experimental/torch/fx/quantization/quantize_model.py b/nncf/experimental/torch/fx/quantization/quantize_model.py index 01aebf68c1f..bdfe839994c 100644 --- a/nncf/experimental/torch/fx/quantization/quantize_model.py +++ b/nncf/experimental/torch/fx/quantization/quantize_model.py @@ -26,13 +26,21 @@ from nncf.common.logging import nncf_logger from nncf.common.quantization.structs import QuantizationPreset from nncf.data import Dataset +from nncf.experimental.torch.fx.quantization.backend_parameters import is_weight_compression_needed from nncf.experimental.torch.fx.transformations import apply_quantization_transformations +from nncf.experimental.torch.fx.transformations import compress_post_quantize_transformation +from nncf.experimental.torch.fx.transformations import fq_weights_transformation from nncf.experimental.torch.fx.transformations import revert_quantization_transformations +from nncf.parameters import BackupMode +from nncf.parameters import CompressWeightsMode from nncf.parameters import ModelType from nncf.parameters import QuantizationMode +from nncf.parameters import SensitivityMetric from nncf.parameters import TargetDevice +from nncf.quantization.advanced_parameters import AdvancedCompressionParameters from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization +from nncf.quantization.algorithms.weight_compression.algorithm import WeightCompression from nncf.scopes import IgnoredScope DEFAULT_RANGE_TYPE = "mean_min_max" @@ -49,7 +57,7 @@ def quantize_impl( model_type: Optional[ModelType] = None, ignored_scope: Optional[IgnoredScope] = None, advanced_parameters: Optional[AdvancedQuantizationParameters] = None, -) -> torch.nn.Module: +) -> torch.fx.GraphModule: """ Implementation of the `quantize()` method for the Torch FX backend. """ @@ -58,8 +66,6 @@ def quantize_impl( " Torch FX PTQ is an experimental feature, consider using Torch or OpenVino PTQ backends" " in case of errors or a poor model performance." ) - if fast_bias_correction is False: - raise ValueError(f"fast_bias_correction={fast_bias_correction} is not supported") if target_device == TargetDevice.CPU_SPR: raise nncf.InternalError("target_device == CPU_SPR is not supported") if mode is not None: @@ -90,6 +96,11 @@ def quantize_impl( # bias configuration. revert_quantization_transformations(quantized_model) + if is_weight_compression_needed(advanced_parameters): + compress_post_quantize_transformation(quantized_model) + else: + fq_weights_transformation(quantized_model) + # Magic. Without this call compiled model # is not preformant quantized_model = GraphModule(quantized_model, quantized_model.graph) @@ -103,5 +114,52 @@ def quantize_impl( quantized_model.meta.update(original_graph_meta) quantized_model = _disallow_eval_train(quantized_model) + # Each transformation adds a duplicate tensor value to the model buffer. + # This step removes the duplicates tensor values from the buffer. + quantized_model = GraphModule(quantized_model, quantized_model.graph) return quantized_model + + +def compress_weights_impl( + model: torch.fx.GraphModule, + dataset: Dataset, + mode: CompressWeightsMode, + ratio: float, + group_size: int, + ignored_scope: IgnoredScope, + all_layers: bool, + sensitivity_metric: SensitivityMetric, + awq: bool, + subset_size: int, + scale_estimation: bool, + gptq: bool, + lora_correction: bool, + backup_mode: BackupMode, + advanced_parameters: Optional[AdvancedCompressionParameters] = None, +) -> torch.fx.GraphModule: + """ + Implementation of the `compress_weights()` method for the Torch Fx backend. + """ + + compression_algorithm = WeightCompression( + mode, + ratio, + group_size, + ignored_scope, + all_layers, + sensitivity_metric, + awq, + subset_size, + scale_estimation, + gptq, + lora_correction, + backup_mode, + advanced_parameters, + ) + graph = NNCFGraphFactory.create(model) + compressed_model = compression_algorithm.apply(model, graph, dataset=dataset) + compressed_model = GraphModule(compressed_model, compressed_model.graph) + compressed_model = _disallow_eval_train(compressed_model) + + return compressed_model diff --git a/nncf/experimental/torch/fx/statistics/aggregator.py b/nncf/experimental/torch/fx/statistics/aggregator.py index 9f109147d83..05af7f19bf5 100644 --- a/nncf/experimental/torch/fx/statistics/aggregator.py +++ b/nncf/experimental/torch/fx/statistics/aggregator.py @@ -20,7 +20,9 @@ from nncf.common.graph.transformations.layout import TransformationLayout from nncf.common.tensor_statistics.aggregator import StatisticPointsContainer from nncf.common.tensor_statistics.aggregator import StatisticsAggregator +from nncf.common.utils.backend import BackendType from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.statistics import TensorStatistic from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand from nncf.experimental.torch.fx.transformations import leaf_module_insertion_transformation_builder from nncf.tensor import Tensor @@ -51,6 +53,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class FXStatisticsAggregator(StatisticsAggregator): + BACKEND: BackendType = BackendType.TORCH_FX HOOKS_GROUP_NAME = "statistics_hooks" def collect_statistics(self, model: NNCFNetwork, graph: NNCFGraph) -> None: @@ -121,3 +124,14 @@ def _get_merged_statistic_points( @staticmethod def _process_outputs(outputs: Dict[str, np.ndarray]) -> Dict[str, Tensor]: return outputs + + def _get_statistics_key(self, statistics: TensorStatistic, target_point: PTTargetPoint) -> str: + """ + Returns key of statistics. + + :param statistics: Statistics value. + :param target_point: Statistics target point. + :return: Statistics key. + """ + target_point_id = f"{target_point.target_node_name}_{target_point.type}_{target_point.input_port_id}" + return f"{statistics.__class__.__name__}_{target_point_id}" diff --git a/nncf/experimental/torch/fx/transformations.py b/nncf/experimental/torch/fx/transformations.py index d15172e93d0..c695220c981 100644 --- a/nncf/experimental/torch/fx/transformations.py +++ b/nncf/experimental/torch/fx/transformations.py @@ -9,7 +9,8 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Callable, List, Optional +from copy import copy +from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch import torch.fx @@ -18,16 +19,59 @@ from torch.quantization.fake_quantize import FakeQuantize import nncf +import nncf.torch from nncf.common.graph.graph import NNCFNode from nncf.common.graph.transformations.commands import TargetType +from nncf.experimental.torch.fx.constant_folding import constant_fold from nncf.experimental.torch.fx.node_utils import get_graph_node_by_name from nncf.experimental.torch.fx.node_utils import get_tensor_constant_from_node from nncf.torch.graph.transformations.commands import PTTargetPoint TransformationFNType = Callable[[torch.fx.GraphModule], None] - -def _set_new_node_meta(new_node: torch.fx.Node, prev_node: torch.fx.Node, target_module: torch.nn.Module): +# Copied from torch.fx.Node +BaseArgumentTypes = Union[ + str, + int, + float, + bool, + complex, + torch.dtype, + torch.Tensor, + torch.device, + torch.memory_format, + torch.layout, + torch._ops.OpOverload, +] +Argument = Optional[Union[Tuple[Any, ...], List[Any], Dict[str, Any], slice, range, torch.fx.Node, BaseArgumentTypes]] + +QUANTIZE_NODE_TARGETS = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.quantize_per_channel.default, +] +DEQUANTIZE_NODE_TARGETS = [ + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_channel.default, +] +# Map quantize_per_tensor to dequantize_per_tensor, the same for per_channel and vice-versa +QDQ_PAIR = { + **{ + quantize_node: dequantize_node + for quantize_node, dequantize_node in zip(QUANTIZE_NODE_TARGETS, DEQUANTIZE_NODE_TARGETS) + }, + **{ + dequantize_node: quantize_node + for quantize_node, dequantize_node in zip(QUANTIZE_NODE_TARGETS, DEQUANTIZE_NODE_TARGETS) + }, +} + + +def _set_new_node_meta( + new_node: torch.fx.Node, + prev_nodes: Tuple[Argument, ...], + target_module: torch.nn.Module, + model: torch.fx.GraphModule, +): """ Sets correct meta \"val\" value to the new node. @@ -36,14 +80,21 @@ def _set_new_node_meta(new_node: torch.fx.Node, prev_node: torch.fx.Node, target New node expected to have only one input node. :param target_module: Module which is being called by the new node. """ - val = prev_node.meta["val"] - val = val if isinstance(val, tuple) else (val,) - retval = [] - for t in val: - retval.append(torch.ones(t.shape)) - + vals = [] + for prev_node in prev_nodes: + val = prev_node + if isinstance(prev_node, torch.fx.Node): + val = ( + prev_node.meta["val"] + if prev_node.op not in ["get_attr"] + else get_tensor_constant_from_node(prev_node, model).data + ) + # TODO(anzr299): Support output port id here. + # Output port 0 is used by default + val = val[0] if isinstance(val, tuple) else val + vals.append(val) with torch.no_grad(): - new_node.meta["val"] = target_module(*val) + new_node.meta["val"] = target_module(*tuple(vals)) def module_insertion_transformation_builder( @@ -70,16 +121,16 @@ def module_insertion_transformation(model: torch.fx.GraphModule): target_node = get_graph_node_by_name(graph, target_point.target_node_name) if target_point.target_type == TargetType.OPERATOR_POST_HOOK: - _set_new_node_meta(new_node, target_node, module_to_insert) + _set_new_node_meta(new_node, [target_node], module_to_insert, model) with graph.inserting_after(target_node): - for user in target_node.users: + for user in list(target_node.users): if user is new_node: continue user.replace_input_with(target_node, new_node) else: prev_node = target_node.args[target_point.input_port_id] - _set_new_node_meta(new_node, prev_node, module_to_insert) + _set_new_node_meta(new_node, [prev_node], module_to_insert, model) target_node.replace_input_with(prev_node, new_node) return module_insertion_transformation @@ -109,12 +160,13 @@ def leaf_module_insertion_transformation(model: torch.fx.GraphModule): return leaf_module_insertion_transformation -def bias_update_transformation_builder(node: NNCFNode, value: torch.Tensor) -> TransformationFNType: +def bias_update_transformation_builder(node: NNCFNode, value: torch.Tensor, input_port_id: int) -> TransformationFNType: """ Return transformation which updates constant of the given node with bias to the given value. :param node: Node with bias which requires bias constant update. :param value: New value to use as the bias constant. + :param input_port_id: Input port id to get constant node from. :return: Transformation which updates constant of the given node with bias to the given value. """ @@ -122,31 +174,44 @@ def bias_update_transformation(model: torch.fx.GraphModule): graph = model.graph target_node_name = node.node_name graph_node = get_graph_node_by_name(graph, target_node_name) - if len(graph_node.users) != 1: - raise nncf.InternalError(f"Node with bias have {len(graph_node.users)} users, 1 expected.") + add_nodes = [] + for user in graph_node.users: + if _is_add(user): + add_nodes.append(user) + if len(add_nodes) != 1: + raise nncf.InternalError(f"Node {graph_node.name} has {len(add_nodes)} outputs with adds, 1 expected") - bias_node = next(iter(graph_node.users)) - constant_update_fn(model, bias_node, value, input_port_id=1) + bias_node = add_nodes[0] + constant_update_fn(model, bias_node, value, input_port_id=input_port_id) return bias_update_transformation -def constant_update_transformation_builder(node: NNCFNode, value: torch.Tensor) -> TransformationFNType: +def constant_update_transformation_builder( + node: NNCFNode, value: torch.Tensor, input_port_id: int = 1 +) -> TransformationFNType: """ Return transformation which updates constant of the given node to the given value. :param node: Node which requires bias constant update. :param value: New value to use as the node constant. + :param input_port_id: Port Id of the constant. :return: Transformation which updates constant of the given node to the given value. """ def constant_update_transformation(model: torch.fx.GraphModule): - constant_update_fn(model, get_graph_node_by_name(model.graph, node.node_name), value, input_port_id=1) + constant_update_fn(model, get_graph_node_by_name(model.graph, node.node_name), value, input_port_id) return constant_update_transformation -def constant_update_fn(model: torch.fx.GraphModule, node: torch.fx.Node, value: torch.Tensor, input_port_id: int = 1): +def constant_update_fn( + model: torch.fx.GraphModule, + node: torch.fx.Node, + value: torch.Tensor, + input_port_id: int = 1, + updated_node_name: Optional[str] = None, +): """ Updates constant of given node on the given input port id with given value. @@ -154,11 +219,10 @@ def constant_update_fn(model: torch.fx.GraphModule, node: torch.fx.Node, value: :param node: Given graph node. :param value: New value to use as the node constant. :param input_port_id: Target constant input port id. + :param updated_node_name: Name of the constant node after updating. Default is `nodename` + `_updated_constant`. """ graph = model.graph - with graph.inserting_before(node): - new_constant = create_getattr_from_value(model, graph, node.name + "_updated_constant", value) - + node_name = updated_node_name if updated_node_name else node.name + "_updated_constant" args = list(node.args) # A bias node suppose to have constant on the second input port. if args[input_port_id].op != "get_attr": @@ -166,8 +230,19 @@ def constant_update_fn(model: torch.fx.GraphModule, node: torch.fx.Node, value: f"Constant on input port {input_port_id} for {node} is expected," f" but node {args[input_port_id]} is present." ) - args[input_port_id] = new_constant - node.args = tuple(args) + + # Update metadata of the new constant node. + previous_const = args[input_port_id] + consumer_nodes = list(previous_const.users) + # This list of consumer nodes is topologically sorted + # To ensure the updated node has the right order, + # we insert constant node before the node placed at the highest order in topological order. + sorted_consumer_nodes = [node for node in graph.nodes if node in consumer_nodes] + + with graph.inserting_before(sorted_consumer_nodes[0]): + new_constant = create_getattr_from_value(model, graph, node_name, value) + + previous_const.replace_all_uses_with(new_constant, propagate_meta=True) graph.eliminate_dead_code() @@ -186,7 +261,7 @@ def qdq_insertion_transformation_builder( def qdq_insertion_transformation(model: torch.fx.GraphModule): if any(tp.target_type != TargetType.OPERATION_WITH_WEIGHTS for tp in target_points) and len(target_points) > 1: - raise RuntimeError( + raise nncf.InternalError( "Insertion of shared qdq pair for the weights is not supported." " Please use non shared qdq pairs for the weights quantization." ) @@ -196,6 +271,69 @@ def qdq_insertion_transformation(model: torch.fx.GraphModule): return qdq_insertion_transformation +def node_removal_transformation_builder(node: NNCFNode, input_port_id: int) -> TransformationFNType: + """ + Returns transformation which removes target node from the model and connects + target node previous node on the given input port id with all target node outputs. + + :param node: Target node to remove. + :param input_port_id: Input port id which points to input node which should be connected + to the target node outputs. + :return: Transformation which removes target node from the model and connects + target node previous node on the given input port id with all target node outputs. + """ + + def node_removal_transformation(model: torch.fx.GraphModule): + target_node = get_graph_node_by_name(model.graph, node.node_name) + input_node = target_node.all_input_nodes[input_port_id] + for user in list(target_node.users): + user.replace_input_with(target_node, input_node) + model.graph.eliminate_dead_code() + + return node_removal_transformation + + +def output_insertion_transformation_builder(target_point: PTTargetPoint) -> TransformationFNType: + """ + Returns transformation which inserts clone operation on the given target point + and extend the model outputs with the inserted cloned value. + + :param target_point: Target point to insert clone and extend the model outputs. + :return: Transformation which inserts clone operation on the given target point + and extend the model outputs with the inserted cloned value. + """ + + def output_insertion_transformation(model: torch.fx.GraphModule): + graph = model.graph + target_node = get_graph_node_by_name(graph, target_point.target_node_name) + input_node = get_input_node(target_point, target_node) + + # Clone node output to safe it from inplace operations affects + with graph.inserting_after(input_node): + cloned_input = graph.create_node( + "call_function", + torch.ops.aten.clone.default, + (input_node,), + name=input_node.name + "_cloned", + ) + cloned_input.meta["val"] = copy(input_node.meta.get("val")) + + # Update args of the output node as one output could be present in the model + # TODO(dlaykhov) Support case when there are no outputs in the input model. + output_nodes = [node for node in model.graph.nodes if node.op == "output"] + assert len(output_nodes) == 1 + output_node = output_nodes[0] + + args = output_node.args + assert len(args) == 1 + if isinstance(args[0], torch.fx.Node): + args = (args,) + args = tuple(args[0]) + (cloned_input,) + output_node.args = (args,) + + return output_insertion_transformation + + def insert_one_qdq(model: torch.fx.GraphModule, target_point: PTTargetPoint, quantizer: FakeQuantize): """ Inserts quantize-dequantize after the target node to the target model. @@ -264,10 +402,12 @@ def insert_one_qdq(model: torch.fx.GraphModule, target_point: PTTargetPoint, qua input_node = get_input_node(target_point, target_node) quantize_op_inputs[0] = input_node + meta_val = input_node.meta.get("val") ctx_manager = get_ctx_manager(graph, target_point) with ctx_manager(target_node): quantized_node = graph.create_node(node_type, quantize_op, tuple(quantize_op_inputs), {}) + quantized_node.meta["val"] = copy(meta_val) # use the same qparams from quantize op dq_inputs = [quantized_node] + quantize_op_inputs[1:] @@ -277,13 +417,16 @@ def insert_one_qdq(model: torch.fx.GraphModule, target_point: PTTargetPoint, qua for user in target_node.users: if user is quantized_node: continue - user_dq_nodes.append((user, graph.call_function(dequantize_op, tuple(dq_inputs), {}))) + dq_node = graph.call_function(dequantize_op, tuple(dq_inputs), {}) + dq_node.meta["val"] = copy(meta_val) + user_dq_nodes.append((user, dq_node)) for user, dq_node in user_dq_nodes: user.replace_input_with(target_node, dq_node) elif target_point.target_type in [TargetType.OPERATOR_PRE_HOOK, TargetType.OPERATION_WITH_WEIGHTS]: with graph.inserting_after(quantized_node): dq_node = graph.call_function(dequantize_op, tuple(dq_inputs), {}) + dq_node.meta["val"] = copy(meta_val) args = list(target_node.args) args[target_point.input_port_id] = dq_node @@ -296,10 +439,10 @@ def _insert_call_module( graph: torch.fx.Graph, target_point: PTTargetPoint, module_attr_name: str, graph_node_name: str ): """ - Inserts module call node to the graph after the target node. + Inserts module call node to the graph after/before the target node using the ctx_manager. :param graph: Graph to insert module call node. - :param target_node: Target node, module call node is being iserted just after the target node. + :param target_node: Target node, module call node is being inserted just after the target node. :param module_attr_name: The name of the graph attribute which keeps the target module. :param graph_node_name: Target name for module call node. :return: Inserted module call node. @@ -376,6 +519,7 @@ def _is_supported_batch_norm_for_training(node: torch.fx.Node): Return True if the given node refers to an aten batch norm op QAT supports. """ supported_ops = [ + torch.ops.aten.batch_norm.default, torch.ops.aten._native_batch_norm_legit.default, torch.ops.aten.cudnn_batch_norm.default, torch.ops.aten.miopen_batch_norm.default, @@ -418,23 +562,249 @@ def fuse_conv_bn(model: torch.fx.GraphModule) -> None: model.recompile() +def _get_pattern_replacement_per_channel() -> ( + Tuple[Callable[[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int, torch.dtype], torch.Tensor]] +): + """ + Returns the patter and replacement function for the subgraph rewriter to + match and replace for per_tensor quantization + """ + + def pattern_per_channel(weight, scale, zero_point, axis, low, high, dtype): + quantized = torch.ops.quantized_decomposed.quantize_per_channel.default( + weight, scale, zero_point, axis, low, high, dtype + ) + dequantized = torch.ops.quantized_decomposed.dequantize_per_channel.default( + quantized, scale, zero_point, axis, low, high, dtype + ) + return dequantized + + def replacement_graph_per_channel(weight, scale, zero_point, axis, low, high, dtype): + return torch.ops.aten.sub.Tensor(torch.ops.aten.mul.Tensor(weight, scale), zero_point) + + return pattern_per_channel, replacement_graph_per_channel + + +def _get_pattern_replacement_per_tensor() -> ( + Tuple[Callable[[torch.Tensor, torch.Tensor, torch.Tensor, int, int, torch.dtype], torch.Tensor]] +): + """ + Returns the patter and replacement function for the subgraph rewriter to + match and replace for per_tensor quantization + """ + + def pattern_per_tensor(weight, scale, zero_point, low, high, dtype): + quantized = torch.ops.quantized_decomposed.quantize_per_tensor.default( + weight, scale, zero_point, low, high, dtype + ) + dequantized = torch.ops.quantized_decomposed.dequantize_per_tensor.default( + quantized, scale, zero_point, low, high, dtype + ) + return dequantized + + def replacement_graph_per_tensor(weight, scale, zero_point, low, high, dtype): + return torch.ops.aten.sub.Tensor(torch.ops.aten.mul.Tensor(weight, scale), zero_point) + + return pattern_per_tensor, replacement_graph_per_tensor + + +def _set_meta_for_matches(model: torch.fx.GraphModule, matches: torch.fx.subgraph_rewriter.ReplacedPatterns) -> None: + """ + Sets the meta for the nodes which were matched by the subgraph_rewriter pattern. + + :param model: Model whose nodes' meta are to be set. + :param matches: matches returned by the subgraph_rewriter + """ + for match in matches: + mul_node = match.replacements[0] + sub_node = match.replacements[1] + _set_new_node_meta(mul_node, mul_node.args, torch.mul, model) + _set_new_node_meta(sub_node, sub_node.args, torch.sub, model) + + +def _get_node_inputs(node: torch.fx.Node, model: torch.fx.GraphModule) -> Optional[Tuple[Union[torch.Tensor, int]]]: + """ + Gets the inputs for the Quantize node which quantize the weights. Otherwise returns None. + + :param node: Node to get the inputs from + :param model: Model to which the node belongs + """ + args = [] + for arg in node.args: + if isinstance(arg, torch.fx.Node): + if arg.op != "get_attr": + return None + arg = get_tensor_constant_from_node(arg, model) + args.append(arg) + return tuple(args) + + +def _get_value( + arg: Optional[Union[torch.fx.Node, float, int]], model: torch.fx.GraphModule +) -> Union[torch.nn.Parameter, float, int]: + """ + Retrieves value from the given argument. It can be either torch.fx.Node or float/int value. + + :param arg: Given arg to retrieve value. + :param model: torch.fx.GraphModule instance. + :return: value from the given argument. + """ + if isinstance(arg, torch.fx.Node): + return get_tensor_constant_from_node(arg, model) + return arg + + +def _compress_qdq_constant_transformation(model: torch.fx.GraphModule, matches) -> None: + """ + Change the FP32 weight value to Int8 and also reshape the scale for per_channel_quantization. + + :param: model: Model to apply transformations to. + """ + + for match in matches: + mul_node = match.replacements[0] + sub_node = match.replacements[1] + nodes_map = {node.name: match.nodes_map[node] for node in match.nodes_map} + + weight_node = _get_value(nodes_map["weight"], model) + scale_node = _get_value(nodes_map["scale"], model) + zp_node = _get_value(nodes_map["zero_point"], model) + axis = _get_value(nodes_map.get("axis"), model) + port_id = 0 + if axis is not None: + result = torch.ops.quantized_decomposed.quantize_per_channel.default( + weight_node, scale_node, zp_node, axis, -128, 127, torch.int8 + ) + _reshape_scale_zp(model, sub_node, mul_node, weight_node, scale_node, zp_node, axis) + else: + result = torch.ops.quantized_decomposed.quantize_per_tensor.default( + weight_node, scale_node, zp_node, -128, 127, torch.int8 + ) + constant_update_fn(model, mul_node, result, port_id, updated_node_name="compressed_weight_updated_constant") + + +def _reshape_scale_zp( + model: torch.fx.GraphModule, + sub_node: torch.fx.Node, + mul_node: torch.fx.Node, + weight: torch.Tensor, + scale: Union[torch.Tensor, float], + zp: Union[torch.Tensor, float, int], + axis: int, +) -> None: + """ + Reshape scale and zero point so that it can be multiplied elementwise with the weight for per channel quantization. + + :param model: Model to apply transformations to. + :param node: quantize node whose scale has to be reshaped + """ + new_shape = [1] * weight.dim() + new_shape[axis] = scale.shape[0] + scale = scale.reshape(new_shape) + zp = zp.reshape(new_shape) + constant_update_fn(model, sub_node, zp, 1, updated_node_name="zero_point_updated_constant") + constant_update_fn(model, mul_node, scale, 1, updated_node_name="scale_updated_constant") + + +def fq_weights_transformation(model: torch.fx.GraphModule) -> None: + """ + This function applies a transformation to replace the FP32 weights with Fake Quantized + FP values to avoid the rounding error when converting to OpenVino model. + + :param model: Model to apply transformations to. + """ + for node in model.graph.nodes: + if node.target not in DEQUANTIZE_NODE_TARGETS: + continue + port_id = 0 + quantize_node = node.all_input_nodes[0] + # Case where Quantize Dequantize nodes are not consecutive + if quantize_node.target != QDQ_PAIR[node.target]: + continue + args = _get_node_inputs(quantize_node, model) + if not args: + continue + result = node.target(quantize_node.target(*args), *args[1:]) + constant_update_fn( + model, + quantize_node, + result, + port_id, + updated_node_name="compressed_weight_updated_constant", + ) + + +def compress_post_quantize_transformation(model: torch.fx.GraphModule) -> None: + """ + Applies transformation to compress the weights to Int8 after the quantization step. + Starts by removing the Quantize/De-Quantize nodes for weight nodes by matching the pattern + to be like follows: + + weight scale zp + \ | / + Quantize + | + Dequantize + + and replace with the following: + weight scale + \ / + Multiply Zero Point + | / + Subtract + + and then also updates the weights to int8 values and reshape the scale and zero point. + + :param model: Model to apply transformations to. + """ + + def match_filters(match, original_graph, graph): + for node in match.nodes_map: + if node.name == "weight" and match.nodes_map[node].op == "get_attr": + return True + return False + + patterns = [_get_pattern_replacement_per_channel(), _get_pattern_replacement_per_tensor()] + + for pattern, replacement in patterns: + matches = torch.fx.subgraph_rewriter.replace_pattern_with_filters(model, pattern, replacement, [match_filters]) + _compress_qdq_constant_transformation(model, matches) + _set_meta_for_matches(model, matches) + + def apply_quantization_transformations(model: torch.fx.GraphModule) -> None: """ Applies quantization transformations to the model. + :param model: Model to apply transformations to. """ # BatchNorm operations have 3 output ports, # to make it easier for algorithms to work # with the target graph BatchNorm operations # are being fused + fold_constant_except_qdq(model) fuse_conv_bn(model) separate_conv_and_bias(model) separate_linear_and_bias(model) +def fold_constant_except_qdq(model: torch.fx.GraphModule): + """ + Performs constant folding avoiding quantize-dequantize pattern. + + :param model: Model to perform constant folding on. + """ + + def constraint_fn(node: torch.fx.Node): + return node.op != "call_function" or node.target not in QUANTIZE_NODE_TARGETS + DEQUANTIZE_NODE_TARGETS + + constant_fold(model, constraint_fn=constraint_fn) + + def revert_quantization_transformations(model: torch.fx.GraphModule) -> None: """ Reverts quantization transformations from the model. + :param model: Model to revert transformations from. """ merge_conv_and_bias(model) @@ -458,6 +828,17 @@ def _is_conv(n: torch.fx.Node): return n.op == "call_function" and n.target in ( torch.ops.aten.conv1d.default, torch.ops.aten.conv2d.default, + torch.ops.aten.conv_transpose2d.input, + ) + + +def _is_add(n: torch.fx.Node): + """ + Return whether the node refers to an aten add op. + """ + return n.op == "call_function" and n.target in ( + torch.ops.aten.add_.Tensor, + torch.ops.aten.add.Tensor, ) @@ -468,11 +849,13 @@ def separate_linear_and_bias(model: torch.fx.GraphModule): :param model: Target model. """ - add_node_target = torch.ops.aten.add_.Tensor + add_node_target = torch.ops.aten.add.Tensor for n in model.graph.nodes: if not _is_linear(n): continue - if len(n.args) < 3 or n.args[2] is None: + # This check also makes sure to ignore linear nodes which might already + # have quantization applied to the weights. + if len(n.args) < 3 or n.args[2] is None or n.args[1].op != "get_attr": continue linear_node = n linear_bias_node = linear_node.args[2] @@ -515,7 +898,9 @@ def separate_conv_and_bias(model: torch.fx.GraphModule): for n in model.graph.nodes: if not _is_conv(n): continue - if len(n.args) < 3 or n.args[2] is None: + # This check also makes sure to ignore convolution nodes which might + # already have quantization applied to the weights. + if len(n.args) < 3 or n.args[2] is None or n.args[1].op != "get_attr": continue conv_node = n dims = len(get_tensor_constant_from_node(conv_node.args[1], model).shape) @@ -560,6 +945,27 @@ def merge_linear_and_bias(model: torch.fx.GraphModule): _merge_node_and_bias(model, _is_linear) +def _get_connected_nodes(graph: torch.fx.Graph) -> List[torch.fx.Node]: + """ + Returns the List of nodes which are directly or indirectly connected + to the output node. + + :param graph: The torch FX graph to get nodes from. + """ + output_nodes = [node for node in graph.nodes if node.op == "output"] + assert len(output_nodes) == 1 + output_node = output_nodes[0] + connected_nodes = set() # Every node is unique in the graph + nodes_to_visit = [output_node] + while nodes_to_visit: + current_node = nodes_to_visit.pop() + if current_node in connected_nodes: + continue + connected_nodes.add(current_node) + nodes_to_visit.extend(current_node.all_input_nodes) + return list(connected_nodes) + + def _merge_node_and_bias(model: torch.fx.GraphModule, is_target_node: Callable[[torch.fx.Node], bool]): """ Merges two separate node and bias node to a one node: node+bias. @@ -568,7 +974,7 @@ def _merge_node_and_bias(model: torch.fx.GraphModule, is_target_node: Callable[[ :param model: Target model. :param is_target_node: Predicate to specify nodes which should be merged with the bias """ - add_node_targets = (torch.ops.aten.add_.Tensor,) + add_node_targets = (torch.ops.aten.add.Tensor, torch.ops.aten.add_.Tensor) for n in model.graph.nodes: if not is_target_node(n): continue @@ -593,5 +999,13 @@ def _merge_node_and_bias(model: torch.fx.GraphModule, is_target_node: Callable[[ for user in list(bias_node.users): user.replace_input_with(bias_node, conv_node) + # Remove nodes which are not connected to output. This removes dead nodes and dead subgraphs in the model graph. + nodes_connected_to_output = _get_connected_nodes(model.graph) + is_impure = lambda node: node in nodes_connected_to_output + + for node in reversed(model.graph.nodes): + if not is_impure(node) and len(node.users) == 0: + model.graph.erase_node(node) + model.graph.eliminate_dead_code() model.recompile() diff --git a/nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py b/nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py index bdb8127d75b..fe57c0b860b 100644 --- a/nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py +++ b/nncf/experimental/torch/nas/bootstrapNAS/training/lr_scheduler.py @@ -147,7 +147,7 @@ def __init__( base_lr: float, num_epochs: float, warmup_epochs: float = 0, - warmup_lr: float = 3.4e-4 + warmup_lr: float = 3.4e-4, ): super().__init__(optimizer, num_steps_in_epoch) self._base_lr = base_lr diff --git a/tests/shared/__init__.py b/nncf/experimental/torch2/__init__.py similarity index 100% rename from tests/shared/__init__.py rename to nncf/experimental/torch2/__init__.py diff --git a/nncf/experimental/torch2/function_hook/__init__.py b/nncf/experimental/torch2/function_hook/__init__.py new file mode 100644 index 00000000000..938d5faea2a --- /dev/null +++ b/nncf/experimental/torch2/function_hook/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nncf.experimental.torch2.function_hook.graph.build_graph_mode import build_graph as build_graph +from nncf.experimental.torch2.function_hook.wrapper import get_hook_storage as get_hook_storage +from nncf.experimental.torch2.function_hook.wrapper import is_wrapped as is_wrapped +from nncf.experimental.torch2.function_hook.wrapper import register_post_function_hook as register_post_function_hook +from nncf.experimental.torch2.function_hook.wrapper import register_pre_function_hook as register_pre_function_hook +from nncf.experimental.torch2.function_hook.wrapper import wrap_model as wrap_model diff --git a/tests/shared/test_templates/__init__.py b/nncf/experimental/torch2/function_hook/graph/__init__.py similarity index 100% rename from tests/shared/test_templates/__init__.py rename to nncf/experimental/torch2/function_hook/graph/__init__.py diff --git a/nncf/experimental/torch2/function_hook/graph/build_graph_mode.py b/nncf/experimental/torch2/function_hook/graph/build_graph_mode.py new file mode 100644 index 00000000000..08be7ad3cec --- /dev/null +++ b/nncf/experimental/torch2/function_hook/graph/build_graph_mode.py @@ -0,0 +1,367 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from typing import Any, Dict, Tuple, Union, cast + +import networkx as nx # type: ignore[import-untyped] +import torch +from torch import nn + +from nncf.common.logging import nncf_logger as logger +from nncf.experimental.torch2.function_hook.graph.graph_utils import ConstMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import EdgeMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import FunctionMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import InOutMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import NodeType +from nncf.experimental.torch2.function_hook.graph.graph_utils import TensorInfo +from nncf.experimental.torch2.function_hook.graph.graph_utils import TensorMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import TensorSource +from nncf.experimental.torch2.function_hook.hook_executor_mode import FunctionHookMode +from nncf.experimental.torch2.function_hook.hook_executor_mode import OpMeta +from nncf.experimental.torch2.function_hook.hook_storage import HookStorage +from nncf.experimental.torch2.function_hook.weak_map import WeakUnhashableKeyMap +from nncf.experimental.torch2.function_hook.wrapper import ForwardWithHooks +from nncf.experimental.torch2.function_hook.wrapper import get_hook_storage + + +class GraphBuilderMode(FunctionHookMode): + """ + A class that extends the `FunctionHookMode` and constructs nx.MultiDiGraph representing + the operations within a PyTorch model. Each operation in the model is represented as a node in the graph. + + :param next_node_id: The index of the last added node in the graph. + :param graph: An instance of `networkx.MultiDiGraph` used to represent the graph of model. + :param tensor_info: A dictionary mapping tensor objects to tensor info. + """ + + def __init__(self, model: nn.Module, hook_storage: HookStorage): + """ + Initialize the GraphBuilderMode. + + :param model: The PyTorch model to which the hooks will be applied. + :param hook_storage: Storage for hooks to be executed. + """ + super().__init__(model=model, hook_storage=hook_storage) + self.next_node_id: int = 0 + self.graph: nx.MultiDiGraph = nx.MultiDiGraph() + self.tensor_info: WeakUnhashableKeyMap[Union[torch.Tensor, torch.nn.Parameter], TensorInfo] = ( + WeakUnhashableKeyMap() + ) + + for name, parameter in self.model.named_parameters(): + self.tensor_info[parameter] = TensorInfo( + tensor_source=TensorSource.parameter, + shape=tuple(parameter.shape), + dtype=parameter.dtype, + output_port_id=0, + source_node_id=None, + name_in_model=name, + ) + for name, buffer in self.model.named_buffers(): + self.tensor_info[buffer] = TensorInfo( + tensor_source=TensorSource.buffer, + shape=tuple(buffer.shape), + dtype=buffer.dtype, + output_port_id=0, + source_node_id=None, + name_in_model=name, + ) + + def __enter__(self) -> GraphBuilderMode: + """ + Overload __enter__ to get correct return type hint. + """ + super().__enter__() + return self + + def register_new_node_id(self) -> int: + """ + Generates and returns a unique id for a new node in the graph. + + :return: A unique id for a new node. + """ + key = self.next_node_id + self.next_node_id += 1 + return key + + def register_node_for_model_input_tensor(self, node_name: str, tensor: torch.Tensor) -> None: + """ + Register a node for model input. + + :param node_name: The name of the input node to be registered. + :param tensor: The tensor associated with the model's input. + """ + node_id = self.register_new_node_id() + with self.disable(): + self.tensor_info[tensor] = TensorInfo( + tensor_source=TensorSource.input, + shape=tuple(tensor.shape), + dtype=tensor.dtype, + output_port_id=0, + source_node_id=node_id, + name_in_model=None, + ) + self.graph.add_node(node_id, type=NodeType.input, meta=InOutMeta.from_tensor(tensor, node_name)) + logger.debug(f"GraphBuilderMode.register_node_for_model_input_tensor: {node_id=} {node_name=}") + + def execute_hooks_for_model_input(self, name: str, value: Any) -> Any: + """ + Overloaded function to register a model input to the graph. + + :param name: The name of the input argument. + :param value: The value of the input argument. + :return: The processed value after the hook is executed. + """ + if isinstance(value, torch.Tensor): + self.register_node_for_model_input_tensor(name, value) + return super().execute_hooks_for_model_input(name, value) + + def register_node_for_model_output_tensor(self, node_name: str, tensor: torch.Tensor) -> None: + """ + Registers a node for a model's output tensor in graph, creating an edge between + the tensor's source node and the output node. + + :param node_name: The name of the output node to be registered. + :param tensor: The tensor associated with the model's output. + """ + node_id = self.register_new_node_id() + tensor_info = self.tensor_info.get(tensor) + if tensor_info is not None and isinstance(tensor_info.output_port_id, int): + with self.disable(): + self.graph.add_edge( + tensor_info.source_node_id, + node_id, + meta=EdgeMeta.from_tensor(tensor, input_port=0, output_port=tensor_info.output_port_id), + ) + self.graph.add_node(node_id, type=NodeType.output, meta=InOutMeta.from_tensor(tensor, node_name)) + logger.debug(f"GraphBuilderMode.register_node_for_model_input_tensor: {node_id=} {node_name=}") + + def execute_hooks_for_model_output(self, name: str, value: Any) -> Any: + """ + Overloaded function to register a model output to the graph. + + :param name: The name of the input argument. + :param value: The value of the input argument. + :return: The processed value after the hook is executed. + """ + value = super().execute_hooks_for_model_output(name, value) + if isinstance(value, torch.Tensor): + self.register_node_for_model_output_tensor(name, value) + return value + + def execute_pre_hooks( + self, args: Tuple[Any, ...], kwargs: Dict[str, Any], op_meta: OpMeta + ) -> Tuple[Tuple[Any, ...], Dict[str, Any]]: + """ + Overloaded function to register a model output to the graph. + + :param args: The arguments to the function. + :param kwargs: The keyword arguments to the function. + :param op_meta: Metadata for the operation. + :return: The modified arguments and keyword arguments after pre-hooks. + """ + _args, _kwargs = super().execute_pre_hooks(args, kwargs, op_meta) + self.register_op_node(_args, _kwargs, op_meta) + return _args, _kwargs + + def process_tensor_attributes(self, output: torch.Tensor, op_meta: OpMeta) -> None: + """ + Processes special functions like attribute access on tensors using grad_fn. + + Process attributes: detect .T and .mT attributes of tensor + .T - permute((1, 0)) + .mT - transpose(-2, -1) + + :param output: The output tensor. + :param op_meta: Metadata about the operation. + """ + fn_name = None + fn_kwargs = None + + if output.grad_fn is not None: + if output.grad_fn.name() == "TransposeBackward0": + fn_name = "transpose" + # grad_fn collect arguments as _saved_dim0=18446744073709551614 + fn_kwargs = { + "dim0": -(2**64 - output.grad_fn._saved_dim0), # type: ignore[attr-defined] + "dim1": -(2**64 - output.grad_fn._saved_dim1), # type: ignore[attr-defined] + } + if output.grad_fn.name() == "PermuteBackward0": + fn_name = "permute" + fn_kwargs = {"dims": output.grad_fn._saved_dims} # type: ignore[attr-defined] + + if fn_name is not None and fn_kwargs is not None: + self.graph.nodes[op_meta.extra_info["node_id"]]["meta"].fn_name = fn_name + self.graph.nodes[op_meta.extra_info["node_id"]]["meta"].kwargs = fn_kwargs + + def execute_post_hooks(self, outputs: Any, op_meta: OpMeta) -> Any: + """ + Overload execute_post_hooks to correct registered node for operation. + Process __get__ function, to detect permute and transpose operation + and remove node if operation return not tensor. + """ + if op_meta.func.__name__ == "__get__": + if isinstance(outputs, torch.Tensor): + self.process_tensor_attributes(outputs, op_meta) + else: + # Remove the node corresponding to this operation from the graph, as non-tensor + # outputs (like `tensor.shape` or similar) are not relevant for further algorithmic use. + self.graph.remove_node(op_meta.extra_info["node_id"]) + outputs = super().execute_post_hooks(outputs, op_meta) + return outputs + + def execute_hooks_for_parameter(self, value: torch.Tensor) -> torch.Tensor: + """ + Overload execute_hooks_for_parameter to register parameters to the graph. + + :param value: The tensor to which the post-hook will be applied. + :return: The processed tensor with the applied post-hook, if applicable. + """ + tensor_info = self.tensor_info.get(value) + if ( + tensor_info is not None + and tensor_info.source_node_id is None + and tensor_info.tensor_source in [TensorSource.buffer, TensorSource.parameter] + and isinstance(tensor_info.name_in_model, str) + ): + node_id = self.register_new_node_id() + with self.disable(): + self.graph.add_node( + node_id, + type=NodeType.const, + meta=ConstMeta.from_tensor(tensor=value, name_in_model=tensor_info.name_in_model), + ) + tensor_info.source_node_id = node_id + logger.debug(f"GraphBuilderMode._maybe_add_node_for_parameters: {node_id=} {tensor_info.name_in_model=}") + return super().execute_hooks_for_parameter(value) + + def register_op_input_tensor(self, tensor: torch.Tensor, op_node_id: int, port_id: int, op_meta: OpMeta) -> None: + """ + Registers a tensor input by creating an edge between the tensor's source node and the current operation node. + + :param tensor: Input tensor for the operation. + :param node_id: Id if operation node. + :param port_id: Port id of input argument. + :param op_meta: Metadata about the operation. + """ + tensor_info = self.tensor_info.get(tensor, None) + if tensor_info is None: + logger.debug(f"GraphBuilderMode.register_op_input_tensor: unknown tensor {op_meta.op_name=} {port_id=}") + return + self.graph.add_edge( + tensor_info.source_node_id, + op_node_id, + meta=EdgeMeta.from_tensor(tensor, input_port=port_id, output_port=tensor_info.output_port_id), + ) + + def register_op_input(self, arg: Any, node_id: int, port_id: int, op_meta: OpMeta) -> Any: + """ + Registers an operation input. Handles tensors or collections of tensors on one input port. + + :param arg: Operation input argument. + :param node_id: Id if operation node. + :param port_id: Port id of input argument. + :param op_meta: Metadata about the operation. + :return: Descriptor of the input. For a Tensor, this is a `TensorMeta` object. + For a collection of Tensors, a collection of `TensorMeta` objects is returned. + For other types, the original input `arg` is returned as-is. + """ + if isinstance(arg, torch.Tensor): + self.register_op_input_tensor(arg, node_id, port_id, op_meta) + return TensorMeta.from_tensor(arg) + elif isinstance(arg, (list, tuple, set)): + op_attr = [] + for x in arg: + if isinstance(x, torch.Tensor): + self.register_op_input_tensor(x, node_id, port_id, op_meta) + op_attr.append(TensorMeta.from_tensor(x)) + else: + op_attr.append(x) + return op_attr + return arg + + def register_op_node(self, args: Tuple[Any], kwargs: Dict[str, Any], op_meta: OpMeta) -> None: + """ + Registers a new operation node in the graph. + + This method is responsible for: + - adding a new node in the graph for the given operation and storing metadata such as the node ID, + operation name, input arguments and keyword arguments; + - adding edge associated with registered tensor. + + :param args: Positional arguments for the operation. + :param kwargs: Keyword arguments for the operation. + :param op_meta: Metadata about the operation, including the function being invoked. + """ + node_id = self.register_new_node_id() + op_meta.extra_info["node_id"] = node_id + op_name = op_meta.op_name + + op_attrs = [] + op_kwargs = {} + for port_id, arg in enumerate(args): + op_attr = self.register_op_input(arg, node_id, port_id, op_meta) + op_attrs.append(op_attr) + + for port_id, (name, arg) in enumerate(kwargs.items(), start=len(args)): + op_attr = self.register_op_input(arg, node_id, port_id, op_meta) + op_kwargs[name] = op_attr + + self.graph.add_node( + node_id, + type=NodeType.fn_call, + meta=FunctionMeta(op_name=op_name, fn_name=op_meta.func.__name__, args=tuple(op_attrs), kwargs=op_kwargs), + ) + + logger.debug(f"GraphBuilderMode.process_op_inputs: {node_id=} {op_name=} {op_attrs=} {op_kwargs=}") + + def process_post_function_hooks_for_value(self, value: Any, op_meta: OpMeta, port_id: int) -> Any: + """ + Overload process_post_function_hooks_for_value to get register output tensors to the graph + + :param output: The output of the function. + :param op_meta: Metadata for the operation. + :return: The modified output after post-hooks. + """ + if isinstance(value, torch.Tensor): + with self.disable(): + self.tensor_info[value] = TensorInfo( + tensor_source=TensorSource.function, + shape=tuple(value.shape), + dtype=value.dtype, + output_port_id=port_id, + source_node_id=op_meta.extra_info["node_id"], + name_in_model=None, + ) + return super().process_post_function_hooks_for_value(value, op_meta, port_id) + + +def build_graph(model: nn.Module, *args: Any, **kwargs: Any) -> nx.MultiDiGraph: + """ + Constructs a computational graph of the given model. + + This function builds a directed graph `nx.MultiDiGraph` representing the operations + and data flow within the model by leveraging hooks by using GraphBuilderMode. + + :param model: The PyTorch model for which the computational graph will be built. + :return: A nx.MultiDiGraph where nodes represent operations of model. + """ + + with torch.enable_grad(): # type: ignore + # Gradient use to get information about __get__ functions to detect tensor.(T, mT) attributes + with GraphBuilderMode(model=model, hook_storage=get_hook_storage(model)) as ctx: + args, kwargs = ctx.process_model_inputs(args, kwargs) + wrapped_forward = cast(ForwardWithHooks, model.forward) + outputs = wrapped_forward._func(*args, **kwargs) + outputs = ctx.process_model_outputs(outputs) + return ctx.graph diff --git a/nncf/experimental/torch2/function_hook/graph/graph_utils.py b/nncf/experimental/torch2/function_hook/graph/graph_utils.py new file mode 100644 index 00000000000..f7954ee0327 --- /dev/null +++ b/nncf/experimental/torch2/function_hook/graph/graph_utils.py @@ -0,0 +1,102 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from enum import Enum +from typing import Any, Dict, Optional, Tuple + +import torch + + +class NodeType(Enum): + const = "const" + fn_call = "function_call" + input = "input" + output = "output" + + def __str__(self) -> str: + return self.value + + +class TensorSource(Enum): + buffer = "buffer" + function = "function" + input = "input" + output = "output" + parameter = "parameter" + + def __str__(self) -> str: + return self.value + + +@dataclass +class TensorMeta: + dtype: torch.dtype + shape: Tuple[int, ...] + requires_grad: bool + + @staticmethod + def from_tensor(tensor: torch.Tensor) -> TensorMeta: + return TensorMeta(tensor.dtype, tuple(tensor.shape), tensor.requires_grad) + + +@dataclass +class ConstMeta: + dtype: torch.dtype + shape: Tuple[int, ...] + name_in_model: str + + @staticmethod + def from_tensor(tensor: torch.Tensor, name_in_model: str) -> ConstMeta: + return ConstMeta(tensor.dtype, tuple(tensor.shape), name_in_model) + + +@dataclass +class InOutMeta: + dtype: torch.dtype + shape: Tuple[int, ...] + name: str + + @staticmethod + def from_tensor(tensor: torch.Tensor, name: str) -> InOutMeta: + return InOutMeta(tensor.dtype, tuple(tensor.shape), name) + + +@dataclass +class FunctionMeta: + op_name: str + fn_name: str + args: Tuple[Any, ...] + kwargs: Dict[str, Any] + + +@dataclass +class EdgeMeta: + dtype: torch.dtype + shape: Tuple[int, ...] + input_port: int + output_port: int + + @staticmethod + def from_tensor(tensor: torch.Tensor, input_port: int, output_port: int) -> EdgeMeta: + return EdgeMeta(tensor.dtype, tuple(tensor.shape), input_port, output_port) + + +@dataclass +class TensorInfo: + tensor_source: TensorSource + shape: Tuple[int, ...] + dtype: torch.dtype + output_port_id: int + source_node_id: Optional[int] + name_in_model: Optional[str] diff --git a/nncf/experimental/torch2/function_hook/graph/graph_visualization.py b/nncf/experimental/torch2/function_hook/graph/graph_visualization.py new file mode 100644 index 00000000000..3574a425e67 --- /dev/null +++ b/nncf/experimental/torch2/function_hook/graph/graph_visualization.py @@ -0,0 +1,226 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import hashlib +from enum import Enum +from typing import Any, Dict, Tuple + +import networkx as nx # type: ignore[import-untyped] +import pydot # type: ignore[import-untyped] + +from nncf.experimental.torch2.function_hook.graph.graph_utils import ConstMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import EdgeMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import FunctionMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import InOutMeta + + +class PydotStyleTemplate(Enum): + """ + Enum to define different styles for Pydot graph representation. + - disable: labels contain only names, used for tests (not recommend to convert to svg) + - short: labels contain names, add edge info + - full: labels contain all full information about nodes + """ + + disable = "disable" + short = "short" + full = "full" + + def __str__(self) -> str: + return self.value + + +def fix_dot_label(label: str) -> str: + """ + Escapes curly braces in a DOT label to avoid syntax errors. + + :param label: The label string to be fixed. + :return: The label with escaped curly braces. + """ + return label.replace("{", r"\{").replace("}", r"\}") + + +def args_to_label(args: Tuple[Any, ...]) -> str: + """ + Converts function arguments to a formatted string label. + + :param args: Function arguments. + :return: Formatted string label of arguments. + """ + if not args: + return "[]" + ret = "[" + for arg in args: + ret += f"\n{arg}," + return ret + "\n]" + + +def kwargs_to_label(kwargs: Dict[str, Any]) -> str: + """ + Converts function keyword arguments to a formatted string label. + + :param kwargs: Function keyword arguments. + :return: Formatted string label of keyword arguments. + """ + if not kwargs: + return "{}" + ret = "{" + for key, val in kwargs.items(): + ret += f"\n{key} : {str(val)[:50]}" + return ret + "\n}" + + +def get_label_from_node_data(node_data: Dict[str, Any], style: PydotStyleTemplate) -> str: + """ + Generates a label for a graph node based on its metadata and the desired style. + + :param node_data: Metadata of the node. + :param style: Style template to determine the label format. + :return: Formatted label for the node. + """ + meta = node_data["meta"] + node_type = node_data["type"] + if style == PydotStyleTemplate.full: + rows = [] + if isinstance(meta, InOutMeta): + rows = [ + f"type: {node_type}", + f"name: {meta.name}", + f"dtype: {meta.dtype}", + f"shape: {meta.shape}", + ] + elif isinstance(meta, ConstMeta): + rows = [ + f"type: {node_type}", + f"name: {meta.name_in_model}", + f"dtype: {meta.dtype}", + f"shape: {meta.shape}", + ] + if isinstance(meta, FunctionMeta): + rows = [ + f"type: {node_type}", + f"op_name: {meta.op_name}", + f"fn_name: {meta.fn_name}", + f"args: {args_to_label(meta.args)}", + f"kwargs: {kwargs_to_label(meta.kwargs)}", + ] + return "{" + fix_dot_label("|".join(rows)) + "}" + else: + if isinstance(meta, InOutMeta): + return f"{meta.name}" + if isinstance(meta, ConstMeta): + return f"{meta.name_in_model}" + if isinstance(meta, FunctionMeta): + return f"{meta.op_name}" + raise ValueError(f"Unknown meta node {type(meta)}") + + +def get_label_from_edge_data(node_data: Dict[str, Any], style: PydotStyleTemplate) -> str: + """ + Generates a label for a graph edge based on its metadata and the desired style. + + :param edge_data: Metadata of the edge. + :param style: Style template to determine the label format. + :return: Formatted label for the edge. + """ + meta = node_data["meta"] + assert isinstance(meta, EdgeMeta) + + if style == PydotStyleTemplate.disable: + return f"{meta.output_port} → {meta.input_port}" + else: + return f"{meta.shape}\n{meta.output_port} → {meta.input_port}" + + +_RAINBOW_COLORS = [ + "#ffadad", + "#ffc2a9", + "#ffd6a5", + "#fdffb6", + "#caffbf", + "#b3fbdf", + "#aae0ef", + "#a0c4ff", + "#bdb2ff", + "#ffc6ff", +] + + +def color_picker(data: str) -> str: + """ + Picks a color from a predefined set of colors based on the input string. + + :param data: Input string to determine the color. + :return: Hex code of the selected color. + """ + data = "".join(d for d in data if d.isalpha()) + hash_object = hashlib.sha256(data.encode()) + hex_int = int(hash_object.hexdigest()[:6], 16) + return _RAINBOW_COLORS[hex_int % len(_RAINBOW_COLORS)] + + +def get_style(node: Dict[str, Any], style: PydotStyleTemplate) -> Dict[str, str]: + """ + Generates a style dictionary for a graph node based on its metadata and the desired style. + + :param node: Metadata of the node. + :param style: Style template to determine the node style. + :return: Dictionary containing style attributes for the node. + """ + if style == PydotStyleTemplate.disable: + return {} + meta = node["meta"] + + if isinstance(meta, InOutMeta): + return { + "fillcolor": "#adadad", + "fontcolor": "#000000", + "shape": "record", + "style": '"filled,rounded"', + } + if isinstance(meta, ConstMeta): + return { + "fillcolor": "#ffffff", + "fontcolor": "#000000", + "shape": "record", + "style": '"filled,rounded"', + } + if isinstance(meta, FunctionMeta): + return { + "fillcolor": color_picker(meta.fn_name), + "fontcolor": "#000000", + "shape": "record", + "style": '"filled,rounded"', + } + + raise ValueError(f"Unknown meta node {type(meta)}") + + +def to_pydot(nx_graph: nx.MultiDiGraph, style_template: PydotStyleTemplate = PydotStyleTemplate.full) -> pydot.Graph: + """ + Converts a NetworkX directed graph to a Pydot graph with specified styling. + + :param nx_graph: Input NetworkX directed graph. + :param style_template: Style template to determine node and edge styles. + :return: Pydot graph representation of the input NetworkX graph. + """ + dot_graph = pydot.Dot("", rankdir="TB") + + for key, data in nx_graph.nodes(data=True): + style = get_style(data, style_template) + dot_node = pydot.Node(key, label=get_label_from_node_data(data, style_template), **style) + dot_graph.add_node(dot_node) + + for key_from, key_to, data in nx_graph.edges(data=True): + dot_edge = pydot.Edge(key_from, key_to, label=get_label_from_edge_data(data, style_template)) + dot_graph.add_edge(dot_edge) + + return dot_graph diff --git a/nncf/experimental/torch2/function_hook/handle_inner_functions.py b/nncf/experimental/torch2/function_hook/handle_inner_functions.py new file mode 100644 index 00000000000..9cdd8ff2b6e --- /dev/null +++ b/nncf/experimental/torch2/function_hook/handle_inner_functions.py @@ -0,0 +1,302 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This module implements selected functions from the `torch` module, excluding the `hand_function` mechanism. + +It processes inner functions to handle exception hooks and graph analysis. The implementation is designed +to support custom handling of inner function exceptions for specific functions. +""" + +import math +from typing import Any, Callable, Dict, Optional, Tuple, Union + +import torch +from torch.nn.functional import _canonical_mask +from torch.nn.functional import _in_projection # type:ignore[attr-defined] +from torch.nn.functional import _in_projection_packed # type:ignore[attr-defined] +from torch.nn.functional import _mha_shape_check # type:ignore[attr-defined] +from torch.nn.functional import _none_or_dtype +from torch.nn.functional import _verify_batch_size # type:ignore[attr-defined] +from torch.nn.functional import dropout +from torch.nn.functional import linear +from torch.nn.functional import pad +from torch.nn.functional import scaled_dot_product_attention +from torch.nn.functional import softmax + +Tensor = torch.Tensor + + +def multi_head_attention_forward( + query: Tensor, + key: Tensor, + value: Tensor, + embed_dim_to_check: int, + num_heads: int, + in_proj_weight: Optional[Tensor], + in_proj_bias: Optional[Tensor], + bias_k: Optional[Tensor], + bias_v: Optional[Tensor], + add_zero_attn: bool, + dropout_p: float, + out_proj_weight: Tensor, + out_proj_bias: Optional[Tensor], + training: bool = True, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + use_separate_proj_weight: bool = False, + q_proj_weight: Optional[Tensor] = None, + k_proj_weight: Optional[Tensor] = None, + v_proj_weight: Optional[Tensor] = None, + static_k: Optional[Tensor] = None, + static_v: Optional[Tensor] = None, + average_attn_weights: bool = True, + is_causal: bool = False, +) -> Tuple[Tensor, Optional[Tensor]]: + + is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads) + + if not is_batched: + query = query.unsqueeze(1) + key = key.unsqueeze(1) + value = value.unsqueeze(1) + if key_padding_mask is not None: + key_padding_mask = key_padding_mask.unsqueeze(0) + + tgt_len, bsz, embed_dim = query.shape + src_len, _, _ = key.shape + + key_padding_mask = _canonical_mask( + mask=key_padding_mask, + mask_name="key_padding_mask", + other_type=_none_or_dtype(attn_mask), + other_name="attn_mask", + target_type=query.dtype, + ) + + if is_causal and attn_mask is None: + raise RuntimeError( + "Need attn_mask if specifying the is_causal hint. " + "You may use the Transformer module method " + "`generate_square_subsequent_mask` to create this mask." + ) + + if is_causal and key_padding_mask is None and not need_weights: + + attn_mask = None + else: + attn_mask = _canonical_mask( + mask=attn_mask, + mask_name="attn_mask", + other_type=None, + other_name="", + target_type=query.dtype, + check_other=False, + ) + + if key_padding_mask is not None: + + is_causal = False + + assert ( + embed_dim == embed_dim_to_check + ), f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}" + if isinstance(embed_dim, torch.Tensor): + head_dim = embed_dim.div(num_heads, rounding_mode="trunc") + else: + head_dim = embed_dim // num_heads + assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}" + if use_separate_proj_weight: + assert ( + key.shape[:2] == value.shape[:2] + ), f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}" + else: + assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}" + + if not use_separate_proj_weight: + assert in_proj_weight is not None, "use_separate_proj_weight is False but in_proj_weight is None" + q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias) + else: + assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None" + assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None" + assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None" + if in_proj_bias is None: + b_q = b_k = b_v = None + else: + b_q, b_k, b_v = in_proj_bias.chunk(3) + q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v) + + if attn_mask is not None: + if attn_mask.dim() == 2: + correct_2d_size = (tgt_len, src_len) + if attn_mask.shape != correct_2d_size: + raise RuntimeError( + f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}." + ) + attn_mask = attn_mask.unsqueeze(0) + elif attn_mask.dim() == 3: + correct_3d_size = (bsz * num_heads, tgt_len, src_len) + if attn_mask.shape != correct_3d_size: + raise RuntimeError( + f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}." + ) + else: + raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported") + + if bias_k is not None and bias_v is not None: + assert static_k is None, "bias cannot be added to static key." + assert static_v is None, "bias cannot be added to static value." + k = torch.cat([k, bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = pad(attn_mask, (0, 1)) + if key_padding_mask is not None: + key_padding_mask = pad(key_padding_mask, (0, 1)) + else: + assert bias_k is None + assert bias_v is None + + q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1) + if static_k is None: + k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1) + else: + assert ( + static_k.size(0) == bsz * num_heads + ), f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}" + assert static_k.size(2) == head_dim, f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}" + k = static_k + if static_v is None: + v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1) + else: + assert ( + static_v.size(0) == bsz * num_heads + ), f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}" + assert static_v.size(2) == head_dim, f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}" + v = static_v + + if add_zero_attn: + zero_attn_shape = (bsz * num_heads, 1, head_dim) + k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1) + v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1) + if attn_mask is not None: + attn_mask = pad(attn_mask, (0, 1)) + if key_padding_mask is not None: + key_padding_mask = pad(key_padding_mask, (0, 1)) + + src_len = k.size(1) + + if key_padding_mask is not None: + assert key_padding_mask.shape == ( + bsz, + src_len, + ), f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}" + key_padding_mask = ( + key_padding_mask.view(bsz, 1, 1, src_len).expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len) + ) + if attn_mask is None: + attn_mask = key_padding_mask + else: + attn_mask = attn_mask + key_padding_mask + + if not training: + dropout_p = 0.0 + + if need_weights: + B, Nt, E = q.shape # noqa: F841 + q_scaled = q * math.sqrt(1.0 / float(E)) + + assert not (is_causal and attn_mask is None), "FIXME: is_causal not implemented for need_weights" + + if attn_mask is not None: + attn_output_weights = torch.baddbmm(attn_mask, q_scaled, k.transpose(-2, -1)) + else: + attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1)) + attn_output_weights = softmax(attn_output_weights, dim=-1) + if dropout_p > 0.0: + attn_output_weights = dropout(attn_output_weights, p=dropout_p) + + attn_output = torch.bmm(attn_output_weights, v) + + attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim) + attn_output = linear(attn_output, out_proj_weight, out_proj_bias) + attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1)) + + attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len) + if average_attn_weights: + attn_output_weights = attn_output_weights.mean(dim=1) + + if not is_batched: + attn_output = attn_output.squeeze(1) + attn_output_weights = attn_output_weights.squeeze(0) + return attn_output, attn_output_weights + else: + if attn_mask is not None: + if attn_mask.size(0) == 1 and attn_mask.dim() == 3: + attn_mask = attn_mask.unsqueeze(0) + else: + attn_mask = attn_mask.view(bsz, num_heads, -1, src_len) + + q = q.view(bsz, num_heads, tgt_len, head_dim) + k = k.view(bsz, num_heads, src_len, head_dim) + v = v.view(bsz, num_heads, src_len, head_dim) + + attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal) + attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim) + + attn_output = linear(attn_output, out_proj_weight, out_proj_bias) + attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1)) + if not is_batched: + attn_output = attn_output.squeeze(1) + return attn_output, None + + +def relu(input: Tensor, inplace: bool = False) -> Tensor: + if inplace: + result = torch.relu_(input) + else: + result = torch.relu(input) + return result + + +def batch_norm( + input: Tensor, + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + weight: Optional[Tensor] = None, + bias: Optional[Tensor] = None, + training: bool = False, + momentum: float = 0.1, + eps: float = 1e-5, +) -> Tensor: + if training: + _verify_batch_size(input.size()) + + return torch.batch_norm( + input, weight, bias, running_mean, running_var, training, momentum, eps, torch.backends.cudnn.enabled + ) + + +MAP_HANDLER_TO_INNER_FUNCTION: Dict[Callable[..., Any], Callable[..., Any]] = { + torch.nn.functional.relu: relu, + torch.nn.functional.multi_head_attention_forward: multi_head_attention_forward, + torch.nn.functional.batch_norm: batch_norm, +} + + +def get_handle_inner_function(fn: Callable[..., Any]) -> Union[Callable[..., Any], None]: + """ + Returns the corresponding function to process inner functions. + + :param fn: The function for which the handler is needed. + :return: The function without handle_function, or None if no handler is found. + """ + return MAP_HANDLER_TO_INNER_FUNCTION.get(fn) diff --git a/nncf/experimental/torch2/function_hook/hook_executor_mode.py b/nncf/experimental/torch2/function_hook/hook_executor_mode.py new file mode 100644 index 00000000000..67d9d8658c2 --- /dev/null +++ b/nncf/experimental/torch2/function_hook/hook_executor_mode.py @@ -0,0 +1,466 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import inspect +import types +from collections import defaultdict +from contextlib import contextmanager +from dataclasses import dataclass +from dataclasses import field +from itertools import chain +from types import MethodType +from types import TracebackType +from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Type +from weakref import ReferenceType +from weakref import ref + +import torch +from torch import Tensor +from torch import nn +from torch.overrides import TorchFunctionMode + +from nncf.common.logging import nncf_logger as logger +from nncf.experimental.torch2.function_hook.handle_inner_functions import get_handle_inner_function +from nncf.experimental.torch2.function_hook.hook_storage import HookStorage +from nncf.experimental.torch2.function_hook.weak_map import WeakUnhashableKeyMap + +IGNORED_FN_NAMES = [ + "__repr__", + "_assert", + "dim", + "size", + "is_floating_point", + "_set_grad_enabled", +] + + +@dataclass +class OpMeta: + """ + Metadata for an operation to be executed, including its name, the callable function, + and any additional information. + + + :param op_name: The name of the operation. + :param func: The function to be executed for the operation. + :param extra_info: A dictionary for storing any additional information about the operation. + """ + + op_name: str + func: Callable[..., Any] + extra_info: Dict[str, Any] = field(default_factory=lambda: dict()) + + +def _get_full_fn_name(fn: Callable[..., Any]) -> str: + """ + Get the full name of a function, including its module if applicable. + + :param fn: The function for which to get the full name. + :return: The full name of the function. + """ + if inspect.ismethoddescriptor(fn) or inspect.ismethod(fn): + return fn.__qualname__ + if inspect.isbuiltin(fn) or inspect.isfunction(fn): + return f"{fn.__module__}.{fn.__name__}" + return f"{fn.__name__}" + + +def generate_normalized_op_name(module_name: str, fn_name: str, call_id: Optional[int] = None) -> str: + """ + Returns a normalized name of operation. + + Returns: The string representation in the format "module_name/fn_name" or "module_name/fn_name/call_id". + """ + if call_id is None: + return f"{module_name}/{fn_name}" + return f"{module_name}/{fn_name}/{call_id}" + + +class FunctionHookMode(TorchFunctionMode): + """ + Executes pre- and post-hooks for PyTorch functions within a model's execution. + + This mode wraps the function calls in the model to allow custom hooks to be executed before + and after the actual function calls. + + + :param model: The PyTorch model to which the hooks will be applied. + :param hook_storage: Storage for hooks to be executed. + :param module_call_stack: A stack tracking the modules being called. + :param nested_enter_count: A counter to track nested context manager entries. + :param op_calls: A dictionary to track operation calls. + """ + + def __init__(self, model: nn.Module, hook_storage: HookStorage) -> None: + """ + Initialize the FunctionHookMode. + + :param model: The PyTorch model to which the hooks will be applied. + :param hook_storage: Storage for hooks to be executed. + """ + super().__init__() + self.hook_storage: HookStorage = hook_storage + self.model: nn.Module = model + self.module_call_stack: List[nn.Module] = [] + self.nested_enter_count = 0 + self.op_calls: Dict[str, int] = defaultdict(int) + self.enabled: bool = True + + # Variables for hooks after constant nodes + self.const_name_map: WeakUnhashableKeyMap[torch.Tensor, str] = WeakUnhashableKeyMap() + for name, parameter in chain(self.model.named_parameters(), self.model.named_buffers()): + self.const_name_map[parameter] = name + self.in_process_const = False + + # Hook names + self.hooks_module_to_group_name: Dict[ReferenceType[nn.Module], str] = {} + self._get_named_hooks(self.hook_storage.pre_hooks, "pre_hook") + self._get_named_hooks(self.hook_storage.post_hooks, "post_hook") + + def _get_named_hooks(self, storage: nn.ModuleDict, prefix: str) -> None: + """ + Associates named hooks from the given module storage with a group name, updating + the `hooks_module_to_group_name` dictionary for each hook. + + The group name is formatted as '{prefix}__{hook_key}[{hook_id}]', and any forward + slashes in the hook key are replaced with dashes to avoid conflicts with separator + characters used by the module hierarchy. + + :param storage: A module that stores hook modules. + :param prefix: A string prefix used to group hooks by their context or origin. + """ + for hook_key, hook_module_dict in storage.named_children(): + for hook_id, hook_module in hook_module_dict.named_children(): + # Replace / to avoid collision with module separator + hook_name = hook_key.replace("/", "-") + self.hooks_module_to_group_name[ref(hook_module)] = f"{prefix}__{hook_name}[{hook_id}]" + + def _get_wrapped_call(self, fn_call: MethodType) -> Callable[..., Any]: + """ + Wrap a function call to include pushing to and popping from the module call stack. + + :param fn_call: The original function call to wrap. + :return: The wrapped function call. + """ + + def wrapped_call(self_: nn.Module, *args: Tuple[Any, ...], **kwargs: Dict[str, Any]) -> Any: + self.push_module_call_stack(self_) + retval = fn_call.__func__(self_, *args, **kwargs) + self.pop_module_call_stack() + return retval + + return wrapped_call + + def __enter__(self) -> FunctionHookMode: + """ + Enter the context manager. + Wrapping the _call_impl function of each module on first nested enter. + + :return: The instance of FunctionHookMode. + """ + super().__enter__() # type: ignore + if self.nested_enter_count == 0: + # Wrap _call_impl function of instance each module. + # Note: __call__ can`t not be overrided for instance, the function can be override only in class namespace. + logger.debug("FunctionHookMode.__enter__: wrap _call_impl function") + for _, module in self.model.named_modules(): + module._call_impl = types.MethodType(self._get_wrapped_call(module._call_impl), module) + self.push_module_call_stack(self.model) + self.nested_enter_count += 1 + return self + + def __exit__( + self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType] + ) -> None: + """ + Exit the context manager, unwrapping the _call_impl function of each module. + + :param exc_type: Exception type. + :param exc_val: Exception value. + :param exc_tb: Traceback. + """ + self.nested_enter_count -= 1 + if self.nested_enter_count == 0: + # Unwrap _call_impl functions + logger.debug("FunctionHookMode.__exit__: unwrap _call_impl function") + for _, module in self.model.named_modules(): + module.__dict__.pop("_call_impl") + self.pop_module_call_stack() + super().__exit__(exc_type, exc_val, exc_tb) # type: ignore + + def __torch_function__( + self, + func: Callable[..., Any], + types: List[Type[Any]], + args: Tuple[Any, ...] = (), + kwargs: Optional[Dict[str, Any]] = None, + ) -> Any: + """ + Override the __torch_function__ method to add pre- and post-hook execution. + + :param func: The function being called. + :param types: List of types. + :param args: The arguments to the function. + :param kwargs: The keyword arguments to the function. + :return: The output of the function call after hooks have been executed. + """ + kwargs = kwargs or {} + + fn_name = func.__name__ + + # WA: to catch nested calls for some functions + # https://github.com/pytorch/pytorch/issues/55093 + fn_for_nested_call = get_handle_inner_function(func) + if fn_for_nested_call is not None: + with self: + return fn_for_nested_call(*args, **kwargs) + + if not self.enabled or fn_name in IGNORED_FN_NAMES: + return func(*args, **kwargs) + + op_name = self.get_current_executed_op_name(fn_name) + full_fn_name = _get_full_fn_name(func) + logger.debug(f"FunctionHookMode.__torch_function__: {full_fn_name=} {op_name=}") + self.register_op(fn_name) + op_meta = OpMeta(op_name=op_name, func=func) + args, kwargs = self.execute_pre_hooks(args, kwargs, op_meta) + output = func(*args, **kwargs) + output = self.execute_post_hooks(output, op_meta) + return output + + def push_module_call_stack(self, module: nn.Module) -> None: + """ + Push a module to the call stack and initialize its operation calls. + + :param module: The module to push onto the call stack. + """ + assert isinstance(module, nn.Module) + self.module_call_stack.append(module) + module_name = self.get_current_relative_name() + logger.debug(f"FunctionHookMode.push_module_call_stack: {module_name=}") + + def pop_module_call_stack(self) -> None: + """ + Pop a module from the call stack and remove its operation calls. + """ + module_name = self.get_current_relative_name() + self.module_call_stack.pop() + logger.debug(f"FunctionHookMode.pop_module_call_stack: {module_name=}") + + def get_current_relative_name(self) -> str: + """ + Get the name of the current module being executed. + + :return: The name of the current module. + """ + relative_module_names = [] + prev_module = self.module_call_stack[0] + + for module in self.module_call_stack[1:]: + hook_name = self.hooks_module_to_group_name.get(ref(module)) + if hook_name is not None: + relative_module_names.append(hook_name) + else: + for n, m in prev_module.named_children(): + if m is module: + relative_module_names.append(n) + break + prev_module = module + + return "/".join(relative_module_names) + + def get_current_executed_op_name(self, fn_name: str) -> str: + """ + Get the name of the current operation being executed. + + :param fn_name: The function name of the operation. + :return: The name of the operation. + """ + module_name = self.get_current_relative_name() + op_name = generate_normalized_op_name(module_name, fn_name) + call_id = self.op_calls[op_name] + return generate_normalized_op_name(module_name, fn_name, call_id) + + def register_op(self, fn_name: str) -> None: + """ + Register an operation call for the current module and increment call counter. + + :param fn_name: The function name of the operation. + """ + module_name = self.get_current_relative_name() + op_name = generate_normalized_op_name(module_name, fn_name) + self.op_calls[op_name] += 1 + + def execute_hooks_for_parameter(self, value: torch.Tensor) -> torch.Tensor: + """ + Executes post-hooks for a model parameter if a hook is defined for it. + If the input is not a `torch.nn.Parameter`, or if no hook is defined, the original tensor is returned unchanged. + + :param value: The tensor to which the post-hook will be applied.. + :return: The processed tensor with the applied post-hook, if applicable. + """ + if not isinstance(value, torch.nn.Parameter): + return value + + name_in_model = self.const_name_map.get(value, None) + if name_in_model is not None and not self.in_process_const: + self.in_process_const = True + value = self.hook_storage.execute_post_function_hooks(name_in_model.replace(".", ":"), 0, value) + self.in_process_const = False + return value + + def process_parameters(self, args: List[Any], kwargs: Dict[str, Any]) -> Tuple[List[Any], Dict[str, Any]]: + """ + Execute post-hooks for parameters. + + :param args: The arguments to the function. + :param kwargs: The keyword arguments to the function. + :return: The modified arguments and keyword arguments after pre-hooks. + """ + for idx, value in enumerate(args): + args[idx] = self.execute_hooks_for_parameter(value) + for kw_name, value in kwargs.items(): + kwargs[kw_name] = self.execute_hooks_for_parameter(value) + return args, kwargs + + def execute_pre_hooks( + self, args: Tuple[Any, ...], kwargs: Dict[str, Any], op_meta: OpMeta + ) -> Tuple[Tuple[Any, ...], Dict[str, Any]]: + """ + Execute pre-hooks for the operation. + + :param args: The arguments to the function. + :param kwargs: The keyword arguments to the function. + :param op_meta: Metadata for the operation. + :return: The modified arguments and keyword arguments after pre-hooks. + """ + _args: List[Any] = list(args) + with self: + _args, kwargs = self.process_parameters(_args, kwargs) + + for idx, value in enumerate(_args): + _args[idx] = self.hook_storage.execute_pre_function_hooks(op_meta.op_name, idx, value) + + for port_id, kw_name in enumerate(kwargs, start=len(_args)): + kwargs[kw_name] = self.hook_storage.execute_pre_function_hooks( + op_meta.op_name, port_id, kwargs[kw_name] + ) + return tuple(_args), kwargs + + def process_post_function_hooks_for_value(self, value: Any, op_meta: OpMeta, port_id: int) -> Any: + """ + Executes post-hooks on a value after an operation. + + :param value: The output value. + :param op_meta: Metadata about the operation. + :param port_id: The id of output port. + :return: The modified output after post-hooks. + """ + return self.hook_storage.execute_post_function_hooks(op_meta.op_name, port_id, value) + + def execute_post_hooks(self, output: Any, op_meta: OpMeta) -> Any: + """ + Execute post-hooks for the operation. + + :param output: The output of the function. + :param op_meta: Metadata for the operation. + :return: The modified output after post-hooks. + """ + with self: + cls_tuple = None + if isinstance(output, tuple): + # Need to return named tuples like torch.return_types.max + cls_tuple = type(output) + output = list(output) + + if isinstance(output, list): + for idx, value in enumerate(output): + output[idx] = self.process_post_function_hooks_for_value(value, op_meta, idx) + if cls_tuple is not None: + output = cls_tuple(output) + else: + output = self.process_post_function_hooks_for_value(output, op_meta, 0) + return output + + def process_model_inputs(self, args: Tuple[Any], kwargs: Dict[str, Any]) -> Tuple[Tuple[Any], Dict[str, Any]]: + """ + Processes the input arguments for the model's forward function. + + :param args: Positional arguments passed to the model's forward method. + :param kwargs: Keyword arguments passed to the model's forward method. + :return: The processed arguments, with hooks applied to any tensor inputs. + """ + forward_signature = inspect.signature(self.model.forward) + bound_arguments = forward_signature.bind(*args, **kwargs) + + # Hooks available only for named arguments + for name, value in bound_arguments.arguments.items(): + if isinstance(value, Tensor): + bound_arguments.arguments[name] = self.execute_hooks_for_model_input(name, value) + + return bound_arguments.args, bound_arguments.kwargs + + def execute_hooks_for_model_input(self, name: str, value: Any) -> Any: + """ + Executes the post-hooks for an input tensor. + + :param name: The name of the input argument. + :param value: The value of the input argument. + :return: The processed value after the hook is executed. + """ + return self.hook_storage.execute_post_function_hooks(name, 0, value) + + def process_model_outputs(self, outputs: Any) -> Any: + """ + Processes the outputs from the model, applying pre-hooks to any tensors found in the output. + + :param outputs: The outputs returned by the model's forward method. + :return: The processed outputs with hooks applied. + """ + if isinstance(outputs, Tensor): + return self.execute_hooks_for_model_output("output", outputs) + + cls_tuple = None + if isinstance(outputs, tuple): + cls_tuple = type(outputs) + outputs = list(outputs) + if isinstance(outputs, list): + outputs = list(outputs) + for idx, val in enumerate(outputs): + if isinstance(val, Tensor): + outputs[idx] = self.execute_hooks_for_model_output(f"output_{idx}", val) + if cls_tuple is not None: + outputs = cls_tuple(outputs) + return outputs + + def execute_hooks_for_model_output(self, name: str, value: Any) -> Any: + """ + Executes the pre-hooks for an input tensor. + + :param name: The name of the input argument. + :param value: The value of the input argument. + :return: The processed value after the hook is executed. + """ + return self.hook_storage.execute_pre_function_hooks(name, 0, value) + + @contextmanager + def disable(self) -> Iterator[None]: + """ + Temporarily disables the function tracing and execution hooks within a context. + + :return: A context manager which temporary disables the function tracing and execution hooks within a context. + """ + ret = self.enabled + self.enabled = False + yield + self.enabled = ret diff --git a/nncf/experimental/torch2/function_hook/hook_storage.py b/nncf/experimental/torch2/function_hook/hook_storage.py new file mode 100644 index 00000000000..ab28d35bae5 --- /dev/null +++ b/nncf/experimental/torch2/function_hook/hook_storage.py @@ -0,0 +1,176 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import weakref +from typing import Any, Optional + +from torch import nn + + +class RemovableHookHandle: + """ + A handle to manage removable hooks that are stored in a dictionary within a PyTorch ModuleDict. + The handle allows safe removal of specific hooks based on their unique identifiers. + + :param storage_ref: A weak reference to the storage (an nn.ModuleDict) containing the hooks. + :param key: The key in the storage dict corresponding to a set of hooks. + :param id: A unique identifier for the specific hook within the set. + """ + + def __init__(self, storage: nn.ModuleDict, key: str, id: str) -> None: + self.storage_ref = weakref.ref(storage) + self.key = key + self.id = id + + def remove(self) -> None: + """ + Removes the hook with the specified id from the storage, if it exists. + If the set of hooks under a particular key becomes empty after removal, the key is also deleted. + Steps: + - Checks if the storage still exists (i.e., if it's not garbage collected). + - Verifies if the key and id are present in the storage. + - If hook exists, hook will removed. + - If no hooks left under the key, the key is also removed from the storage. + """ + storage: Optional[nn.ModuleDict] = self.storage_ref() + if storage is not None and self.key in storage and self.id in storage[self.key]: + del storage[self.key][self.id] + if not storage[self.key]: + del storage[self.key] + + +class HookStorage(nn.Module): + """ + A module for storing and executing hooks. + + :param pre_hooks: A instance of nn.ModuleDict for storing pre-hooks. + :param post_hooks: A instance of nn.ModuleDict for storing post-hooks. + """ + + def __init__(self) -> None: + """ + Initialize an empty HookStorage. + """ + super().__init__() + self.pre_hooks: nn.ModuleDict = nn.ModuleDict() + self.post_hooks: nn.ModuleDict = nn.ModuleDict() + + @staticmethod + def _generate_key(op_name: str, port_id: int) -> str: + """ + Return key for module dict that generated by operation name and port id. + + :param op_name: The operation name the hook is associated with. + :param port_id: The port ID the hook is associated with. + :return: Generate key for module dict. + """ + return f"{op_name}__{port_id}" + + @staticmethod + def _get_next_hook_id(hooks_dict: nn.ModuleDict) -> str: + """ + Determines the next available hook ID by finding the highest existing hook ID and incrementing it. + + :param hooks_dict: A dictionary containing existing hooks. + :return: The next available hook ID as a string. Starts from '0' if no hooks exist. + """ + + if not hooks_dict: + return "0" + return str(max([int(k) for k in hooks_dict]) + 1) + + @classmethod + def _insert_hook( + cls, storage_dict: nn.ModuleDict, op_name: str, port_id: int, hook: nn.Module + ) -> RemovableHookHandle: + """ + Inserts a hook into the storage under the appropriate key. + + :param storage_dict: The storage of hook. + :param op_name: The operation name the hook is associated with. + :param port_id: The port ID the hook is associated with. + :param hook: The hook module to be stored. + :return: A handle that can be used to remove the hook later. + """ + + hook_key = cls._generate_key(op_name, port_id) + if hook_key not in storage_dict: + storage_dict[hook_key] = nn.ModuleDict() + + hook_id = cls._get_next_hook_id(storage_dict[hook_key]) + storage_dict[hook_key][hook_id] = hook + + return RemovableHookHandle(storage_dict, hook_key, hook_id) + + def register_pre_function_hook(self, op_name: str, port_id: int, hook: nn.Module) -> RemovableHookHandle: + """ + Registers a pre-function hook to be executed before a specific operation. + + :param op_name: The operation name the hook is associated with. + :param port_id: The port ID the hook is associated with. + :param hook: The pre-function hook to be stored. + :return: A handle for removing the registered hook. + """ + return self._insert_hook(self.pre_hooks, op_name, port_id, hook) + + def register_post_function_hook(self, op_name: str, port_id: int, hook: nn.Module) -> RemovableHookHandle: + """ + Registers a post-function hook to be executed before a specific operation. + + :param op_name: The operation name the hook is associated with. + :param port_id: The port ID the hook is associated with. + :param hook: The pre-function hook to be stored. + :return: A handle for removing the registered hook. + """ + return self._insert_hook(self.post_hooks, op_name, port_id, hook) + + @classmethod + def _execute_hooks(cls, storage_dict: nn.ModuleDict, op_name: str, port_id: int, value: Any) -> Any: + """ + Executes all hooks of a given type (pre or post) for a specific operation and port, + passing and potentially modifying a value. + + :param storage_dict: The storage of hook. + :param op_name: The operation name the hooks are associated with. + :param port_id: The port ID the hooks are associated with. + :param value: The input value to be passed through the hooks. + :return: The modified value after all hooks have been applied. + """ + hook_key = cls._generate_key(op_name, port_id) + if hook_key not in storage_dict: + return value + for hook in storage_dict[hook_key].values(): + value = hook(value) + return value + + def execute_pre_function_hooks(self, op_name: str, port_id: int, value: Any) -> Any: + """ + Executes all pre-function hooks for a given operation and port. + + :param op_name: The operation name the hooks are associated with. + :param port_id: The port ID the hooks are associated with. + :param value: The input value to be passed through the pre-function hooks. + :return: The value after all pre-function hooks have been executed. + """ + return self._execute_hooks(self.pre_hooks, op_name, port_id, value) + + def execute_post_function_hooks(self, op_name: str, port_id: int, value: Any) -> Any: + """ + Executes all post-function hooks for a given operation and port. + + :param op_name: The operation name the hooks are associated with. + :param port_id: The port ID the hooks are associated with. + :param value: The input value to be passed through the pre-function hooks. + :return: The value after all post-function hooks have been executed. + """ + return self._execute_hooks(self.post_hooks, op_name, port_id, value) diff --git a/nncf/experimental/torch2/function_hook/nncf_graph/nncf_graph_builder.py b/nncf/experimental/torch2/function_hook/nncf_graph/nncf_graph_builder.py new file mode 100644 index 00000000000..03361e85e23 --- /dev/null +++ b/nncf/experimental/torch2/function_hook/nncf_graph/nncf_graph_builder.py @@ -0,0 +1,132 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections import defaultdict +from typing import Any, Dict, List, Tuple, Union + +import networkx as nx # type: ignore +import torch +from torch import nn + +import nncf +from nncf.common.graph.graph import NNCFGraph +from nncf.common.graph.graph import NNCFNode +from nncf.common.graph.layer_attributes import Dtype +from nncf.experimental.torch2.function_hook.graph.build_graph_mode import build_graph +from nncf.experimental.torch2.function_hook.graph.graph_utils import ConstMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import EdgeMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import FunctionMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import InOutMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import NodeType + + +def get_node_type(type: NodeType, meta: Union[ConstMeta, FunctionMeta, InOutMeta]) -> str: + """ + Convert a given NodeType to its corresponding string representation. + + :param type: The type of the node, which can be one of the following: + :param meta: Metadata associated with the node. + :return: The string representation of the node type. + """ + if isinstance(meta, InOutMeta) and type == NodeType.input: + return "nncf_model_input" + if isinstance(meta, InOutMeta) and type == NodeType.output: + return "nncf_model_output" + if isinstance(meta, ConstMeta): + return "nncf_model_const" + if isinstance(meta, FunctionMeta): + return meta.fn_name + raise nncf.InternalError("Unexpected metadata type") + + +def get_name_of_node(meta: Union[ConstMeta, FunctionMeta, InOutMeta]) -> str: + """ + Get the name of a node based on its metadata. + + :param meta: The metadata of the node. + :return: The name of the node. + """ + if isinstance(meta, ConstMeta): + return meta.name_in_model + if isinstance(meta, FunctionMeta): + return meta.op_name + if isinstance(meta, InOutMeta): + return meta.name + raise nncf.InternalError("Unexpected metadata type") + + +def get_dtype(dtype: torch.dtype) -> Dtype: + """ + Converts a torch dtype to a NNCF Dtype enum. + + :param dtype: The torch dtype to be converted. + :return: The corresponding NNCF Dtype enum value. + """ + if dtype in [torch.float, torch.float16, torch.bfloat16, torch.float32, torch.float64]: + return Dtype.FLOAT + return Dtype.INTEGER + + +def convert_to_nncf_graph(nx_graph: nx.MultiDiGraph) -> NNCFGraph: + """ + Converts a graph to an NNCFGraph. + + :param nx_graph: The graph to convert. + :return: The converted NNCFGraph. + """ + nncf_graph = NNCFGraph() + + map_nx_node_to_nncf_node: Dict[int, NNCFNode] = {} + for node, data in nx_graph.nodes(data=True): + meta: Union[ConstMeta, FunctionMeta, InOutMeta] = data["meta"] + node_type = get_node_type(data["type"], meta) + node_metatype = None # TODO(AlexanderDokuchaev): add node_metatype + nncf_node = nncf_graph.add_nncf_node( + node_name=get_name_of_node(meta), + node_type=node_type, + node_metatype=node_metatype, # type: ignore[arg-type] + ) + map_nx_node_to_nncf_node[node] = nncf_node + + map_edges: Dict[Tuple[int, int], List[EdgeMeta]] = defaultdict(list) + + for s_node, t_node, data in nx_graph.edges(data=True): + meta = data["meta"] + if isinstance(meta, EdgeMeta): + map_edges[(s_node, t_node)].append(meta) + + for (s_node, t_node), list_meta in map_edges.items(): + source_node = map_nx_node_to_nncf_node[s_node] + target_node = map_nx_node_to_nncf_node[t_node] + nncf_graph.add_edge_between_nncf_nodes( + source_node.node_id, + target_node.node_id, + tensor_shape=list_meta[0].shape, + input_port_id=list_meta[0].input_port, + output_port_id=list_meta[0].output_port, + dtype=get_dtype(list_meta[0].dtype), + parallel_input_port_ids=[meta.input_port for meta in list_meta[1:]] if len(list_meta) > 1 else None, + ) + return nncf_graph + + +def build_nncf_graph(model: nn.Module, *args: Any, **kwargs: Any) -> NNCFGraph: + """ + Builds an NNCF graph from the given PyTorch model. + + :param model: The PyTorch model to build the graph from. + :param *args: Positional arguments to model inference. + :param **kwargs: Keyword arguments to model inference. + :return: The NNCF graph representation of the model. + """ + graph = build_graph(model, *args, **kwargs) + return convert_to_nncf_graph(graph) diff --git a/nncf/experimental/torch2/function_hook/weak_map.py b/nncf/experimental/torch2/function_hook/weak_map.py new file mode 100644 index 00000000000..1a3575f67a4 --- /dev/null +++ b/nncf/experimental/torch2/function_hook/weak_map.py @@ -0,0 +1,79 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import weakref +from typing import Any, Dict, Generic, Optional, Tuple, TypeVar + +_K = TypeVar("_K") +_V = TypeVar("_V") + + +class WeakUnhashableKeyMap(Generic[_K, _V]): + """ + A dictionary-like object that uses weak references to unhashable objects as keys. + + This class allows the use of unhashable objects as keys in a dictionary by using their `id`. + It ensures that the entries are removed when the objects are garbage collected. + + :param _data: Internal storage for the key-value pairs, + where the key is the id of the object and the value is a tuple containing a weak reference + to the object and the actual value. + """ + + def __init__(self) -> None: + """Initialize an empty WeakUnhashableKeyMap.""" + self._data: Dict[int, Tuple[weakref.ReferenceType[_K], _V]] = {} + + def __getitem__(self, obj: Any) -> Any: + """ + Get the value associated with the given object. + + :param obj (Any): The key object. + :return: The value associated with the object. + """ + ref_obj, val = self._data[id(obj)] + if ref_obj() is not obj: + raise KeyError(obj) + return val + + def __setitem__(self, obj: _K, value: _V) -> None: + """ + Set the value for the given object. + + :param obj: The key object. + :param value: The value to associate with the object. + """ + key = id(obj) + ref_obj, _ = self._data.get(key, (None, None)) + + if ref_obj is not None and ref_obj() is obj: + self._data[key] = ref_obj, value + else: + + def on_destroy(_: Any) -> None: + del self._data[key] + + self._data[key] = weakref.ref(obj, on_destroy), value + + def get(self, obj: _K, default: Optional[Any] = None) -> Optional[_V]: + """ + Get the value associated with the given object, or a default value if the object is not in the map. + + :param obj: The key object. + :param default: The default value to return if the object is not found. Defaults to None. + :return: The value associated with the object, or the default value if the object is not found. + """ + key = id(obj) + try: + _, val = self._data[key] + return val + except KeyError: + return default diff --git a/nncf/experimental/torch2/function_hook/wrapper.py b/nncf/experimental/torch2/function_hook/wrapper.py new file mode 100644 index 00000000000..f56154f412b --- /dev/null +++ b/nncf/experimental/torch2/function_hook/wrapper.py @@ -0,0 +1,230 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import inspect +import types +from types import MethodType +from typing import Any, Callable, Dict, Tuple, cast + +from torch import nn + +import nncf +from nncf.experimental.torch2.function_hook.hook_executor_mode import FunctionHookMode +from nncf.experimental.torch2.function_hook.hook_storage import HookStorage +from nncf.experimental.torch2.function_hook.hook_storage import RemovableHookHandle + +ATR_HOOK_STORAGE = "__nncf_hooks" + + +class ForwardWithHooks: + """Class to wrap forward function of nn.Module, to forward function of the model with enabled FunctionHookMode""" + + __slots__ = "_func", "__dict__", "__weakref__" + _func: Callable[..., Any] + + def __new__(cls, orig_forward: Callable[..., Any]) -> ForwardWithHooks: + if not callable(orig_forward): + raise TypeError("the first argument must be callable") + + if isinstance(orig_forward, ForwardWithHooks): + raise TypeError("Func already wrapped") + + self = super(ForwardWithHooks, cls).__new__(cls) + + self._func = orig_forward + return self + + def __call__(self, *args: Any, **kwargs: Any) -> Any: + model = cast(nn.Module, self._func.__self__) # type: ignore[attr-defined] + with FunctionHookMode(model=model, hook_storage=get_hook_storage(model)) as ctx: + args, kwargs = ctx.process_model_inputs(args, kwargs) + outputs = self._func(*args, **kwargs) + outputs = ctx.process_model_outputs(outputs) + return outputs + + def __repr__(self) -> str: + return f"ForwardWithHooks.{repr(self._func)}" + + def __reduce__(self) -> Tuple[Callable[..., Any], Tuple[Any, ...], Tuple[Any, ...]]: + return type(self), (self._func,), (self._func, self.__dict__ or None) + + def __setstate__(self, state: Tuple[Any, Any]) -> None: + if not isinstance(state, tuple): + raise TypeError("argument to __setstate__ must be a tuple") + if len(state) != 2: + raise TypeError(f"expected 2 items in state, got {len(state)}") + func, namespace = state + if not callable(func) or (namespace is not None and not isinstance(namespace, dict)): + raise TypeError("invalid partial state") + + if namespace is None: + namespace = {} + + self.__dict__ = namespace + self._func = func + + @property + def __code__(self) -> types.CodeType: + return self.__call__.__code__ + + @property + def __globals__(self) -> Dict[str, Any]: + return self._func.__globals__ + + @property + def __name__(self) -> str: + return self._func.__name__ + + @property + def __signature__(self) -> inspect.Signature: + return inspect.signature(self._func) + + +class ReplicateForDataParallel: + """ + Class to wrap _replicate_for_data_parallel function of nn.Module, + to correctly wrap forward with enabled FunctionHookMode. + """ + + __slots__ = "_func", "__dict__", "__weakref__" + _func: Callable[..., Any] + + def __new__(cls, func: Callable[..., Any]) -> ReplicateForDataParallel: + if not callable(func): + raise TypeError("the first argument must be callable") + + if isinstance(func, ReplicateForDataParallel): + raise TypeError("Func already wrapped") + + self = super(ReplicateForDataParallel, cls).__new__(cls) + + self._func = func + return self + + def __call__(self, *args: Any, **kwargs: Any) -> nn.Module: + module = cast(nn.Module, self._func.__self__) # type: ignore[attr-defined] + saved_wrapped_forward = module.forward + module.__dict__.pop("forward") + + replica: nn.Module = self._func(*args, **kwargs) + + replica.forward = ForwardWithHooks(replica.forward) + module.forward = saved_wrapped_forward + + return replica + + def __repr__(self) -> str: + return f"ReplicateForDataParallel.{repr(self._func)}" + + def __reduce__(self) -> Tuple[Callable[..., Any], Tuple[Any, ...], Tuple[Any, ...]]: + return type(self), (self._func,), (self._func, self.__dict__ or None) + + def __setstate__(self, state: Dict[str, Any]) -> None: + if not isinstance(state, tuple): + raise TypeError("argument to __setstate__ must be a tuple") + if len(state) != 2: + raise TypeError(f"expected 2 items in state, got {len(state)}") + func, namespace = state + if not callable(func) or (namespace is not None and not isinstance(namespace, dict)): + raise TypeError("invalid partial state") + + if namespace is None: + namespace = {} + + self.__dict__ = namespace + self._func = func + + @property + def func(self) -> MethodType: + return cast(MethodType, self._func) + + +def wrap_model(model: nn.Module) -> nn.Module: + """ + Wraps a nn.Module to inject custom behavior into the forward pass and replication process. + + This function modifies the given model by: + 1. Replacing the model's `forward` method with a wrapped version (`ForwardWithHooks`) that allows + additional hooks to be executed during the forward pass by using FunctionHookMode. + 2. Wrapping the model's `_replicate_for_data_parallel` method with `ReplicateForDataParallel`, + which allows custom behavior when the model is replicated across multiple devices (e.g., for + data parallelism). + 3. Adding a new module, `HookStorage`, to the model under the attribute `ATR_HOOK_STORAGE`. + + :param model: The nn.Module to be wrapped. + :return: The modified model with the custom behavior injected. + """ + + if "forward" in model.__dict__: + raise nncf.InternalError("Wrapper does not supported models with overrided forward function") + model.forward = ForwardWithHooks(model.forward) + model._replicate_for_data_parallel = ReplicateForDataParallel(model._replicate_for_data_parallel) # type: ignore + model.add_module(ATR_HOOK_STORAGE, HookStorage()) + return model + + +def is_wrapped(model: nn.Module) -> bool: + """ + Checks if a given model has been wrapped by the `wrap_model` function. + + :param model: The nn.Module to check. + :return: `True` if the model's `forward` method is an instance of `ForwardWithHooks`, + indicating that the model has been wrapped; `False` otherwise. + """ + return isinstance(model.forward, ForwardWithHooks) + + +def get_hook_storage(model: nn.Module) -> HookStorage: + """ + Retrieves the `HookStorage` module from the given model. + + This function accesses the model's attribute defined by `ATR_HOOK_STORAGE` + and returns the `HookStorage` module associated with it. + + + :param model: The PyTorch model from which to retrieve the `HookStorage`. + :return: The `HookStorage` module associated with the model. + """ + storage = getattr(model, ATR_HOOK_STORAGE) + if storage is None: + raise nncf.InstallationError("Hook storage is not exist in the model") + return cast(HookStorage, getattr(model, ATR_HOOK_STORAGE)) + + +def register_pre_function_hook(model: nn.Module, op_name: str, port_id: int, hook: nn.Module) -> RemovableHookHandle: + """ + Registers a pre-function hook for a specific operation in the model. + + :param model: The model to register the hook to. + :param op_name: The name of the operation associated with the hook. + :param port_id: The port ID associated with the hook. + :param hook: The pre-function hook module to be executed. + + :return: A handle that can be used to remove the hook later. + """ + hook_storage = get_hook_storage(model) + return hook_storage.register_pre_function_hook(op_name, port_id, hook) + + +def register_post_function_hook(model: nn.Module, op_name: str, port_id: int, hook: nn.Module) -> RemovableHookHandle: + """ + Registers a post-function hook for a specific operation in the model. + + :param model: The model to register the hook to. + :param op_name: The name of the operation associated with the hook. + :param port_id: The port ID associated with the hook. + :param hook: The pre-function hook module to be executed. + :return: A handle that can be used to remove the hook later. + """ + hook_storage = get_hook_storage(model) + return hook_storage.register_post_function_hook(op_name, port_id, hook) diff --git a/nncf/onnx/graph/metatypes/groups.py b/nncf/onnx/graph/metatypes/groups.py index 561e1340595..56ea16842dc 100644 --- a/nncf/onnx/graph/metatypes/groups.py +++ b/nncf/onnx/graph/metatypes/groups.py @@ -104,6 +104,25 @@ onnx_metatypes.ONNXDivLayerMetatype, ] +ELEMENTWISE_OPERATIONS = [ + onnx_metatypes.ONNXAddLayerMetatype, + onnx_metatypes.ONNXMulLayerMetatype, + onnx_metatypes.ONNXSubMetatype, + onnx_metatypes.ONNXDivLayerMetatype, + onnx_metatypes.ONNXLessMetatype, + onnx_metatypes.ONNXLessOrEqualMetatype, + onnx_metatypes.ONNXGreaterMetatype, + onnx_metatypes.ONNXGreaterOrEqualMetatype, + onnx_metatypes.ONNXEqualMetatype, + onnx_metatypes.ONNXModMetatype, + onnx_metatypes.ONNXOrMetatype, + onnx_metatypes.ONNXNotMetatype, + onnx_metatypes.ONNXAndMetatype, + onnx_metatypes.ONNXXOrMetatype, + onnx_metatypes.ONNXMaximumMetatype, + onnx_metatypes.ONNXMinimumMetatype, + onnx_metatypes.ONNXMeanMetatype, +] OPERATIONS_WITH_WEIGHTS = [ *CONSTANT_WEIGHT_LAYER_METATYPES, @@ -123,7 +142,11 @@ # TODO: Need to add MatMul with the separate bias support (CVS-135433) ] -OPERATIONS_WITH_BIAS = [*OPERATIONS_WITH_BIAS_REDUCED, onnx_metatypes.ONNXDepthwiseConvolutionMetatype] +OPERATIONS_WITH_BIAS = [ + *OPERATIONS_WITH_BIAS_REDUCED, + onnx_metatypes.ONNXDepthwiseConvolutionMetatype, + onnx_metatypes.ONNXConvolutionTransposeMetatype, +] QUANTIZE_DEQUANTIZE_OPERATIONS = [ diff --git a/nncf/onnx/graph/metatypes/onnx_metatypes.py b/nncf/onnx/graph/metatypes/onnx_metatypes.py index 2105f31a216..075b2597643 100644 --- a/nncf/onnx/graph/metatypes/onnx_metatypes.py +++ b/nncf/onnx/graph/metatypes/onnx_metatypes.py @@ -343,6 +343,13 @@ class ONNXLessMetatype(ONNXOpMetatype): hw_config_names = [HWConfigOpName.LESS] +@ONNX_OPERATION_METATYPES.register() +class ONNXLessOrEqualMetatype(ONNXOpMetatype): + name = "LessOrEqualOp" + op_names = ["LessOrEqual"] + hw_config_names = [HWConfigOpName.LESSEQUAL] + + @ONNX_OPERATION_METATYPES.register() class ONNXGreaterMetatype(ONNXOpMetatype): name = "GreaterOp" @@ -350,6 +357,13 @@ class ONNXGreaterMetatype(ONNXOpMetatype): hw_config_names = [HWConfigOpName.GREATER] +@ONNX_OPERATION_METATYPES.register() +class ONNXGreaterOrEqualMetatype(ONNXOpMetatype): + name = "GreaterOrEqualOp" + op_names = ["GreaterOrEqual"] + hw_config_names = [HWConfigOpName.GREATEREQUAL] + + @ONNX_OPERATION_METATYPES.register() class ONNXEqualMetatype(ONNXOpMetatype): name = "EqualOp" @@ -378,6 +392,20 @@ class ONNXOrMetatype(ONNXOpMetatype): hw_config_names = [HWConfigOpName.LOGICALOR] +@ONNX_OPERATION_METATYPES.register() +class ONNXXOrMetatype(ONNXOpMetatype): + name = "XorOp" + op_names = ["Xor"] + hw_config_names = [HWConfigOpName.LOGICALXOR] + + +@ONNX_OPERATION_METATYPES.register() +class ONNXModMetatype(ONNXOpMetatype): + name = "ModOp" + op_names = ["Mod"] + hw_config_names = [HWConfigOpName.FLOORMOD] + + @ONNX_OPERATION_METATYPES.register() class ONNXMaximumMetatype(ONNXOpMetatype): name = "MaxOp" @@ -392,11 +420,16 @@ class ONNXMinimumMetatype(ONNXOpMetatype): hw_config_names = [HWConfigOpName.MINIMUM] +@ONNX_OPERATION_METATYPES.register() +class ONNXMeanMetatype(ONNXOpMetatype): + name = "MeanOp" + op_names = ["Mean"] + + @ONNX_OPERATION_METATYPES.register() class ONNXFloorMetatype(ONNXOpMetatype): name = "FloorOp" op_names = ["Floor"] - hw_config_names = [HWConfigOpName.FLOORMOD] @ONNX_OPERATION_METATYPES.register() @@ -644,6 +677,18 @@ class ONNXErfMetatype(ONNXOpMetatype): op_names = ["Erf"] +@ONNX_OPERATION_METATYPES.register() +class ONNXCosMetatype(ONNXOpMetatype): + name = "CosOp" + op_names = ["Cos"] + + +@ONNX_OPERATION_METATYPES.register() +class ONNXSinMetatype(ONNXOpMetatype): + name = "SinOp" + op_names = ["Sin"] + + def get_operator_metatypes() -> List[Type[OperatorMetatype]]: """ Returns a list of the operator metatypes. diff --git a/nncf/onnx/quantization/ignored_patterns.py b/nncf/onnx/quantization/ignored_patterns.py index 43eb29f0915..8e1255f3ca1 100644 --- a/nncf/onnx/quantization/ignored_patterns.py +++ b/nncf/onnx/quantization/ignored_patterns.py @@ -95,28 +95,27 @@ def create_multihead_attention_output() -> GraphPattern: @ONNX_IGNORED_PATTERNS.register(IgnoredPatternNames.SE_BLOCK) def create_se_block() -> GraphPattern: - # NON_PATTERN_NODE-------- - # (PATTERN_NODE_TO_EXCLUDE) | - # | | - # REDUCE_MEAN | - # | | - # CONVOLUTION | - # | | - # ACTIVATION | - # | | - # CONVOLUTION | - # | | - # SIGMOID||HARDSIGMOID | - # | | - # | | - # MUL--------------- + # NON_PATTERN_NODE-------------- + # (PATTERN_NODE_TO_EXCLUDE) | + # | | + # REDUCE_MEAN||GLOBAL_AVERAGE_POOL | + # | | + # CONVOLUTION | + # | | + # ACTIVATION | + # | | + # CONVOLUTION | + # | | + # SIGMOID||HARDSIGMOID | + # | | + # MUL--------------------- # (PATTERN_NODE_TO_EXCLUDE) pattern = GraphPattern() non_pattern_node = pattern.add_node(label="NON_PATTERN_NODE", type=GraphPattern.NON_PATTERN_NODE_TYPE) - reduce_mean_node = pattern.add_node( + reduce_node_or_global_average_pool = pattern.add_node( **{ - GraphPattern.LABEL_ATTR: "REDUCE_MEAN", - GraphPattern.METATYPE_ATTR: om.ONNXReduceMeanMetatype, + GraphPattern.LABEL_ATTR: "REDUCE_MEAN || GLOBAL_AVERAGE_POOL", + GraphPattern.METATYPE_ATTR: [om.ONNXReduceMeanMetatype, om.ONNXGlobalAveragePoolMetatype], GraphPattern.PATTERN_NODE_TO_EXCLUDE: True, } ) @@ -124,8 +123,8 @@ def create_se_block() -> GraphPattern: **{GraphPattern.LABEL_ATTR: "CONVOLUTION", GraphPattern.METATYPE_ATTR: om.ONNXConvolutionMetatype} ) - pattern.add_edge(non_pattern_node, reduce_mean_node) - pattern.add_edge(reduce_mean_node, conv_node_1) + pattern.add_edge(non_pattern_node, reduce_node_or_global_average_pool) + pattern.add_edge(reduce_node_or_global_average_pool, conv_node_1) pattern.join_patterns(atomic_activations_operations()) rest_pattern = GraphPattern() @@ -158,3 +157,25 @@ def create_se_block() -> GraphPattern: multiply_node = node_id pattern.add_edge(non_pattern_node, multiply_node) return pattern + + +@ONNX_IGNORED_PATTERNS.register(IgnoredPatternNames.ROPE) +def create_rope() -> GraphPattern: + pattern = GraphPattern() + matmul_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "MATMUL", GraphPattern.METATYPE_ATTR: om.ONNXMatMulMetatype} + ) + transpose_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "TRANSPOSE", GraphPattern.METATYPE_ATTR: om.ONNXTransposeMetatype} + ) + concat_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "CONCAT", GraphPattern.METATYPE_ATTR: om.ONNXConcatMetatype} + ) + cos_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "COS", GraphPattern.METATYPE_ATTR: om.ONNXCosMetatype}) + sin_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "SIN", GraphPattern.METATYPE_ATTR: om.ONNXSinMetatype}) + + pattern.add_edge(matmul_node, transpose_node) + pattern.add_edge(transpose_node, concat_node) + pattern.add_edge(concat_node, cos_node) + pattern.add_edge(concat_node, sin_node) + return pattern diff --git a/nncf/onnx/quantization/quantize_model.py b/nncf/onnx/quantization/quantize_model.py index 094b98e81af..7a4665d1a0c 100644 --- a/nncf/onnx/quantization/quantize_model.py +++ b/nncf/onnx/quantization/quantize_model.py @@ -32,15 +32,11 @@ from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization from nncf.quantization.quantize_model import quantize_with_tune_hyperparams from nncf.quantization.quantize_model import warning_model_no_batchwise_support -from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeApi from nncf.scopes import IgnoredScope -from nncf.telemetry import tracked_function -from nncf.telemetry.events import NNCF_ONNX_CATEGORY TTensor = TypeVar("TTensor") -@tracked_function(NNCF_ONNX_CATEGORY, [CompressionStartedWithQuantizeApi(), "target_device", "preset"]) def quantize_impl( model: onnx.ModelProto, calibration_dataset: Dataset, diff --git a/nncf/onnx/statistics/aggregator.py b/nncf/onnx/statistics/aggregator.py index 0ae00618eb1..1240f5ee1d0 100644 --- a/nncf/onnx/statistics/aggregator.py +++ b/nncf/onnx/statistics/aggregator.py @@ -20,15 +20,20 @@ from nncf.common.graph.transformations.layout import TransformationLayout from nncf.common.tensor_statistics.aggregator import StatisticsAggregator from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer +from nncf.common.utils.backend import BackendType from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.statistics import TensorStatistic from nncf.onnx.graph.node_utils import get_input_edge from nncf.onnx.graph.node_utils import get_input_edges_mapping from nncf.onnx.graph.onnx_helper import get_name_to_node_map from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand +from nncf.onnx.graph.transformations.commands import ONNXTargetPoint from nncf.tensor import Tensor class ONNXStatisticsAggregator(StatisticsAggregator): + BACKEND: BackendType = BackendType.ONNX + def collect_statistics(self, model: onnx.ModelProto, graph: NNCFGraph) -> None: self.input_edges_mapping = get_input_edges_mapping(graph) self.node_mapping = get_name_to_node_map(model) @@ -87,3 +92,14 @@ def _get_merged_statistic_points( @staticmethod def _process_outputs(outputs: Dict[str, np.ndarray]) -> Dict[str, Tensor]: return {n: Tensor(v) for n, v in outputs.items()} + + def _get_statistics_key(self, statistics: TensorStatistic, target_point: ONNXTargetPoint) -> str: + """ + Returns key of statistics. + + :param statistics: Statistics value. + :param target_point: Statistics target point. + :return: Statistics key. + """ + target_point_id = f"{target_point.target_node_name}_{target_point.type}_{target_point.port_id}" + return f"{statistics.__class__.__name__}_{target_point_id}" diff --git a/nncf/onnx/statistics/collectors.py b/nncf/onnx/statistics/collectors.py index 0df9c72e6e5..60a2d40a049 100644 --- a/nncf/onnx/statistics/collectors.py +++ b/nncf/onnx/statistics/collectors.py @@ -25,49 +25,11 @@ from nncf.experimental.common.tensor_statistics.collectors import RawReducer from nncf.experimental.common.tensor_statistics.collectors import ShapeAggregator from nncf.experimental.common.tensor_statistics.collectors import TensorCollector -from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase from nncf.experimental.common.tensor_statistics.statistics import MeanTensorStatistic from nncf.experimental.common.tensor_statistics.statistics import RawTensorStatistic from nncf.quantization.advanced_parameters import StatisticsType -class ONNXBasicReducer(TensorReducerBase): - def get_inplace_fn(self): - raise NotImplementedError("ONNX backend has no support of inplace statistics yet.") - - -class ONNXMinReducer(ONNXBasicReducer, MinReducer): - pass - - -class ONNXMaxReducer(ONNXBasicReducer, MaxReducer): - pass - - -class ONNXAbsMaxReducer(ONNXBasicReducer, AbsMaxReducer): - pass - - -class ONNXMeanReducer(ONNXBasicReducer, MeanReducer): - pass - - -class ONNXQuantileReducer(ONNXBasicReducer, QuantileReducer): - pass - - -class ONNXAbsQuantileReducer(ONNXBasicReducer, AbsQuantileReducer): - pass - - -class ONNXBatchMeanReducer(ONNXBasicReducer, BatchMeanReducer): - pass - - -class ONNXMeanPerChanelReducer(ONNXBasicReducer, MeanPerChReducer): - pass - - def get_mean_statistic_collector( num_samples: int, channel_axis: int, window_size: Optional[int] = None, inplace: bool = True ) -> TensorCollector: @@ -83,9 +45,9 @@ def get_mean_statistic_collector( """ inplace = False if channel_axis == 0: - reducer = ONNXBatchMeanReducer(inplace) + reducer = BatchMeanReducer(inplace) else: - reducer = ONNXMeanPerChanelReducer(channel_axis=channel_axis, inplace=inplace) + reducer = MeanPerChReducer(channel_axis=channel_axis, inplace=inplace) noop_reducer = NoopReducer() kwargs = { @@ -118,10 +80,10 @@ def get_raw_stat_collector(num_samples: int) -> TensorCollector: ONNX_REDUCERS_MAP = { - StatisticsType.MIN: ONNXMinReducer, - StatisticsType.MAX: ONNXMaxReducer, - StatisticsType.ABS_MAX: ONNXAbsMaxReducer, - StatisticsType.MEAN: ONNXMeanReducer, - StatisticsType.QUANTILE: ONNXQuantileReducer, - StatisticsType.ABS_QUANTILE: ONNXAbsQuantileReducer, + StatisticsType.MIN: MinReducer, + StatisticsType.MAX: MaxReducer, + StatisticsType.ABS_MAX: AbsMaxReducer, + StatisticsType.MEAN: MeanReducer, + StatisticsType.QUANTILE: QuantileReducer, + StatisticsType.ABS_QUANTILE: AbsQuantileReducer, } diff --git a/nncf/openvino/engine.py b/nncf/openvino/engine.py index 04805fac31f..1591cf7c362 100644 --- a/nncf/openvino/engine.py +++ b/nncf/openvino/engine.py @@ -13,6 +13,8 @@ import numpy as np import openvino.runtime as ov +from openvino import Type +from openvino.properties.hint import inference_precision from nncf.common.engine import Engine from nncf.openvino.graph.model_utils import model_has_state @@ -62,10 +64,18 @@ class OVNativeEngine(Engine): to infer the model. """ - def __init__(self, model: ov.Model): + def __init__(self, model: ov.Model, use_fp32_precision: bool = True): + """ + :param model: Model. + :param use_fp32_precision: A flag that determines whether to force the engine to use FP32 + precision during inference. + """ + config = None + if use_fp32_precision: + config = {inference_precision: Type.f32} ie = ov.Core() stateful = model_has_state(model) - compiled_model = ie.compile_model(model, device_name="CPU") + compiled_model = ie.compile_model(model, device_name="CPU", config=config) self.engine = OVCompiledModelEngine(compiled_model, stateful) def infer( diff --git a/nncf/openvino/graph/metatypes/groups.py b/nncf/openvino/graph/metatypes/groups.py index 799b6ce92bd..f382db6228a 100644 --- a/nncf/openvino/graph/metatypes/groups.py +++ b/nncf/openvino/graph/metatypes/groups.py @@ -13,6 +13,7 @@ QUANTIZE_AGNOSTIC_OPERATIONS = [ ov_metatypes.OVMaxPoolMetatype, + ov_metatypes.OVAdaptiveMaxPoolMetatype, ov_metatypes.OVReduceMaxMetatype, ov_metatypes.OVReshapeMetatype, ov_metatypes.OVSqueezeMetatype, @@ -62,6 +63,7 @@ ov_metatypes.OVMaximumMetatype, ov_metatypes.OVMinimumMetatype, ov_metatypes.OVAvgPoolMetatype, + ov_metatypes.OVAdaptiveAvgPoolMetatype, ov_metatypes.OVReduceMeanMetatype, ov_metatypes.OVMVNMetatype, ov_metatypes.OVNormalizeL2Metatype, @@ -190,7 +192,11 @@ ov_metatypes.OVMatMulMetatype, ] -OPERATIONS_WITH_BIAS = [*OPERATIONS_WITH_BIAS_REDUCED, ov_metatypes.OVDepthwiseConvolutionMetatype] +OPERATIONS_WITH_BIAS = [ + *OPERATIONS_WITH_BIAS_REDUCED, + ov_metatypes.OVDepthwiseConvolutionMetatype, + ov_metatypes.OVConvolutionBackpropDataMetatype, +] CONV_OPERATIONS = [ ov_metatypes.OVConvolutionMetatype, diff --git a/nncf/openvino/graph/metatypes/openvino_metatypes.py b/nncf/openvino/graph/metatypes/openvino_metatypes.py index 87a2677c0d7..d05a547c170 100644 --- a/nncf/openvino/graph/metatypes/openvino_metatypes.py +++ b/nncf/openvino/graph/metatypes/openvino_metatypes.py @@ -157,6 +157,13 @@ class OVAvgPoolMetatype(OVOpMetatype): hw_config_names = [HWConfigOpName.AVGPOOL] +@OV_OPERATOR_METATYPES.register() +class OVAdaptiveAvgPoolMetatype(OVOpMetatype): + name = "AdaptiveAvgPoolOp" + op_names = ["AdaptiveAvgPool"] + hw_config_names = [HWConfigOpName.AVGPOOL] + + @OV_OPERATOR_METATYPES.register() class OVMaxPoolMetatype(OVOpMetatype): name = "MaxPoolOp" @@ -164,6 +171,13 @@ class OVMaxPoolMetatype(OVOpMetatype): hw_config_names = [HWConfigOpName.MAXPOOL] +@OV_OPERATOR_METATYPES.register() +class OVAdaptiveMaxPoolMetatype(OVOpMetatype): + name = "AdaptiveMaxPoolOp" + op_names = ["AdaptiveMaxPool"] + hw_config_names = [HWConfigOpName.MAXPOOL] + + @OV_OPERATOR_METATYPES.register() class OVConstantMetatype(OVOpMetatype): name = "ConstantOp" @@ -712,6 +726,18 @@ class OVScaledDotProductAttentionMetatype(OVOpMetatype): target_input_ports = [0, 1] +@OV_OPERATOR_METATYPES.register() +class OVCosMetatype(OVOpMetatype): + name = "CosOp" + op_names = ["Cos"] + + +@OV_OPERATOR_METATYPES.register() +class OVSinMetatype(OVOpMetatype): + name = "SinOp" + op_names = ["Sin"] + + def get_operator_metatypes() -> List[Type[OperatorMetatype]]: """ Returns a list of the operator metatypes. diff --git a/nncf/openvino/graph/model_transformer.py b/nncf/openvino/graph/model_transformer.py index 85aeb90585c..a331b82314e 100644 --- a/nncf/openvino/graph/model_transformer.py +++ b/nncf/openvino/graph/model_transformer.py @@ -205,7 +205,7 @@ def _insert_outputs(model: ov.Model, outputs: List[Tuple[ov.Output, int, Callabl output_name = node_output.get_node().get_friendly_name() result_name = get_result_node_name(output_name, port_id) - if node_output.get_element_type() != dtype: + if dtype is not None and node_output.get_element_type() != dtype: node_output = opset.convert(output, destination_type=dtype) result = opset.result(node_output, name=result_name) diff --git a/nncf/openvino/graph/nncf_graph_builder.py b/nncf/openvino/graph/nncf_graph_builder.py index b1a1abd448f..18ae96a149a 100644 --- a/nncf/openvino/graph/nncf_graph_builder.py +++ b/nncf/openvino/graph/nncf_graph_builder.py @@ -199,6 +199,7 @@ def create_nncf_graph(model: ov.Model) -> NNCFGraph: const_attrs[const_port_id] = { "name": const_node.get_friendly_name(), "shape": tuple(const_node.get_output_shape(0)), + "dtype": const_node.output(0).get_element_type().get_type_name(), } if metatype == OVMatMulMetatype: diff --git a/nncf/openvino/graph/node_utils.py b/nncf/openvino/graph/node_utils.py index e39c4e49467..7496187adb1 100644 --- a/nncf/openvino/graph/node_utils.py +++ b/nncf/openvino/graph/node_utils.py @@ -42,7 +42,7 @@ from nncf.openvino.graph.metatypes.openvino_metatypes import OVOpMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import get_node_metatype -InplaceInsertionFnType = Callable[[ov.Node, int], ov.Node] +InplaceInsertionFnType = Callable[[ov.Node, int, str], ov.Node] def get_add_bias_node(node: NNCFNode, nncf_graph: NNCFGraph) -> Optional[NNCFNode]: @@ -212,7 +212,10 @@ def get_ov_model_reduce_node_name(output_name: str, reduce_node_name: str, port_ def get_inplace_reduce_op( - op: Type[ov.Node], reduction_axes: Optional[ReductionAxes], use_abs: bool + op: Type[ov.Node], + reduction_axes: Optional[ReductionAxes], + use_abs: bool, + keep_dims: bool = True, ) -> InplaceInsertionFnType: """ Returns inplace insertion function that adds reduce node to a passed node. @@ -220,7 +223,8 @@ def get_inplace_reduce_op( :param op: OpenVINO reduction operation type to insert. :param reduction_axes: Target reduction axes for the reduction node. Reduce along all axes in case reduction_axes are None. - :param use_abs: Wheather reduce absolute values of input tensors or not. + :param use_abs: Whether reduce absolute values of input tensors or not. + :param keep_dims: Whether to keep the original dimension length or return result as a scalar. :returns: Inplace insertion function to use in ModelTransformer. """ @@ -239,7 +243,7 @@ def get_reduce_op(node: ov.Node, output_port_id: int, output_node_name: str) -> return op( op_input.output(output_port_id), reduction_axes=np.array(reduction_axes_, dtype=np.int64), - keep_dims=True, + keep_dims=keep_dims, name=output_node_name, ) @@ -257,16 +261,19 @@ def get_inplace_min_op(reduction_axes: Optional[ReductionAxes]) -> InplaceInsert return get_inplace_reduce_op(opset.reduce_min, reduction_axes, False) -def get_inplace_max_op(reduction_axes: Optional[ReductionAxes], use_abs_max: bool) -> InplaceInsertionFnType: +def get_inplace_max_op( + reduction_axes: Optional[ReductionAxes], use_abs_max: bool, keep_dims: bool = True +) -> InplaceInsertionFnType: """ Returns inplace max function that adds reduce max node to a passed node. :param reduction_axes: Target reduction axes for the reduction node. Reduce along all axes in case reduction_axes are None. - :param use_abs: Wheather reduce absolute values of input tensors or not. + :param use_abs_max: Whether reduce absolute values of input tensors or not. + :param keep_dims: Whether to keep the original dimension length or return result as a scalar. :returns: Inplace insertion function to use in ModelTransformer. """ - return get_inplace_reduce_op(opset.reduce_max, reduction_axes, use_abs_max) + return get_inplace_reduce_op(opset.reduce_max, reduction_axes, use_abs_max, keep_dims) def get_inplace_mean_op(reduction_axes: Optional[ReductionAxes]) -> InplaceInsertionFnType: @@ -280,6 +287,135 @@ def get_inplace_mean_op(reduction_axes: Optional[ReductionAxes]) -> InplaceInser return get_inplace_reduce_op(opset.reduce_mean, reduction_axes, False) +def var_op( + op_input: ov.Output, output_node_name: str, reduction_axes: Optional[np.ndarray] = None, keep_dims: bool = True +) -> ov.Node: + """ + Return a subgraph computing variance on a given output. + + :param op_input: An output to compute variance for. + :param output_node_name: Variance output name. + :param keep_dims: Whether to keep the original dimension length or return result as a scalar. + :param reduction_axes: Axes along which to compute variance. + """ + mean = opset.reduce_mean( + op_input, + reduction_axes=reduction_axes, + keep_dims=True, + name=f"{output_node_name}/mean", + ) + diff = opset.squared_difference(mean, op_input, name=f"{output_node_name}/squared_diff") + variance = opset.reduce_mean( + diff, + reduction_axes=reduction_axes, + keep_dims=keep_dims, + name=output_node_name, + ) + return variance + + +def get_inplace_mean_var_op(reduction_axes: Optional[ReductionAxes] = None) -> InplaceInsertionFnType: + """ + Return an operation getter function that computes variance across given axes and then mean-reduces the result across + the remaining axes. + + :param reduction_axes: Axes along which to compute variance. + """ + + def get_mean_var_reduce_op(node: ov.Node, output_port_id: int, output_node_name: str) -> ov.Node: + partial_shape = get_partial_shape_safe(node, output_port_id) + all_axes = np.arange(partial_shape.rank.get_length()).astype(np.int64) + reduction_axes_ = np.array(all_axes if reduction_axes is None else reduction_axes, dtype=np.int64) + + reduce_all = np.array_equal(reduction_axes_, all_axes) + var_op_name = output_node_name if reduce_all else f"{output_node_name}/var" + result = var_op(node.output(output_port_id), var_op_name, reduction_axes_, keep_dims=not reduce_all) + if not reduce_all: + result = opset.reduce_mean( + result, + reduction_axes=all_axes, + keep_dims=False, + name=output_node_name, + ) + + return result + + return get_mean_var_reduce_op + + +def get_inplace_max_var_op(reduction_axes: Optional[ReductionAxes] = None) -> InplaceInsertionFnType: + """ + Return an operation getter function that computes variance across given axes and then max-reduces the result across + the remaining axes. + + :param reduction_axes: Axes along which to compute variance. + """ + + def get_max_var_reduce_op(node: ov.Node, output_port_id: int, output_node_name: str) -> ov.Node: + partial_shape = get_partial_shape_safe(node, output_port_id) + all_axes = np.arange(partial_shape.rank.get_length()).astype(np.int64) + reduction_axes_ = np.array(all_axes if reduction_axes is None else reduction_axes, dtype=np.int64) + + reduce_all = np.array_equal(reduction_axes_, all_axes) + var_op_name = output_node_name if reduce_all else f"{output_node_name}/var" + result = var_op(node.output(output_port_id), var_op_name, reduction_axes_, keep_dims=not reduce_all) + if not reduce_all: + result = opset.reduce_max( + result, + reduction_axes=all_axes, + keep_dims=False, + name=output_node_name, + ) + + return result + + return get_max_var_reduce_op + + +def get_inplace_mean_max_op(reduction_axes: Optional[ReductionAxes], use_abs_max: bool) -> InplaceInsertionFnType: + """ + Return an operation getter function that computes maximum across given axes and then mean-reduces the result across + the remaining axes. + + :param reduction_axes: Axes to compute maximum across. + :param use_abs_max: Whether to apply abs() operation before the max operation. + """ + + def get_mean_max_reduce_op(node: ov.Node, output_port_id: int, output_node_name: str) -> ov.Node: + partial_shape = get_partial_shape_safe(node, output_port_id) + all_axes = np.arange(partial_shape.rank.get_length()).astype(np.int64) + reduction_axes_ = np.array(all_axes if reduction_axes is None else reduction_axes, dtype=np.int64) + + reduce_all = np.array_equal(reduction_axes_, all_axes) + max_op_name = output_node_name if reduce_all else f"{output_node_name}/max" + result = get_inplace_max_op(reduction_axes, use_abs_max, keep_dims=not reduce_all)( + node, output_port_id, max_op_name + ) + if not reduce_all: + result = opset.reduce_mean( + result, + reduction_axes=all_axes, + keep_dims=False, + name=output_node_name, + ) + + return result + + return get_mean_max_reduce_op + + +def get_inplace_shape_op() -> InplaceInsertionFnType: + """ + Return an operation returning a shape on the given output. + """ + + def get_shape_op(node: ov.Node, output_port_id: int, output_node_name: str) -> ov.Node: + result = opset.shape_of(node.output(output_port_id), name=output_node_name) + return result + + return get_shape_op + + def get_inplace_batch_mean_op() -> InplaceInsertionFnType: """ Returns inplace batch mean function that adds reduce batch mean node to a passed node. diff --git a/nncf/openvino/graph/transformations/commands.py b/nncf/openvino/graph/transformations/commands.py index 2f8f891a1ac..9e7a0d4ab1c 100644 --- a/nncf/openvino/graph/transformations/commands.py +++ b/nncf/openvino/graph/transformations/commands.py @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Tuple +from typing import List, Optional, Tuple import numpy as np import openvino.runtime as ov @@ -64,7 +64,7 @@ def __init__( inplace_op_fn: InplaceInsertionFnType, fn_output_port_id: int, last_inplace_node_name: str, - output_dtype: ov.Type = ov.Type.f32, + output_dtype: Optional[ov.Type] = None, ): super().__init__(target_point) self.inplace_op_fn = inplace_op_fn diff --git a/nncf/openvino/quantization/ignored_patterns.py b/nncf/openvino/quantization/ignored_patterns.py index 11483507d04..862a078f903 100644 --- a/nncf/openvino/quantization/ignored_patterns.py +++ b/nncf/openvino/quantization/ignored_patterns.py @@ -169,3 +169,25 @@ def create_se_block() -> GraphPattern: pattern.add_edge(activation_node_2, multiply_node) pattern.add_edge(any_node, multiply_node) return pattern + + +@OPENVINO_IGNORED_PATTERNS.register(IgnoredPatternNames.ROPE) +def create_rope() -> GraphPattern: + pattern = GraphPattern() + matmul_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "MATMUL", GraphPattern.METATYPE_ATTR: om.OVMatMulMetatype} + ) + transpose_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "TRANSPOSE", GraphPattern.METATYPE_ATTR: om.OVTransposeMetatype} + ) + concat_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "CONCAT", GraphPattern.METATYPE_ATTR: om.OVConcatMetatype} + ) + cos_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "COS", GraphPattern.METATYPE_ATTR: om.OVCosMetatype}) + sin_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "SIN", GraphPattern.METATYPE_ATTR: om.OVSinMetatype}) + + pattern.add_edge(matmul_node, transpose_node) + pattern.add_edge(transpose_node, concat_node) + pattern.add_edge(concat_node, cos_node) + pattern.add_edge(concat_node, sin_node) + return pattern diff --git a/nncf/openvino/quantization/quantize_model.py b/nncf/openvino/quantization/quantize_model.py index dbd8b7adf77..46db1c50cca 100644 --- a/nncf/openvino/quantization/quantize_model.py +++ b/nncf/openvino/quantization/quantize_model.py @@ -10,12 +10,14 @@ # limitations under the License. from copy import deepcopy +from pathlib import Path from typing import Any, Callable, Iterable, List, Optional, Tuple, TypeVar, Union import openvino.runtime as ov from openvino._offline_transformations import compress_quantize_weights_transformation from nncf.common.factory import NNCFGraphFactory +from nncf.common.factory import StatisticsAggregatorFactory from nncf.common.logging import nncf_logger from nncf.common.quantization.structs import QuantizationPreset from nncf.data import Dataset @@ -29,6 +31,7 @@ from nncf.openvino.quantization.backend_parameters import is_weight_compression_needed from nncf.openvino.quantization.quantize_ifmodel import apply_algorithm_if_bodies from nncf.openvino.rt_info import dump_parameters +from nncf.parameters import BackupMode from nncf.parameters import CompressWeightsMode from nncf.parameters import DropType from nncf.parameters import ModelType @@ -48,16 +51,14 @@ from nncf.quantization.quantize_model import is_model_no_batchwise_support from nncf.quantization.quantize_model import quantize_with_tune_hyperparams from nncf.quantization.quantize_model import warning_model_no_batchwise_support -from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeApi +from nncf.quantization.statistics_caching import cache_weight_compression_statistics +from nncf.quantization.statistics_caching import register_statistics_for_algorithm from nncf.scopes import IgnoredScope from nncf.scopes import validate_ignored_scope -from nncf.telemetry.decorator import tracked_function -from nncf.telemetry.events import NNCF_OV_CATEGORY TTensor = TypeVar("TTensor") -@tracked_function(NNCF_OV_CATEGORY, [CompressionStartedWithQuantizeApi(), "target_device", "preset"]) def native_quantize_if_op_impl( model: ov.Model, calibration_dataset: Dataset, @@ -140,7 +141,6 @@ def _extract_all_subgraphs(model: ov.Model, current_id: str) -> None: return quantized_model -@tracked_function(NNCF_OV_CATEGORY, [CompressionStartedWithQuantizeApi(), "target_device", "preset"]) def native_quantize_impl( model: ov.Model, calibration_dataset: Dataset, @@ -188,10 +188,7 @@ def native_quantize_impl( return quantized_model -@tracked_function( - NNCF_OV_CATEGORY, [CompressionStartedWithQuantizeApi(), "target_device", "preset", "max_drop", "drop_type"] -) -def native_quantize_with_accuracy_control_impl( +def quantize_with_accuracy_control_impl( model: ov.Model, calibration_dataset: Dataset, validation_dataset: Dataset, @@ -365,65 +362,6 @@ def quantize_impl( ) -def wrap_validation_fn(validation_fn): - """ - Wraps validation function to support case when it only returns metric value. - - :param validation_fn: Validation function to wrap. - :return: Wrapped validation function. - """ - - def wrapper(*args, **kwargs): - retval = validation_fn(*args, **kwargs) - if isinstance(retval, tuple): - return retval - return retval, None - - return wrapper - - -def quantize_with_accuracy_control_impl( - model: ov.Model, - calibration_dataset: Dataset, - validation_dataset: Dataset, - validation_fn: Callable[[Any, Iterable[Any]], float], - max_drop: float = 0.01, - drop_type: DropType = DropType.ABSOLUTE, - preset: Optional[QuantizationPreset] = None, - target_device: TargetDevice = TargetDevice.ANY, - subset_size: int = 300, - fast_bias_correction: bool = True, - model_type: Optional[ModelType] = None, - ignored_scope: Optional[IgnoredScope] = None, - advanced_quantization_parameters: Optional[AdvancedQuantizationParameters] = None, - advanced_accuracy_restorer_parameters: Optional[AdvancedAccuracyRestorerParameters] = None, -) -> ov.Model: - """ - Implementation of the `quantize_with_accuracy_control()` method for the OpenVINO backend. - """ - - quantize_with_accuracy_control_fn = native_quantize_with_accuracy_control_impl - - val_func = wrap_validation_fn(validation_fn) - - return quantize_with_accuracy_control_fn( - model, - calibration_dataset, - validation_dataset, - val_func, - max_drop, - drop_type, - preset, - target_device, - subset_size, - fast_bias_correction, - model_type, - ignored_scope, - advanced_quantization_parameters, - advanced_accuracy_restorer_parameters, - ) - - def compress_weights_impl( model: ov.Model, dataset: Dataset, @@ -438,13 +376,14 @@ def compress_weights_impl( scale_estimation: bool, gptq: bool, lora_correction: bool, + backup_mode: BackupMode, advanced_parameters: Optional[AdvancedCompressionParameters] = None, ) -> ov.Model: """ Implementation of the `compress_weights()` method for the OpenVINO backend. """ - model = remove_friendly_name_duplicates(model) + graph = NNCFGraphFactory.create(model) compression_algorithm = WeightCompression( mode, ratio, @@ -457,7 +396,26 @@ def compress_weights_impl( scale_estimation, gptq, lora_correction, + backup_mode, advanced_parameters, ) - graph = NNCFGraphFactory.create(model) - return compression_algorithm.apply(model, graph, dataset=dataset) + + statistics_points = None + if advanced_parameters and advanced_parameters.statistics_path: + # If there is no such directory, then caches statistics + if not Path(advanced_parameters.statistics_path).exists(): + cache_weight_compression_statistics(model, graph, dataset, subset_size, advanced_parameters.statistics_path) + statistics_aggregator = StatisticsAggregatorFactory.create(model, dataset) + compression_algorithm.set_backend_entity(model) + _, matmul_input_to_output_nodes_map = compression_algorithm.get_compression_nodes_info(graph) + register_statistics_for_algorithm( + statistics_aggregator, + model, + graph, + compression_algorithm, + matmul_input_to_output_nodes_map, + ) + statistics_aggregator.load_statistics_from_dir(advanced_parameters.statistics_path) + statistics_points = statistics_aggregator.statistic_points + + return compression_algorithm.apply(model, graph, statistics_points, dataset) diff --git a/nncf/openvino/statistics/aggregator.py b/nncf/openvino/statistics/aggregator.py index 86a27c71e08..0f0dbd37ecd 100644 --- a/nncf/openvino/statistics/aggregator.py +++ b/nncf/openvino/statistics/aggregator.py @@ -21,16 +21,21 @@ from nncf.common.tensor_statistics.aggregator import StatisticsAggregator from nncf.common.tensor_statistics.statistic_point import StatisticPoint from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer +from nncf.common.utils.backend import BackendType from nncf.experimental.common.tensor_statistics.collectors import MergedTensorCollector from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.statistics import TensorStatistic from nncf.openvino.graph.node_utils import get_ov_model_reduce_node_name from nncf.openvino.graph.node_utils import get_reducer_output_node_names from nncf.openvino.graph.transformations.commands import OVInplaceFnInsertionCommand from nncf.openvino.graph.transformations.commands import OVOutputInsertionCommand +from nncf.openvino.graph.transformations.commands import OVTargetPoint from nncf.tensor import Tensor class OVStatisticsAggregator(StatisticsAggregator): + BACKEND: BackendType = BackendType.OPENVINO + def collect_statistics(self, model: ov.Model, graph: NNCFGraph) -> None: self._name_to_node_mapping = {op.get_friendly_name(): op for op in model.get_ops()} super().collect_statistics(model, graph) @@ -125,3 +130,14 @@ def _translate_to_post_layer_operation(self, statistic_point: StatisticPoint): @staticmethod def _process_outputs(outputs: Dict[str, np.ndarray]) -> Dict[str, Tensor]: return {n: Tensor(v) for n, v in outputs.items()} + + def _get_statistics_key(self, statistics: TensorStatistic, target_point: OVTargetPoint) -> str: + """ + Returns key of statistics. + + :param statistics: Statistics value. + :param target_point: Statistics target point. + :return: Statistics key. + """ + target_point_id = f"{target_point.target_node_name}_{target_point.type}_{target_point.port_id}" + return f"{statistics.__class__.__name__}_{target_point_id}" diff --git a/nncf/openvino/statistics/collectors.py b/nncf/openvino/statistics/collectors.py index 9960203af7a..a851e49f832 100644 --- a/nncf/openvino/statistics/collectors.py +++ b/nncf/openvino/statistics/collectors.py @@ -16,23 +16,31 @@ from nncf.experimental.common.tensor_statistics.collectors import BatchMeanReducer from nncf.experimental.common.tensor_statistics.collectors import InplaceInsertionFNType from nncf.experimental.common.tensor_statistics.collectors import MaxReducer +from nncf.experimental.common.tensor_statistics.collectors import MaxVarianceReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanAbsMaxReducer from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator from nncf.experimental.common.tensor_statistics.collectors import MeanPerChReducer from nncf.experimental.common.tensor_statistics.collectors import MeanReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanVarianceReducer from nncf.experimental.common.tensor_statistics.collectors import MinReducer from nncf.experimental.common.tensor_statistics.collectors import NoopAggregator from nncf.experimental.common.tensor_statistics.collectors import NoopReducer from nncf.experimental.common.tensor_statistics.collectors import QuantileReducer from nncf.experimental.common.tensor_statistics.collectors import RawReducer from nncf.experimental.common.tensor_statistics.collectors import ShapeAggregator +from nncf.experimental.common.tensor_statistics.collectors import ShapeReducer from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.experimental.common.tensor_statistics.statistics import MeanTensorStatistic from nncf.experimental.common.tensor_statistics.statistics import RawTensorStatistic from nncf.openvino.graph.node_utils import get_inplace_batch_mean_op from nncf.openvino.graph.node_utils import get_inplace_max_op +from nncf.openvino.graph.node_utils import get_inplace_max_var_op +from nncf.openvino.graph.node_utils import get_inplace_mean_max_op from nncf.openvino.graph.node_utils import get_inplace_mean_op from nncf.openvino.graph.node_utils import get_inplace_mean_per_ch +from nncf.openvino.graph.node_utils import get_inplace_mean_var_op from nncf.openvino.graph.node_utils import get_inplace_min_op +from nncf.openvino.graph.node_utils import get_inplace_shape_op from nncf.quantization.advanced_parameters import StatisticsType @@ -60,6 +68,26 @@ def get_inplace_fn(self): return get_inplace_mean_op(self._reduction_axes) +class OVMeanVarianceReducer(MeanVarianceReducer): + def get_inplace_fn(self): + return get_inplace_mean_var_op(self._reduction_axes) + + +class OVMaxVarianceReducer(MaxVarianceReducer): + def get_inplace_fn(self): + return get_inplace_max_var_op(self._reduction_axes) + + +class OVMeanAbsMaxReducer(MeanAbsMaxReducer): + def get_inplace_fn(self): + return get_inplace_mean_max_op(self._reduction_axes, True) + + +class OVShapeReducer(ShapeReducer): + def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]: + return get_inplace_shape_op() + + class OVBatchMeanReducer(BatchMeanReducer): def get_inplace_fn(self): diff --git a/nncf/parameters.py b/nncf/parameters.py index d4d43ea7d87..b7d27ef8018 100644 --- a/nncf/parameters.py +++ b/nncf/parameters.py @@ -96,6 +96,23 @@ class CompressWeightsMode(StrEnum): E2M1 = "e2m1" +@api(canonical_alias="nncf.BackupMode") +class BackupMode(StrEnum): + """ + Defines a backup mode for weight compression. + :param NONE: Stands for original floating-point precision of the model weights. + In this mode, weights are retained in their original precision without any quantization. + :param INT8_SYM: Stands for 8-bit integer symmetric quantization without zero point. + https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#symmetric-quantization + :param INT8_ASYM: Stands for 8-bit integer asymmetric quantization with a typical non-fixed zero point. + https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#asymmetric-quantization + """ + + NONE = "none" + INT8_SYM = "int8_sym" + INT8_ASYM = "int8_asym" + + @api(canonical_alias="nncf.SensitivityMetric") class SensitivityMetric(StrEnum): """ diff --git a/nncf/quantization/advanced_parameters.py b/nncf/quantization/advanced_parameters.py index 6702ae8bae2..cad3f1ba969 100644 --- a/nncf/quantization/advanced_parameters.py +++ b/nncf/quantization/advanced_parameters.py @@ -11,6 +11,7 @@ """ Structures and functions for passing advanced parameters to NNCF post-training quantization APIs. """ + import sys from dataclasses import dataclass from dataclasses import field @@ -20,6 +21,7 @@ from typing import Any, Dict, Optional, Union import nncf +from nncf.common.quantization.quantizer_propagation.structs import QuantizerPropagationRule from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode from nncf.common.utils.api_marker import api from nncf.parameters import StrEnum @@ -197,6 +199,17 @@ class AdvancedQuantizationParameters: the calibration dataset, then in case batch_size of the data source > 1 batchwise_statistics sets to True, otherwise sets to False. :type batchwise_statistics: Optional[bool] + :param quantizer_propagation_rule: An instance of the `QuantizerPropagationRule` enum that + specifies how quantizers should be propagated and merged across branching nodes in the + model's computational graph. The strategies are as follows: + - DO_NOT_MERGE_BRANCHES: No merging of quantization parameters across branches. + - MERGE_IF_ALL_BRANCHES_SAME : Merge only if all branch quantization configurations are identical. + - MERGE_WITH_POTENTIAL_REQUANTIZATION: Merge common configurations and allow for requantization + on branches with additional options. + - MERGE_ALL_IN_ONE: Attempt to merge into a single global quantization configuration + if possible given hardware constraints. + MERGE_ALL_IN_ONE is a default value. + :type quantizer_propagation_rule: QuantizerPropagationRule :param activations_quantization_params: Quantization parameters for activations. :type activations_quantization_params: nncf.quantization.advanced_parameters.QuantizationParameters :param weights_quantization_params: Quantization parameters for weights. @@ -211,7 +224,7 @@ class AdvancedQuantizationParameters: It regulates the calculation of the smooth scale. The default value stored in AdvancedSmoothQuantParameters. A negative value for each field switches off type smoothing. In case of inaccurate results, fields may be adjusted in the range from 0 to 1 or set -1 to disable smoothing for type. - :type smooth_quant_alpha: AdvancedSmoothQuantParameters + :type smooth_quant_alphas: nncf.quantization.advanced_parameters.AdvancedSmoothQuantParameters :param smooth_quant_alpha: Deprecated SmoothQuant-related parameter. :type smooth_quant_alpha: float :param backend_params: Backend-specific parameters. @@ -229,6 +242,7 @@ class AdvancedQuantizationParameters: # Advanced Quantization parameters activations_quantization_params: Union[QuantizationParameters, FP8QuantizationParameters] = None weights_quantization_params: Union[QuantizationParameters, FP8QuantizationParameters] = None + quantizer_propagation_rule: QuantizerPropagationRule = QuantizerPropagationRule.MERGE_ALL_IN_ONE # Range estimator parameters activations_range_estimator_params: RangeEstimatorParameters = field(default_factory=RangeEstimatorParameters) @@ -347,12 +361,15 @@ class AdvancedCompressionParameters: """ Contains advanced parameters for compression algorithms. + :param statistics_path: Directory path to dump statistics. + :type statistics_path: str :param awq_params: Advanced parameters for AWQ algorithm. :type awq_params: AdvancedAWQParameters :param scale_estimation_params: Advanced parameters for scale estimation algorithm. :type scale_estimation_params: AdvancedScaleEstimationParameters """ + statistics_path: Optional[str] = None # Advanced AWQ algorithm parameters awq_params: AdvancedAWQParameters = field(default_factory=AdvancedAWQParameters) diff --git a/nncf/quantization/algorithms/accuracy_control/openvino_backend.py b/nncf/quantization/algorithms/accuracy_control/openvino_backend.py index 694156edce9..37961be77e2 100644 --- a/nncf/quantization/algorithms/accuracy_control/openvino_backend.py +++ b/nncf/quantization/algorithms/accuracy_control/openvino_backend.py @@ -13,6 +13,8 @@ import numpy as np import openvino.runtime as ov +from openvino import Type +from openvino.properties.hint import inference_precision from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode @@ -40,9 +42,12 @@ class OVPreparedModel(PreparedModel): Implementation of the `PreparedModel` for OpenVINO backend. """ - def __init__(self, model: ov.Model): + def __init__(self, model: ov.Model, use_fp32_precision: bool = True): self._stateful = model_has_state(model) - self._compiled_model = ov.compile_model(model, device_name="CPU") + config = None + if use_fp32_precision: + config = {inference_precision: Type.f32} + self._compiled_model = ov.compile_model(model, device_name="CPU", config=config) self._engine = None @property diff --git a/nncf/quantization/algorithms/bias_correction/algorithm.py b/nncf/quantization/algorithms/bias_correction/algorithm.py index fe9c4716a38..938a1166f44 100644 --- a/nncf/quantization/algorithms/bias_correction/algorithm.py +++ b/nncf/quantization/algorithms/bias_correction/algorithm.py @@ -10,7 +10,7 @@ # limitations under the License. from collections import defaultdict -from typing import Any, Dict, List, Optional, Tuple, TypeVar +from typing import Any, Dict, List, Optional, Set, Tuple, TypeVar import nncf from nncf import Dataset @@ -105,7 +105,7 @@ def __init__( @property def available_backends(self) -> List[BackendType]: - return [BackendType.ONNX, BackendType.OPENVINO] + return [BackendType.ONNX, BackendType.OPENVINO, BackendType.TORCH_FX] def _set_backend_entity(self, model: TModel) -> None: """ @@ -122,6 +122,10 @@ def _set_backend_entity(self, model: TModel) -> None: from nncf.quantization.algorithms.bias_correction.openvino_backend import OVBiasCorrectionAlgoBackend self._backend_entity = OVBiasCorrectionAlgoBackend() + elif model_backend == BackendType.TORCH_FX: + from nncf.quantization.algorithms.bias_correction.torch_fx_backend import FXBiasCorrectionAlgoBackend + + self._backend_entity = FXBiasCorrectionAlgoBackend() else: raise nncf.UnsupportedBackendError( "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) @@ -304,7 +308,7 @@ def _find_subgraph_input_ids( edges_queue.extend(nncf_graph.get_input_edges(node)) return subgraph_input_ids - def _get_subgraph_data_for_node(self, node: NNCFNode, nncf_graph: NNCFGraph) -> Dict[str, List[str]]: + def _get_subgraph_data_for_node(self, node: NNCFNode, nncf_graph: NNCFGraph) -> Dict[str, Set[Tuple[str, int]]]: """ This method collects necessary data for the specified node and its subgraph. This data contains the nodes (NNCFNode) for the subgraph building @@ -511,8 +515,8 @@ def input_filter_func(point): # For the cases when the layer has more than one (0) output port. return ( self._algorithm_key in point.algorithm_to_tensor_collectors - and point.target_point.type == TargetType.POST_LAYER_OPERATION - and point.target_point.port_id == port_id + and point.target_point.type in [TargetType.POST_LAYER_OPERATION, TargetType.OPERATOR_POST_HOOK] + and self._backend_entity.get_port_id(point.target_point) == port_id ) input_id = (node_name, port_id) @@ -537,10 +541,10 @@ def _get_fp_outputs(self, statistic_points: StatisticPointsContainer, node_name: """ def output_filter_func(point): - return ( - self._algorithm_key in point.algorithm_to_tensor_collectors - and point.target_point.type == TargetType.POST_LAYER_OPERATION - ) + return self._algorithm_key in point.algorithm_to_tensor_collectors and point.target_point.type in [ + TargetType.POST_LAYER_OPERATION, + TargetType.OPERATOR_POST_HOOK, + ] output_fp = [] for tensor_collector in statistic_points.get_algo_statistics_for_node( @@ -587,10 +591,11 @@ def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPoin input_id = (biased_after_input_node.node_name, edge.input_port_id) output_id = (edge.from_node.node_name, edge.output_port_id) + self._collected_stat_inputs_map[input_id] = output_id + if edge.from_node.node_name in statistic_container: continue - self._collected_stat_inputs_map[input_id] = output_id statistic_point = self._backend_entity.target_point( TargetType.POST_LAYER_OPERATION, edge.from_node.node_name, port_id=edge.output_port_id ) @@ -668,9 +673,7 @@ def traverse_to_biased(node, condition_container): return list(biased_nodes - dependant_nodes) - def extract_model( - self, model: TModel, input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] - ) -> TModel: + def extract_model(self, model: TModel, input_ids: Set[Tuple[str, int]], output_ids: Set[Tuple[str, int]]) -> TModel: """ Returns the backend-specific model that bounded by the specified input & output layers. diff --git a/nncf/quantization/algorithms/bias_correction/backend.py b/nncf/quantization/algorithms/bias_correction/backend.py index 943671d72de..ab165eb8891 100644 --- a/nncf/quantization/algorithms/bias_correction/backend.py +++ b/nncf/quantization/algorithms/bias_correction/backend.py @@ -11,7 +11,7 @@ from abc import ABC from abc import abstractmethod -from typing import List, Optional, Tuple, TypeVar +from typing import Optional, Set, Tuple, TypeVar, Union import numpy as np @@ -55,14 +55,14 @@ def create_bias_correction_command(node: NNCFNode, bias_value: Tensor) -> Transf @staticmethod @abstractmethod def model_extraction_command( - input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + input_ids: Set[Tuple[str, int]], output_ids: Set[Tuple[str, int]] ) -> TransformationCommand: """ Returns backend-specific command to extract sub-model based on input & output names. - :param input_ids: List of the input IDs: pairs of node names and correspondent input port ids. + :param input_ids: Set of the input IDs: pairs of node names and correspondent input port ids. Each pair denotes the sub-graph beginning. - :param output_ids: List of the output IDs: pairs of node names and correspondent output port ids. + :param output_ids: Set of the output IDs: pairs of node names and correspondent output port ids. Each pair denotes the sub-graph ending. :return: Backend-specific TransformationCommand for the model extraction. """ @@ -109,7 +109,7 @@ def raw_statistic_collector(num_samples: Optional[int] = None) -> TensorStatisti @staticmethod @abstractmethod - def process_model_output(raw_data: OutputType, output_name: str) -> NNCFTensor: + def process_model_output(raw_data: OutputType, output_name: Union[str, int]) -> NNCFTensor: """ Returns backend-specific processed output from the model. @@ -200,3 +200,17 @@ def remove_fq_from_inputs(model: TModel, nncf_graph: NNCFGraph) -> TModel: :param nncf_graph: NNCFGraph instance. :return: TModel without activation Fake Quantize nodes (or Quantize-Dequantize pairs). """ + + @staticmethod + @abstractmethod + def get_port_id(target_point: TargetPoint) -> int: + """ + Returns port id from the given backend-specific target point. + Port id is an input port id in case target point target type is + TargetType.PRE_LAYER_OPERATION or TargetType.OPERATOR_PRE_HOOK and + is an output port id in case target point target type is + TargetType.POST_LAYER_OPERATION or TargetType.OPERATOR_POST_HOOK. + + :param target_point: TargetPoint instance. + :return: Port id from the given backend-specific target point + """ diff --git a/nncf/quantization/algorithms/bias_correction/onnx_backend.py b/nncf/quantization/algorithms/bias_correction/onnx_backend.py index b2d520b89d3..42779c7c97d 100644 --- a/nncf/quantization/algorithms/bias_correction/onnx_backend.py +++ b/nncf/quantization/algorithms/bias_correction/onnx_backend.py @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, List, Optional, Tuple +from typing import Dict, Optional, Set, Tuple import onnx @@ -47,7 +47,7 @@ def create_bias_correction_command( @staticmethod def model_extraction_command( - input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + input_ids: Set[Tuple[str, int]], output_ids: Set[Tuple[str, int]] ) -> ONNXModelExtractionCommand: return ONNXModelExtractionCommand(input_ids, output_ids) @@ -105,3 +105,7 @@ def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: @staticmethod def remove_fq_from_inputs(model: onnx.ModelProto, nncf_graph: NNCFGraph) -> onnx.ModelProto: return remove_fq_from_inputs(model, nncf_graph) + + @staticmethod + def get_port_id(target_point: ONNXTargetPoint) -> int: + return target_point.port_id diff --git a/nncf/quantization/algorithms/bias_correction/openvino_backend.py b/nncf/quantization/algorithms/bias_correction/openvino_backend.py index cbcdd662c82..0180d4a53c5 100644 --- a/nncf/quantization/algorithms/bias_correction/openvino_backend.py +++ b/nncf/quantization/algorithms/bias_correction/openvino_backend.py @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, List, Optional, Tuple +from typing import Dict, Optional, Set, Tuple import openvino.runtime as ov @@ -48,7 +48,7 @@ def create_bias_correction_command( @staticmethod def model_extraction_command( - input_ids: List[Tuple[str, int]], output_ids: List[Tuple[str, int]] + input_ids: Set[Tuple[str, int]], output_ids: Set[Tuple[str, int]] ) -> OVModelExtractionCommand: return OVModelExtractionCommand(input_ids, output_ids) @@ -110,3 +110,7 @@ def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: @staticmethod def remove_fq_from_inputs(model: ov.Model, nncf_graph: NNCFGraph) -> ov.Model: return remove_fq_from_inputs(model, nncf_graph) + + @staticmethod + def get_port_id(target_point: OVTargetPoint) -> int: + return target_point.port_id diff --git a/nncf/quantization/algorithms/bias_correction/torch_fx_backend.py b/nncf/quantization/algorithms/bias_correction/torch_fx_backend.py new file mode 100644 index 00000000000..1f62eef00b6 --- /dev/null +++ b/nncf/quantization/algorithms/bias_correction/torch_fx_backend.py @@ -0,0 +1,121 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Optional, Set, Tuple + +import torch.fx + +import nncf +from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFNode +from nncf.common.graph.transformations.commands import TargetType +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand +from nncf.experimental.torch.fx.model_utils import get_target_point +from nncf.experimental.torch.fx.model_utils import remove_fq_from_inputs +from nncf.experimental.torch.fx.node_utils import get_bias_value +from nncf.experimental.torch.fx.node_utils import get_graph_node_by_name +from nncf.experimental.torch.fx.node_utils import is_node_with_bias +from nncf.experimental.torch.fx.transformations import bias_update_transformation_builder +from nncf.experimental.torch.fx.transformations import output_insertion_transformation_builder +from nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend +from nncf.tensor import Tensor +from nncf.torch.graph.transformations.commands import PTModelExtractionCommand +from nncf.torch.graph.transformations.commands import PTTargetPoint +from nncf.torch.model_graph_manager import is_quantized_weights +from nncf.torch.tensor_statistics.collectors import get_mean_statistic_collector +from nncf.torch.tensor_statistics.collectors import get_raw_stat_collector + + +class FXBiasCorrectionAlgoBackend(BiasCorrectionAlgoBackend): + + @staticmethod + def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: + return get_target_point(target_type, target_node_name, port_id) + + @staticmethod + def create_bias_correction_command( + node: NNCFNode, bias_value: Tensor, nncf_graph: NNCFGraph + ) -> FXApplyTransformationCommand: + return FXApplyTransformationCommand(bias_update_transformation_builder(node, bias_value.data, input_port_id=1)) + + @staticmethod + def model_extraction_command( + input_ids: Set[Tuple[str, int]], output_ids: Set[Tuple[str, int]] + ) -> PTModelExtractionCommand: + return PTModelExtractionCommand([inp_id[0] for inp_id in input_ids], [out_id[0] for out_id in output_ids]) + + @staticmethod + def output_insertion_command(nncf_graph: NNCFGraph, target_point: PTTargetPoint) -> FXApplyTransformationCommand: + return FXApplyTransformationCommand(output_insertion_transformation_builder(target_point)) + + @staticmethod + def mean_statistic_collector( + channel_axis: int, + inplace: bool, + num_samples: Optional[int] = None, + window_size: Optional[int] = None, + ) -> TensorCollector: + return get_mean_statistic_collector(num_samples, channel_axis, window_size) + + @staticmethod + def raw_statistic_collector(num_samples: Optional[int] = None) -> TensorCollector: + return get_raw_stat_collector(num_samples) + + @staticmethod + def process_model_output(raw_data: Dict, output_name: int) -> Tensor: + return Tensor(raw_data[output_name]) + + @staticmethod + def get_activation_port_id(node: NNCFNode, nncf_graph: NNCFGraph) -> int: + return 0 + + @staticmethod + def get_bias_value(node: NNCFNode, model: torch.fx.GraphModule, nncf_graph: NNCFGraph) -> Tensor: + return Tensor(get_bias_value(node, nncf_graph, model)) + + @staticmethod + def get_input_name(model: torch.fx.GraphModule, node_name: str, input_port_id: int) -> str: + graph_node = get_graph_node_by_name(model.graph, node_name) + return graph_node.all_input_nodes[input_port_id].name + + @staticmethod + def get_output_name(model: torch.fx.GraphModule, node_name: str, output_port_id: int) -> int: + graph_node = get_graph_node_by_name(model.graph, node_name) + nodes = list(graph_node.users) + while nodes: + node = nodes.pop() + if node.op == "call_function" and node.target == torch.ops.aten.clone.default: + nodes = list(node.users) + graph_node = node + elif node.op == "output": + return node.all_input_nodes.index(graph_node) + + raise nncf.InternalError(f"Node with name {node_name} expected to have an output," " no outputs were found.") + + @staticmethod + def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + return is_quantized_weights(node, nncf_graph) + + @staticmethod + def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + return is_node_with_bias(node, nncf_graph) + + @staticmethod + def remove_fq_from_inputs(model: torch.fx.GraphModule, nncf_graph: NNCFGraph) -> torch.fx.GraphModule: + return remove_fq_from_inputs(model, nncf_graph) + + @staticmethod + def get_port_id(target_point: PTTargetPoint) -> int: + if target_point.target_type == TargetType.OPERATOR_POST_HOOK: + # Return 0 as default value for post hook port id. + return 0 + return target_point.input_port_id diff --git a/nncf/quantization/algorithms/fast_bias_correction/backend.py b/nncf/quantization/algorithms/fast_bias_correction/backend.py index 71e46c7d5e7..110e05161cd 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/backend.py @@ -88,7 +88,7 @@ def mean_statistic_collector( @staticmethod @abstractmethod - def get_sub_input_output_names(subgraph: TModel) -> Tuple[str, str]: + def get_sub_input_output_names(subgraph: TModel) -> Tuple[Union[str, int], Union[str]]: """ Returns tuple of the subgraph's the input & output tensor names respectively. @@ -149,7 +149,7 @@ def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: @staticmethod @abstractmethod - def process_model_output(raw_data: OutputType, output_name: str) -> Tensor: + def process_model_output(raw_data: OutputType, output_name: Union[str, int]) -> Tensor: """ Returns backend-specific processed output from the model. diff --git a/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py b/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py index 134706415a1..7eda61ce64a 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/torch_backend.py @@ -69,7 +69,7 @@ def mean_statistic_collector( return get_mean_statistic_collector(num_samples, channel_axis, window_size) @staticmethod - def get_sub_input_output_names(subgraph: NNCFNetwork) -> Tuple[str, str]: + def get_sub_input_output_names(subgraph: NNCFNetwork) -> Tuple[Optional[str], Optional[str]]: # Pytorch does not have name for extracted node return None, None @@ -90,7 +90,7 @@ def get_activation_port_ids_for_bias_node(node: NNCFNode) -> Tuple[int, int]: return 0, 0 @staticmethod - def process_model_output(raw_data: Dict, output_name: str) -> Tensor: + def process_model_output(raw_data: Dict, output_name: Optional[str]) -> Tensor: return Tensor(raw_data) @staticmethod diff --git a/nncf/quantization/algorithms/fast_bias_correction/torch_fx_backend.py b/nncf/quantization/algorithms/fast_bias_correction/torch_fx_backend.py index 883e72283ed..abbbf7ea21d 100644 --- a/nncf/quantization/algorithms/fast_bias_correction/torch_fx_backend.py +++ b/nncf/quantization/algorithms/fast_bias_correction/torch_fx_backend.py @@ -15,43 +15,33 @@ import torch import torch.fx -import nncf.torch.graph.operator_metatypes as om from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode -from nncf.common.graph.definitions import NNCFGraphNodeType from nncf.common.graph.transformations.commands import TargetType from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand -from nncf.experimental.torch.fx.node_utils import get_graph_node_by_name -from nncf.experimental.torch.fx.node_utils import get_tensor_constant_from_node +from nncf.experimental.torch.fx.model_utils import get_target_point +from nncf.experimental.torch.fx.node_utils import get_bias_value +from nncf.experimental.torch.fx.node_utils import is_node_with_bias from nncf.experimental.torch.fx.transformations import bias_update_transformation_builder from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend from nncf.tensor import Tensor from nncf.torch.graph.transformations.commands import PTModelExtractionCommand from nncf.torch.graph.transformations.commands import PTTargetPoint -from nncf.torch.nncf_network import NNCFNetwork +from nncf.torch.model_graph_manager import is_quantized_weights from nncf.torch.tensor_statistics.collectors import get_mean_statistic_collector class FXFastBiasCorrectionAlgoBackend(FastBiasCorrectionAlgoBackend): - TARGET_TYPE_TO_PT_INS_TYPE_MAP = { - TargetType.PRE_LAYER_OPERATION: TargetType.OPERATOR_PRE_HOOK, - TargetType.POST_LAYER_OPERATION: TargetType.OPERATOR_POST_HOOK, - } - @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: - if NNCFGraphNodeType.INPUT_NODE in target_node_name or target_type == TargetType.POST_LAYER_OPERATION: - port_id = None - if target_type in FXFastBiasCorrectionAlgoBackend.TARGET_TYPE_TO_PT_INS_TYPE_MAP: - target_type = FXFastBiasCorrectionAlgoBackend.TARGET_TYPE_TO_PT_INS_TYPE_MAP[target_type] - return PTTargetPoint(target_type, target_node_name, input_port_id=port_id) + return get_target_point(target_type, target_node_name, port_id) @staticmethod def create_bias_correction_command( node: NNCFNode, bias_value: Tensor, nncf_graph: NNCFGraph ) -> FXApplyTransformationCommand: - return FXApplyTransformationCommand(bias_update_transformation_builder(node, bias_value.data)) + return FXApplyTransformationCommand(bias_update_transformation_builder(node, bias_value.data, input_port_id=1)) @staticmethod def model_extraction_command( @@ -69,9 +59,9 @@ def mean_statistic_collector( return get_mean_statistic_collector(num_samples, channel_axis, window_size) @staticmethod - def get_sub_input_output_names(subgraph: NNCFNetwork) -> Tuple[str, str]: + def get_sub_input_output_names(subgraph: torch.fx.GraphModule) -> Tuple[Optional[int], int]: # Pytorch does not have name for extracted node - return None, None + return None, 0 @staticmethod def create_input_data(shape: Tuple[int], data: List[Tensor], input_name: str, channel_axis: int) -> torch.Tensor: @@ -83,32 +73,23 @@ def create_input_data(shape: Tuple[int], data: List[Tensor], input_name: str, ch @staticmethod def get_bias_value(node: NNCFNode, nncf_graph: NNCFGraph, model: torch.fx.GraphModule) -> Tensor: - bias_node = nncf_graph.get_next_nodes(node)[0] - # TODO(dlyakhov): make a node_name_vs_node map to speed up the process - graph_bias_node = get_graph_node_by_name(model.graph, bias_node.node_name) - return Tensor(get_tensor_constant_from_node(graph_bias_node.all_input_nodes[1], model)) + return get_bias_value(node, nncf_graph, model) @staticmethod def get_activation_port_ids_for_bias_node(node: NNCFNode) -> Tuple[int, int]: return 0, 0 @staticmethod - def process_model_output(raw_data: Dict, output_name: str) -> Tensor: - return Tensor(raw_data) + def process_model_output(raw_data: Dict, output_name: int) -> Tensor: + return Tensor(raw_data[output_name]) @staticmethod def is_quantized_weights(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: - weight_node = nncf_graph.get_previous_nodes(node)[1] - return "dequantize" in weight_node.node_type + return is_quantized_weights(node, nncf_graph) @staticmethod def is_node_with_bias(node: NNCFNode, nncf_graph: NNCFGraph) -> bool: - # Assumes that all biases were unfused - if node.metatype in (om.PTConv1dMetatype, om.PTConv2dMetatype, om.PTConv3dMetatype, om.PTLinearMetatype): - next_nodes = nncf_graph.get_next_nodes(node) - if len(next_nodes) != 1: - return False - return next_nodes[0].metatype in (om.PTAddMetatype,) + return is_node_with_bias(node, nncf_graph) @staticmethod def get_node_names_for_input_output_statistics(node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[str, str]: diff --git a/nncf/quantization/algorithms/hyperparameter_tuner/param_grid.py b/nncf/quantization/algorithms/hyperparameter_tuner/param_grid.py index 48766ab985c..95db6e3380d 100644 --- a/nncf/quantization/algorithms/hyperparameter_tuner/param_grid.py +++ b/nncf/quantization/algorithms/hyperparameter_tuner/param_grid.py @@ -12,6 +12,7 @@ import itertools from typing import Any, Dict, List +from nncf.common.quantization.quantizer_propagation.structs import QuantizerPropagationRule from nncf.common.quantization.structs import QuantizationPreset from nncf.common.utils.backend import BackendType from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters @@ -64,6 +65,9 @@ def _get_minmax_quantization_param_grid() -> ParamGrid: ] param_grid = { + "advanced_parameters:quantizer_propagation_rule": [ + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME, + ], "preset": [QuantizationPreset.PERFORMANCE, QuantizationPreset.MIXED], "advanced_parameters:weights_range_estimator_params": [ RangeEstimatorParameters( diff --git a/nncf/quantization/algorithms/layerwise/openvino_iterator.py b/nncf/quantization/algorithms/layerwise/openvino_iterator.py index d2915476ddb..50d438e0c6b 100644 --- a/nncf/quantization/algorithms/layerwise/openvino_iterator.py +++ b/nncf/quantization/algorithms/layerwise/openvino_iterator.py @@ -10,6 +10,7 @@ # limitations under the License. from collections import defaultdict +from itertools import islice from typing import Dict, List, Optional, Tuple import openvino.runtime as ov @@ -200,7 +201,7 @@ def collect_output_tensors(self, step: LayerwiseStep) -> Dict[NodeOutputPort, Li and input_id not in subgraph_outputs ): subgraph_outputs.append(input_id) - feed_dicts = self._dataset.get_inference_data(range(self._subset_size)) + feed_dicts = islice(self._dataset.get_inference_data(), self._subset_size) else: subgraph_inputs = step.subgraph_inputs subgraph_outputs = step.subgraph_outputs diff --git a/nncf/quantization/algorithms/min_max/algorithm.py b/nncf/quantization/algorithms/min_max/algorithm.py index 6306f6736c2..dea9211b734 100644 --- a/nncf/quantization/algorithms/min_max/algorithm.py +++ b/nncf/quantization/algorithms/min_max/algorithm.py @@ -33,6 +33,7 @@ from nncf.common.logging import nncf_logger from nncf.common.quantization.config_assignment import assign_qconfig_lists_to_modules from nncf.common.quantization.initialization.range import RangeInitCollectorParams +from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationRule from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationSolver from nncf.common.quantization.quantizer_propagation.structs import IgnoreReason from nncf.common.quantization.quantizer_setup import SingleConfigQuantizationPoint @@ -148,6 +149,7 @@ def __init__( weights_quantization_params: Union[QuantizationParameters, FP8QuantizationParameters] = None, activations_range_estimator_params: Optional[RangeEstimatorParameters] = None, weights_range_estimator_params: Optional[RangeEstimatorParameters] = None, + quantizer_propagation_rule: Optional[QuantizerPropagationRule] = None, backend_params: Optional[Dict[str, Any]] = None, ): """ @@ -183,6 +185,7 @@ def __init__( parameters for activation. :param weights_range_estimator_params: Quantization range estimation parameters for weights. + :param quantizer_propagation_rule: The strategy to be used while propagating and merging quantizers. :param backend_params: Backend specific parameters. """ self._target_device = target_device @@ -200,6 +203,7 @@ def __init__( self._weights_range_estimator_params = weights_range_estimator_params self._preset = preset self._ignored_scope = IgnoredScope() if ignored_scope is None else ignored_scope + self.quantizer_propagation_rule = quantizer_propagation_rule # preset definition if self._preset is None: @@ -611,6 +615,7 @@ def _get_quantizer_setup( weight_ignored_scopes=list(ignored_names.keys()), hw_config=hw_config, default_trait_to_metatype_map=self._backend_entity.quant_trait_op_dict, + propagation_strategy=self.quantizer_propagation_rule, default_qconfig_list=[ self._get_default_qconfig(self._global_quantizer_constraints[QuantizerGroup.ACTIVATIONS]) ], @@ -623,7 +628,7 @@ def _get_quantizer_setup( scope_overrides=scope_overrides, ) - quantization_proposal = solver.run_on_ip_graph(ip_graph) + quantization_proposal = solver.run_on_ip_graph(ip_graph, self._backend_entity.elementwise_metatypes) multi_config_setup = quantization_proposal.quantizer_setup single_config_setup = multi_config_setup.select_first_qconfig_for_each_point() finalized_proposal = quantization_proposal.finalize(single_config_setup) @@ -1026,7 +1031,7 @@ def _apply_model_type_pass( continue if ( quantization_point.qconfig.mode != QuantizationScheme.SYMMETRIC - and node.layer_attributes is None + and not self._backend_entity.is_matmul_with_constant(node, nncf_graph) ): quantization_point.qconfig.mode = QuantizationScheme.SYMMETRIC nncf_logger.debug( diff --git a/nncf/quantization/algorithms/min_max/backend.py b/nncf/quantization/algorithms/min_max/backend.py index 73befbe90c7..de562139307 100644 --- a/nncf/quantization/algorithms/min_max/backend.py +++ b/nncf/quantization/algorithms/min_max/backend.py @@ -75,6 +75,13 @@ def dropout_metatypes(self) -> List[OperatorMetatype]: Property for the backend-specific Dropout metatypes. """ + @property + @abstractmethod + def elementwise_metatypes(self) -> List[OperatorMetatype]: + """ + Property for the backend-specific Elementwises metatypes. + """ + @property @abstractmethod def overflow_fix_metatypes(self) -> List[OperatorMetatype]: @@ -296,12 +303,21 @@ def get_ignored_names_by_layer_attributes(nncf_graph: NNCFGraph) -> Set[str]: :return: List of ignored names. """ - @staticmethod @abstractmethod - def get_weight_nodes(nncf_graph: NNCFGraph) -> List[NNCFNode]: + def get_weight_nodes(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: """ Returns nodes that have weights. :param nncf_graph: Instance of NNCFGraph. :return: All nodes with weights. """ + + @abstractmethod + def is_matmul_with_constant(self, node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + """ + Returns true if given nncf matmul node is a matmul with a constant, False otherwise. + + :param Node: Instance of NNCFNode. + :param nncf_graph: Instance of NNCFGraph. + :return: True if given nncf matmul node is a matmul with a constant, False otherwise. + """ diff --git a/nncf/quantization/algorithms/min_max/onnx_backend.py b/nncf/quantization/algorithms/min_max/onnx_backend.py index ce750026a43..a09ca2b7861 100644 --- a/nncf/quantization/algorithms/min_max/onnx_backend.py +++ b/nncf/quantization/algorithms/min_max/onnx_backend.py @@ -25,6 +25,7 @@ from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.experimental.common.tensor_statistics.statistics import MinMaxTensorStatistic from nncf.onnx.graph.metatypes import onnx_metatypes as om +from nncf.onnx.graph.metatypes.groups import ELEMENTWISE_OPERATIONS from nncf.onnx.graph.metatypes.groups import MATMUL_METATYPES from nncf.onnx.graph.node_utils import get_input_edges_mapping from nncf.onnx.graph.node_utils import get_quantized_tensor_shape @@ -62,6 +63,10 @@ def post_processing_metatypes(self) -> List[OperatorMetatype]: def conv_metatypes(self) -> List[OperatorMetatype]: return [om.ONNXConvolutionMetatype] + @property + def elementwise_metatypes(self) -> List[OperatorMetatype]: + return ELEMENTWISE_OPERATIONS + @property def overflow_fix_metatypes(self) -> List[OperatorMetatype]: return [ @@ -250,8 +255,7 @@ def get_ignored_metatypes(model_type: ModelType, device: TargetDevice) -> List[O def get_ignored_names_by_layer_attributes(nncf_graph: NNCFGraph) -> Set[str]: return set() - @staticmethod - def get_weight_nodes(nncf_graph: NNCFGraph) -> List[NNCFNode]: + def get_weight_nodes(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: return [node for node in nncf_graph.get_all_nodes() if node.layer_attributes.has_weight()] @staticmethod @@ -263,3 +267,6 @@ def get_weight_name(nncf_graph: NNCFGraph, target_point: ONNXTargetPoint) -> str def should_quantize_weight(weight_name: str, quantized_weight_names: Set[str]) -> bool: # If the nodes share one weight tensor, we should have only one quantizer on that return weight_name not in quantized_weight_names + + def is_matmul_with_constant(self, node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + return node.metatype in self.mat_mul_metatypes and node.layer_attributes.has_weight() diff --git a/nncf/quantization/algorithms/min_max/openvino_backend.py b/nncf/quantization/algorithms/min_max/openvino_backend.py index fbb420f308e..e1db64346bc 100644 --- a/nncf/quantization/algorithms/min_max/openvino_backend.py +++ b/nncf/quantization/algorithms/min_max/openvino_backend.py @@ -23,6 +23,7 @@ from nncf.experimental.common.tensor_statistics.statistics import MinMaxTensorStatistic from nncf.openvino.graph.layer_attributes import OVLayerAttributes from nncf.openvino.graph.metatypes import openvino_metatypes as om +from nncf.openvino.graph.metatypes.groups import ELEMENTWISE_OPERATIONS from nncf.openvino.graph.metatypes.groups import OPERATIONS_WITH_WEIGHTS from nncf.openvino.graph.model_utils import get_start_nodes_for_activation_path_tracing from nncf.openvino.graph.node_utils import get_weight_channel_axes @@ -59,6 +60,10 @@ def post_processing_metatypes(self) -> List[OperatorMetatype]: def conv_metatypes(self) -> List[OperatorMetatype]: return [om.OVConvolutionMetatype] + @property + def elementwise_metatypes(self) -> List[OperatorMetatype]: + return ELEMENTWISE_OPERATIONS + @property def overflow_fix_metatypes(self) -> List[OperatorMetatype]: return [ @@ -223,6 +228,7 @@ def get_ignored_metatypes(model_type: ModelType, device: TargetDevice) -> List[O om.OVSumMetatype, om.OVSquaredDifferenceMetatype, om.OVMVNMetatype, + om.OVGroupNormalizationMetatype, om.OVBatchNormMetatype, om.OVDivideMetatype, om.OVSqrtMetatype, @@ -251,14 +257,16 @@ def get_ignored_names_by_layer_attributes(nncf_graph: NNCFGraph) -> Set[str]: ignored_names.add(node.node_name) return ignored_names - @staticmethod - def get_weight_nodes(nncf_graph: NNCFGraph) -> List[NNCFNode]: + def get_weight_nodes(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: return [ node for node in nncf_graph.get_all_nodes() if isinstance(node.layer_attributes, OVLayerAttributes) and node.metatype in OPERATIONS_WITH_WEIGHTS ] + def is_matmul_with_constant(self, node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + return node.metatype in self.mat_mul_metatypes and node.layer_attributes is not None + @staticmethod def get_weight_name(nncf_graph: NNCFGraph, target_point: OVTargetPoint) -> str: node = nncf_graph.get_node_by_name(target_point.target_node_name) diff --git a/nncf/quantization/algorithms/min_max/torch_backend.py b/nncf/quantization/algorithms/min_max/torch_backend.py index 98e41e03745..92b15c40708 100644 --- a/nncf/quantization/algorithms/min_max/torch_backend.py +++ b/nncf/quantization/algorithms/min_max/torch_backend.py @@ -37,6 +37,7 @@ from nncf.quantization.range_estimator import RangeEstimatorParameters from nncf.torch.graph.graph import PTNNCFGraph from nncf.torch.graph.graph import PTTargetPoint +from nncf.torch.graph.operator_metatypes import ELEMENTWISE_OPERATIONS from nncf.torch.graph.transformations.command_creation import create_quantizer_insertion_command from nncf.torch.graph.transformations.command_creation import create_shared_quantizer_insertion_command from nncf.torch.graph.transformations.commands import PTInsertionCommand @@ -89,6 +90,10 @@ def read_variable_metatypes(self) -> List[OperatorMetatype]: def conv_metatypes(self) -> List[OperatorMetatype]: return [om.PTConv1dMetatype, om.PTConv2dMetatype, om.PTConv3dMetatype] + @property + def elementwise_metatypes(self) -> List[OperatorMetatype]: + return ELEMENTWISE_OPERATIONS + @property def overflow_fix_metatypes(self) -> List[OperatorMetatype]: return [ @@ -365,10 +370,18 @@ def get_ignored_metatypes(model_type: ModelType, device: TargetDevice) -> List[O def get_ignored_names_by_layer_attributes(nncf_graph: NNCFGraph) -> Set[str]: return set() - @staticmethod - def get_weight_nodes(nncf_graph: NNCFGraph) -> List[NNCFNode]: - return [ + def get_weight_nodes(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: + weight_nodes_candidates = [ node for node in nncf_graph.get_all_nodes() if issubclass(node.metatype, om.PTOperatorMetatype) and node.metatype.weight_port_ids ] + weight_nodes = [] + for node in weight_nodes_candidates: + if node.metatype in self.mat_mul_metatypes and not self.is_matmul_with_constant(node, nncf_graph): + continue + weight_nodes.append(node) + return weight_nodes + + def is_matmul_with_constant(self, node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + return node.metatype in self.mat_mul_metatypes and len(get_weight_tensor_port_ids(node, nncf_graph)) > 0 diff --git a/nncf/quantization/algorithms/min_max/torch_fx_backend.py b/nncf/quantization/algorithms/min_max/torch_fx_backend.py index 4f82a1e0c8c..a181ad41a3e 100644 --- a/nncf/quantization/algorithms/min_max/torch_fx_backend.py +++ b/nncf/quantization/algorithms/min_max/torch_fx_backend.py @@ -12,10 +12,10 @@ from typing import Dict, List, Optional, Set, Tuple import torch +from torch.quantization.fake_quantize import FakeQuantize import nncf import nncf.torch.graph.operator_metatypes as om -from nncf.common.graph.definitions import NNCFGraphNodeType from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode from nncf.common.graph.operator_metatypes import OperatorMetatype @@ -28,6 +28,7 @@ from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.experimental.common.tensor_statistics.statistics import MinMaxTensorStatistic from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand +from nncf.experimental.torch.fx.model_utils import get_target_point from nncf.experimental.torch.fx.transformations import qdq_insertion_transformation_builder from nncf.parameters import ModelType from nncf.parameters import TargetDevice @@ -39,6 +40,7 @@ from nncf.quantization.range_estimator import RangeEstimatorParameters from nncf.torch.graph.graph import PTNNCFGraph from nncf.torch.graph.graph import PTTargetPoint +from nncf.torch.graph.operator_metatypes import ELEMENTWISE_OPERATIONS from nncf.torch.graph.transformations.commands import PTSharedFnInsertionCommand from nncf.torch.hardware.config import PTHWConfig from nncf.torch.model_graph_manager import get_weight_tensor_port_ids @@ -54,10 +56,6 @@ class FXMinMaxAlgoBackend(MinMaxAlgoBackend): - TARGET_TYPE_TO_PT_INS_TYPE_MAP = { - TargetType.PRE_LAYER_OPERATION: TargetType.OPERATOR_PRE_HOOK, - TargetType.POST_LAYER_OPERATION: TargetType.OPERATOR_POST_HOOK, - } @property def preserved_metatypes(self) -> List[OperatorMetatype]: @@ -87,6 +85,10 @@ def read_variable_metatypes(self) -> List[OperatorMetatype]: def conv_metatypes(self) -> List[OperatorMetatype]: return [om.PTConv1dMetatype, om.PTConv2dMetatype, om.PTConv3dMetatype] + @property + def elementwise_metatypes(self) -> List[OperatorMetatype]: + return ELEMENTWISE_OPERATIONS + @property def overflow_fix_metatypes(self) -> List[OperatorMetatype]: return [ @@ -129,11 +131,7 @@ def get_start_nodes_for_activation_path_tracing(nncf_graph: PTNNCFGraph) -> List @staticmethod def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: - if NNCFGraphNodeType.INPUT_NODE in target_node_name or target_type == TargetType.POST_LAYER_OPERATION: - port_id = None - if target_type in FXMinMaxAlgoBackend.TARGET_TYPE_TO_PT_INS_TYPE_MAP: - target_type = FXMinMaxAlgoBackend.TARGET_TYPE_TO_PT_INS_TYPE_MAP[target_type] - return PTTargetPoint(target_type, target_node_name, input_port_id=port_id) + return get_target_point(target_type, target_node_name, port_id) @staticmethod def create_convert_insertion_command( @@ -206,7 +204,8 @@ def get_weight_tensor_port_ids(node: NNCFNode, graph: NNCFGraph) -> List[Optiona @staticmethod def get_weight_name(nncf_graph: NNCFGraph, target_point: PTTargetPoint) -> str: weighted_node = nncf_graph.get_node_by_name(target_point.target_node_name) - weight = nncf_graph.get_previous_nodes(weighted_node)[target_point.input_port_id] + weight_edge = nncf_graph.get_input_edge_by_port_id(weighted_node, target_point.input_port_id) + weight = weight_edge.from_node return weight.node_name @staticmethod @@ -242,7 +241,7 @@ def _create_quantizer( scale_shape: Tuple, parameters: FakeQuantizeParameters, target_type: TargetType, - ) -> BaseQuantizer: + ) -> FakeQuantize: mode = quantizer_config.mode quantizer_cls = QUANTIZATION_MODULES.get(mode) narrow_range = target_type == TargetType.OPERATION_WITH_WEIGHTS and mode == QuantizationMode.SYMMETRIC @@ -348,10 +347,18 @@ def get_ignored_metatypes(model_type: ModelType, device: TargetDevice) -> List[O def get_ignored_names_by_layer_attributes(nncf_graph: NNCFGraph) -> Set[str]: return set() - @staticmethod - def get_weight_nodes(nncf_graph: NNCFGraph) -> List[NNCFNode]: - return [ + def get_weight_nodes(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: + weight_nodes_candidates = [ node for node in nncf_graph.get_all_nodes() if issubclass(node.metatype, om.PTOperatorMetatype) and node.metatype.weight_port_ids ] + weight_nodes = [] + for node in weight_nodes_candidates: + if node.metatype in self.mat_mul_metatypes and not self.is_matmul_with_constant(node, nncf_graph): + continue + weight_nodes.append(node) + return weight_nodes + + def is_matmul_with_constant(self, node: NNCFNode, nncf_graph: NNCFGraph) -> bool: + return node.metatype in self.mat_mul_metatypes and len(get_weight_tensor_port_ids(node, nncf_graph)) > 0 diff --git a/nncf/quantization/algorithms/post_training/pipeline.py b/nncf/quantization/algorithms/post_training/pipeline.py index 018f02f5e75..0c034342686 100644 --- a/nncf/quantization/algorithms/post_training/pipeline.py +++ b/nncf/quantization/algorithms/post_training/pipeline.py @@ -121,6 +121,7 @@ def create_ptq_pipeline( weights_quantization_params=advanced_parameters.weights_quantization_params, activations_range_estimator_params=advanced_parameters.activations_range_estimator_params, weights_range_estimator_params=advanced_parameters.weights_range_estimator_params, + quantizer_propagation_rule=advanced_parameters.quantizer_propagation_rule, backend_params=advanced_parameters.backend_params, ) ] diff --git a/nncf/quantization/algorithms/smooth_quant/torch_backend.py b/nncf/quantization/algorithms/smooth_quant/torch_backend.py index 15d247bb64d..910c5e886e0 100644 --- a/nncf/quantization/algorithms/smooth_quant/torch_backend.py +++ b/nncf/quantization/algorithms/smooth_quant/torch_backend.py @@ -21,6 +21,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.quantization.quantizer_propagation.structs import QuantizationTrait from nncf.common.tensor_statistics.statistic_point import StatisticPoint +from nncf.experimental.common.tensor_statistics.collectors import AbsMaxReducer from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.openvino.graph.transformations.commands import OVMultiplyInsertionCommand @@ -37,7 +38,6 @@ from nncf.torch.model_graph_manager import get_const_node from nncf.torch.nncf_network import NNCFNetwork from nncf.torch.quantization.default_quantization import DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT -from nncf.torch.tensor_statistics.collectors import PTAbsMaxReducer @COMPRESSION_MODULES.register() @@ -113,7 +113,7 @@ def get_abs_max_channel_collector( num_samples: int, stats_reduction_axes: Tuple[int], inplace: bool, branch_key: str ) -> TensorCollector: collector = TensorCollector() - reducer = PTAbsMaxReducer(reduction_axes=stats_reduction_axes) + reducer = AbsMaxReducer(reduction_axes=stats_reduction_axes) aggregator = MaxAggregator(num_samples=num_samples) collector.register_statistic_branch(branch_key, reducer, aggregator) return collector diff --git a/nncf/quantization/algorithms/smooth_quant/torch_fx_backend.py b/nncf/quantization/algorithms/smooth_quant/torch_fx_backend.py index 2b4bb7b37b9..6dee64469ed 100644 --- a/nncf/quantization/algorithms/smooth_quant/torch_fx_backend.py +++ b/nncf/quantization/algorithms/smooth_quant/torch_fx_backend.py @@ -20,6 +20,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.quantization.quantizer_propagation.structs import QuantizationTrait from nncf.common.tensor_statistics.statistic_point import StatisticPoint +from nncf.experimental.common.tensor_statistics.collectors import AbsMaxReducer from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand @@ -34,7 +35,6 @@ from nncf.torch.graph.transformations.commands import PTTargetPoint from nncf.torch.model_graph_manager import get_const_node from nncf.torch.quantization.default_quantization import DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT -from nncf.torch.tensor_statistics.collectors import PTAbsMaxReducer PT_PRE_LAYER_TARGET_TYPE = TargetType.OPERATOR_PRE_HOOK @@ -89,7 +89,7 @@ def get_abs_max_channel_collector( num_samples: int, stats_reduction_axes: Tuple[int], inplace: bool, branch_key: str ) -> TensorCollector: collector = TensorCollector() - reducer = PTAbsMaxReducer(reduction_axes=stats_reduction_axes) + reducer = AbsMaxReducer(reduction_axes=stats_reduction_axes) aggregator = MaxAggregator(num_samples=num_samples) collector.register_statistic_branch(branch_key, reducer, aggregator) return collector @@ -105,7 +105,10 @@ def get_weight_value(node_with_weight: NNCFNode, model: torch.fx.GraphModule, nn @staticmethod def weight_update_command(node_with_weight: NNCFNode, weight_value: torch.Tensor) -> OVWeightUpdateCommand: - return FXApplyTransformationCommand(constant_update_transformation_builder(node_with_weight, weight_value.data)) + # TODO(dlyakhov): Use input port id depending on the node metatype/attributes. + return FXApplyTransformationCommand( + constant_update_transformation_builder(node_with_weight, weight_value.data, input_port_id=1) + ) @staticmethod def scale_insertion_command( diff --git a/nncf/quantization/algorithms/weight_compression/activation_stats.py b/nncf/quantization/algorithms/weight_compression/activation_stats.py index 359887e7769..5d9d528e57b 100644 --- a/nncf/quantization/algorithms/weight_compression/activation_stats.py +++ b/nncf/quantization/algorithms/weight_compression/activation_stats.py @@ -9,34 +9,35 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Tuple +from functools import reduce +from operator import mul +from typing import Tuple +from nncf.experimental.common.tensor_statistics.statistics import WCTensorStatistic from nncf.tensor import Tensor from nncf.tensor import functions as fns -def process_stats(stats: List[Tensor], subset_size: int) -> Tuple[Tensor, Tensor]: +def process_stats(stats: WCTensorStatistic, subset_size: int) -> Tuple[Tensor, Tensor]: """ - It's a processing of activations shared between AWQ, Scale Estimation and LoRA Correction algorithms. + A function for processing activations. Shared between AWQ, Scale Estimation and LoRA Correction algorithms. - :param stats: list of activation statistics for a layer that contains N tensors with shape [SeqLen, HiddenDim] - :type stats: List[TTensor] + :param stats: An object containing statistics for the layer. :param subset_size: The number of samples for AWQ. - :type subset_size: int :return: tuple of the following tensors: s - maximum channel magnitude across samples [HiddenDim] - X - average channel magnitude across tokens in the sequence [HiddenDim, SampleSize] - :rtype: Tuple[TTensor, TTensor] + X - average channel magnitude across tokens in the sequence [HiddenDim, min(SampleSize, ~subset_size)] """ - X = fns.stack([fns.mean(stat, axis=0) for stat in stats]) # [Batch, HiddenDim] - X_full = fns.transpose(X) # [HiddenDim, Batch] + X = fns.stack(stats.mean_values) # [SampleSize, HiddenDim] + X_full = fns.transpose(X) # [HiddenDim, SampleSize] # prevent high memory and time consumption if X_full.shape[1] > subset_size: - lens = [stat.shape[0] for stat in stats] + # activations were reduced across all but the last dimension + lens = [reduce(mul, shape[:-1], 1) for shape in stats.shape_values] step = X_full.shape[1] // subset_size idxs = [i[0] for i in sorted(enumerate(lens), key=lambda x: -x[1])][::step] - X = X_full[:, idxs] # [HiddenDim, SampleSize] + X = X_full[:, idxs] # [HiddenDim, ~SubsetSize] else: X = X_full s = fns.max(fns.abs(X_full), axis=1) # [HiddenDim] diff --git a/nncf/quantization/algorithms/weight_compression/algorithm.py b/nncf/quantization/algorithms/weight_compression/algorithm.py index 1b2af0fd9a3..c5a4e2d221c 100644 --- a/nncf/quantization/algorithms/weight_compression/algorithm.py +++ b/nncf/quantization/algorithms/weight_compression/algorithm.py @@ -8,9 +8,13 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - +import copy +import operator +from collections import OrderedDict from collections import defaultdict -from typing import Dict, List, Optional, OrderedDict, Tuple, TypeVar +from functools import partial +from functools import reduce +from typing import Any, Dict, Iterable, List, Optional, Tuple, TypeVar import nncf from nncf import Dataset @@ -26,6 +30,8 @@ from nncf.common.utils.backend import BackendType from nncf.common.utils.backend import get_backend from nncf.common.utils.helpers import create_table +from nncf.experimental.common.tensor_statistics.statistics import WCTensorStatistic +from nncf.parameters import BackupMode from nncf.parameters import CompressWeightsMode from nncf.parameters import SensitivityMetric from nncf.quantization.advanced_parameters import AdvancedCompressionParameters @@ -40,12 +46,131 @@ from nncf.quantization.algorithms.weight_compression.weight_lowering import WeightCompressionConfig from nncf.scopes import IgnoredScope from nncf.scopes import get_ignored_node_names_from_ignored_scope -from nncf.tensor import Tensor -from nncf.tensor.definitions import TensorDataType TModel = TypeVar("TModel") TTensor = TypeVar("TTensor") +INT8_MODES = [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM] +NON_INT8_MODES = [ + CompressWeightsMode.INT4_SYM, + CompressWeightsMode.INT4_ASYM, + CompressWeightsMode.NF4, + CompressWeightsMode.E2M1, +] + + +def get_weight_compression_configuration( + mode: CompressWeightsMode = CompressWeightsMode.INT8_ASYM, + dataset: Optional[Dataset] = None, + ratio: Optional[float] = None, + group_size: Optional[int] = None, + all_layers: Optional[bool] = None, + awq: Optional[bool] = None, + scale_estimation: Optional[bool] = None, + gptq: Optional[bool] = None, + lora_correction: Optional[bool] = None, + ignored_scope: Optional[IgnoredScope] = None, + sensitivity_metric: Optional[SensitivityMetric] = None, + backup_mode: Optional[BackupMode] = None, + advanced_parameters: Optional[AdvancedCompressionParameters] = None, +) -> Dict[str, Any]: + """ + Generates a configuration dictionary for weight compression based on the provided parameters. + """ + group_size = ( + -1 + if group_size is None and mode in INT8_MODES + else 128 if group_size is None and mode in NON_INT8_MODES else group_size + ) + + return { + "mode": mode, + "ratio": ratio or 1, + "group_size": group_size, + "all_layers": all_layers or False, + "awq": awq or False, + "scale_estimation": scale_estimation or False, + "gptq": gptq or False, + "lora_correction": lora_correction or False, + "ignored_scope": ignored_scope or IgnoredScope(), + "sensitivity_metric": ( + ( + SensitivityMetric.WEIGHT_QUANTIZATION_ERROR + if dataset is None + else SensitivityMetric.MAX_ACTIVATION_VARIANCE + ) + if sensitivity_metric is None + else sensitivity_metric + ), + "backup_mode": backup_mode or BackupMode.INT8_ASYM, + "advanced_parameters": advanced_parameters or AdvancedCompressionParameters(), + } + + +def check_user_compression_configuration( + mode: CompressWeightsMode, + subset_size: int, + dataset: Optional[Dataset], + ratio: Optional[float], + group_size: Optional[int], + all_layers: Optional[bool], + awq: Optional[bool], + scale_estimation: Optional[bool], + gptq: Optional[bool], + lora_correction: Optional[bool], + ignored_scope: Optional[IgnoredScope], + sensitivity_metric: Optional[SensitivityMetric], + backup_mode: Optional[BackupMode], + advanced_parameters: Optional[AdvancedCompressionParameters], +) -> None: + """ + Validates the user's weight compression configuration for correctness. + """ + if mode in INT8_MODES: + if (ratio and ratio != 1) or (group_size and group_size != -1): + raise nncf.ParameterNotSupportedError( + "INT8 modes require per-channel quantization of all layers in 8 bit. " + "Default values of `ratio` (1) and `group_size` (-1) cannot be overridden." + ) + + if advanced_parameters and advanced_parameters.statistics_path: + raise nncf.ParameterNotSupportedError( + "INT8 modes do not support the `statistics_path` option in `AdvancedCompressionParameters`." + ) + + unsupported_options = { + "all_layers": all_layers, + "sensitivity_metric": sensitivity_metric, + "dataset": dataset, + "awq": awq, + "scale_estimation": scale_estimation, + "gptq": gptq, + "lora_correction": lora_correction, + "backup_mode": backup_mode, + } + unsupported_for_int8 = [name for name, value in unsupported_options.items() if value is not None] + if unsupported_for_int8: + raise nncf.ParameterNotSupportedError( + f"INT8 modes do not support {', '.join(unsupported_for_int8)} option(s). Set them to None." + ) + + if ratio is not None and not (0 <= ratio <= 1): + raise nncf.ValidationError(f"The ratio should be between 0 and 1, but ratio={ratio} is specified.") + + if subset_size <= 0: + raise nncf.ValidationError(f"The subset_size value should be positive, but subset_size={subset_size} is given.") + + if ( + ratio + and dataset is None + and sensitivity_metric is not None + and sensitivity_metric != SensitivityMetric.WEIGHT_QUANTIZATION_ERROR + ): + raise nncf.ValidationError( + f"Mixed precision selection with sensitivity metric={sensitivity_metric.value} \ + requires a dataset, but it's not provided." + ) + class WeightCompression(Algorithm): """ @@ -68,6 +193,7 @@ def __init__( scale_estimation: bool, gptq: bool, lora_correction: bool, + backup_mode: BackupMode = BackupMode.INT8_ASYM, advanced_parameters: Optional[AdvancedCompressionParameters] = None, ): """ @@ -78,15 +204,15 @@ def __init__( with a typical non-fixed zero point. INT4_SYM stands for a mixed-precision weights quantization with 4-bit integer as a primary precision. Weights are quantized to a primary precision symmetrically without zero point. - All embeddings and the last layer are always compressed to a backup precision, which is INT8_ASYM, - by default. All others are quantized whether to 4-bit integer or to a backup precision depending on + All embeddings and the last layer are always compressed to a backup_mode, which is INT8_ASYM, + by default. All others are quantized whether to 4-bit integer or to a backup_mode depending on criteria and the given ratio. INT4_ASYM is the same as INT4_SYM mode, but weights are quantized to a primary precision asymmetrically with a typical non-fixed zero point. NF4 is the same as INT4_SYM mode, but primary precision is NF4 data type without zero point. E2M1 is the same as INT4_SYM mode, but primary precision is E2M1 data type without zero point. :param ratio: the ratio between primary and backup precisions (e.g. 0.9 means 90% of layers quantized to NF4 - and the rest to INT8_ASYM). + and the rest to backup_mode). :param group_size: number of weights (e.g. 128) in the channel dimension that share quantization parameters (scale). The value -1 means no grouping. :param ignored_scope: An ignored scope that defined the list of model control @@ -101,6 +227,11 @@ def __init__( :param scale_estimation: determines whether to use or not scale estimation for 4 bit layers. :param gptq: determines whether to use or not GPTQ algorithm. :param lora_correction: determines whether to use or not LoRA Correction algorithm. + :param backup_mode: Defines a backup mode for mixed-precision weight compression. + NONE stands for original floating-point precision of the model weights. + In this mode, weights are retained in their original precision without any quantization. + INT8_SYM stands for 8-bit integer symmetric quantization without zero point. + INT8_ASYM stands for 8-bit integer asymmetric quantization with a typical non-fixed zero point. :param advanced_parameters: advanced parameters for algorithms in compression pipeline. """ super().__init__() @@ -110,7 +241,7 @@ def __init__( self._ignored_scope = ignored_scope self._backend_entity = None self._algorithm_key = f"CW_{hash(self)}" - self._fp_inputs = defaultdict(list) + self._statistics = {} self._all_layers = all_layers self._sensitivity_metric = sensitivity_metric self._awq = awq @@ -118,10 +249,15 @@ def __init__( self._scale_estimation = scale_estimation self._gptq = gptq self._lora_correction = lora_correction + self._backup_mode = backup_mode self._advanced_parameters = ( advanced_parameters if advanced_parameters is not None else AdvancedCompressionParameters() ) + primary_config = WeightCompressionConfig(mode=self._mode, group_size=self._group_size) + criterion_cls = MIXED_PRECISION_CRITERIA.get(self._sensitivity_metric) + self._mixed_precision_algo = criterion_cls(primary_config, self._ratio, self._subset_size) + self._statistics_path = self._advanced_parameters.statistics_path if self._gptq: gptq_params = self._advanced_parameters.gptq_params self._gptq_algo = GPTQ( @@ -130,13 +266,17 @@ def __init__( subset_size=gptq_params.subset_size, scale_estimation=self._scale_estimation, ) - self._gptq_statistics = None + + self._data_aware_mixed_precision = ( + self._sensitivity_metric != SensitivityMetric.WEIGHT_QUANTIZATION_ERROR and self._ratio != 1.0 + ) + self._data_aware_compression = self._awq or self._scale_estimation or self._lora_correction or self._gptq @property def available_backends(self) -> List[BackendType]: - return [BackendType.OPENVINO, BackendType.TORCH] + return [BackendType.OPENVINO, BackendType.TORCH, BackendType.TORCH_FX] - def _set_backend_entity(self, model: TModel) -> None: + def set_backend_entity(self, model: TModel) -> None: """ Creates a helper class with a backed-specific logic of the algorithm. @@ -151,12 +291,16 @@ def _set_backend_entity(self, model: TModel) -> None: from nncf.quantization.algorithms.weight_compression.torch_backend import PTWeightCompressionAlgoBackend self._backend_entity = PTWeightCompressionAlgoBackend() + elif model_backend == BackendType.TORCH_FX: + from nncf.quantization.algorithms.weight_compression.torch_fx_backend import FXWeightCompressionAlgoBackend + + self._backend_entity = FXWeightCompressionAlgoBackend() else: raise nncf.UnsupportedBackendError( "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) ) - def _get_nodes_to_compress(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: + def get_nodes_to_compress(self, nncf_graph: NNCFGraph) -> List[NNCFNode]: """ Collects nodes in the model's graph corresponding to the layers for weight compression. @@ -224,7 +368,7 @@ def _set_weight_compression_config( ratio_defining_params: List[WeightCompressionParameters], model: TModel, graph: NNCFGraph, - activations: Optional[Dict[str, List[Tensor]]] = None, + statistics_points: StatisticPointsContainer, ) -> None: """ Sets the appropriate compression configuration for weights based on some criteria. @@ -233,18 +377,14 @@ def _set_weight_compression_config( backup precisions. :param model: The model. :param graph: The model graph associated with the model. - :param activations: The input activations of the layers considered for compression. + :param statistics_points: Statistics points. """ primary_config = WeightCompressionConfig(mode=self._mode, group_size=self._group_size) if self._ratio == 1: for weight_param in ratio_defining_params: weight_param.compression_config = primary_config else: - criterion_cls = MIXED_PRECISION_CRITERIA.get(self._sensitivity_metric) - criterion = criterion_cls( - model, graph, self._backend_entity, ratio_defining_params, primary_config, self._ratio, activations - ) - criterion.assign_mixed_precision() + self._mixed_precision_algo.apply(model, graph, statistics_points, weight_params=ratio_defining_params) @staticmethod def _proportion_str(num_weights_list: List[int], total_num_weights: int, total_num_params: int) -> str: @@ -260,7 +400,10 @@ def _proportion_str(num_weights_list: List[int], total_num_weights: int, total_n return f"{percentage:.0f}% ({len(num_weights_list)} / {total_num_params})" def _get_bitwidth_distribution_str( - self, all_params: List[WeightCompressionParameters], ratio_defining_params: List[WeightCompressionParameters] + self, + all_params: List[WeightCompressionParameters], + ratio_defining_params: List[WeightCompressionParameters], + ignored_scope_weight_statistics: List[int], ) -> str: """ Generates a table that shows the ratio of weights quantized to different number of bits. @@ -268,27 +411,32 @@ def _get_bitwidth_distribution_str( :param all_params: Information about each weight node. :param ratio_defining_params: Information about weights that are used for calculating ratio between primary and backup precisions. + :param ignored_scope_weight_statistics: Information about weight nodes from IgnoredScope. :return: A string containing the table. """ - num_bits_vs_num_weights_map = {} + dtype_vs_num_weights_map = {} ratio_defining_weight_names = set(wp.weight_name for wp in ratio_defining_params) for data in all_params: - num_bits = data.compression_config.num_bits - n_total, n_ratio_defining = num_bits_vs_num_weights_map.get(num_bits, ([], [])) + dtype = data.compression_config.mode if data.compression_config is not None else "float" + n_total, n_ratio_defining = dtype_vs_num_weights_map.get(dtype, ([], [])) if data.weight_name in ratio_defining_weight_names: n_ratio_defining.append(data.num_weights) n_total.append(data.num_weights) - num_bits_vs_num_weights_map[num_bits] = (n_total, n_ratio_defining) + dtype_vs_num_weights_map[dtype] = (n_total, n_ratio_defining) + + if ignored_scope_weight_statistics: + n_total, n_ratio_defining = dtype_vs_num_weights_map.get("float", ([], [])) + dtype_vs_num_weights_map["float"] = (n_total + ignored_scope_weight_statistics, n_ratio_defining) num_ratio_defining_weights = sum(ws.num_weights for ws in ratio_defining_params) num_ratio_defining_params = len(ratio_defining_params) - num_total_weights = sum(ws.num_weights for ws in all_params) - num_params = len(all_params) - num_bits_vs_num_weights_map = OrderedDict(sorted(num_bits_vs_num_weights_map.items(), reverse=True)) + num_total_weights = sum(ws.num_weights for ws in all_params) + sum(ignored_scope_weight_statistics) + num_params = len(all_params) + len(ignored_scope_weight_statistics) + dtype_vs_num_weights_map = OrderedDict(sorted(dtype_vs_num_weights_map.items(), reverse=True)) # Table creation - header = ["Num bits (N)", "% all parameters (layers)", "% ratio-defining parameters (layers)"] + header = ["Weight compression mode", "% all parameters (layers)", "% ratio-defining parameters (layers)"] rows = [] - for bitwidth, (n_total, n_ratio_defining) in num_bits_vs_num_weights_map.items(): + for bitwidth, (n_total, n_ratio_defining) in dtype_vs_num_weights_map.items(): rows.append( [ bitwidth, @@ -301,6 +449,35 @@ def _get_bitwidth_distribution_str( pretty_string = f"Statistics of the bitwidth distribution:\n{table}" return pretty_string + def _get_ignored_scope_weight_statistics(self, model: TModel, graph: NNCFGraph) -> List[int]: + """ + Collect the weight statistics for nodes in the ignored scope. + + :param model: Model for statistics collection. + :param graph: Model graph. + :return: A list of weight sizes for the ignored nodes. + """ + ignored_names = get_ignored_node_names_from_ignored_scope(self._ignored_scope, graph, strict=False) + weighted_metatypes = ( + self._backend_entity.matmul_metatypes + + self._backend_entity.embedding_metatypes + + self._backend_entity.convolution_metatypes + ) + ignored_scope_weight_statistics = [] + for node_name in ignored_names: + node = graph.get_node_by_name(node_name) + is_node_with_weights = self._backend_entity.is_node_with_weights(node, graph) + if not is_node_with_weights or node.metatype not in weighted_metatypes: + continue + for _, weight_port_id in self._backend_entity.get_weight_names_and_port_ids(node, graph): + weight_dtype = self._backend_entity.get_weight_dtype(node, weight_port_id, model, graph) + if weight_dtype.is_float(): + continue + weight_shape = self._backend_entity.get_weight_shape(node, weight_port_id, graph) + weight_size = reduce(operator.mul, weight_shape, 1) + ignored_scope_weight_statistics.append(weight_size) + return ignored_scope_weight_statistics + def apply( self, model: TModel, @@ -308,12 +485,25 @@ def apply( statistic_points: Optional[StatisticPointsContainer] = None, dataset: Optional[Dataset] = None, ) -> TModel: - self._set_backend_entity(model) - nodes_to_compress = self._get_nodes_to_compress(graph) + self.set_backend_entity(model) + + nodes_to_compress = self.get_nodes_to_compress(graph) + + statistics = None + if self._data_aware_mixed_precision or self._data_aware_compression: + matmul_nodes_to_compress = [ + node for node in nodes_to_compress if node.metatype in self._backend_entity.matmul_metatypes + ] + matmul_input_to_output_nodes_map = self.get_matmul_input_to_output_nodes_map( + matmul_nodes_to_compress, graph + ) + if statistic_points is None: + statistic_points = self.get_statistic_points(model, graph, matmul_input_to_output_nodes_map.keys()) + statistic_points = self._collect_statistics(dataset, graph, model, statistic_points) + statistics = self._get_statistics_for_weights_compression( + matmul_input_to_output_nodes_map, statistic_points + ) - activations = {} - if dataset is not None and self._sensitivity_metric != SensitivityMetric.WEIGHT_QUANTIZATION_ERROR: - activations = self._get_activations(dataset, self._subset_size, nodes_to_compress, graph, model) all_weight_params: List[WeightCompressionParameters] = [] weight_names = set() @@ -326,14 +516,11 @@ def apply( is_last_layer_shared = True continue - weight = self._backend_entity.get_weight(node, weight_port_id, model, graph) - if weight.dtype not in [ - TensorDataType.float16, - TensorDataType.bfloat16, - TensorDataType.float32, - TensorDataType.float64, - ]: + weight_dtype = self._backend_entity.get_weight_dtype(node, weight_port_id, model, graph) + if not weight_dtype.is_float(): continue + weight_shape = self._backend_entity.get_weight_shape(node, weight_port_id, graph) + weight_size = reduce(operator.mul, weight_shape, 1) reduction_axes = self._backend_entity.get_reduction_axes(node, weight_port_id, graph) if ( self._group_size != -1 @@ -343,37 +530,65 @@ def apply( and len(reduction_axes) != 1 ): # NNCF supports multiple reduction axes only for ops with group_size != -1. - # Convolution ops are always quantized to 8-bits (without groups). + # Convolution ops are always kept in backup mode. # Embedding layers are quantized to 4-bits only if all_layers=True. # MatMul ops can't have multiple reduction axes. nncf_logger.warning( f"Weight compression expects a single reduction axis, but {len(reduction_axes)} given. " - f"Weight shape: {weight.shape}, reduction axes: {reduction_axes}, " - f"node name: {node.node_name}. The node will be asymmetrically quantized to 8 bits." + f"Weight shape: {weight_shape}, reduction axes: {reduction_axes}, " + f"node name: {node.node_name}. The node will be in {self._backup_mode} mode." ) + if self._backup_mode == BackupMode.NONE: + wc_config = None + else: + mode = ( + CompressWeightsMode.INT8_ASYM + if self._backup_mode == BackupMode.INT8_ASYM + else CompressWeightsMode.INT8_SYM + ) + wc_config = WeightCompressionConfig(mode=mode) weight_params = WeightCompressionParameters( - weight_name, node, weight_port_id, weight.size, reduction_axes + weight_name, node, weight_port_id, weight_size, reduction_axes, wc_config ) all_weight_params.append(weight_params) weight_names.add(weight_name) ratio_defining_params = self._get_ratio_defining_params(all_weight_params, is_last_layer_shared) - self._set_weight_compression_config(ratio_defining_params, model, graph, activations) - nncf_logger.info(self._get_bitwidth_distribution_str(all_weight_params, ratio_defining_params)) - - if ( - self._awq - and activations is not None - and self._mode not in [CompressWeightsMode.NF4, CompressWeightsMode.E2M1] - ): + self._set_weight_compression_config(ratio_defining_params, model, graph, statistic_points) + ignored_scope_weight_statistics = self._get_ignored_scope_weight_statistics(model, graph) + nncf_logger.info( + self._get_bitwidth_distribution_str( + all_weight_params, ratio_defining_params, ignored_scope_weight_statistics + ) + ) + + if self._backup_mode == BackupMode.NONE: + # Filter all_weight_params and nodes_to_compress by excluding nodes + # that should remain in their original floating-point precision + nodes_names_to_exclude = { + w_params.node_with_weight.node_name + for w_params in all_weight_params + if w_params.compression_config is None + } + all_weight_params = list( + filter( + lambda w_params: w_params.node_with_weight.node_name not in nodes_names_to_exclude, + all_weight_params, + ) + ) + nodes_to_compress = list( + filter(lambda node: node.node_name not in nodes_names_to_exclude, nodes_to_compress) + ) + + if self._awq: awq_params = self._advanced_parameters.awq_params awq_algo = AWQ( model, self._backend_entity.name_to_node_mapping, all_weight_params, nodes_to_compress, - activations, + statistics, awq_params.subset_size, awq_params.percent_to_apply, awq_params.alpha_min, @@ -381,44 +596,44 @@ def apply( awq_params.steps, ) awq_algo.apply(model, graph) + # After applying AWQ we need to update statistics since AWQ alters the activations + statistics = awq_algo.update_statistics(statistics) + # del is used to prematurely mark non-necessary data as free for garbage collection + del awq_algo scales = {} zero_points = {} lora_correction_algo = None description = "Applying Weight Compression" if self._gptq: + del statistics model, scales, zero_points = self._gptq_algo.apply( model=model, graph=graph, dataset=dataset, weight_compression_parameters=all_weight_params, - statistic_points=self._gptq_statistics, backend_entity=self._backend_entity, ) else: - if ( - self._scale_estimation - and activations is not None - and self._mode not in [CompressWeightsMode.NF4, CompressWeightsMode.E2M1] - ): + if self._scale_estimation: scale_estimation_params = self._advanced_parameters.scale_estimation_params - scale_algo = ScaleEstimation( + scales, zero_points = ScaleEstimation( model, self._backend_entity.name_to_node_mapping, all_weight_params, nodes_to_compress, - activations, + statistics, scale_estimation_params.subset_size, scale_estimation_params.initial_steps, scale_estimation_params.scale_steps, scale_estimation_params.weight_penalty, - ) - scales = scale_algo.apply(model, graph) + ).apply(model, graph) if self._lora_correction: lora_correction_params = self._advanced_parameters.lora_correction_params - lora_correction_algo = LoraCorrectionAlgorithm(activations, lora_correction_params) + lora_correction_algo = LoraCorrectionAlgorithm(statistics, lora_correction_params) description += " with correction of low-rank adapters" + del statistics # Sort weight params to start compression with the bigger constants. This lowers peak memory footprint. all_weight_params = sorted(all_weight_params, key=lambda wp: wp.num_weights, reverse=True) @@ -447,15 +662,13 @@ def apply( "scale_estimation": self._scale_estimation, "gptq": self._gptq, "lora_correction": self._lora_correction, + "backup_mode": self._backup_mode.value, "advanced_parameters": convert_to_dict_recursively(self._advanced_parameters), }, algo_name="weight_compression", ) return transformed_model - def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPointsContainer: - pass - def _get_activation_node_and_port(self, node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[NNCFNode, int]: """ This method returns the activation layer and corresponding port id for the node. @@ -470,98 +683,163 @@ def _get_activation_node_and_port(self, node: NNCFNode, nncf_graph: NNCFGraph) - port_id = activation_edge.output_port_id return activation_node, port_id - def _get_fp_inputs(self, statistic_points: StatisticPointsContainer, node_name: str, port_id: int) -> List[Tensor]: + def get_matmul_input_to_output_nodes_map( + self, matmul_nodes: List[NNCFNode], graph: NNCFGraph + ) -> Dict[Tuple[NNCFNode, int], List[NNCFNode]]: + """ + Maps activation nodes to their corresponding MatMul nodes in the graph. + + Each weighted MatMul node takes two inputs: an activation and a weight. + An activation node may serve as an input to multiple MatMul nodes. + This function returns a mapping where each key is a tuple consisting of an + activation node and its output port ID, and the value is a list of MatMul + nodes that use this activation as input. + + :param matmul_nodes: A list of MatMul nodes from the computation graph. + :param graph: An instance of NNCFGraph representing the computation graph. + :return: A dictionary mapping from a tuple of (activation node, port ID) + to a list of corresponding MatMul nodes that accept the activation as input. """ - Collects floating-point statistics for the given node and port id. + matmul_input_to_output_nodes_map = defaultdict(list) + for node in matmul_nodes: + if node.layer_attributes.input_attributes["transpose"]: # It works only for OV + raise nncf.UnsupportedModelError("Transposed input is not supported") + act_node, output_port_id = self._get_activation_node_and_port(node, graph) + matmul_input_to_output_nodes_map[(act_node, output_port_id)].append(node) + return matmul_input_to_output_nodes_map - :param statistic_points: Filled StatisticPointsContainer. - :param node_name: Name of the current layer. - :param port_id: Port id for statistics collection. - :return: Collected list of tensor data. + def get_compression_nodes_info( + self, graph: NNCFGraph + ) -> Tuple[List[NNCFNode], Dict[Tuple[NNCFNode, int], List[NNCFNode]]]: + """ + Retrieves the nodes to compress along with a mapping of activation nodes + to their corresponding MatMul nodes. + + This function first identifies all nodes that can be compressed from the + provided graph. It then filters these nodes to find those that are of + MatMul type and generates a mapping of activation nodes to their + corresponding MatMul nodes using the + `get_matmul_input_to_output_nodes_map` function. + + :param graph: An instance of NNCFGraph representing the computation graph. + :return: A tuple containing: + - Nodes for compression. + - A dictionary mapping from a tuple of (activation node, port ID) + to a list of MatMul nodes that accept the activation as input. + """ + nodes_to_compress = self.get_nodes_to_compress(graph) + matmul_nodes_to_compress = [ + node for node in nodes_to_compress if node.metatype in self._backend_entity.matmul_metatypes + ] + matmul_input_to_output_nodes_map = self.get_matmul_input_to_output_nodes_map(matmul_nodes_to_compress, graph) + return nodes_to_compress, matmul_input_to_output_nodes_map + + def _collect_statistics( + self, + dataset: Dataset, + graph: NNCFGraph, + model: TModel, + statistic_points: StatisticPointsContainer, + ): """ + Creates statistics aggregator, registers all statistics specified for algorithm, and then collect them. - def input_filter_func(point): - # For the floating-point statistics collected in POST_LAYER style, - # we also need to determine the output port id. - # For the cases when the layer has more than one (0) output port. - return ( - self._algorithm_key in point.algorithm_to_tensor_collectors - and point.target_point.type == TargetType.POST_LAYER_OPERATION - and point.target_point.port_id == port_id - ) + :param dataset: Dataset to collect values. + :param graph: Model graph. + :param model: Model for statistics collection. + :param statistic_points: Statistics points. + """ + statistics_aggregator = StatisticsAggregatorFactory.create(model, dataset) + statistics_aggregator.register_statistic_points(statistic_points) + statistics_aggregator.collect_statistics(model, graph) + return statistics_aggregator.statistic_points - input_id = (node_name, port_id) - if input_id in self._fp_inputs: - return self._fp_inputs[input_id] - - input_fp = [] - for tensor_collector in statistic_points.get_algo_statistics_for_node( - node_name, input_filter_func, self._algorithm_key - ): - for value in tensor_collector.get_statistics().values: - input_fp.append(value) - self._fp_inputs[input_id] = input_fp - return self._fp_inputs[input_id] - - def _get_activations( - self, dataset: Dataset, subset_size: int, nodes_to_compress: List[NNCFNode], graph: NNCFGraph, model: TModel - ) -> Dict[str, List[Tensor]]: + def get_statistic_points( + self, + model: TModel, + graph: NNCFGraph, + nodes_and_port_ids: Iterable[Tuple[NNCFNode, int]], + ) -> StatisticPointsContainer: """ - Collects input activations for the given nodes on the dataset. + Returns statistic points, for which StatisticsCollector should collect statistics. - :param dataset: Dataset to collect values. - :param subset_size: Number of data samples to calculate activation statistics used for assigning different - quantization precision. - :param nodes_to_compress: List of nodes, whose inputs are collected. :param model: Model for statistics collection. :param graph: Model graph. - :return: statistics values itself per node name. + :param nodes_and_port_ids: Nodes and port ids for which statistics should be collected. + :return: Statistic points, for which StatisticsCollector should collect statistics. """ - activations = {} - _collected_stat_inputs_map = {} statistic_container = StatisticPointsContainer() - all_act_nodes = set() - act_vs_shared_node_names_mapping = defaultdict(list) - matmul_metatypes = self._backend_entity.matmul_metatypes - filtered_nodes = filter(lambda node: node.metatype in matmul_metatypes, nodes_to_compress) - for node in filtered_nodes: - act_node, output_port_id = self._get_activation_node_and_port(node, graph) - act_node_name = act_node.node_name - if act_node_name in all_act_nodes: - act_vs_shared_node_names_mapping[act_node_name].append(node.node_name) - continue - all_act_nodes.add(act_node_name) - output_id = (act_node_name, output_port_id) - _collected_stat_inputs_map[node.node_name] = output_id - - statistic_point = self._backend_entity.target_point( - TargetType.POST_LAYER_OPERATION, act_node_name, port_id=output_port_id - ) - stat_collector = self._backend_entity.raw_statistic_collector(num_samples=subset_size) - statistic_container.add_statistic_point( - StatisticPoint( - target_point=statistic_point, tensor_collector=stat_collector, algorithm=self._algorithm_key + # Statistics for data aware algorithms + if self._data_aware_compression: + for node, output_port_id in nodes_and_port_ids: + statistic_point = self._backend_entity.target_point( + TargetType.POST_LAYER_OPERATION, node.node_name, port_id=output_port_id + ) + # Reduce activations across all but the last dimension. The last dimension is assumed to be the hidden + # size dimension. + n_dims = len(graph.get_output_edges_by_port_id(node, output_port_id)[0].tensor_shape) + stat_collector = self._backend_entity.mean_statistic_collector( + reduction_axes=tuple(range(n_dims - 1)), subset_size=self._subset_size ) + statistic_container.add_statistic_point( + StatisticPoint( + target_point=statistic_point, tensor_collector=stat_collector, algorithm=self._algorithm_key + ) + ) + # Statistics for mixed precision algorithm + if self._data_aware_mixed_precision: + mixed_precision_statistics = self._mixed_precision_algo.get_statistic_points( + model, graph, nodes_and_port_ids ) + for points in mixed_precision_statistics.values(): + for point in points: + statistic_container.add_statistic_point(point) - statistics_aggregator = StatisticsAggregatorFactory.create(model, dataset) - statistics_aggregator.register_statistic_points(statistic_container) - - if self._gptq and not self._awq: - self._gptq_statistics = self._gptq_algo.get_statistic_points( - model, graph, nodes_to_compress, self._backend_entity - ) - statistics_aggregator.register_statistic_points(self._gptq_statistics) + return statistic_container - statistics_aggregator.collect_statistics(model, graph) + def _get_statistics_for_weights_compression( + self, + matmul_input_to_output_nodes_map: Dict[Tuple[NNCFNode, int], List[NNCFNode]], + statistic_points: StatisticPointsContainer, + ) -> Dict[str, WCTensorStatistic]: + """ + Retrieve collected statistics only for WeightCompression algorithm and not for MixedPrecision. - for node_name, output_id in _collected_stat_inputs_map.items(): - act_node_name, output_port_id = output_id - x_fp = self._get_fp_inputs(statistic_container, node_name=act_node_name, port_id=output_port_id) - x_fp = [i.squeeze() for i in x_fp] # List[tensor(seq_length, hidden_dim)] - activations[node_name] = x_fp + :param matmul_input_to_output_nodes_map: A mapping from activation node and a port id to corresponding matmul + nodes which accept this activation as an input. + :param statistic_points: Statistic points object. + :return: Collected statistics. + """ - for shared_node_name in act_vs_shared_node_names_mapping[act_node_name]: - activations[shared_node_name] = x_fp + def input_filter_func(point, port_id): + # For the floating-point statistics collected in POST_LAYER style, + # we also need to determine the output port id. + # For the cases when the layer has more than one (0) output port. + return ( + self._algorithm_key in point.algorithm_to_tensor_collectors + and point.target_point.type == TargetType.POST_LAYER_OPERATION + and point.target_point.port_id == port_id + ) - return activations + # For each node we store statistics in a WCTensorStatistics data-class. It contains the following fields: + # mean_values=[mean_value_1, ..., mean_value_n] + # shapes=[shape_1, ..., shape_n] + # Where mean_value is a 1D tensor representing an activation reduced over batch and sequence length dimensions, + # shape is an original shape of an activation before reduction, n is the size of the dataset (or subset_size). + statistics = {} + for (act_node, output_port_id), matmul_nodes in matmul_input_to_output_nodes_map.items(): + tensor_collectors = list( + statistic_points.get_algo_statistics_for_node( + act_node.node_name, partial(input_filter_func, port_id=output_port_id), self._algorithm_key + ) + ) + # Statistics could be empty in case when the statistics is registered for another algorithm, + # e.g. mixed precision. + if tensor_collectors: + assert len(tensor_collectors) == 1 + stats = tensor_collectors[0].get_statistics() + + # Each activation node may have multiple MatMul nodes which it is an input to + for node in matmul_nodes: + statistics[node.node_name] = copy.deepcopy(stats) + return statistics diff --git a/nncf/quantization/algorithms/weight_compression/awq.py b/nncf/quantization/algorithms/weight_compression/awq.py index c0c05d76e5b..6de2a3659cf 100644 --- a/nncf/quantization/algorithms/weight_compression/awq.py +++ b/nncf/quantization/algorithms/weight_compression/awq.py @@ -13,6 +13,7 @@ from dataclasses import dataclass from typing import Any, Dict, List, Optional, TypeVar +import nncf from nncf import Dataset from nncf import nncf_logger from nncf.common.factory import ModelTransformerFactory @@ -24,12 +25,18 @@ from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.common.utils.backend import BackendType from nncf.common.utils.backend import get_backend +from nncf.experimental.common.tensor_statistics.statistics import WCTensorStatistic +from nncf.parameters import CompressWeightsMode from nncf.quantization.algorithms.algorithm import Algorithm from nncf.quantization.algorithms.weight_compression.activation_stats import process_stats from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters +from nncf.quantization.algorithms.weight_compression.weight_lowering import calculate_nf4_scale from nncf.quantization.algorithms.weight_compression.weight_lowering import do_int_dequantization from nncf.quantization.algorithms.weight_compression.weight_lowering import do_int_quantization +from nncf.quantization.algorithms.weight_compression.weight_lowering import do_nf4_dequantization +from nncf.quantization.algorithms.weight_compression.weight_lowering import do_nf4_quantization from nncf.quantization.passes import transform_to_inference_graph +from nncf.tensor import TensorDataType from nncf.tensor import functions as fns TModel = TypeVar("TModel") @@ -59,7 +66,7 @@ def __init__( name_to_node_mapping: Dict[str, Any], all_weight_params: List[WeightCompressionParameters], nodes_to_compress: List[NNCFNode], - activations: Optional[Dict[str, TTensor]] = None, + statistics: Dict[str, WCTensorStatistic], subset_size: int = 32, percent_to_apply=0.002, alpha_min=0.0, @@ -71,7 +78,7 @@ def __init__( :param name_to_node_mapping: Name to node mapping for updating node weights. :param all_weight_params: List of all weight parameters. :param nodes_to_compress: List of nodes for processing. - :param activations: The input activations of the layers considered for compression. + :param statistics: Input activation statistics for each node. :param subset_size: The number of samples for AWQ. :param percent_to_apply: The percent of outliers for correction. :param alpha_min: Minimum value of smoothness parameter for grid search. @@ -82,7 +89,7 @@ def __init__( self.name_to_node_mapping = name_to_node_mapping self._all_weight_params = all_weight_params self._nodes_to_compress = nodes_to_compress - self._activations = activations + self._statistics = statistics self._subset_size = subset_size self._percent_to_apply = percent_to_apply self._alpha_min = alpha_min @@ -90,6 +97,7 @@ def __init__( self._steps = steps self._backend_entity = None self._patterns = None + self._scale_per_target_node = {} self._set_backend_entity(model) @@ -111,7 +119,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = OVAWQAlgoAlgoBackend(model, self.name_to_node_mapping) self._patterns = self._backend_entity.get_awq_patterns() else: - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot return backend-specific AWQ entity because {} is not supported!".format(model_backend.value) ) @@ -157,6 +165,10 @@ def apply( for weight_op_friendly_name, _ in self._backend_entity.get_weight_names_and_port_ids(nncf_node, graph): target_node_names.append(weight_op_friendly_name) + # skip node if it is in IgnoredScope or should not be compressed + if target_node_names[-1] not in name_mapping: + continue + weight_params = self._all_weight_params[name_mapping[target_node_names[-1]]] if weight_params.compression_config.num_bits != 4: @@ -195,7 +207,7 @@ def apply( config = wp.compression_config - s, X = process_stats(self._activations[k], self._subset_size) + s, X = process_stats(self._statistics[k], self._subset_size) top_k = max(int(s.shape[0] * self._percent_to_apply), 1) topk_idxs = fns.argsort(-s)[:top_k] @@ -230,7 +242,7 @@ def apply( offset = gi * group_size gscale = s[offset : offset + group_size] - a_min = fns.quantile(gscale, 0.1) + a_min = fns.astype(fns.quantile(gscale, 0.1), TensorDataType.float32) a_max = 1e2 gscale = fns.clip(gscale, a_min=a_min, a_max=a_max) @@ -244,11 +256,16 @@ def apply( alpha = self._alpha_min for _ in range(self._steps): cur_scale = gscale**alpha - - g_compressed_weighs, g_c_scale, g_c_zp = do_int_quantization( - gweight * cur_scale, reduction_axis, awq_config - ) - g_decompressed_weighs = do_int_dequantization(g_compressed_weighs, g_c_scale, g_c_zp) + weights_to_fake_quantize = gweight * cur_scale + if config.mode == CompressWeightsMode.NF4: + g_c_scale = calculate_nf4_scale(weights_to_fake_quantize, reduction_axis) + g_compressed_weighs = do_nf4_quantization(weights_to_fake_quantize, g_c_scale) + g_decompressed_weighs = do_nf4_dequantization(g_compressed_weighs, g_c_scale) + else: + g_compressed_weighs, g_c_scale, g_c_zp = do_int_quantization( + weights_to_fake_quantize, reduction_axis, awq_config + ) + g_decompressed_weighs = do_int_dequantization(g_compressed_weighs, g_c_scale, g_c_zp) sacts = gacts / fns.unsqueeze(cur_scale, 1) cur_out = fns.matmul(g_decompressed_weighs, sacts) @@ -290,15 +307,19 @@ def apply( ) transformation_layout.register(scale_insertion_command) - # update activations for next usage - for i, stat in enumerate(self._activations[k]): - stat = stat * a_scale - self._activations[k][i] = stat + self._scale_per_target_node[k] = a_scale transformed_model = model_transformer.transform(transformation_layout) return transformed_model + def update_statistics(self, statistics): + # Multiply activations by the computed scales + for node_name, scale in self._scale_per_target_node.items(): + for mean_stat in statistics[node_name].mean_values: + mean_stat *= fns.squeeze(scale) + return statistics + def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPointsContainer: """ Returns statistic points, for which StatisticsCollector should collect statistics. diff --git a/nncf/quantization/algorithms/weight_compression/backend.py b/nncf/quantization/algorithms/weight_compression/backend.py index c14b860a093..eeda65a5bd7 100644 --- a/nncf/quantization/algorithms/weight_compression/backend.py +++ b/nncf/quantization/algorithms/weight_compression/backend.py @@ -19,8 +19,13 @@ from nncf.common.graph.transformations.commands import TargetPoint from nncf.common.graph.transformations.commands import TargetType from nncf.common.tensor_statistics.collectors import TensorStatisticCollectorBase +from nncf.experimental.common.tensor_statistics.collectors import HAWQAggregator +from nncf.experimental.common.tensor_statistics.collectors import NoopReducer +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.statistics import HessianTensorStatistic from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters from nncf.tensor import Tensor +from nncf.tensor import TensorDataType TModel = TypeVar("TModel") @@ -93,6 +98,32 @@ def get_weight(self, node_with_weight: NNCFNode, weight_port_id: int, model: TMo :return: The weight tensor. """ + @abstractmethod + def get_weight_dtype( + self, node_with_weight: NNCFNode, weight_port_id: int, model: TModel, graph: NNCFGraph + ) -> TensorDataType: + """ + Returns a weight data type associated with the given node on the given port id. + + :param node_with_weight: The node with weight. + :param weight_port_id: The weight port id for given node with weight. + :param model: The model. + :param graph: The model graph associated with the model. + :return: The weight data type. + """ + + @staticmethod + @abstractmethod + def get_weight_shape(node_with_weight: NNCFNode, weight_port_id: int, graph: NNCFGraph) -> Tuple: + """ + Returns a weight shape associated with the given node on the given port id. + + :param node_with_weight: The node with weight. + :param weight_port_id: The weight port id for given node with weight. + :param graph: The model graph associated with the model. + :return: The weight shape. + """ + @abstractmethod def set_weight( self, node_with_weight: NNCFNode, weight_port_id: int, model: TModel, graph: NNCFGraph, weight: Tensor @@ -131,7 +162,7 @@ def transform_model( def insert_adapters( self, wc_params: WeightCompressionParameters, lora_A: Tensor, lora_B: Tensor, int8_lora: bool ) -> None: - """ + r""" Expands a model's execution graph following the Low-Rank Adaptation (LoRA) concept. It inserts two additional Linear layers with weight matrices of low rank that are executed in parallel to the @@ -167,15 +198,15 @@ def target_point(target_type: TargetType, target_node_name: str, port_id: int) - :return: Backend-specific TargetPoint. """ - @staticmethod @abstractmethod - def raw_statistic_collector(num_samples: Optional[int] = None) -> TensorStatisticCollectorBase: + def mean_statistic_collector( + self, reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorStatisticCollectorBase: """ - Returns backend-specific raw statistic collector. - This statistic collector is used for raw data calculation, without aggregating. + Return mean statistic collector - :param num_samples: Maximum number of samples to collect. - :return: Backend-specific TensorStatisticCollectorBase for the statistics calculation. + :param reduction_axes: Axes along which to apply mean reduction + :param subset_size: Number of samples to collect """ @staticmethod @@ -216,3 +247,34 @@ def scale_insertion_command(source_node, next_nodes, source_node_output_port, sc """ Returns scale insertion command/transformation for applying AWQ algorithm. """ + + +class MixedPrecisionAlgoBackend(ABC): + @staticmethod + def hawq_statistic_collector(subset_size: Optional[int] = None) -> TensorCollector: + reducer = NoopReducer() + aggregator = HAWQAggregator(num_samples=subset_size) + collector = TensorCollector(HessianTensorStatistic) + collector.register_statistic_branch(HessianTensorStatistic.HESSIAN_INPUT_ACTIVATION_STATS, reducer, aggregator) + return collector + + @staticmethod + @abstractmethod + def mean_variance_statistic_collector( + reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + pass + + @staticmethod + @abstractmethod + def max_variance_statistic_collector( + reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + pass + + @staticmethod + @abstractmethod + def mean_abs_max_statistic_collector( + reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + pass diff --git a/nncf/quantization/algorithms/weight_compression/config.py b/nncf/quantization/algorithms/weight_compression/config.py index 3e1a460b012..56dbc24f2e2 100644 --- a/nncf/quantization/algorithms/weight_compression/config.py +++ b/nncf/quantization/algorithms/weight_compression/config.py @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass +from dataclasses import field from typing import Optional, Tuple, TypeVar import numpy as np @@ -64,7 +65,7 @@ class WeightCompressionParameters: weight_port_id: int num_weights: np.uint64 reduction_axes: Tuple[int, ...] - compression_config = WeightCompressionConfig() + compression_config: Optional[WeightCompressionConfig] = field(default_factory=WeightCompressionConfig) def __post_init__(self): # Explicitly cast num_weights to avoid overflow on finding total number of weights. diff --git a/nncf/quantization/algorithms/weight_compression/gptq.py b/nncf/quantization/algorithms/weight_compression/gptq.py index bd6518c86ad..d2178b19e91 100644 --- a/nncf/quantization/algorithms/weight_compression/gptq.py +++ b/nncf/quantization/algorithms/weight_compression/gptq.py @@ -169,9 +169,9 @@ def _calculate_hessian(self, node: NNCFNode, inputs: List[Tensor]) -> Tensor: nsamples = 0 if node.metatype in self._backend_entity.convolution_metatypes: - raise RuntimeError("Convolution metatypes are not supported") + raise nncf.UnsupportedModelError("Convolution metatypes are not supported") if node.layer_attributes.input_attributes["transpose"]: - raise RuntimeError("Transpose is not supported") + raise nncf.UnsupportedModelError("Transposed input is not supported") hessian = fns.zeros( (inputs[0].shape[-1], inputs[0].shape[-1]), backend=inputs[0].backend, dtype=TensorDataType.float32 @@ -264,10 +264,11 @@ def _quantize_weights( scales.append(scale) else: if self._scale_estimation and block_compression_config.num_bits == 4: - activations = [inp.squeeze()[:, (i1 + i) : (i1 + i + group_size)] for inp in inputs] + activations = [inp[..., (i1 + i) : (i1 + i + group_size)] for inp in inputs] + wc_statistics = ScaleEstimation.activations_to_wc_statistics(activations) scale, zero_point = ScaleEstimation.calculate_quantization_params( self._backend_entity, - activations, + wc_statistics, weight_tensor[:, (i1 + i) : (i1 + i + group_size)], reduction_axes, wc_params.compression_config, diff --git a/nncf/quantization/algorithms/weight_compression/lora_correction.py b/nncf/quantization/algorithms/weight_compression/lora_correction.py index 8907cb64a2c..83c0acbd228 100644 --- a/nncf/quantization/algorithms/weight_compression/lora_correction.py +++ b/nncf/quantization/algorithms/weight_compression/lora_correction.py @@ -14,9 +14,11 @@ import matplotlib.pyplot as plt import pandas as pd +import nncf from nncf.common.logging import nncf_logger from nncf.common.utils.debug import DEBUG_LOG_DIR from nncf.common.utils.debug import is_debug +from nncf.experimental.common.tensor_statistics.statistics import WCTensorStatistic from nncf.parameters import CompressWeightsMode from nncf.quantization.advanced_parameters import AdvancedLoraCorrectionParameters from nncf.quantization.algorithms.weight_compression.activation_stats import process_stats @@ -80,12 +82,14 @@ class LoraCorrectionAlgorithm: The method reduces quantization noise after weight compression using low rank adapters. """ - def __init__(self, activations: Dict[str, List[Tensor]], lora_correction_params: AdvancedLoraCorrectionParameters): + def __init__( + self, statistics: Dict[str, WCTensorStatistic], lora_correction_params: AdvancedLoraCorrectionParameters + ): """ - :param activations: The input activations of the layers considered for compression. + :param statistics: Input activation statistics for each node. :param lora_correction_params: parameters to configure the algorithm. """ - self._activations = activations + self._statistics = statistics self._lora_correction_params = lora_correction_params self._debug_interface = DebugInterface() if is_debug() else None @@ -112,7 +116,7 @@ def calculate_adapters( :return: two low rank matrices in the order of execution of corresponding linear layers. """ layer_name = wc_params.node_with_weight.node_name - layer_activations = self._activations[layer_name] + layer_statistics = self._statistics[layer_name] is_debug = self._debug_interface is not None lora_A, lora_B, mean_noises = self.calculate_low_rank_matrices( weight, @@ -120,7 +124,7 @@ def calculate_adapters( wc_params.compression_config, wc_params.reduction_axes, self._lora_correction_params, - layer_activations, + layer_statistics, is_debug, ) if is_debug: @@ -134,7 +138,7 @@ def calculate_low_rank_matrices( compression_config: WeightCompressionConfig, reduction_axes: Tuple[int, ...], lora_correction_params: AdvancedLoraCorrectionParameters, - layer_activations: List[Tensor], + layer_statistics: WCTensorStatistic, is_debug: Optional[bool] = False, ): """ @@ -149,8 +153,7 @@ def calculate_low_rank_matrices( :param compression_config: configuration of weight compression for the weight node. :param reduction_axes: axes along which different statistics reduced. :param lora_correction_params: parameters to configure the algorithm. - :param layer_activations: list of activation statistics for a layer that contains - N tensors with shape [SeqLen, HiddenDim]. + :param layer_statistics: an object containing statistics for the layer. :param is_debug: whether to collect debug information, defaults to False. :return: two low rank matrices in the order of execution of corresponding linear layers and list of mean noises. Noises are collected from each step of the algorithm if debug was enabled. @@ -176,7 +179,7 @@ def calculate_low_rank_matrices( indexes = do_nf4_quantization(compressed_weight.tensor, compressed_weight.scale, is_normalized_weight=True) fq_weights = do_nf4_dequantization(indexes, compressed_weight.scale, reduction_axis) else: - raise ValueError( + raise nncf.InternalError( f"{mode.value} mode is invalid for Lora Correction algorithm. Supported modes: INT4_SYM, INT4_ASYM, NF4" ) # fq_w + residual = w => residual = w - fq_w @@ -188,7 +191,7 @@ def calculate_low_rank_matrices( svd_residual = fns.transpose(svd_residual) residual = svd_residual.clone() # [H, O] - s, X = process_stats(layer_activations, subset_size) # [H], [H, SS] + s, X = process_stats(layer_statistics, subset_size) # [H], [H, SS] X = fns.transpose(X) # [SS, H] if compression_config.group_size > 0: # Multiply residual of weights by maximum channel magnitude of activations normalized per quantization diff --git a/nncf/quantization/algorithms/weight_compression/mixed_precision.py b/nncf/quantization/algorithms/weight_compression/mixed_precision.py index 63f90b2677c..f2fbae67a5c 100644 --- a/nncf/quantization/algorithms/weight_compression/mixed_precision.py +++ b/nncf/quantization/algorithms/weight_compression/mixed_precision.py @@ -9,14 +9,23 @@ # See the License for the specific language governing permissions and # limitations under the License. +from abc import ABC from abc import abstractmethod -from typing import Dict, List, Optional, TypeVar +from typing import Iterable, List, Optional, Tuple, TypeVar +import nncf +from nncf import Dataset from nncf.common.graph import NNCFGraph +from nncf.common.graph import NNCFNode +from nncf.common.graph.transformations.commands import TargetType from nncf.common.logging.track_progress import track +from nncf.common.tensor_statistics.statistic_point import StatisticPoint +from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer +from nncf.common.utils.backend import BackendType +from nncf.common.utils.backend import get_backend from nncf.common.utils.registry import Registry from nncf.parameters import SensitivityMetric -from nncf.quantization.algorithms.weight_compression.backend import WeightCompressionAlgoBackend +from nncf.quantization.algorithms.algorithm import Algorithm from nncf.quantization.algorithms.weight_compression.config import WeightCompressionConfig from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters from nncf.quantization.algorithms.weight_compression.weight_lowering import do_int_dequantization @@ -31,67 +40,91 @@ THE_LOWEST_SENSITIVITY = 0 -class MixedPrecisionCriterion: +class MixedPrecisionCriterion(Algorithm): """ Assigns mixed quantization scheme (e.g. uniform int8 or uniform int4/non-uniform fp4) for weights based on some criteria. """ - def __init__( - self, - model: TModel, - graph: NNCFGraph, - backend_entity: WeightCompressionAlgoBackend, - weight_params: List[WeightCompressionParameters], - primary_config: WeightCompressionConfig, - ratio: float, - activations: Optional[Dict[str, List[Tensor]]] = None, - ): + def __init__(self, primary_config: WeightCompressionConfig, ratio: float, subset_size: Optional[int] = None): """ - :param model: The model. - :param graph: The model graph associated with the model. - :param backend_entity: The instance of the WeightCompressionAlgoBackend. - :param weight_params: Weight compression parameters which determines how and what weight should be compressed. :param primary_config: Configuration on how to compress (quantize) weights to primary precision. :param ratio: The ratio between primary and backup precisions (e.g. 0.9 means 90% of layers quantized to NF4 and the rest to INT8_ASYM). - :param activations: The input activations of the nodes to be quantized. + :param subset_size: Size of dataset subset for statistics. """ - self._model = model - self._graph = graph - self._backend_entity = backend_entity - self._weight_params = weight_params - self._activations = activations self._primary_config = primary_config self._ratio = ratio + self._subset_size = subset_size + self._algorithm_key = f"MPC_{hash(self)}" + self._backend_entity = None @abstractmethod - def _calc_sensitivity(self) -> List[float]: + def _calc_sensitivity( + self, + model: TModel, + graph: NNCFGraph, + weight_params: List[WeightCompressionParameters], + statistic_points: Optional[StatisticPointsContainer] = None, + ) -> List[float]: """ Calculates sensitivity of each layer according to a criterion. :return: List of values per node to be quantized. """ - def assign_mixed_precision(self) -> None: + def apply( + self, + model: TModel, + graph: NNCFGraph, + statistic_points: Optional[StatisticPointsContainer] = None, + dataset: Optional[Dataset] = None, + weight_params: List[WeightCompressionParameters] = None, + ) -> None: """ Assigns quantization precision based on computed layers' sensitivities, ratio of parameters. """ - scores = self._calc_sensitivity() - num_all_weights = sum(wp.num_weights for wp in self._weight_params) + self._set_backend_entity(model) + + scores = self._calc_sensitivity(model, graph, weight_params, statistic_points) + num_all_weights = sum(wp.num_weights for wp in weight_params) indexes_of_layers_in_ascending_order_of_scores = [ i[0] for i in sorted(enumerate(scores), reverse=False, key=lambda x: x[1]) ] num_weights_in_4bit = 0 for index in indexes_of_layers_in_ascending_order_of_scores: - weight_param = self._weight_params[index] + weight_param = weight_params[index] current_ratio = (num_weights_in_4bit + weight_param.num_weights) / num_all_weights if current_ratio >= self._ratio: break weight_param.compression_config = self._primary_config num_weights_in_4bit += weight_param.num_weights + @abstractmethod + def _set_backend_entity(self, model: TModel) -> None: + """ + Creates a helper class with a backed-specific logic of the algorithm. + + :param model: Backend-specific input model. + """ + + @abstractmethod + def get_statistic_points( + self, + model: TModel, + graph: NNCFGraph, + nodes_and_port_ids: Iterable[Tuple[NNCFNode, int]], + ) -> StatisticPointsContainer: + """ + Returns statistic points, for which StatisticsCollector should collect statistics. + + :param model: Model for statistics collection. + :param graph: Model graph. + :param nodes_and_port_ids: Nodes and port ids for which statistics should be collected. + :return: Statistic points, for which StatisticsCollector should collect statistics. + """ + @MIXED_PRECISION_CRITERIA.register(SensitivityMetric.WEIGHT_QUANTIZATION_ERROR) class DataFreeCriterion(MixedPrecisionCriterion): @@ -99,39 +132,114 @@ class DataFreeCriterion(MixedPrecisionCriterion): A baseline mixed precision criterion that is based on quantization noise of weights only. """ - def _calc_weight_sensitivity(self, weight_param: WeightCompressionParameters) -> float: + @property + def available_backends(self) -> List[BackendType]: + return [BackendType.OPENVINO, BackendType.TORCH, BackendType.TORCH_FX] + + def _set_backend_entity(self, model: TModel) -> None: + model_backend = get_backend(model) + if model_backend == BackendType.OPENVINO: + from nncf.quantization.algorithms.weight_compression.openvino_backend import OVWeightCompressionAlgoBackend + + self._backend_entity = OVWeightCompressionAlgoBackend(model) + elif model_backend == BackendType.TORCH: + from nncf.quantization.algorithms.weight_compression.torch_backend import PTWeightCompressionAlgoBackend + + self._backend_entity = PTWeightCompressionAlgoBackend() + elif model_backend == BackendType.TORCH_FX: + from nncf.quantization.algorithms.weight_compression.torch_fx_backend import FXWeightCompressionAlgoBackend + + self._backend_entity = FXWeightCompressionAlgoBackend() + else: + raise nncf.UnsupportedBackendError( + "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) + ) + + def _calc_weight_sensitivity( + self, + weight_param: WeightCompressionParameters, + model: TModel, + graph: NNCFGraph, + ) -> float: weight = self._backend_entity.get_weight( - weight_param.node_with_weight, weight_param.weight_port_id, self._model, self._graph + weight_param.node_with_weight, weight_param.weight_port_id, model, graph ) - backup_config = weight_param.compression_config + backup_config = WeightCompressionConfig() reduction_axes = weight_param.reduction_axes int_error = get_integer_quantization_error(weight, reduction_axes, backup_config) eps = fns.finfo(weight).eps return 1 / (int_error + eps) - def _calc_score_per_node(self, weight_param: WeightCompressionParameters) -> float: - weight_score = self._calc_weight_sensitivity(weight_param) + def _calc_score_per_node( + self, + weight_param: WeightCompressionParameters, + model: TModel, + graph: NNCFGraph, + statistic_points: Optional[StatisticPointsContainer] = None, + ) -> float: + weight_score = self._calc_weight_sensitivity(weight_param, model, graph) return weight_score - def _calc_sensitivity(self) -> List[float]: + def _calc_sensitivity( + self, + model: TModel, + graph: NNCFGraph, + weight_params: List[WeightCompressionParameters], + statistic_points: Optional[StatisticPointsContainer] = None, + ) -> List[float]: scores = [] - for weight_param in track(self._weight_params, description="Mixed-Precision assignment"): - scores.append(self._calc_score_per_node(weight_param)) + for weight_param in track(weight_params, description="Mixed-Precision assignment"): + scores.append(self._calc_score_per_node(weight_param, model, graph, statistic_points)) return scores + def get_statistic_points( + self, + model: TModel, + graph: NNCFGraph, + nodes_and_port_ids: Iterable[Tuple[NNCFNode, int]], + ) -> StatisticPointsContainer: + raise RuntimeError("No statistics collection intended for data-free mixed precision criterion") + -class DataBasedCriterion(DataFreeCriterion): +class DataBasedCriterion(DataFreeCriterion, ABC): """ - Data-based mixed precision criterion that takes into account outliers in the input activations. - Expecting activations of the following shape: [seq_length, hidden_dim] + Data-based mixed precision criterion that takes into account outliers in the input statistics. + Expecting statistics of the following shape: [hidden_dim] """ - @staticmethod - @abstractmethod - def _calc_activation_sensitivity(activations: List[Tensor]) -> float: - pass + STAT_KEY = None + + @property + def available_backends(self) -> List[BackendType]: + return [BackendType.OPENVINO] + + def _set_backend_entity(self, model: TModel) -> None: + model_backend = get_backend(model) + if model_backend == BackendType.OPENVINO: + from nncf.quantization.algorithms.weight_compression.openvino_backend import OVMixedPrecisionAlgoBackend + + self._backend_entity = OVMixedPrecisionAlgoBackend(model) + else: + raise nncf.UnsupportedBackendError( + "Cannot return backend-specific entity because {} is not supported!".format(model_backend.value) + ) + + def _calc_activation_sensitivity( + self, + weight_param: WeightCompressionParameters, + graph: NNCFGraph, + statistic_points: StatisticPointsContainer, + ) -> float: + stats = self._get_statistics_for_node(statistic_points, weight_param.node_with_weight, graph, self.STAT_KEY) + return stats[0].item() - def _calc_score_per_node(self, weight_param: WeightCompressionParameters): + def _calc_score_per_node( + self, + weight_param: WeightCompressionParameters, + model: TModel, + graph: NNCFGraph, + statistic_points: Optional[StatisticPointsContainer] = None, + ): """ NOTE: Data-based criteria for assigning 4-bit/8-bit precisions are valid for Matmul operations only. However, in some cases it can be beneficial to quantize Gather layers to 4-bit. @@ -140,10 +248,85 @@ def _calc_score_per_node(self, weight_param: WeightCompressionParameters): """ if weight_param.node_with_weight.metatype in self._backend_entity.embedding_metatypes: return THE_LOWEST_SENSITIVITY - weight_score = self._calc_weight_sensitivity(weight_param) - activation_score = self._calc_activation_sensitivity(self._activations[weight_param.node_with_weight.node_name]) + weight_score = self._calc_weight_sensitivity(weight_param, model, graph) + activation_score = self._calc_activation_sensitivity(weight_param, graph, statistic_points) return weight_score * activation_score + def get_statistic_points( + self, + model: TModel, + graph: NNCFGraph, + nodes_and_port_ids: Iterable[Tuple[NNCFNode, int]], + ) -> StatisticPointsContainer: + self._set_backend_entity(model) + + statistic_container = StatisticPointsContainer() + for act_node, output_port_id in nodes_and_port_ids: + n_dims = len(graph.get_output_edges_by_port_id(act_node, output_port_id)[0].tensor_shape) + if n_dims < 2: + raise RuntimeError( + f"Data-aware mixed precision criteria are not supported for MatMuls with 1D inputs. " + f"Node: {act_node.node_name}, number of dimensions: {n_dims}." + ) + statistic_point = self._backend_entity.target_point( + TargetType.POST_LAYER_OPERATION, act_node.node_name, port_id=output_port_id + ) + stat_collector = self._get_statistic_collector() + statistic_container.add_statistic_point( + StatisticPoint( + target_point=statistic_point, tensor_collector=stat_collector, algorithm=self._algorithm_key + ) + ) + + return statistic_container + + @abstractmethod + def _get_statistic_collector(self): + """ + Get statistic collector + """ + + def _get_activation_node_and_port(self, node: NNCFNode, nncf_graph: NNCFGraph) -> Tuple[NNCFNode, int]: + """ + This method returns the activation layer and corresponding port id for the node. + + :param node: NNCFGraph node for which the activation is sought. + :param nncf_graph: NNCFGraph instance with the node. + :return: Tuple with the activation node and port id. + """ + activation_port = self._backend_entity.get_activation_port_id(node, nncf_graph) + activation_edge = nncf_graph.get_input_edge_by_port_id(node, activation_port) + activation_node = activation_edge.from_node + port_id = activation_edge.output_port_id + return activation_node, port_id + + def _get_statistics_for_node( + self, statistic_points: StatisticPointsContainer, node: NNCFNode, nncf_graph: NNCFGraph, stat_key: str + ) -> List[Tensor]: + act_node, output_port_id = self._get_activation_node_and_port(node, nncf_graph) + + def input_filter_func(point): + # For the floating-point statistics collected in POST_LAYER style, + # we also need to determine the output port id. + # For the cases when the layer has more than one (0) output port. + return ( + self._algorithm_key in point.algorithm_to_tensor_collectors + and point.target_point.type == TargetType.POST_LAYER_OPERATION + and point.target_point.port_id == output_port_id + ) + + stats = [] + for tensor_collector in statistic_points.get_algo_statistics_for_node( + act_node.node_name, input_filter_func, self._algorithm_key + ): + statistics = tensor_collector.get_statistics() + for data in statistics.get_data().values(): + if isinstance(data, Tensor): + stats.append(data) + else: + stats.extend(data) + return stats + @MIXED_PRECISION_CRITERIA.register(SensitivityMetric.HESSIAN_INPUT_ACTIVATION) class HAWQCriterion(DataBasedCriterion): @@ -152,24 +335,18 @@ class HAWQCriterion(DataBasedCriterion): multiplied by L2 norm of 8-bit quantization noise. """ - @staticmethod - def _calc_activation_sensitivity(activations: List[Tensor]) -> float: - htrace = 0 - nsamples = len(activations) - for inp in activations: - # NOTE: average trace?? divide by number of diagonal elements - htrace += fns.sum(fns.multiply(inp, inp)).item() - # normalize by sequence_length - the same for all activations - # normalize by hidden dimension - htrace /= inp.size - htrace *= 2 / nsamples - return htrace - - def _calc_weight_sensitivity(self, weight_param: WeightCompressionParameters) -> float: + STAT_KEY = SensitivityMetric.HESSIAN_INPUT_ACTIVATION.value + + def _calc_weight_sensitivity( + self, + weight_param: WeightCompressionParameters, + model: TModel, + graph: NNCFGraph, + ) -> float: weight = self._backend_entity.get_weight( - weight_param.node_with_weight, weight_param.weight_port_id, self._model, self._graph + weight_param.node_with_weight, weight_param.weight_port_id, model, graph ) - backup_config = weight_param.compression_config + backup_config = WeightCompressionConfig() reduction_axes = weight_param.reduction_axes orig_shape = weight.shape @@ -182,6 +359,9 @@ def _calc_weight_sensitivity(self, weight_param: WeightCompressionParameters) -> decompressed_weight = decompressed_weight.reshape(orig_shape) return fns.linalg.norm(decompressed_weight - weight, ord="fro").item() + def _get_statistic_collector(self): + return self._backend_entity.hawq_statistic_collector(self._subset_size) + @MIXED_PRECISION_CRITERIA.register(SensitivityMetric.MEAN_ACTIVATION_VARIANCE) class MeanVarianceCriterion(DataBasedCriterion): @@ -189,9 +369,13 @@ class MeanVarianceCriterion(DataBasedCriterion): The mean variance of the layers' inputs multiplied by inverted 8-bit quantization noise. """ - @staticmethod - def _calc_activation_sensitivity(activations: List[Tensor]) -> float: - return fns.mean(fns.stack([fns.mean(fns.var(inp, axis=0)) for inp in activations])).item() + STAT_KEY = SensitivityMetric.MEAN_ACTIVATION_VARIANCE.value + + def _get_statistic_collector(self): + # Reducing across the second-last dimension, assuming it is the sequence length dimension + return self._backend_entity.mean_variance_statistic_collector( + reduction_axes=(-2,), subset_size=self._subset_size + ) @MIXED_PRECISION_CRITERIA.register(SensitivityMetric.MAX_ACTIVATION_VARIANCE) @@ -200,9 +384,13 @@ class MaxVarianceCriterion(DataBasedCriterion): The maximum variance of the layers' inputs multiplied by inverted 8-bit quantization noise. """ - @staticmethod - def _calc_activation_sensitivity(activations: List[Tensor]) -> float: - return fns.mean(fns.stack([fns.max(fns.var(inp, axis=0)) for inp in activations])).item() + STAT_KEY = SensitivityMetric.MAX_ACTIVATION_VARIANCE.value + + def _get_statistic_collector(self): + # Reducing across the second-last dimension, assuming it is the sequence length dimension + return self._backend_entity.max_variance_statistic_collector( + reduction_axes=(-2,), subset_size=self._subset_size + ) @MIXED_PRECISION_CRITERIA.register(SensitivityMetric.MEAN_ACTIVATION_MAGNITUDE) @@ -211,6 +399,10 @@ class MeanMaxCriterion(DataBasedCriterion): The mean magnitude of the layers' inputs multiplied by inverted 8-bit quantization noise. """ - @staticmethod - def _calc_activation_sensitivity(activations: List[Tensor]) -> float: - return fns.mean(fns.stack([fns.mean(fns.max(fns.abs(inp), axis=0)) for inp in activations])).item() + STAT_KEY = SensitivityMetric.MEAN_ACTIVATION_MAGNITUDE.value + + def _get_statistic_collector(self): + # Reducing across the second-last dimension, assuming it is the sequence length dimension + return self._backend_entity.mean_abs_max_statistic_collector( + reduction_axes=(-2,), subset_size=self._subset_size + ) diff --git a/nncf/quantization/algorithms/weight_compression/openvino_backend.py b/nncf/quantization/algorithms/weight_compression/openvino_backend.py index 78313374d9f..ec4dfab4711 100644 --- a/nncf/quantization/algorithms/weight_compression/openvino_backend.py +++ b/nncf/quantization/algorithms/weight_compression/openvino_backend.py @@ -11,14 +11,23 @@ from typing import Dict, Iterable, List, Optional, Tuple import openvino as ov +from openvino import Type +from openvino.properties.hint import inference_precision from openvino.runtime import opset13 as opset +import nncf from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.utils import get_reduction_axes +from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator +from nncf.experimental.common.tensor_statistics.collectors import NoopAggregator from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.statistics import MaxVarianceTensorStatistic +from nncf.experimental.common.tensor_statistics.statistics import MeanMagnitudeTensorStatistic +from nncf.experimental.common.tensor_statistics.statistics import MeanVarianceTensorStatistic +from nncf.experimental.common.tensor_statistics.statistics import WCTensorStatistic from nncf.openvino.graph.metatypes import openvino_metatypes as om from nncf.openvino.graph.metatypes.groups import ATOMIC_ACTIVATIONS_OPERATIONS from nncf.openvino.graph.model_transformer import OVModelTransformer @@ -27,10 +36,15 @@ from nncf.openvino.graph.transformations.command_creation import OVCommandCreator from nncf.openvino.graph.transformations.commands import OVTargetPoint from nncf.openvino.rt_info import dump_parameters -from nncf.openvino.statistics.collectors import get_raw_stat_collector +from nncf.openvino.statistics.collectors import OVMaxVarianceReducer +from nncf.openvino.statistics.collectors import OVMeanAbsMaxReducer +from nncf.openvino.statistics.collectors import OVMeanReducer +from nncf.openvino.statistics.collectors import OVMeanVarianceReducer +from nncf.openvino.statistics.collectors import OVShapeReducer from nncf.parameters import CompressWeightsMode from nncf.quantization.algorithms.weight_compression.awq_patterns import get_awq_patterns from nncf.quantization.algorithms.weight_compression.backend import AWQAlgoBackend +from nncf.quantization.algorithms.weight_compression.backend import MixedPrecisionAlgoBackend from nncf.quantization.algorithms.weight_compression.backend import WeightCompressionAlgoBackend from nncf.quantization.algorithms.weight_compression.config import WeightCompressionConfig from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters @@ -79,9 +93,15 @@ def get_reduction_axes(node_with_weight: NNCFNode, weight_port_id: int, graph: N def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> OVTargetPoint: return OVTargetPoint(target_type, target_node_name, port_id) - @staticmethod - def raw_statistic_collector(num_samples: Optional[int] = None) -> TensorCollector: - return get_raw_stat_collector(num_samples) + def mean_statistic_collector( + self, reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + mean_reducer = OVMeanReducer(reduction_axes, inplace=True) + shape_reducer = OVShapeReducer(inplace=True) + collector = TensorCollector(WCTensorStatistic) + collector.register_statistic_branch(WCTensorStatistic.MEAN_STAT, mean_reducer, NoopAggregator(subset_size)) + collector.register_statistic_branch(WCTensorStatistic.SHAPE_STAT, shape_reducer, NoopAggregator(subset_size)) + return collector @staticmethod def get_activation_port_id(node: NNCFNode, nncf_graph: NNCFGraph) -> int: @@ -106,6 +126,26 @@ def get_weight(self, node_with_weight: NNCFNode, weight_port_id: int, model: ov. weight_tensor = get_const_value(weight_node) return Tensor(weight_tensor) + def get_weight_dtype( + self, node_with_weight: NNCFNode, weight_port_id: int, model: ov.Model, graph: NNCFGraph + ) -> TensorDataType: + ov_type_name = node_with_weight.layer_attributes.constant_attributes[weight_port_id]["dtype"] + dtype_map = { + "f16": TensorDataType.float16, + "bf16": TensorDataType.bfloat16, + "f32": TensorDataType.float32, + "f64": TensorDataType.float64, + "i8": TensorDataType.int8, + "i32": TensorDataType.int32, + "i64": TensorDataType.int64, + "u8": TensorDataType.uint8, + } + return dtype_map.get(ov_type_name) + + @staticmethod + def get_weight_shape(node_with_weight: NNCFNode, weight_port_id: int, graph: NNCFGraph) -> Tuple: + return node_with_weight.layer_attributes.constant_attributes[weight_port_id]["shape"] + def set_weight( self, node_with_weight: NNCFNode, weight_port_id: int, model: ov.Model, graph: NNCFGraph, weight: Tensor ): @@ -201,7 +241,7 @@ def _create_compression_subgraph( elif compression_config.mode == CompressWeightsMode.INT8_ASYM: compression_dtype = ov.Type.u8 else: - raise ValueError(f"{compression_config.mode.value} is not supported.") + raise nncf.ParameterNotSupportedError(f"{compression_config.mode.value} is not supported.") original_shape = weight.shape compressed_weight = compress_weight(weight, reduction_axes, compression_config, layer_scales, layer_zero_points) @@ -310,14 +350,17 @@ def get_compress_decompress_pipeline(config: WeightCompressionConfig, w_shape, s model = ov.Model([result], parameters) - compiled_model = ov.compile_model(model, device_name="CPU") + compiled_model = ov.compile_model(model, device_name="CPU", config={inference_precision: Type.f32}) return lambda parameters: compiled_model(parameters)[0] @staticmethod def get_compress_pipeline(config: WeightCompressionConfig, w_shape, s_shape, z_p_shape=None, return_nodes=False): mode = config.mode - assert mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM] + assert mode in [ + CompressWeightsMode.INT4_SYM, + CompressWeightsMode.INT4_ASYM, + ], f"Only int4 supported, but given={mode}" num_bits = config.num_bits asym_quant = mode in [CompressWeightsMode.INT4_ASYM] @@ -340,7 +383,7 @@ def get_compress_pipeline(config: WeightCompressionConfig, w_shape, s_shape, z_p model = ov.Model([result], parameters) - compiled_model = ov.compile_model(model, device_name="CPU") + compiled_model = ov.compile_model(model, device_name="CPU", config={inference_precision: Type.f32}) return lambda parameters: compiled_model(parameters)[0] @@ -355,3 +398,35 @@ def scale_insertion_command(source_node, next_nodes, source_node_output_port, sc return OVCommandCreator.multiply_insertion_command( source_node, next_nodes, source_node_output_port, scale, f"{source_node.node_name}/awq_mul" ) + + +class OVMixedPrecisionAlgoBackend(MixedPrecisionAlgoBackend, OVWeightCompressionAlgoBackend): + @staticmethod + def mean_variance_statistic_collector( + reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + reducer = OVMeanVarianceReducer(reduction_axes, inplace=True) + aggregator = MeanAggregator(num_samples=subset_size) + collector = TensorCollector(MeanVarianceTensorStatistic) + collector.register_statistic_branch(MeanVarianceTensorStatistic.MEAN_VARIANCE_STAT, reducer, aggregator) + return collector + + @staticmethod + def max_variance_statistic_collector( + reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + reducer = OVMaxVarianceReducer(reduction_axes, inplace=True) + aggregator = MeanAggregator(num_samples=subset_size) + collector = TensorCollector(MaxVarianceTensorStatistic) + collector.register_statistic_branch(MaxVarianceTensorStatistic.MAX_VARIANCE_STAT, reducer, aggregator) + return collector + + @staticmethod + def mean_abs_max_statistic_collector( + reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + reducer = OVMeanAbsMaxReducer(reduction_axes, inplace=True) + aggregator = MeanAggregator(num_samples=subset_size) + collector = TensorCollector(MeanMagnitudeTensorStatistic) + collector.register_statistic_branch(MeanMagnitudeTensorStatistic.MEAN_MAGNITUDE_STAT, reducer, aggregator) + return collector diff --git a/nncf/quantization/algorithms/weight_compression/scale_estimation.py b/nncf/quantization/algorithms/weight_compression/scale_estimation.py index 712c5fd955d..a5572530857 100644 --- a/nncf/quantization/algorithms/weight_compression/scale_estimation.py +++ b/nncf/quantization/algorithms/weight_compression/scale_estimation.py @@ -12,6 +12,7 @@ from copy import deepcopy from typing import Any, Dict, List, Optional, Tuple, TypeVar +import nncf from nncf import Dataset from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.graph import NNCFNode @@ -19,12 +20,17 @@ from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer from nncf.common.utils.backend import BackendType from nncf.common.utils.backend import get_backend +from nncf.experimental.common.tensor_statistics.statistics import WCTensorStatistic +from nncf.parameters import CompressWeightsMode from nncf.quantization.algorithms.weight_compression.activation_stats import process_stats from nncf.quantization.algorithms.weight_compression.backend import WeightCompressionAlgoBackend from nncf.quantization.algorithms.weight_compression.config import WeightCompressionConfig from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters +from nncf.quantization.algorithms.weight_compression.weight_lowering import calculate_normalized_weight_and_fp4_scale from nncf.quantization.algorithms.weight_compression.weight_lowering import do_int_dequantization from nncf.quantization.algorithms.weight_compression.weight_lowering import do_int_quantization +from nncf.quantization.algorithms.weight_compression.weight_lowering import do_nf4_dequantization +from nncf.quantization.algorithms.weight_compression.weight_lowering import do_nf4_quantization from nncf.quantization.algorithms.weight_compression.weight_lowering import reshape_weight_for_grouped_quantization from nncf.tensor import Tensor from nncf.tensor import TensorDataType @@ -46,7 +52,7 @@ def __init__( name_to_node_mapping: Dict[str, Any], all_weight_params: List[WeightCompressionParameters], nodes_to_compress: List[NNCFNode], - activations: Optional[Dict[str, List[Tensor]]] = None, + statistics: Dict[str, WCTensorStatistic], subset_size: int = 32, initial_steps: int = 5, scale_steps: int = 10, @@ -57,7 +63,7 @@ def __init__( :param name_to_node_mapping: Name to node mapping for updating node weights. :param all_weight_params: List of all weight parameters. :param nodes_to_compress: List of nodes for processing. - :param activations: The input activations of the layers considered for compression. + :param statistics: Input activation statistics for each node. :param subset_size: The number of samples for scale estimation. :param initial_steps: The number of the steps for absmax scale rectification. :param scale_steps: The number of the steps for grid search scale rectification @@ -68,7 +74,7 @@ def __init__( self.name_to_node_mapping = name_to_node_mapping self._all_weight_params = all_weight_params self._nodes_to_compress = nodes_to_compress - self._activations = activations + self._statistics = statistics self._subset_size = subset_size self._initial_steps = initial_steps self._scale_steps = scale_steps @@ -96,7 +102,7 @@ def _set_backend_entity(self, model: TModel) -> None: self._backend_entity = OVWeightCompressionAlgoBackend(model, self.name_to_node_mapping) else: - raise RuntimeError( + raise nncf.UnsupportedBackendError( "Cannot return backend-specific AWQ entity because {} is not supported!".format(model_backend.value) ) @@ -106,7 +112,7 @@ def apply( graph: NNCFGraph, statistic_points: Optional[StatisticPointsContainer] = None, dataset: Optional[Dataset] = None, - ) -> Dict[str, Tensor]: + ) -> Tuple[Dict[str, Tensor], Dict[str, Tensor]]: """ Estimates better scale for the int4 nodes in the model. Minimizes per-group difference between floating point MatMul and @@ -118,21 +124,21 @@ def apply( :param graph: Model graph. :param statistic_points: Statistic points with collected statistics values. :param dataset: A representative dataset for the calibration process. - :return: Dict with pairs (weight name, estimated scale). + :return: Two dictionaries for estimated scales and zero points for each weight name. """ - scales = dict() + scales, zero_points = dict(), dict() for wp in track(self._all_weight_params, description="Applying Scale Estimation"): weight_name = wp.weight_name node_name = wp.node_with_weight.node_name config = wp.compression_config - if config.num_bits != 4 or node_name not in self._activations: + if config.num_bits != 4 or node_name not in self._statistics: scales[weight_name] = None continue - stats = self._activations[node_name] + stats = self._statistics[node_name] weight_data = self._backend_entity.get_weight_names_and_port_ids(wp.node_with_weight, graph) if len(weight_data) != 1: # not supported by the algorithm @@ -141,7 +147,7 @@ def apply( weight = self._backend_entity.get_weight(wp.node_with_weight, weight_port_id, model, graph) - scales[weight_name], _ = self.calculate_quantization_params( + scales[weight_name], zero_points[weight_name] = self.calculate_quantization_params( self._backend_entity, stats, weight, @@ -153,12 +159,12 @@ def apply( self._weight_penalty, ) - return scales + return scales, zero_points @staticmethod def calculate_quantization_params( backend_entity: WeightCompressionAlgoBackend, - activations: List[Tensor], + statistics: WCTensorStatistic, weight: Tensor, reduction_axes: Tuple[int, ...], config: WeightCompressionConfig, @@ -178,7 +184,8 @@ def calculate_quantization_params( 2. A grid search to further refine the scale parameters. :param backend_entity: The backend-specific implementation of the weight compression algorithm. - :param activations: List of activation tensors corresponding to the layers being quantized. + :param statistics: The input activations of the layer reduced over batch and sequence length dimensions, + together with original activation tensor shapes. :param weight: The weight tensor that is being quantized. :param reduction_axes: Tuple specifying the axes along which the reduction is performed for quantization. :param config: Configuration parameters for the weight compression, including quantization settings. @@ -192,7 +199,7 @@ def calculate_quantization_params( """ reduction_axis = reduction_axes[0] - s, X = process_stats(activations, subset_size) + s, X = process_stats(statistics, subset_size) weight = weight.astype(TensorDataType.float32) eps = fns.finfo(weight).eps @@ -206,11 +213,18 @@ def calculate_quantization_params( cur_config.group_size = group_size original_weight = fns.zeros_like(weight) + weight - - compressed_weights, scale, zp = do_int_quantization(original_weight, reduction_axis, cur_config) - if zp is not None: - zp = zp.astype(scale.dtype) - q_weights = do_int_dequantization(compressed_weights, scale, zp, reduction_axis) + if config.mode == CompressWeightsMode.NF4: + norm_weight, scale = calculate_normalized_weight_and_fp4_scale( + original_weight, reduction_axis, cur_config.group_size + ) + compressed_weights = do_nf4_quantization(norm_weight, scale, is_normalized_weight=True) + q_weights = do_nf4_dequantization(compressed_weights, scale, reduction_axis) + zp = None + else: + compressed_weights, scale, zp = do_int_quantization(original_weight, reduction_axis, cur_config) + if zp is not None: + zp = zp.astype(scale.dtype) + q_weights = do_int_dequantization(compressed_weights, scale, zp, reduction_axis) s = fns.unsqueeze(s, 0) s, _ = reshape_weight_for_grouped_quantization(s, reduction_axis, group_size) @@ -246,18 +260,19 @@ def calculate_quantization_params( key = (config.mode, config.num_bits) + q_weights.shape + scale.shape if zp is not None: key += zp_shape - if key in ScaleEstimation.compress_decompress_cache: - compress_decompress_model = ScaleEstimation.compress_decompress_cache[key]["compress_decompress_model"] - compress_model = ScaleEstimation.compress_decompress_cache[key]["compress_model"] - else: - compress_decompress_model = backend_entity.get_compress_decompress_pipeline( - config, q_weights.shape, scale.shape, zp_shape - ) - compress_model = backend_entity.get_compress_pipeline(config, q_weights.shape, scale.shape, zp_shape) - ScaleEstimation.compress_decompress_cache[key] = { - "compress_decompress_model": compress_decompress_model, - "compress_model": compress_model, - } + if config.mode != CompressWeightsMode.NF4: + if key in ScaleEstimation.compress_decompress_cache: + compress_decompress_model = ScaleEstimation.compress_decompress_cache[key]["compress_decompress_model"] + compress_model = ScaleEstimation.compress_decompress_cache[key]["compress_model"] + else: + compress_decompress_model = backend_entity.get_compress_decompress_pipeline( + config, q_weights.shape, scale.shape, zp_shape + ) + compress_model = backend_entity.get_compress_pipeline(config, q_weights.shape, scale.shape, zp_shape) + ScaleEstimation.compress_decompress_cache[key] = { + "compress_decompress_model": compress_decompress_model, + "compress_model": compress_model, + } scale_sign = scale / fns.abs(scale) zero_scale = 0.001 zero_mask = zero_scale * zero_mask.astype(original_weight.dtype) @@ -271,7 +286,11 @@ def calculate_quantization_params( near_to_ideal_scale = near_to_ideal_scale * scale_sign input_tensors[1] = near_to_ideal_scale.data - out = compress_decompress_model(input_tensors) + if config.mode == CompressWeightsMode.NF4: + g_compressed_weighs = do_nf4_quantization(original_weight, near_to_ideal_scale) + out = do_nf4_dequantization(g_compressed_weighs, near_to_ideal_scale) + else: + out = compress_decompress_model(input_tensors) q_weights_ = fns.zeros_like(original_weight) + out q_outs = fns.matmul(fns.transpose(q_weights_, (1, 0, 2)), X) @@ -297,18 +316,24 @@ def calculate_quantization_params( input_tensors[1] = near_to_ideal_scale.data if i < initial_steps - 1: - out = compress_model(input_tensors) + if config.mode == CompressWeightsMode.NF4: + out = do_nf4_quantization(original_weight, near_to_ideal_scale) + else: + out = compress_model(input_tensors) compressed_weights = fns.zeros_like(original_weight) + out target, zero_mask = get_target_zero_mask(compressed_weights, zp) zero_mask = zero_scale * zero_mask.astype(original_weight.dtype) # iterative rectification of scale based on grid search - for scale_steps in range(scale_steps): - factor = 1.0 - 0.05 * scale_steps + for scale_step in range(scale_steps): + factor = 1.0 - 0.05 * scale_step scaled_scale = factor * scale input_tensors[1] = scaled_scale.data - out = compress_model(input_tensors) + if config.mode == CompressWeightsMode.NF4: + out = do_nf4_quantization(original_weight, scaled_scale) + else: + out = compress_model(input_tensors) compressed_weights = fns.zeros_like(original_weight) + out target, zero_mask = get_target_zero_mask(compressed_weights, zp) @@ -317,7 +342,11 @@ def calculate_quantization_params( near_to_ideal_scale = near_to_ideal_scale * scale_sign input_tensors[1] = near_to_ideal_scale.data - out = compress_decompress_model(input_tensors) + if config.mode == CompressWeightsMode.NF4: + g_compressed_weighs = do_nf4_quantization(original_weight, near_to_ideal_scale) + out = do_nf4_dequantization(g_compressed_weighs, near_to_ideal_scale) + else: + out = compress_decompress_model(input_tensors) q_weights_ = fns.zeros_like(original_weight) + out q_outs = fns.matmul(fns.transpose(q_weights_, (1, 0, 2)), X) @@ -340,9 +369,28 @@ def calculate_quantization_params( if config.group_size == -1: result_scale = fns.squeeze(result_scale, axis=1) + if zp is not None and config.group_size == -1: + zp = fns.squeeze(zp, axis=1) return result_scale, zp + @staticmethod + def activations_to_wc_statistics(activations: List[Tensor]) -> WCTensorStatistic: + """ + Mimic the activation reducing logic from WeightCompression.get_statistic_points. + + :param activations: List of raw activations. + :return: Instance of WCTensorStatistic class containing reduced activations and shapes. + """ + mean_values = [] + shapes = [] + for act in activations: + shapes.append(act.shape) + reduction_shape = tuple(range(act.ndim - 1)) + mean_values.append(fns.mean(act, axis=reduction_shape)) + wc_statistics = WCTensorStatistic(mean_values, shapes) + return wc_statistics + def get_target_zero_mask(compressed_weights: Tensor, zp: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: """ diff --git a/nncf/quantization/algorithms/weight_compression/torch_backend.py b/nncf/quantization/algorithms/weight_compression/torch_backend.py index eacdc5e1028..136c38413ab 100644 --- a/nncf/quantization/algorithms/weight_compression/torch_backend.py +++ b/nncf/quantization/algorithms/weight_compression/torch_backend.py @@ -21,7 +21,11 @@ from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.layout import TransformationLayout +from nncf.experimental.common.tensor_statistics.collectors import MeanReducer +from nncf.experimental.common.tensor_statistics.collectors import NoopAggregator +from nncf.experimental.common.tensor_statistics.collectors import ShapeReducer from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.statistics import WCTensorStatistic from nncf.parameters import CompressWeightsMode from nncf.quantization.algorithms.weight_compression.backend import WeightCompressionAlgoBackend from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters @@ -39,9 +43,10 @@ from nncf.torch.model_graph_manager import split_const_name from nncf.torch.model_transformer import PTModelTransformer from nncf.torch.nncf_network import NNCFNetwork -from nncf.torch.quantization.layers import AsymmetricWeightsDecompressor -from nncf.torch.quantization.layers import SymmetricWeightsDecompressor -from nncf.torch.tensor_statistics.collectors import get_raw_stat_collector +from nncf.torch.quantization.layers import INT4AsymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT4SymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor class PTWeightCompressionAlgoBackend(WeightCompressionAlgoBackend): @@ -50,7 +55,7 @@ class PTWeightCompressionAlgoBackend(WeightCompressionAlgoBackend): TargetType.POST_LAYER_OPERATION: TargetType.OPERATOR_POST_HOOK, } MATMUL_METATYPES = [om.PTLinearMetatype, om.PTMatMulMetatype, om.PTAddmmMetatype] - EMBEDDING_METATYPES = [om.PTEmbeddingMetatype] + EMBEDDING_METATYPES = [om.PTEmbeddingMetatype, om.PTAtenEmbeddingMetatype] CONVOLUTION_METATYPES = [ om.PTConv1dMetatype, om.PTConv2dMetatype, @@ -142,9 +147,15 @@ def target_point(target_type: TargetType, target_node_name: str, port_id: int) - target_type = PTWeightCompressionAlgoBackend.TARGET_TYPE_TO_PT_INS_TYPE_MAP[target_type] return PTTargetPoint(target_type, target_node_name, input_port_id=port_id) - @staticmethod - def raw_statistic_collector(num_samples: Optional[int] = None) -> TensorCollector: - return get_raw_stat_collector(num_samples) + def mean_statistic_collector( + self, reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + mean_reducer = MeanReducer(reduction_axes) + shape_reducer = ShapeReducer() + collector = TensorCollector(WCTensorStatistic) + collector.register_statistic_branch(WCTensorStatistic.MEAN_STAT, mean_reducer, NoopAggregator(subset_size)) + collector.register_statistic_branch(WCTensorStatistic.SHAPE_STAT, shape_reducer, NoopAggregator(subset_size)) + return collector @staticmethod def get_activation_port_id(node: NNCFNode, graph: NNCFGraph) -> int: @@ -170,6 +181,16 @@ def get_weight( return Tensor(weight) + def get_weight_dtype( + self, node_with_weight: NNCFNode, weight_port_id: int, model: torch.nn.Module, graph: NNCFGraph + ) -> TensorDataType: + return self.get_weight(node_with_weight, weight_port_id, model, graph).dtype + + @staticmethod + def get_weight_shape(node_with_weight: NNCFNode, weight_port_id: int, graph: NNCFGraph) -> Tuple: + weight_node = get_const_node(node_with_weight, weight_port_id, graph) + return tuple(weight_node.layer_attributes.shape) + def set_weight( self, node_with_weight: NNCFNode, weight_port_id: int, model: torch.nn.Module, graph: NNCFGraph, weight: Tensor ): @@ -193,12 +214,11 @@ def transform_model( for wc_params in weight_compression_parameters: compression_config = wc_params.compression_config - if compression_config.mode not in [ - CompressWeightsMode.INT8_ASYM, - CompressWeightsMode.INT8_SYM, - CompressWeightsMode.INT8, + if compression_config.mode in [ + CompressWeightsMode.NF4, + CompressWeightsMode.E2M1, ]: - raise ValueError(f"{compression_config.mode.value} is not supported.") + raise nncf.ParameterNotSupportedError(f"{compression_config.mode.value} is not supported.") weight_node = get_const_node(wc_params.node_with_weight, wc_params.weight_port_id, graph) weight_name = weight_node.layer_attributes.name @@ -216,17 +236,35 @@ def transform_model( None if precomputed_scales is None else precomputed_scales.get(wc_params.weight_name), None if precomputed_zero_points is None else precomputed_zero_points.get(wc_params.weight_name), ) - compressed_weight.scale = compressed_weight.scale.astype(dtype=TensorDataType.float16) - # pack compressed tensor + # creates weight decompressor if compression_config.mode == CompressWeightsMode.INT8_SYM: - dtype = TensorDataType.int8 - else: - dtype = TensorDataType.uint8 - packed_tensor = compressed_weight.tensor.astype(dtype) + decompressor = INT8SymmetricWeightsDecompressor(compressed_weight.scale.data, result_dtype=weight.dtype) + elif compression_config.mode == CompressWeightsMode.INT8_ASYM: + decompressor = INT8AsymmetricWeightsDecompressor( + compressed_weight.scale.data, compressed_weight.zero_point.data, result_dtype=weight.dtype + ) + elif compression_config.mode == CompressWeightsMode.INT4_SYM: + decompressor = INT4SymmetricWeightsDecompressor( + scale=compressed_weight.scale.data, + compressed_weight_shape=compressed_weight.tensor.shape, + result_shape=weight.shape, + result_dtype=weight.dtype, + ) + elif compression_config.mode == CompressWeightsMode.INT4_ASYM: + decompressor = INT4AsymmetricWeightsDecompressor( + scale=compressed_weight.scale.data, + zero_point=compressed_weight.zero_point.data, + compressed_weight_shape=compressed_weight.tensor.shape, + result_shape=weight.shape, + result_dtype=weight.dtype, + ) + + # pack tensor + packed_tensor = decompressor.pack_weight(compressed_weight.tensor.data) # sets compressed tensor - compressed_parameter = torch.nn.Parameter(packed_tensor.data, requires_grad=False) + compressed_parameter = torch.nn.Parameter(packed_tensor, requires_grad=False) setattr(module, weight_attr_name, compressed_parameter) consumer_nodes = graph.get_next_nodes(weight_node) @@ -237,15 +275,6 @@ def transform_model( if id(param) == id(weight): setattr(c_module, name, compressed_parameter) - # creates weight decompressor - if compression_config.mode == CompressWeightsMode.INT8_SYM: - decompressor = SymmetricWeightsDecompressor(compressed_weight.scale.data, result_dtype=weight.dtype) - else: - packed_zero_point = compressed_weight.zero_point.astype(dtype) - decompressor = AsymmetricWeightsDecompressor( - compressed_weight.scale.data, packed_zero_point.data, result_dtype=weight.dtype - ) - # registry weight decompression module in the model decompressor_name = f"weights_decompressor_{weight_node.node_name.replace('.', '_')}" diff --git a/nncf/quantization/algorithms/weight_compression/torch_fx_backend.py b/nncf/quantization/algorithms/weight_compression/torch_fx_backend.py new file mode 100644 index 00000000000..c7c0a685244 --- /dev/null +++ b/nncf/quantization/algorithms/weight_compression/torch_fx_backend.py @@ -0,0 +1,253 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Iterable, List, Optional, Tuple + +import torch +import torch.fx + +import nncf +import nncf.errors +import nncf.tensor +from nncf.common.graph.graph import NNCFGraph +from nncf.common.graph.graph import NNCFNode +from nncf.common.graph.operator_metatypes import OperatorMetatype +from nncf.common.graph.transformations.commands import TargetType +from nncf.common.graph.transformations.layout import TransformationLayout +from nncf.experimental.common.tensor_statistics.collectors import MeanReducer +from nncf.experimental.common.tensor_statistics.collectors import NoopAggregator +from nncf.experimental.common.tensor_statistics.collectors import ShapeReducer +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.statistics import WCTensorStatistic +from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand +from nncf.experimental.torch.fx.model_transformer import FXModelTransformer +from nncf.experimental.torch.fx.node_utils import get_graph_node_by_name +from nncf.experimental.torch.fx.node_utils import get_tensor_constant_from_node +from nncf.experimental.torch.fx.transformations import constant_update_transformation_builder +from nncf.experimental.torch.fx.transformations import module_insertion_transformation_builder +from nncf.parameters import CompressWeightsMode +from nncf.quantization.algorithms.weight_compression.backend import WeightCompressionAlgoBackend +from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters +from nncf.quantization.algorithms.weight_compression.lora_correction import LoraCorrectionAlgorithm +from nncf.quantization.algorithms.weight_compression.torch_backend import PTWeightCompressionAlgoBackend +from nncf.quantization.algorithms.weight_compression.weight_lowering import compress_weight +from nncf.tensor import Tensor +from nncf.tensor.definitions import TensorDataType +from nncf.torch.graph import operator_metatypes as om +from nncf.torch.graph.transformations.commands import PTTargetPoint +from nncf.torch.model_graph_manager import get_const_node +from nncf.torch.model_graph_manager import get_weight_tensor_port_ids +from nncf.torch.quantization.layers import INT4AsymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT4SymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor + + +class FXWeightCompressionAlgoBackend(WeightCompressionAlgoBackend): + MATMUL_METATYPES = PTWeightCompressionAlgoBackend.MATMUL_METATYPES + EMBEDDING_METATYPES = PTWeightCompressionAlgoBackend.EMBEDDING_METATYPES + CONVOLUTION_METATYPES = PTWeightCompressionAlgoBackend.CONVOLUTION_METATYPES + + @property + def matmul_metatypes(self) -> List[OperatorMetatype]: + return FXWeightCompressionAlgoBackend.MATMUL_METATYPES + + @property + def embedding_metatypes(self) -> List[OperatorMetatype]: + return FXWeightCompressionAlgoBackend.EMBEDDING_METATYPES + + @property + def convolution_metatypes(self) -> List[OperatorMetatype]: + return FXWeightCompressionAlgoBackend.CONVOLUTION_METATYPES + + @staticmethod + def is_node_with_weights(node: NNCFNode, graph: NNCFGraph) -> bool: + return PTWeightCompressionAlgoBackend.is_node_with_weights(node, graph) + + @staticmethod + def get_weight_names_and_port_ids(node: NNCFNode, graph: NNCFGraph) -> List[Tuple[str, int]]: + port_ids = get_weight_tensor_port_ids(node, graph) + weight_name_port_ids = [(get_const_node(node, pid, graph).node_name, pid) for pid in port_ids] + return weight_name_port_ids + + @staticmethod + def get_reduction_axes(node_with_weight: NNCFNode, weight_port_id: int, graph: NNCFGraph) -> Optional[Tuple[int]]: + weight_node = get_const_node(node_with_weight, weight_port_id, graph) + edge = graph.get_edge(weight_node, graph.get_next_nodes(weight_node)[0]) + + ndims = len(edge.tensor_shape) + reduction_axes = None + if node_with_weight.metatype == om.PTAtenEmbeddingMetatype: + reduction_axes = [1] + elif node_with_weight.metatype == om.PTLinearMetatype: + reduction_axes = [ndims - 1] + elif node_with_weight.metatype == om.PTMatMulMetatype: + if weight_port_id == 0: + reduction_axes = [ndims - 1] + elif weight_port_id == 1: + reduction_axes = [max(0, ndims - 2)] + elif node_with_weight.metatype == om.PTAddmmMetatype: + if weight_port_id == 1: + reduction_axes = [ndims - 1] + elif weight_port_id == 2: + reduction_axes = [max(0, ndims - 2)] + elif node_with_weight.metatype in FXWeightCompressionAlgoBackend.CONVOLUTION_METATYPES: + channel_idx = ( + 1 + if node_with_weight.metatype + in [om.PTConvTranspose1dMetatype, om.PTConvTranspose2dMetatype, om.PTConvTranspose3dMetatype] + else 0 + ) + reduction_axes = [i for i in range(ndims) if i != channel_idx] + return tuple(reduction_axes) + + @staticmethod + def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: + return PTWeightCompressionAlgoBackend.target_point(target_type, target_node_name, port_id) + + def mean_statistic_collector( + self, reduction_axes: Tuple[int], subset_size: Optional[int] = None + ) -> TensorCollector: + mean_reducer = MeanReducer(reduction_axes) + shape_reducer = ShapeReducer() + collector = TensorCollector(WCTensorStatistic) + collector.register_statistic_branch(WCTensorStatistic.MEAN_STAT, mean_reducer, NoopAggregator(subset_size)) + collector.register_statistic_branch(WCTensorStatistic.SHAPE_STAT, shape_reducer, NoopAggregator(subset_size)) + return collector + + @staticmethod + def get_activation_port_id(node: NNCFNode, graph: NNCFGraph) -> int: + return PTWeightCompressionAlgoBackend.get_activation_port_id(node, graph) + + def get_weight( + self, node_with_weight: NNCFNode, weight_port_id: int, model: torch.fx.GraphModule, graph: NNCFGraph + ) -> Tensor: + weight_edge = graph.get_input_edge_by_port_id(node_with_weight, weight_port_id) + weight_node = weight_edge.from_node + graph_weight_node = get_graph_node_by_name(model.graph, weight_node.node_name) + weight = get_tensor_constant_from_node(graph_weight_node, model).data + if weight is None: + raise nncf.InternalError(f"Could not find a node in the model by name {weight_node}.") + + return Tensor(weight) + + def get_weight_dtype( + self, node_with_weight: NNCFNode, weight_port_id: int, model: torch.fx.GraphModule, graph: NNCFGraph + ) -> TensorDataType: + return self.get_weight(node_with_weight, weight_port_id, model, graph).dtype + + @staticmethod + def get_weight_shape(node_with_weight: NNCFNode, weight_port_id: int, graph: NNCFGraph) -> Tuple: + weight_node = get_const_node(node_with_weight, weight_port_id, graph) + edge = graph.get_edge(weight_node, node_with_weight) + return tuple(edge.tensor_shape) + + def set_weight( + self, + node_with_weight: NNCFNode, + weight_port_id: int, + model: torch.fx.GraphModule, + graph: NNCFGraph, + weight: Tensor, + ) -> None: + constant_update_transformation_builder(node_with_weight, weight.data, input_port_id=weight_port_id)(model) + + def insert_adapters( + self, wc_params: WeightCompressionParameters, lora_A: Tensor, lora_B: Tensor, int8_lora: bool + ) -> None: + pass + + def transform_model( + self, + model: torch.fx.GraphModule, + graph: NNCFGraph, + weight_compression_parameters: Iterable[WeightCompressionParameters], + precomputed_scales: Dict[str, Tensor] = None, + precomputed_zero_points: Dict[str, Tensor] = None, + lora_correction_algo: LoraCorrectionAlgorithm = None, + ) -> torch.fx.GraphModule: + transformation_layout = TransformationLayout() + + for wc_params in weight_compression_parameters: + compression_config = wc_params.compression_config + if compression_config.mode in [ + CompressWeightsMode.NF4, + CompressWeightsMode.E2M1, + ]: + raise nncf.ParameterNotSupportedError(f"{compression_config.mode.value} is not supported.") + weight_node = get_const_node(wc_params.node_with_weight, wc_params.weight_port_id, graph) + weight_name = weight_node.node_name + weight = self.get_weight(wc_params.node_with_weight, wc_params.weight_port_id, model, graph) + if weight is None or not isinstance(weight, Tensor): + raise nncf.InternalError(f"Could not find a nncf.tensor in the model by name {weight_name}.") + + # calculates compressed weights and decompression parameters + compressed_weight = compress_weight( + weight, + wc_params.reduction_axes, + compression_config, + None if precomputed_scales is None else precomputed_scales.get(wc_params.weight_name), + None if precomputed_zero_points is None else precomputed_zero_points.get(wc_params.weight_name), + ) + + # creates weight decompressor + if compression_config.mode == CompressWeightsMode.INT8_SYM: + decompressor = INT8SymmetricWeightsDecompressor( + compressed_weight.scale.data, result_dtype=weight.data.dtype + ) + elif compression_config.mode == CompressWeightsMode.INT8_ASYM: + decompressor = INT8AsymmetricWeightsDecompressor( + compressed_weight.scale.data, compressed_weight.zero_point.data, result_dtype=weight.data.dtype + ) + elif compression_config.mode == CompressWeightsMode.INT4_SYM: + decompressor = INT4SymmetricWeightsDecompressor( + scale=compressed_weight.scale.data, + compressed_weight_shape=compressed_weight.tensor.shape, + result_shape=weight.shape, + result_dtype=weight.data.dtype, + ) + elif compression_config.mode == CompressWeightsMode.INT4_ASYM: + decompressor = INT4AsymmetricWeightsDecompressor( + scale=compressed_weight.scale.data, + zero_point=compressed_weight.zero_point.data, + compressed_weight_shape=compressed_weight.tensor.shape, + result_shape=weight.shape, + result_dtype=weight.data.dtype, + ) + + # pack tensor + packed_tensor = decompressor.pack_weight(compressed_weight.tensor.data) + + # sets compressed tensor + self.set_weight(wc_params.node_with_weight, wc_params.weight_port_id, model, graph, packed_tensor) + + # register weight decompression module in the model + graph_weight_node = get_graph_node_by_name(model.graph, wc_params.node_with_weight.node_name) + compressed_weight_name = graph_weight_node.all_input_nodes[wc_params.weight_port_id].name + + decompressor_suffix = "_".join(compressed_weight_name.replace(".", "_").split("_")[:-2]) + decompressor_name = f"{decompressor.quantization_mode}_weights_decompressor_{decompressor_suffix}" + + # inserts the weight decompressor into the model as the post hook on the model weight + transformation_layout.register( + FXApplyTransformationCommand( + module_insertion_transformation_builder( + decompressor, + [PTTargetPoint(TargetType.OPERATOR_POST_HOOK, target_node_name=compressed_weight_name)], + decompressor_name, + ) + ) + ) + + # apply transformations + transformed_model = FXModelTransformer(model).transform(transformation_layout) + + return transformed_model diff --git a/nncf/quantization/algorithms/weight_compression/weight_lowering.py b/nncf/quantization/algorithms/weight_compression/weight_lowering.py index cf424b5becc..13406c0b288 100644 --- a/nncf/quantization/algorithms/weight_compression/weight_lowering.py +++ b/nncf/quantization/algorithms/weight_compression/weight_lowering.py @@ -82,12 +82,14 @@ def reshape_weight_for_grouped_quantization( if isinstance(reduction_axes, tuple) and len(reduction_axes) == 1: reduction_axes = reduction_axes[0] if not isinstance(reduction_axes, int): - raise NotImplementedError( + raise nncf.UnsupportedModelError( f"Group-wise quantization expects a single reduction axis, but given: {reduction_axes}." ) channel_size = weight.shape[reduction_axes] if channel_size % group_size != 0: - raise nncf.ValidationError(f"Channel size {channel_size} should be divisible by size of group {group_size}") + raise nncf.UnsupportedModelError( + f"Channel size {channel_size} should be divisible by size of group {group_size}" + ) num_groups_per_channel = channel_size // group_size shape = list(weight.shape) # [a1, r, a2] - "r" refers to number of channels along reduction axis @@ -178,39 +180,37 @@ def calculate_normalized_weight(weight: Tensor, scale: Tensor) -> Tensor: def do_nf4_quantization(weight: Tensor, scale: Tensor, is_normalized_weight: bool = False) -> Tensor: """ - Performs NF4 quantization - the floating point value is represented by floating point scale, look-up table of - 16 NF4 values Quantizes the weight tensor to NF4 format. + Performs NF4 quantization. The floating point values are represented by floating point scale and look-up with + 16 floating-point values on [-1, 1]. Scale normalizes original values to [-1, 1] interval and look-up table + "rounds" or "quantize" to the closest quant. :param weight: Weight tensor to quantize. :param scale: Scale tensor used for normalization. :param is_normalized_weight: Whether weight was scaled to [-1, 1] interval. Defaults to False. - :return: Tensor of indexes from 0 to 15 that represents the position in look-up table with the corresponding - NF4 values from -1 to 1. + :return: Tensor with floating-point values, where each of them corresponds to 1 out of 16 quants on [-1, 1]. """ norm_weight = weight if is_normalized_weight else calculate_normalized_weight(weight, scale) center_nf4_quantiles = fns.from_numpy(CENTER_OF_NF4_QUANTILES, backend=norm_weight.backend) indexes = fns.searchsorted(center_nf4_quantiles, norm_weight) - return indexes + nf4_quantiles = fns.from_numpy(NF4_QUANTILES, backend=indexes.backend) + nf4_weight = nf4_quantiles[indexes] + return nf4_weight -def do_nf4_dequantization(indexes: Tensor, scale: Tensor, reduction_axis: int = -1) -> Tensor: +def do_nf4_dequantization(nf4_weight: Tensor, scale: Tensor, reduction_axis: int = -1) -> Tensor: """ Decompresses the NF4 quantized weight tensor. - :param indexes: Tensor of indexes from 0 to 15 that represents the position in look-up table with the corresponding - NF4 values from -1 to 1. + :param nf4_weight: Tensor with floating-point values, + where each of them corresponds to 1 out of 16 quants on [-1, 1]. :param scale: Scale tensor used for decompression. :param reduction_axis: axis along which weights were reshaped for group quantization and will be reshaped back to original shapes. If equals to -1, weights are not reshaped, assumed not a group quantization. Defaults to -1. :return: Decompressed weight tensor. """ - nf4_quantiles = fns.from_numpy(NF4_QUANTILES, backend=indexes.backend) - nf4_weight = nf4_quantiles[indexes] - decompressed_weight = nf4_weight * scale if reduction_axis != -1: decompressed_weight = ungroup_weights(decompressed_weight, reduction_axis) - return decompressed_weight @@ -358,6 +358,12 @@ def do_int_quantization( """ assert config.is_integer(), "The function supports integer quantization only" group_size = config.group_size + is_asym = config.mode in [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT4_ASYM] + if is_asym and (precomputed_scale is None) != (precomputed_zero_point is None): + raise ValueError( + "If precomputed quantization parameters are provided, both scale and zero point are required " + "for asymmetric quantization." + ) if weight.dtype != TensorDataType.float32: weight = weight.astype(TensorDataType.float32) @@ -366,7 +372,8 @@ def do_int_quantization( # weights are reshaped from [a1, r, a2] to [a1, r//gs, gs, a2] weight, reduction_axes = reshape_weight_for_grouped_quantization(weight, reduction_axes, group_size) - if precomputed_zero_point is None or precomputed_zero_point is None: + scale, zero_point = None, None + if precomputed_scale is None or (is_asym and precomputed_zero_point is None): scale, zero_point = calculate_integer_quantization_params(weight, reduction_axes, config) if precomputed_scale is not None: scale = precomputed_scale diff --git a/nncf/quantization/quantize_model.py b/nncf/quantization/quantize_model.py index 60baeacc48e..eb520bfcd1b 100644 --- a/nncf/quantization/quantize_model.py +++ b/nncf/quantization/quantize_model.py @@ -22,6 +22,7 @@ from nncf.common.utils.backend import BackendType from nncf.common.utils.backend import get_backend from nncf.data import Dataset +from nncf.parameters import BackupMode from nncf.parameters import CompressWeightsMode from nncf.parameters import DropType from nncf.parameters import ModelType @@ -35,7 +36,14 @@ from nncf.quantization.algorithms.hyperparameter_tuner.algorithm import HyperparameterTuner from nncf.quantization.algorithms.hyperparameter_tuner.param_grid import get_quantization_param_grids from nncf.quantization.algorithms.post_training.pipeline import create_ptq_pipeline +from nncf.quantization.algorithms.weight_compression.algorithm import check_user_compression_configuration +from nncf.quantization.algorithms.weight_compression.algorithm import get_weight_compression_configuration +from nncf.quantization.telemetry_extractors import CompressionStartedWithCompressWeightsApi +from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeApi +from nncf.quantization.telemetry_extractors import CompressionStartedWithQuantizeWithAccuracyControlApi from nncf.scopes import IgnoredScope +from nncf.telemetry.decorator import tracked_function +from nncf.telemetry.events import MODEL_BASED_CATEGORY TTensor = TypeVar("TTensor") @@ -110,6 +118,14 @@ def _update_advanced_quantization_parameters( @api(canonical_alias="nncf.quantize") +@tracked_function( + MODEL_BASED_CATEGORY, + [ + CompressionStartedWithQuantizeApi(), + "target_device", + "preset", + ], +) def quantize( model: TModel, calibration_dataset: Dataset, @@ -160,7 +176,7 @@ def quantize( :rtype: TModel """ if subset_size < 1: - raise ValueError("Subset size must be positive.") + raise nncf.ValidationError("Subset size must be positive.") advanced_parameters = _update_advanced_quantization_parameters(advanced_parameters, calibration_dataset) @@ -246,12 +262,39 @@ def quantize( raise nncf.UnsupportedBackendError(f"Unsupported type of backend: {backend}") +def wrap_validation_fn(validation_fn): + """ + Wraps validation function to support case when it only returns metric value. + + :param validation_fn: Validation function to wrap. + :return: Wrapped validation function. + """ + + def wrapper(*args, **kwargs): + retval = validation_fn(*args, **kwargs) + if isinstance(retval, tuple): + return retval + return retval, None + + return wrapper + + @api(canonical_alias="nncf.quantize_with_accuracy_control") +@tracked_function( + MODEL_BASED_CATEGORY, + [ + CompressionStartedWithQuantizeWithAccuracyControlApi(), + "target_device", + "preset", + "max_drop", + "drop_type", + ], +) def quantize_with_accuracy_control( model: TModel, calibration_dataset: Dataset, validation_dataset: Dataset, - validation_fn: Callable[[Any, Iterable[Any]], float], + validation_fn: Callable[[Any, Iterable[Any]], Tuple[float, Union[None, List[float], List[List[TTensor]]]]], max_drop: float = 0.01, drop_type: DropType = DropType.ABSOLUTE, preset: Optional[QuantizationPreset] = None, @@ -316,6 +359,9 @@ def quantize_with_accuracy_control( ) backend = get_backend(model) + + validation_fn = wrap_validation_fn(validation_fn) + if backend == BackendType.OPENVINO: from nncf.openvino.quantization.quantize_model import quantize_with_accuracy_control_impl @@ -359,6 +405,18 @@ def quantize_with_accuracy_control( @api(canonical_alias="nncf.compress_weights") +@tracked_function( + MODEL_BASED_CATEGORY, + [ + CompressionStartedWithCompressWeightsApi(), + "mode", + "awq", + "scale_estimation", + "gptq", + "lora_correction", + "backup_mode", + ], +) def compress_weights( model: TModel, mode=CompressWeightsMode.INT8_ASYM, @@ -369,11 +427,12 @@ def compress_weights( dataset: Optional[Dataset] = None, sensitivity_metric: Optional[SensitivityMetric] = None, *, - subset_size: Optional[int] = 128, + subset_size: int = 128, awq: Optional[bool] = None, scale_estimation: Optional[bool] = None, gptq: Optional[bool] = None, lora_correction: Optional[bool] = None, + backup_mode: Optional[BackupMode] = None, advanced_parameters: Optional[AdvancedCompressionParameters] = None, ) -> TModel: """ @@ -424,6 +483,12 @@ def compress_weights( :type gptq: bool :param lora_correction: Indicates whether to use Lora Correction algorithm. :type lora_correction: bool + :param backup_mode: Defines a backup mode for mixed-precision weight compression. + NONE stands for original floating-point precision of the model weights. + In this mode, weights are retained in their original precision without any quantization. + INT8_SYM stands for 8-bit integer symmetric quantization without zero point. + INT8_ASYM stands for 8-bit integer asymmetric quantization with a typical non-fixed zero point. + :type backup_mode: nncf.BackupMode :param advanced_parameters: Advanced parameters for compression algorithms. :type advanced_parameters: nncf.AdvancedCompressionParameters :return: The non-trainable model with compressed weights. @@ -442,130 +507,143 @@ def compress_weights( from nncf.torch.model_creation import wrap_model from nncf.torch.quantization.quantize_model import compress_weights_impl as pt_compression_weights_impl - if mode not in [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM]: - raise AttributeError( - "Torch backend supports only INT8_ASYM, INT8_SYM modes for weight compression, " - f"but given {mode.value} mode." + if mode in [CompressWeightsMode.NF4, CompressWeightsMode.E2M1]: + raise nncf.ParameterNotSupportedError( + "Torch backend does not support NF4 and E2M1 modes for weight compression." ) - if True in [awq, scale_estimation, gptq, lora_correction]: - raise AttributeError( - "Torch backend does not support 'awq', 'scale_estimation', 'gptq' and 'lora_correction' options. " - "Set them to None." + options = { + "awq": awq, + "scale_estimation": scale_estimation, + "gptq": gptq, + "lora_correction": lora_correction, + } + unsupported_options = [name for name, value in options.items() if value is not None] + if unsupported_options: + raise nncf.ParameterNotSupportedError( + f"Torch backend does not support {', '.join(unsupported_options)} option(s). Set them to None." ) + if sensitivity_metric not in [None, SensitivityMetric.WEIGHT_QUANTIZATION_ERROR]: + raise nncf.ParameterNotSupportedError( + "Torch backend only supports data-free sensitivity metric. " + "Set None or SensitivityMetric.WEIGHT_QUANTIZATION_ERROR." + ) + + if advanced_parameters and advanced_parameters.statistics_path: + raise nncf.ParameterNotSupportedError("Torch does not support statistics caching.") + if is_wrapped_model(model): if not model.nncf.trace_parameters: - raise ValueError( + raise nncf.ValidationError( "Tracing capabilities with tracing parameters are required in the PyTorch model " "for nncf.compress_weights(). Please wrap the model using " "nncf.torch.wrap_model(model, example_input, trace_parameters=True) before calling " "nncf.compress_weights()." ) elif dataset is None: - raise AttributeError("Please provide a dataset of at least one element for PyTorch model tracing.") + raise nncf.ValidationError("Please provide a dataset of at least one element for PyTorch model tracing.") else: example_input = next(iter(dataset.get_inference_data())) model = wrap_model(model, example_input=example_input, trace_parameters=True) dataset = None compression_weights_impl = pt_compression_weights_impl - if backend == BackendType.OPENVINO: - from nncf.openvino.quantization.quantize_model import compress_weights_impl as ov_compress_weights_impl + if backend == BackendType.TORCH_FX: + from nncf.experimental.torch.fx.quantization.quantize_model import ( + compress_weights_impl as fx_compression_weights_impl, + ) - if any((awq, scale_estimation)) and ( - dataset is None or mode in [CompressWeightsMode.NF4, CompressWeightsMode.E2M1] - ): - raise AttributeError( - "Scale estimation or AWQ algorithm is defined, but dataset is None or mode is (NF4 or E2M1)." + if mode in [CompressWeightsMode.NF4, CompressWeightsMode.E2M1]: + raise nncf.ParameterNotSupportedError( + "Torch backend does not support NF4 and E2M1 modes for weight compression." ) - if any((gptq, lora_correction)) and (dataset is None or mode == CompressWeightsMode.E2M1): - raise AttributeError("GPTQ or Lora Correction algorithm is defined, but dataset is None or mode is E2M1.") - if gptq and lora_correction: - raise AttributeError( - "Simultaneous use of Lora correction and GPTQ algorithms is not supported. Select one of them." - ) - - compression_weights_impl = ov_compress_weights_impl - - if mode in [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM]: - if ratio is None: - ratio = 1 - if group_size is None: - group_size = -1 - if ratio != 1 or group_size != -1: - raise AttributeError( - "INT8 mode assumes per-channel quantization of all layers in 8 bit. " - "Default values of `ratio` (1) and `group_size` (-1) parameters can not be overridden" - ) options = { - "all_layers": all_layers, - "sensitivity_metric": sensitivity_metric, - "dataset": dataset, "awq": awq, "scale_estimation": scale_estimation, "gptq": gptq, "lora_correction": lora_correction, } - unsupported_for_int8 = [name for name, value in options.items() if value is not None] - if unsupported_for_int8: - raise AttributeError( - f"INT8 modes do not support {', '.join(unsupported_for_int8)} option(s). Set them to None." + unsupported_options = [name for name, value in options.items() if value is not None] + if unsupported_options: + raise nncf.ParameterNotSupportedError( + f"TorchFX backend does not support {', '.join(unsupported_options)} option(s). Set them to None." ) - if ratio is None: - ratio = 1 - if group_size is None: - group_size = 128 - if all_layers is None: - all_layers = False - if awq is None: - awq = False - if scale_estimation is None: - scale_estimation = False - if gptq is None: - gptq = False - if lora_correction is None: - lora_correction = False - if ignored_scope is None: - ignored_scope = IgnoredScope() - if sensitivity_metric is None: - sensitivity_metric = ( - SensitivityMetric.WEIGHT_QUANTIZATION_ERROR - if dataset is None - else SensitivityMetric.MAX_ACTIVATION_VARIANCE - ) - if ratio != 1 and dataset is None and sensitivity_metric != SensitivityMetric.WEIGHT_QUANTIZATION_ERROR: - raise AttributeError( - f"Mixed precision selection based on the given sensitivity metric={sensitivity_metric.value} requires " - "a dataset, but it's not provided." - ) - if ratio < 0 or ratio > 1: - raise ValueError(f"The ratio should be between 0 and 1, but ratio={ratio} is specified.") - if subset_size is None or subset_size <= 0: - raise ValueError(f"The subset_size value should be positive, but subset_size={subset_size} is given.") + if sensitivity_metric not in [None, SensitivityMetric.WEIGHT_QUANTIZATION_ERROR]: + raise nncf.ParameterNotSupportedError( + "TorchFX backend only supports data-free sensitivity metric. " + "Set None or SensitivityMetric.WEIGHT_QUANTIZATION_ERROR." + ) - if compression_weights_impl is None: - raise nncf.UnsupportedBackendError(f"Unsupported type of backend: {backend}") + if dataset: + raise nncf.ParameterNotSupportedError( + "TorchFX only supports data-free weights compression," "Set the 'dataset' option to None" + ) + if advanced_parameters and advanced_parameters.statistics_path: + raise nncf.ParameterNotSupportedError("TorchFX does not supports statistics caching.") + compression_weights_impl = fx_compression_weights_impl - return compression_weights_impl( - model, - dataset, + if backend == BackendType.OPENVINO: + from nncf.openvino.quantization.quantize_model import compress_weights_impl as ov_compress_weights_impl + + if any((awq, scale_estimation, gptq, lora_correction)) and ( + dataset is None or mode == CompressWeightsMode.E2M1 + ): + raise nncf.ParameterNotSupportedError( + "Scale estimation, AWQ, GPTQ or Lora Correction algorithm is defined, " + "but dataset is None or mode is E2M1." + ) + + if gptq and lora_correction: + raise nncf.ValidationError( + "Simultaneous use of Lora correction and GPTQ algorithms is not supported. Select one of them." + ) + + compression_weights_impl = ov_compress_weights_impl + check_user_compression_configuration( mode, + subset_size, + dataset, ratio, group_size, - ignored_scope, all_layers, + awq, + scale_estimation, + gptq, + lora_correction, + ignored_scope, sensitivity_metric, + backup_mode, + advanced_parameters, + ) + weight_compression_configuration = get_weight_compression_configuration( + mode, + dataset, + ratio, + group_size, + all_layers, awq, - subset_size, scale_estimation, gptq, lora_correction, + ignored_scope, + sensitivity_metric, + backup_mode, advanced_parameters, ) + if compression_weights_impl is None: + raise nncf.UnsupportedBackendError(f"Unsupported type of backend: {backend}") + + return compression_weights_impl( + model=model, + dataset=dataset, + subset_size=subset_size, + **weight_compression_configuration, + ) + def quantize_with_tune_hyperparams( model: TModel, diff --git a/nncf/quantization/statistics_caching.py b/nncf/quantization/statistics_caching.py new file mode 100644 index 00000000000..d6253f2fdda --- /dev/null +++ b/nncf/quantization/statistics_caching.py @@ -0,0 +1,120 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Dict, List, Tuple + +from nncf.api.compression import TModel +from nncf.common.factory import StatisticsAggregatorFactory +from nncf.common.graph.graph import NNCFGraph +from nncf.common.graph.graph import NNCFNode +from nncf.common.tensor_statistics.aggregator import StatisticsAggregator +from nncf.data import Dataset +from nncf.parameters import SensitivityMetric +from nncf.quantization.algorithms.weight_compression.algorithm import WeightCompression +from nncf.quantization.algorithms.weight_compression.algorithm import get_weight_compression_configuration +from nncf.quantization.algorithms.weight_compression.mixed_precision import MIXED_PRECISION_CRITERIA + + +def register_statistics_for_algorithm( + aggregator: StatisticsAggregator, + model: TModel, + graph: NNCFGraph, + compression_algo: WeightCompression, + matmul_input_to_output_nodes_map: Dict[Tuple[NNCFNode, int], List[NNCFNode]], +) -> None: + """ + Registers the statistics required for the given compression algorithm. + + :param aggregator: Aggregator to register statistics. + :param model: Model being analyzed. + :param graph: Model's computational graph. + :param compression_algo: WeightCompression algorithm instance. + :param matmul_input_to_output_nodes_map: A dictionary mapping from a tuple of (activation node, port ID) + to a list of MatMul nodes that accept the activation as input. + """ + statistic_points = compression_algo.get_statistic_points(model, graph, matmul_input_to_output_nodes_map.keys()) + aggregator.register_statistic_points(statistic_points) + + +def _register_mixed_precision( + aggregator: StatisticsAggregator, + model: TModel, + graph: NNCFGraph, + matmul_input_to_output_nodes_map: Dict[Tuple[NNCFNode, int], List[NNCFNode]], + subset_size: int, +) -> None: + """ + Registers statistics for mixed precision compression algorithm. + + :param aggregator: Aggregator to register statistics. + :param model: Model being analyzed. + :param graph: Model's computational graph. + :param matmul_input_to_output_nodes_map: A dictionary mapping from a tuple of (activation node, port ID) + to a list of MatMul nodes that accept the activation as input. + :param subset_size: Size of dataset subset for statistics. + """ + sensitivities = [ + SensitivityMetric.HESSIAN_INPUT_ACTIVATION, + SensitivityMetric.MEAN_ACTIVATION_VARIANCE, + SensitivityMetric.MAX_ACTIVATION_VARIANCE, + SensitivityMetric.MEAN_ACTIVATION_MAGNITUDE, + ] + + for sensitivity in sensitivities: + criterion_cls = MIXED_PRECISION_CRITERIA.get(sensitivity) + mixed_prec_algo = criterion_cls(None, None, subset_size) + statistic_points = mixed_prec_algo.get_statistic_points(model, graph, matmul_input_to_output_nodes_map.keys()) + aggregator.register_statistic_points(statistic_points) + + +def register_all_statistics( + aggregator: StatisticsAggregator, + model: TModel, + graph: NNCFGraph, + subset_size: int, + compression_algo: WeightCompression, + enable_mixed_precision: bool = True, +) -> None: + """ + Registers all required statistics for the model compression. + + :param aggregator: Aggregator to register statistics. + :param model: Model being analyzed. + :param graph: Model's computational graph. + :param compression_algo: WeightCompression algorithm instance. + :param enable_mixed_precision: Whether to enable mixed precision statistics. + """ + _, matmul_input_to_output_nodes_map = compression_algo.get_compression_nodes_info(graph) + + register_statistics_for_algorithm(aggregator, model, graph, compression_algo, matmul_input_to_output_nodes_map) + + if enable_mixed_precision: + _register_mixed_precision(aggregator, model, graph, matmul_input_to_output_nodes_map, subset_size) + + +def cache_weight_compression_statistics( + model: TModel, graph: NNCFGraph, dataset: Dataset, subset_size: int, statistics_path: str +) -> None: + """ + Caches compression statistics for a given model and dataset. + + :param model: Model being analyzed. + :param graph: Model's computational graph. + :param dataset: Dataset to analyze model statistics. + :param subset_size: Size of dataset subset for statistics. + :param statistics_path: Path to save cached statistics. + """ + config = get_weight_compression_configuration(awq=True, scale_estimation=True, lora_correction=True) + compression_algo = WeightCompression(**config, subset_size=subset_size) + compression_algo.set_backend_entity(model) + aggregator = StatisticsAggregatorFactory.create(model, dataset) + register_all_statistics(aggregator, model, graph, subset_size, compression_algo) + aggregator.collect_statistics(model, graph) + aggregator.dump_statistics(statistics_path) diff --git a/nncf/quantization/telemetry_extractors.py b/nncf/quantization/telemetry_extractors.py index 72158e53136..6607ceb6991 100644 --- a/nncf/quantization/telemetry_extractors.py +++ b/nncf/quantization/telemetry_extractors.py @@ -23,3 +23,8 @@ def extract(self, _: Any) -> CollectedEvent: class CompressionStartedWithQuantizeWithAccuracyControlApi(TelemetryExtractor): def extract(self, _: Any) -> CollectedEvent: return CollectedEvent(name="compression_started", data="quantize_with_accuracy_control_api") + + +class CompressionStartedWithCompressWeightsApi(TelemetryExtractor): + def extract(self, _: Any) -> CollectedEvent: + return CollectedEvent(name="compression_started", data="compress_weights_api") diff --git a/nncf/scopes.py b/nncf/scopes.py index a2cee4b319b..6e3c32849da 100644 --- a/nncf/scopes.py +++ b/nncf/scopes.py @@ -48,6 +48,11 @@ def get_ignored_node_names_from_subgraph(graph: NNCFGraph, subgraph: Subgraph) - ignored_names = set() for start_node_name in subgraph.inputs: for end_node_name in subgraph.outputs: + if start_node_name == end_node_name: + # For networkx<3.3 nx.get_all_simple_paths returns empty path for this case + node = graph.get_node_by_name(start_node_name) + ignored_names.add(node.node_name) + continue for path in graph.get_all_simple_paths(start_node_name, end_node_name): for node_key in path: node = graph.get_node_by_key(node_key) diff --git a/nncf/telemetry/decorator.py b/nncf/telemetry/decorator.py index 3ccee0f9235..23ee56acc86 100644 --- a/nncf/telemetry/decorator.py +++ b/nncf/telemetry/decorator.py @@ -12,7 +12,9 @@ import inspect from typing import Callable, List, Union +from nncf.telemetry.events import MODEL_BASED_CATEGORY from nncf.telemetry.events import get_current_category +from nncf.telemetry.events import get_model_based_category from nncf.telemetry.events import telemetry_category from nncf.telemetry.extractors import CollectedEvent from nncf.telemetry.extractors import TelemetryExtractor @@ -49,6 +51,11 @@ def __call__(self, fn: Callable) -> Callable: def wrapped(*args, **kwargs): bound_args = fn_signature.bind(*args, **kwargs) bound_args.apply_defaults() + + if self._category == MODEL_BASED_CATEGORY: + model_argument = bound_args.arguments.get("model", None) + self._category = get_model_based_category(model_argument) + events: List[CollectedEvent] = [] for collector in self._collectors: argname = collector.argname diff --git a/nncf/telemetry/events.py b/nncf/telemetry/events.py index b7f6ebdc95b..a048fbbb0c7 100644 --- a/nncf/telemetry/events.py +++ b/nncf/telemetry/events.py @@ -10,15 +10,25 @@ # limitations under the License. from contextlib import contextmanager -from typing import Optional +from typing import Optional, TypeVar +from nncf.common.utils.backend import BackendType +from nncf.common.utils.backend import get_backend + +# Backend categories NNCF_TF_CATEGORY = "nncf_tf" NNCF_PT_CATEGORY = "nncf_pt" +NNCF_PT_FX_CATEGORY = "nncf_pt_fx" NNCF_ONNX_CATEGORY = "nncf_onnx" NNCF_OV_CATEGORY = "nncf_ov" +# Dynamic categories +MODEL_BASED_CATEGORY = "model_based" + CURRENT_CATEGORY = None +TModel = TypeVar("TModel") + def _set_current_category(category: str): global CURRENT_CATEGORY @@ -29,6 +39,22 @@ def get_current_category() -> Optional[str]: return CURRENT_CATEGORY +def get_model_based_category(model: TModel) -> str: + category_by_backend = { + BackendType.ONNX: NNCF_ONNX_CATEGORY, + BackendType.OPENVINO: NNCF_OV_CATEGORY, + BackendType.TORCH: NNCF_PT_CATEGORY, + BackendType.TENSORFLOW: NNCF_TF_CATEGORY, + BackendType.TORCH_FX: NNCF_PT_FX_CATEGORY, + } + category = None + if model is not None: + model_backend = get_backend(model) + category = category_by_backend[model_backend] + + return category + + @contextmanager def telemetry_category(category: str) -> str: previous_category = get_current_category() diff --git a/nncf/telemetry/extractors.py b/nncf/telemetry/extractors.py index b037104de0d..a06d8f8daa5 100644 --- a/nncf/telemetry/extractors.py +++ b/nncf/telemetry/extractors.py @@ -54,4 +54,6 @@ class VerbatimTelemetryExtractor(TelemetryExtractor): def extract(self, argvalue: SerializableData) -> CollectedEvent: if isinstance(argvalue, Enum): argvalue = str(argvalue.value) + if isinstance(argvalue, bool): + argvalue = "enabled" if argvalue else "disabled" return CollectedEvent(name=self._argname, data=argvalue) diff --git a/nncf/telemetry/wrapper.py b/nncf/telemetry/wrapper.py index 355e3debb47..2cc32af0158 100644 --- a/nncf/telemetry/wrapper.py +++ b/nncf/telemetry/wrapper.py @@ -16,10 +16,10 @@ from typing import Callable, Optional from unittest.mock import MagicMock -from nncf import __version__ from nncf.common.logging import nncf_logger from nncf.definitions import NNCF_CI_ENV_VAR_NAME from nncf.definitions import NNCF_DEV_ENV_VAR_NAME +from nncf.version import __version__ as __version__ NNCFTelemetryStub = MagicMock @@ -92,10 +92,12 @@ class NNCFTelemetry(ITelemetry): MEASUREMENT_ID = "G-W5E9RNLD4H" def __init__(self): + self._app_name = "nncf" + self._app_version = __version__ try: self._impl = Telemetry( - app_name="nncf", - app_version=__version__, + app_name=self._app_name, + app_version=self._app_version, tid=self.MEASUREMENT_ID, backend="ga4", enable_opt_in_dialog=False, @@ -121,7 +123,16 @@ def send_event( ): if event_value is None: event_value = 1 - self._impl.send_event(event_category, event_action, event_label, event_value, force_send, **kwargs) + self._impl.send_event( + event_category=event_category, + event_action=event_action, + event_label=event_label, + event_value=event_value, + app_name=self._app_name, + app_version=self._app_version, + force_send=force_send, + **kwargs, + ) @skip_if_raised def end_session(self, category: str, **kwargs): diff --git a/nncf/tensor/README.md b/nncf/tensor/README.md index dc62dc16fd5..ff912d8a9d4 100644 --- a/nncf/tensor/README.md +++ b/nncf/tensor/README.md @@ -166,7 +166,7 @@ tensor_a[0:2] # Tensor(array([[1],[2]])) return torch.foo(a, arg1) ``` -4. Add test of method to [test template](/tests/shared/test_templates/template_test_nncf_tensor.py) for Tensor class +4. Add test of method to [test template](/tests/cross_fw/test_templates/template_test_nncf_tensor.py) for Tensor class ### Add new backend @@ -191,7 +191,6 @@ tensor_a[0:2] # Tensor(array([[1],[2]])) "onnxruntime", "openvino", "tensorflow", - "tensorflow_addons", "nncf.tensor.functions.torch_*", "nncf.tensor.functions.numpy_*", "nncf.tensor.functions._*", diff --git a/nncf/tensor/definitions.py b/nncf/tensor/definitions.py index 447a6dd8bb5..5d2df4ac035 100644 --- a/nncf/tensor/definitions.py +++ b/nncf/tensor/definitions.py @@ -37,6 +37,12 @@ class TensorDataType(Enum): int64 = auto() uint8 = auto() + def is_float(self): + """ + :return: True if the tensor data type is a floating-point type, else False. + """ + return self in [TensorDataType.float16, TensorDataType.bfloat16, TensorDataType.float32, TensorDataType.float64] + class TensorDeviceType(Enum): """ diff --git a/nncf/tensor/tensor.py b/nncf/tensor/tensor.py index 52966be1ad1..9edbd4acb50 100644 --- a/nncf/tensor/tensor.py +++ b/nncf/tensor/tensor.py @@ -162,6 +162,13 @@ def __gt__(self, other: Union[Tensor, float]) -> Tensor: def __ge__(self, other: Union[Tensor, float]) -> Tensor: return Tensor(self.data >= unwrap_tensor_data(other)) + # Methods to support pickling and unpickling + def __getstate__(self): + return self._data + + def __setstate__(self, state): + self._data = state + # Tensor functions def squeeze(self, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Tensor: diff --git a/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py b/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py index 5fddbc8d1ec..538a522fd27 100644 --- a/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py +++ b/nncf/tensorflow/accuracy_aware_training/keras_model_utils.py @@ -38,8 +38,9 @@ def accuracy_aware_fit( if result_dict_to_val_metric_fn is None: result_dict_to_val_metric_fn = lambda metric: metric - with cls_instance.distribute_strategy.scope(), tf_internals.keras_engine.training_utils.RespectCompiledTrainableState( # noqa: E501 - cls_instance + with ( + cls_instance.distribute_strategy.scope(), + tf_internals.keras_engine.training_utils.RespectCompiledTrainableState(cls_instance), # noqa: E501 ): data_handler = tf_internals.keras_engine.data_adapter.DataHandler( x=train_dataset, diff --git a/nncf/tensorflow/accuracy_aware_training/runner.py b/nncf/tensorflow/accuracy_aware_training/runner.py index d21411835ab..42553286f1f 100644 --- a/nncf/tensorflow/accuracy_aware_training/runner.py +++ b/nncf/tensorflow/accuracy_aware_training/runner.py @@ -12,7 +12,6 @@ import os.path as osp import tensorflow as tf -import tensorflow_addons as tfa from tensorflow.keras.optimizers import schedules from nncf.common.accuracy_aware_training.runner import BaseAccuracyAwareTrainingRunner @@ -35,14 +34,14 @@ def validate(self, model): def reset_training(self): self.configure_optimizers() - if isinstance(self.optimizer, tfa.optimizers.MultiOptimizer): - optimizers = [optimizer_spec.optimizer for optimizer_spec in self.optimizer.optimizer_specs] - else: - optimizers = self.optimizer if isinstance(self.optimizer, (tuple, list)) else [self.optimizer] + optimizers = self.optimizer if isinstance(self.optimizer, (tuple, list)) else [self.optimizer] for optimizer in optimizers: scheduler = optimizer.learning_rate - if isinstance(scheduler, tf.Variable): + # pylint: disable=protected-access + if isinstance(scheduler, tf.Variable) and not isinstance( + optimizer._learning_rate, schedules.LearningRateSchedule + ): scheduler = scheduler * self.base_lr_reduction_factor_during_search optimizer.learning_rate = scheduler optimizer.lr = scheduler diff --git a/nncf/tensorflow/quantization/algorithm.py b/nncf/tensorflow/quantization/algorithm.py index fab9707ce96..dd41cb2bad5 100644 --- a/nncf/tensorflow/quantization/algorithm.py +++ b/nncf/tensorflow/quantization/algorithm.py @@ -358,6 +358,8 @@ def _build_insertion_commands_for_quantizer_setup( non_unified_scales_quantization_point_ids = set(range(len(quantization_points))) for unified_scales_group in quantizer_setup.get_unified_scale_groups(): + if not unified_scales_group: + continue us_qp_id = unified_scales_group[0] qp = quantization_points[us_qp_id] quantizer_spec = qp.quantizer_spec @@ -602,7 +604,7 @@ def _get_quantizer_propagation_solution( custom_layer_node_names: List[NNCFNodeName], model: tf.keras.Model, ) -> SingleConfigQuantizerSetup: - ip_graph = InsertionPointGraph(nncf_graph, [qn.node.node_name for qn in quantizable_weighted_layer_nodes]) + ip_graph = InsertionPointGraph(nncf_graph) pattern = TF_HW_FUSED_PATTERNS.get_full_pattern_graph() ip_graph = ip_graph.get_ip_graph_with_merged_hw_optimized_operations(pattern) @@ -640,7 +642,7 @@ def _get_quantizer_propagation_solution( scales_unification_map=scales_unification_map, ) - quantization_proposal = solver.run_on_ip_graph(ip_graph) + quantization_proposal = solver.run_on_ip_graph(ip_graph, ELEMENTWISE_LAYER_METATYPES) multi_config_setup = quantization_proposal.quantizer_setup single_config_setup = multi_config_setup.select_first_qconfig_for_each_point() finalized_proposal = quantization_proposal.finalize(single_config_setup) diff --git a/nncf/torch/__init__.py b/nncf/torch/__init__.py index 0b74414cfce..633eda6e3c7 100644 --- a/nncf/torch/__init__.py +++ b/nncf/torch/__init__.py @@ -68,4 +68,12 @@ from nncf.torch.extensions import force_build_cpu_extensions, force_build_cuda_extensions +# This is required since torchvision changes a dictionary inside of pytorch mapping +# different ops and their role in torch fx graph. Once the nncf mapping is done, it is +# represented as a different custom operation which is how it is changed in +# the said mapping. The polyfills loader is the specific file to be imported +# before making wrapping changes +if torch.__version__ >= "2.5.0": + from torch._dynamo.polyfills import loader + patch_torch_operators() diff --git a/nncf/torch/dynamic_graph/wrappers.py b/nncf/torch/dynamic_graph/wrappers.py index efb8bdac12d..bf42ef2e030 100644 --- a/nncf/torch/dynamic_graph/wrappers.py +++ b/nncf/torch/dynamic_graph/wrappers.py @@ -13,7 +13,6 @@ from typing import Callable, List, Tuple import torch -from torch._dynamo import OptimizedModule from torch.nn import DataParallel from nncf.common.graph.definitions import MODEL_CONST_OP_NAME @@ -136,7 +135,7 @@ def wrapped(self, *args, **kwargs): from nncf.torch.dynamic_graph.patch_pytorch import unpatching_module_call # If called on a model compiled by torch dynamo, we unpatch torch operators and invoke original module call - if isinstance(self, OptimizedModule): + if "_torchdynamo_orig_callable" in self.forward.__dict__: return unpatching_module_call(self, *args, **kwargs) ctx = get_current_context() diff --git a/nncf/torch/graph/operator_metatypes.py b/nncf/torch/graph/operator_metatypes.py index 15966d62130..86c1b8f689c 100644 --- a/nncf/torch/graph/operator_metatypes.py +++ b/nncf/torch/graph/operator_metatypes.py @@ -28,6 +28,7 @@ ModuleAttributes = TypeVar("ModuleAttributes", bound=BaseLayerAttributes) PT_OPERATOR_METATYPES = OperatorMetatypeRegistry("operator_metatypes") +FX_OPERATOR_METATYPES = OperatorMetatypeRegistry("operator_metatypes") class PTOperatorMetatype(OperatorMetatype): @@ -801,7 +802,7 @@ class PTPadMetatype(PTOperatorMetatype): @PT_OPERATOR_METATYPES.register() class PTCatMetatype(PTOperatorMetatype): name = "CatOp" - module_to_function_names = {NamespaceTarget.TORCH: ["cat", "stack"]} + module_to_function_names = {NamespaceTarget.TORCH: ["cat", "stack", "concat"]} hw_config_names = [HWConfigOpName.CONCAT] @@ -918,6 +919,14 @@ class PTEmbeddingMetatype(PTOperatorMetatype): weight_port_ids = [1] +@FX_OPERATOR_METATYPES.register() +class PTAtenEmbeddingMetatype(OperatorMetatype): + name = "EmbeddingOp" + module_to_function_names = {NamespaceTarget.ATEN: ["embedding"]} + hw_config_names = [HWConfigOpName.EMBEDDING] + weight_port_ids = [0] + + @PT_OPERATOR_METATYPES.register(is_subtype=True) class PTModuleEmbeddingBagMetatype(PTModuleOperatorSubtype): name = "EmbeddingBagOp" @@ -1101,6 +1110,18 @@ class PTScaledDotProductAttentionMetatype(PTOperatorMetatype): target_input_ports = [0, 1] +@PT_OPERATOR_METATYPES.register() +class PTCosMetatype(PTOperatorMetatype): + name = "CosOp" + module_to_function_names = {NamespaceTarget.TORCH: ["cos"]} + + +@PT_OPERATOR_METATYPES.register() +class PTSinMetatype(PTOperatorMetatype): + name = "SinOp" + module_to_function_names = {NamespaceTarget.TORCH: ["sin"]} + + def get_operator_metatypes() -> List[Type[OperatorMetatype]]: """ Returns a list of the operator metatypes. @@ -1141,9 +1162,35 @@ def get_operator_metatypes() -> List[Type[OperatorMetatype]]: PTModuleLinearMetatype, ] +ELEMENTWISE_OPERATIONS = [ + PTAddMetatype, + PTMulMetatype, + PTSubMetatype, + PTDivMetatype, + PTLessMetatype, + PTLessEqualMetatype, + PTGreaterMetatype, + PTGreaterEqualMetatype, + PTEqualsMetatype, + PTNotEqualMetatype, + PTModMetatype, + PTLogicalOrMetatype, + PTLogicalXorMetatype, + PTLogicalAndMetatype, + PTMaxMetatype, + PTMinMetatype, +] + OP_NAMES_WITH_WEIGHTS = [x for meta in OPERATORS_WITH_WEIGHTS_METATYPES for x in meta.get_all_aliases()] -QUANTIZE_NODE_TYPES = ["symmetric_quantize", "asymmetric_quantize"] +QUANTIZE_NODE_TYPES = [ + "symmetric_quantize", + "asymmetric_quantize", + "quantize_per_tensor", + "dequantize_per_tensor", + "quantize_per_channel", + "dequantize_per_channel", +] # These metatypes mix outputs for different samples into one axis. # If reducers and aggregators collect statistics at the output of the following operations, diff --git a/nncf/torch/model_creation.py b/nncf/torch/model_creation.py index 319ada120ab..a74d9f33002 100644 --- a/nncf/torch/model_creation.py +++ b/nncf/torch/model_creation.py @@ -19,6 +19,7 @@ import nncf from nncf.api.compression import CompressionAlgorithmController from nncf.common.compression import BaseCompressionAlgorithmController as BaseController +from nncf.common.deprecation import warning_deprecated from nncf.common.logging import nncf_logger from nncf.common.utils.api_marker import api from nncf.common.utils.debug import set_debug_log_dir @@ -100,6 +101,18 @@ def create_compressed_model( is an instance of CompositeCompressionController) and the model ready for compression parameter training wrapped as an object of NNCFNetwork. """ + + warning_deprecated( + "The 'nncf.torch.create_compressed_model' function is deprecated and will be removed in a future release.\n" + "To perform post training quantization (PTQ) or quantization aware training (QAT)," + " use the new nncf.quantize() API:\n" + " - https://github.com/openvinotoolkit/nncf?tab=readme-ov-file#post-training-quantization\n" + " - https://github.com/openvinotoolkit/nncf?tab=readme-ov-file#training-time-quantization\n" + "Examples:\n" + " - https://github.com/openvinotoolkit/nncf/tree/develop/examples/post_training_quantization/torch\n" + " - https://github.com/openvinotoolkit/nncf/tree/develop/examples/quantization_aware_training/torch" + ) + if isinstance(model, NNCFNetwork): raise nncf.InternalError( "The model object has already been compressed.\n" diff --git a/nncf/torch/nncf_network.py b/nncf/torch/nncf_network.py index 6a80ebd709e..ba3573acf10 100644 --- a/nncf/torch/nncf_network.py +++ b/nncf/torch/nncf_network.py @@ -679,12 +679,8 @@ def get_original_insertion_point_graph(self) -> InsertionPointGraph: post_hook_ip = PostHookInsertionPoint(node.node_name) post_hooks.append(post_hook_ip) - weighted_nodes = self.get_weighted_original_graph_nodes() - weighted_node_names = [weighted_node.node_name for weighted_node in weighted_nodes] - ip_graph = InsertionPointGraph( self._original_graphs_pair.nncf_graph, - weight_modifiable_node_names=weighted_node_names, allowed_pre_hook_insertion_points=pre_hooks, allowed_post_hook_insertion_points=post_hooks, ) diff --git a/nncf/torch/quantization/algo.py b/nncf/torch/quantization/algo.py index 1766290fe08..331ccbdd545 100644 --- a/nncf/torch/quantization/algo.py +++ b/nncf/torch/quantization/algo.py @@ -81,6 +81,7 @@ from nncf.torch.compression_method_api import PTCompressionAlgorithmBuilder from nncf.torch.compression_method_api import PTCompressionAlgorithmController from nncf.torch.graph.graph import PTNNCFGraph +from nncf.torch.graph.operator_metatypes import ELEMENTWISE_OPERATIONS from nncf.torch.graph.operator_metatypes import UNIFICATION_PRODUCING_METATYPES from nncf.torch.graph.operator_metatypes import PTCatMetatype from nncf.torch.graph.operator_metatypes import PTModuleConv2dMetatype @@ -375,7 +376,7 @@ def generate_setup(self) -> SingleConfigQuantizerSetup: merged_ip_graph = insertion_point_graph.get_ip_graph_with_merged_hw_optimized_operations( self._pattern_fusing_graph ) - quantization_proposal = prop_graph_solver.run_on_ip_graph(merged_ip_graph) + quantization_proposal = prop_graph_solver.run_on_ip_graph(merged_ip_graph, ELEMENTWISE_OPERATIONS) self._num_potential_quantized_activations = prop_graph_solver.get_num_potential_quantized_activations() quantizer_setup = deepcopy(quantization_proposal.quantizer_setup) diff --git a/nncf/torch/quantization/ignored_patterns.py b/nncf/torch/quantization/ignored_patterns.py index 895849c244e..e5bd1d93e16 100644 --- a/nncf/torch/quantization/ignored_patterns.py +++ b/nncf/torch/quantization/ignored_patterns.py @@ -227,3 +227,23 @@ def get_se_block_with_bias_and_reshape() -> GraphPattern: main_pattern.add_pattern_alternative(get_se_block_with_reshape()) main_pattern.add_pattern_alternative(get_se_block_with_bias_and_reshape()) return main_pattern + + +@PT_IGNORED_PATTERNS.register(IgnoredPatternNames.ROPE) +def create_rope() -> GraphPattern: + pattern = GraphPattern() + matmul_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "MATMUL", GraphPattern.METATYPE_ATTR: om.PTMatMulMetatype} + ) + transpose_node = pattern.add_node( + **{GraphPattern.LABEL_ATTR: "TRANSPOSE", GraphPattern.METATYPE_ATTR: om.PTTransposeMetatype} + ) + concat_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "CONCAT", GraphPattern.METATYPE_ATTR: om.PTCatMetatype}) + cos_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "COS", GraphPattern.METATYPE_ATTR: om.PTCosMetatype}) + sin_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "SIN", GraphPattern.METATYPE_ATTR: om.PTSinMetatype}) + + pattern.add_edge(matmul_node, transpose_node) + pattern.add_edge(transpose_node, concat_node) + pattern.add_edge(concat_node, cos_node) + pattern.add_edge(concat_node, sin_node) + return pattern diff --git a/nncf/torch/quantization/layers.py b/nncf/torch/quantization/layers.py index b84d325526f..85e41428846 100644 --- a/nncf/torch/quantization/layers.py +++ b/nncf/torch/quantization/layers.py @@ -49,7 +49,11 @@ from nncf.torch.quantization.quantize_functions import decompress_asymmetric from nncf.torch.quantization.quantize_functions import decompress_symmetric from nncf.torch.quantization.quantize_functions import get_scale_zp_from_input_low_input_high +from nncf.torch.quantization.quantize_functions import pack_int4 +from nncf.torch.quantization.quantize_functions import pack_uint4 from nncf.torch.quantization.quantize_functions import symmetric_quantize +from nncf.torch.quantization.quantize_functions import unpack_int4 +from nncf.torch.quantization.quantize_functions import unpack_uint4 from nncf.torch.return_types import maybe_get_values_from_torch_return_type from nncf.torch.return_types import maybe_wrap_to_torch_return_type from nncf.torch.utils import get_flat_tensor_contents_string @@ -1045,43 +1049,189 @@ def get_scale_shape(input_shape: List[int], is_weights: bool, per_channel: bool, return get_per_channel_scale_shape(input_shape, is_weights, channel_idx) -class AsymmetricWeightsDecompressor(nn.Module): +class BaseWeightsDecompressor(nn.Module, ABC): + """ + Base class for implementing weights decompression modules within NNCF. + + This class is intended to serve as the foundation for modules that handle the decompression + of quantized model weights. It provides an interface for defining the quantization mode and + packing the weights according to the specified quantization strategy. Classes inheriting from + this base class must implement the abstract methods for packing and handling the quantization mode. + """ + + @property + @abstractmethod + def quantization_mode(self) -> QuantizationMode: + """ + Property that specifies the quantization mode used for compressing weights. + + This method must be implemented to return the specific mode of quantization that + the decompressor is using, such as symmetric or asymmetric quantization. + + :return: The quantization mode as an instance of `QuantizationMode`. + """ + + @abstractmethod + def pack_weight(self, weight: torch.Tensor) -> torch.Tensor: + """ + Pack the given weight tensor according to the selected quantization mode. + + :param weight: The tensor containing the weight values to be packed. + :return: The packed tensor. + """ + + +class INT8AsymmetricWeightsDecompressor(BaseWeightsDecompressor): """ Applies asymmetric decompression of compressed weights in the forward pass """ - def __init__(self, scale: torch.Tensor, zero_point: torch.Tensor, result_dtype: torch.dtype = None): + def __init__(self, scale: torch.Tensor, zero_point: torch.Tensor, result_dtype: Optional[torch.dtype] = None): """ :param scale: A scale in quantization scheme :param zero_point: A zero point in quantization scheme :param result_dtype: (Optional) A data type that result should be cast to """ super().__init__() - self.register_buffer("_scale", scale) - self.register_buffer("_zero_point", zero_point) + self.register_buffer("_scale", scale.type(dtype=torch.float16)) + self.register_buffer("_zero_point", self.pack_weight(zero_point)) self.result_dtype = result_dtype - def forward(self, x): + @property + def quantization_mode(self) -> QuantizationMode: + return QuantizationMode.ASYMMETRIC + + def pack_weight(self, weight: torch.Tensor) -> torch.Tensor: + if torch.is_floating_point(weight): + raise ValueError(f"Invalid weight dtype {weight.type}. Integer types are supported.") + if torch.any((weight < 0) | (weight > 255)): + raise ValueError("Weight values are not in [0, 255].") + return weight.type(dtype=torch.uint8) + + def forward(self, x) -> torch.Tensor: result = decompress_asymmetric(x, self._scale, self._zero_point) result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result return result -class SymmetricWeightsDecompressor(nn.Module): +class INT8SymmetricWeightsDecompressor(BaseWeightsDecompressor): """ Applies symmetric decompression of compressed weights in the forward pass """ - def __init__(self, scale: torch.Tensor, result_dtype: torch.dtype = None): + def __init__(self, scale: torch.Tensor, result_dtype: Optional[torch.dtype] = None): """ :param scale: A scale in quantization scheme :param result_dtype: (Optional) A data type that result should be cast to """ super().__init__() - self.register_buffer("_scale", scale) + self.register_buffer("_scale", scale.type(dtype=torch.float16)) self.result_dtype = result_dtype + @property + def quantization_mode(self) -> QuantizationMode: + return QuantizationMode.SYMMETRIC + + def pack_weight(self, weight: torch.Tensor) -> torch.Tensor: + if torch.is_floating_point(weight): + raise ValueError(f"Invalid weight dtype {weight.type}. Integer types are supported.") + if torch.any((weight < -128) | (weight > 127)): + raise ValueError("Weight values are not in [-128, 127].") + return weight.type(dtype=torch.int8) + + def forward(self, x) -> torch.Tensor: + result = decompress_symmetric(x, self._scale) + result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result + return result + + +class INT4AsymmetricWeightsDecompressor(BaseWeightsDecompressor): + def __init__( + self, + scale: torch.Tensor, + zero_point: torch.Tensor, + compressed_weight_shape: Tuple[int, ...], + result_shape: Optional[Tuple[int, ...]] = None, + result_dtype: Optional[torch.dtype] = None, + ): + """ + :param scale: A scale in quantization scheme + :param zero_point: A zero point in quantization scheme + :param compressed_weight_shape: A compressed weight shape + :param result_shape: (Optional) A shape that result should be reshaped + :param result_dtype: (Optional) A data type that result should be cast to + """ + super().__init__() + self.register_buffer("_scale", scale.type(dtype=torch.float16)) + + self.zero_point_shape = zero_point.shape + self.register_buffer("_zero_point", self.pack_weight(zero_point)) + + self.compressed_weight_shape = compressed_weight_shape + self.result_shape = result_shape + self.result_dtype = result_dtype + + @property + def quantization_mode(self) -> QuantizationMode: + return QuantizationMode.ASYMMETRIC + + def pack_weight(self, weight: torch.Tensor) -> torch.Tensor: + if torch.is_floating_point(weight): + raise ValueError(f"Invalid weight dtype {weight.type}. Integer types are supported.") + if torch.any((weight < 0) | (weight > 15)): + raise ValueError("Weight values are not in [0, 15].") + return pack_uint4(weight.type(dtype=torch.uint8)) + def forward(self, x): + x = unpack_uint4(x) + x = x.reshape(self.compressed_weight_shape) + + zero_point = unpack_uint4(self._zero_point) + zero_point = zero_point.reshape(self.zero_point_shape) + + result = decompress_asymmetric(x, self._scale, zero_point) + result = result.reshape(self.result_shape) if self.result_shape is not None else result + result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result + return result + + +class INT4SymmetricWeightsDecompressor(BaseWeightsDecompressor): + def __init__( + self, + scale: torch.Tensor, + compressed_weight_shape: Tuple[int, ...], + result_shape: Optional[Tuple[int, ...]] = None, + result_dtype: Optional[torch.dtype] = None, + ): + """ + :param scale: A scale in quantization scheme + :param compressed_weight_shape: A compressed weight shape + :param result_shape: (Optional) A shape that result should be reshaped + :param result_dtype: (Optional) A data type that result should be cast to + """ + super().__init__() + self.register_buffer("_scale", scale.type(dtype=torch.float16)) + + self.compressed_weight_shape = compressed_weight_shape + self.result_shape = result_shape + self.result_dtype = result_dtype + + @property + def quantization_mode(self) -> QuantizationMode: + return QuantizationMode.SYMMETRIC + + def pack_weight(self, weight: torch.Tensor) -> torch.Tensor: + if torch.is_floating_point(weight): + raise ValueError(f"Invalid weight dtype {weight.type}. Integer types are supported.") + if torch.any((weight < -8) | (weight > 7)): + raise ValueError("Tensor values are not in [-8, 7].") + return pack_int4(weight.type(dtype=torch.int8)) + + def forward(self, x): + x = unpack_int4(x) + x = x.reshape(self.compressed_weight_shape) + result = decompress_symmetric(x, self._scale) + result = result.reshape(self.result_shape) if self.result_shape is not None else result result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result return result diff --git a/nncf/torch/quantization/quantize_functions.py b/nncf/torch/quantization/quantize_functions.py index 9b4055c4586..b967bb57683 100644 --- a/nncf/torch/quantization/quantize_functions.py +++ b/nncf/torch/quantization/quantize_functions.py @@ -13,6 +13,7 @@ import torch from nncf.common.logging import nncf_logger +from nncf.errors import ValidationError from nncf.torch.dynamic_graph.patch_pytorch import register_operator from nncf.torch.functions import STRound from nncf.torch.functions import clamp @@ -259,6 +260,7 @@ def decompress_asymmetric(input: torch.Tensor, scale: torch.Tensor, zero_point: :return: The decompressed tensor """ input = input.type(dtype=scale.dtype) + zero_point = zero_point.type(dtype=scale.dtype) decompressed_input = (input - zero_point) * scale return decompressed_input @@ -275,3 +277,62 @@ def decompress_symmetric(input: torch.Tensor, scale: torch.Tensor) -> torch.Tens input = input.type(dtype=scale.dtype) decompressed_input = input * scale return decompressed_input + + +def pack_uint4(tensor: torch.Tensor) -> torch.Tensor: + """ + Packs a tensor containing uint4 values (in the range [0, 15]) into a tensor with uint8 values, + where each element stores two uint4 values. + + :param tensor: A tensor of dtype `torch.uint8` where each element represents a uint4 value. + The tensor should contain values in the range [0, 15]. + :return: A packed tensor of dtype `torch.uint8` where each element packs two uint4 values. + :raises nncf.errors.ValidationError: If the input tensor is not of type `torch.uint8`. + """ + if tensor.dtype != torch.uint8: + raise ValidationError(f"Invalid tensor dtype {tensor.type}. torch.uint8 type is supported.") + packed_tensor = tensor.contiguous() + packed_tensor = packed_tensor.reshape(-1, 2) + packed_tensor = torch.bitwise_and(packed_tensor[..., ::2], 15) | packed_tensor[..., 1::2] << 4 + return packed_tensor + + +@register_operator() +def unpack_uint4(packed_tensor: torch.Tensor) -> torch.Tensor: + """ + Unpacks a tensor, where each uint8 element stores two uint4 values, back into a tensor with + individual uint4 values. + + :param packed_tensor: A tensor of dtype `torch.uint8` where each element packs two uint4 values. + :return: A tensor of dtype `torch.uint8` where each element represents a uint4 value. + """ + return torch.stack((torch.bitwise_and(packed_tensor, 15), torch.bitwise_right_shift(packed_tensor, 4)), dim=-1) + + +def pack_int4(tensor: torch.Tensor) -> torch.Tensor: + """ + Packs a tensor containing int4 values (in the range [-8, 7]) into a tensor with uint8 values, + where each element stores two int4 values. + + :param tensor: A tensor of dtype `torch.int8` where each element represents an int4 value. + The tensor should contain values in the range [-8, 7]. + :return: A packed tensor of dtype `torch.uint8` where each element packs two int4 values. + :raises nncf.errors.ValidationError: If the input tensor is not of type `torch.int8`. + """ + if tensor.dtype != torch.int8: + raise ValidationError(f"Invalid tensor dtype {tensor.type}. torch.int8 type is supported.") + tensor = tensor + 8 + return pack_uint4(tensor.type(torch.uint8)) + + +@register_operator() +def unpack_int4(packed_tensor: torch.Tensor) -> torch.Tensor: + """ + Unpacks a tensor, where each uint8 element stores two int4 values, back into a tensor with + individual int4 values. + + :param packed_tensor: A tensor of dtype `torch.uint8` where each element packs two int4 values. + :return: A tensor of dtype `torch.int8` where each element represents an int4 value. + """ + t = unpack_uint4(packed_tensor) + return t.type(torch.int8) - 8 diff --git a/nncf/torch/quantization/quantize_model.py b/nncf/torch/quantization/quantize_model.py index d15b2890efb..23cb451f5fe 100644 --- a/nncf/torch/quantization/quantize_model.py +++ b/nncf/torch/quantization/quantize_model.py @@ -18,6 +18,7 @@ from nncf.common.factory import NNCFGraphFactory from nncf.common.quantization.structs import QuantizationPreset from nncf.data import Dataset +from nncf.parameters import BackupMode from nncf.parameters import CompressWeightsMode from nncf.parameters import ModelType from nncf.parameters import QuantizationMode @@ -94,6 +95,7 @@ def compress_weights_impl( scale_estimation: bool, gptq: bool, lora_correction: bool, + backup_mode: BackupMode, advanced_parameters: Optional[AdvancedCompressionParameters] = None, ) -> torch.nn.Module: """ @@ -112,6 +114,7 @@ def compress_weights_impl( scale_estimation, gptq, lora_correction, + backup_mode, advanced_parameters, ) graph = NNCFGraphFactory.create(model) diff --git a/nncf/torch/statistics/aggregator.py b/nncf/torch/statistics/aggregator.py index 2d0a1ba0a8b..c52c2eb4c33 100644 --- a/nncf/torch/statistics/aggregator.py +++ b/nncf/torch/statistics/aggregator.py @@ -19,13 +19,17 @@ from nncf.common.graph.transformations.layout import TransformationLayout from nncf.common.tensor_statistics.aggregator import StatisticPointsContainer from nncf.common.tensor_statistics.aggregator import StatisticsAggregator +from nncf.common.utils.backend import BackendType +from nncf.experimental.common.tensor_statistics.statistics import TensorStatistic from nncf.tensor import Tensor from nncf.torch.graph.transformations.commands import PTInsertionCommand +from nncf.torch.graph.transformations.commands import PTTargetPoint from nncf.torch.nncf_network import NNCFNetwork from nncf.torch.tensor_statistics.algo import create_register_input_hook class PTStatisticsAggregator(StatisticsAggregator): + BACKEND: BackendType = BackendType.TORCH HOOKS_GROUP_NAME = "statistics_hooks" def collect_statistics(self, model: NNCFNetwork, graph: NNCFGraph) -> None: @@ -72,3 +76,14 @@ def _get_merged_statistic_points( def _process_outputs(outputs: torch.Tensor) -> Dict[str, Tensor]: # PyTorch backend doesn't use outputs to register statistics return {} + + def _get_statistics_key(self, statistics: TensorStatistic, target_point: PTTargetPoint) -> str: + """ + Returns key of statistics. + + :param statistics: Statistics value. + :param target_point: Statistics target point. + :return: Statistics key. + """ + target_point_id = f"{target_point.target_node_name}_{target_point.type}_{target_point.input_port_id}" + return f"{statistics.__class__.__name__}_{target_point_id}" diff --git a/nncf/torch/tensor_statistics/collectors.py b/nncf/torch/tensor_statistics/collectors.py index 9fbb95710ec..abe7aa60acd 100644 --- a/nncf/torch/tensor_statistics/collectors.py +++ b/nncf/torch/tensor_statistics/collectors.py @@ -10,7 +10,7 @@ # limitations under the License. from functools import partial -from typing import List, Optional, Tuple, Type +from typing import Optional, Tuple, Type import numpy as np @@ -42,47 +42,6 @@ from nncf.tensor import Tensor -class PTReducerMixIn: - - def get_inplace_fn(self): - return None - - def get_output_names(self, target_node_name: str, port_id: int) -> List[str]: - return [] - - -class PTMinReducer(PTReducerMixIn, MinReducer): - pass - - -class PTMaxReducer(PTReducerMixIn, MaxReducer): - pass - - -class PTAbsMaxReducer(PTReducerMixIn, AbsMaxReducer): - pass - - -class PTMeanReducer(PTReducerMixIn, MeanReducer): - pass - - -class PTQuantileReducer(PTReducerMixIn, QuantileReducer): - pass - - -class PTAbsQuantileReducer(PTReducerMixIn, AbsQuantileReducer): - pass - - -class PTBatchMeanReducer(PTReducerMixIn, BatchMeanReducer): - pass - - -class PTMeanPerChanelReducer(PTReducerMixIn, MeanPerChReducer): - pass - - def _reshape_all(targets: Tuple[Tensor, ...], target_shape: Tuple[int, ...]): return map(lambda stat: stat.reshape(target_shape), targets) @@ -145,11 +104,11 @@ def get_min_max_statistic_collector( "num_samples": num_samples, "aggregation_axes": aggregation_axes, } - min_reducer = PTMinReducer(reduction_axes) + min_reducer = MinReducer(reduction_axes) min_aggregator = MinAggregator(**aggregator_kwargs) tensor_collector.register_statistic_branch(MinMaxTensorStatistic.MIN_STAT, min_reducer, min_aggregator) - max_reducer_cls = PTAbsMaxReducer if use_abs_max else PTMaxReducer + max_reducer_cls = AbsMaxReducer if use_abs_max else MaxReducer max_reducer = max_reducer_cls(reduction_axes) max_aggregator = MaxAggregator(**aggregator_kwargs) tensor_collector.register_statistic_branch(MinMaxTensorStatistic.MAX_STAT, max_reducer, max_aggregator) @@ -181,7 +140,7 @@ def get_mixed_min_max_statistic_collector( :return: Mixed min max statistic collector. """ tensor_collector = TensorCollector(_get_wrapped_min_max_tensor_statistic(target_shape=scale_shape)) - min_reducer = PTMinReducer(reduction_axes) + min_reducer = MinReducer(reduction_axes) kwargs = { "num_samples": num_samples, @@ -192,7 +151,7 @@ def get_mixed_min_max_statistic_collector( min_aggregator = min_aggregator_cls(**kwargs) tensor_collector.register_statistic_branch(MinMaxTensorStatistic.MIN_STAT, min_reducer, min_aggregator) - max_reducer_cls = PTAbsMaxReducer if use_abs_max else PTMaxReducer + max_reducer_cls = AbsMaxReducer if use_abs_max else MaxReducer max_reducer = max_reducer_cls(reduction_axes) max_aggregator_cls = MeanAggregator if use_means_of_maxs else MaxAggregator max_aggregator = max_aggregator_cls(**kwargs) @@ -323,7 +282,7 @@ def get_mean_percentile_statistic_collector( """ tensor_collector = TensorCollector(_get_wrapped_percentile_tensor_statistic(target_shape=scale_shape)) quantiles_to_collect = np.true_divide(percentiles_to_collect, 100) - reducer = PTQuantileReducer(reduction_axes=reduction_axes, quantile=quantiles_to_collect) + reducer = QuantileReducer(reduction_axes=reduction_axes, quantile=quantiles_to_collect) for output_port_id, p in enumerate(percentiles_to_collect): aggregator = MeanAggregator( aggregation_axes=aggregation_axes, @@ -349,9 +308,9 @@ def get_mean_statistic_collector( :return: Mean statistic collector. """ if channel_axis == 0: - reducer = PTBatchMeanReducer() + reducer = BatchMeanReducer() else: - reducer = PTMeanPerChanelReducer(channel_axis=channel_axis) + reducer = MeanPerChReducer(channel_axis=channel_axis) noop_reducer = NoopReducer() kwargs = { @@ -383,10 +342,10 @@ def get_raw_stat_collector(num_samples: Optional[int] = None) -> TensorCollector PT_REDUCERS_MAP = { - StatisticsType.MIN: PTMinReducer, - StatisticsType.MAX: PTMaxReducer, - StatisticsType.ABS_MAX: PTAbsMaxReducer, - StatisticsType.MEAN: PTMeanReducer, - StatisticsType.QUANTILE: PTQuantileReducer, - StatisticsType.ABS_QUANTILE: PTAbsQuantileReducer, + StatisticsType.MIN: MinReducer, + StatisticsType.MAX: MaxReducer, + StatisticsType.ABS_MAX: AbsMaxReducer, + StatisticsType.MEAN: MeanReducer, + StatisticsType.QUANTILE: QuantileReducer, + StatisticsType.ABS_QUANTILE: AbsQuantileReducer, } diff --git a/nncf/version.py b/nncf/version.py index c193ef76ddb..fbeac0d9964 100644 --- a/nncf/version.py +++ b/nncf/version.py @@ -9,9 +9,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "2.13.0" +__version__ = "2.14.0" -BKC_TORCH_SPEC = "==2.4.*" +BKC_TORCH_SPEC = "==2.5.*" BKC_TF_SPEC = "==2.15.*" STRICT_TF_SPEC = ">=2.9.3,<2.16.0" diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000000..2835da0a68b --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,154 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "nncf" +description = "Neural Networks Compression Framework" +readme="docs/PyPiPublishing.md" +license = { text = "Apache-2.0" } +authors = [{ name = "Intel" }, { email = "alexander.kozlov@intel.com" }] +requires-python = ">=3.9" +dynamic = ["version"] +keywords = [ + "bert", + "classification", + "compression", + "hawq", + "mixed-precision-training", + "mmdetection", + "nas", + "nlp", + "object-detection", + "pruning", + "quantization", + "quantization-aware-training", + "semantic-segmentation", + "sparsity", + "transformers", +] +classifiers = [ + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", +] +dependencies = [ + "jsonschema>=3.2.0", + "jstyleson>=0.0.2", + "natsort>=7.1.0", + "networkx>=2.6, <=3.3", + "ninja>=1.10.0.post2, <1.12", + "numpy>=1.19.1, <2.2.0", + "openvino-telemetry>=2023.2.0", + "packaging>=20.0", + "pandas>=1.1.5,<2.3", + "psutil", + "pydot>=1.4.1, <3.0.0", + "pymoo>=0.6.0.1", + "rich>=13.5.2", + "scikit-learn>=0.24.0", + "scipy>=1.3.2", + "tabulate>=0.9.0", + "tqdm>=4.54.1", +] + +[project.optional-dependencies] +plots = [ + "kaleido>=0.2.1", + "matplotlib>=3.3.4, <3.6", + "pillow>=9.0.0", + "plotly-express>=0.4.1", +] + +[project.urls] +Homepage = "https://github.com/openvinotoolkit/nncf" + +[tool.setuptools.dynamic] +version = { attr = "custom_version.version" } + +[tool.setuptools.packages.find] +where = ["."] +exclude = ["tests", "tests.*", "examples", "examples.*", "tools", "tools.*"] +namespaces = false + +[tool.black] +line-length = 120 + +[tool.md_dead_link_check] +exclude_files = ["ReleaseNotes.md"] + +[tool.isort] +line_length = 120 +force_single_line = true +profile = "black" +single_line_exclusions = "typing" +skip_glob = "examples/post_training_quantization/torch/ssd300_vgg16/main.py" + +[tool.mypy] +follow_imports = "silent" +strict = true +# should be removed later +# mypy recommends the following tool as an autofix: +# https://github.com/hauntsaninja/no_implicit_optional +implicit_optional = true +files = [ + "nncf/common/sparsity", + "nncf/common/graph", + "nncf/common/accuracy_aware_training/", + "nncf/common/utils/", + "nncf/common/tensor_statistics", + "nncf/experimental/torch2", +] + +[tool.ruff] +line-length = 120 +exclude = ["nncf/tensorflow/__init__.py"] + +[tool.ruff.lint] +preview = true +ignore-init-module-imports = true +ignore = [ + "E201", # whitespace-after-open-bracket + "E203", # whitespace-before-punctuation + "E231", # missing-whitespace + "E251", # unexpected-spaces-around-keyword-parameter-equals + "E731", # lambda-assignment + "SIM108", # if-else-block-instead-of-if-exp + "SIM110", # reimplemented-builtin + "SIM117", # multiple-with-statements + "SIM103", # needless-bool + "NPY002", # numpy-legacy-random +] +select = [ + "E", # pycodestyle rules + "F", # pyflakes rules + "CPY001", # copyright check + "NPY", # numpy rules +] +extend-select = [ + "SIM", # https://pypi.org/project/flake8-simplify +] + +[tool.ruff.lint.per-file-ignores] +"nncf/experimental/torch/nas/bootstrapNAS/__init__.py" = ["F401"] +"nncf/torch/__init__.py" = ["F401", "E402"] +"tests/**/*.py" = ["F403"] +"tests/**/__init__.py" = ["F401"] +"examples/**/*.py" = ["F403"] + +[tool.ruff.lint.flake8-copyright] +notice-rgx = """\ +# Copyright \\(c\\) (\\d{4}|\\d{4}-\\d{4}) Intel Corporation +# Licensed under the Apache License, Version 2.0 \\(the "License"\\); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" + +[tool.pytest.ini_options] +pythonpath = "." diff --git a/ruff.toml b/ruff.toml deleted file mode 100644 index 517eed635cc..00000000000 --- a/ruff.toml +++ /dev/null @@ -1,49 +0,0 @@ -line-length = 120 -exclude = ["nncf/tensorflow/__init__.py"] - -[lint] -preview = true -ignore-init-module-imports = true -ignore = [ - "E201", # whitespace-after-open-bracket - "E203", # whitespace-before-punctuation - "E231", # missing-whitespace - "E251", # unexpected-spaces-around-keyword-parameter-equals - "E731", # lambda-assignment - "SIM108", # if-else-block-instead-of-if-exp - "SIM110", # reimplemented-builtin - "SIM117", # multiple-with-statements - "SIM103", # needless-bool - "NPY002", # numpy-legacy-random -] -select = [ - "E", # pycodestyle rules - "F", # pyflakes rules - "CPY001", # copyright check - "NPY", # numpy rules -] -extend-select = [ - "SIM", # https://pypi.org/project/flake8-simplify -] - -[lint.per-file-ignores] -"nncf/experimental/torch/nas/bootstrapNAS/__init__.py" = ["F401"] -"nncf/torch/__init__.py" = ["F401", "E402"] -"tests/**/*.py" = ["F403"] -"tests/**/__init__.py" = ["F401"] -"examples/**/*.py" = ["F403"] - - -[lint.flake8-copyright] -notice-rgx = """\ -# Copyright \\(c\\) (\\d{4}|\\d{4}-\\d{4}) Intel Corporation -# Licensed under the Apache License, Version 2.0 \\(the "License"\\); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" diff --git a/setup.py b/setup.py deleted file mode 100644 index 8685c9f9982..00000000000 --- a/setup.py +++ /dev/null @@ -1,178 +0,0 @@ -# Copyright (c) 2024 Intel Corporation -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# *WARNING*: Do not run this file directly by `python setup.py install` -# or with any other parameter - this is an outdated and error-prone way -# to install Python packages and causes particular problems with namespace -# packages such as `protobuf`. -# Refer to the table below for well-known and supported alternatives with -# the same behavior: -# +-------------------------------------+---------------------------------------------+ -# | Old command | New command | -# +-------------------------------------+---------------------------------------------+ -# | python setup.py install | pip install . | -# | python setup.py develop | pip install -e . | -# | python setup.py develop --*arg* | pip install --install-option="*arg*" -e . | -# | python setup.py sdist | python -m build -s | <-- using the "build" package -# | python setup.py bdist_wheel | python -m build -w | <-- pypi.org/project/build/ -# | python setup.py bdist_wheel --*arg* | python -m build -w -C--global-option=--*arg*| -# +-------------------------------------+---------------------------------------------+ -# -# PyPA in general recommends to move away from setup.py and use pyproject.toml -# instead. This doesn't fit us as we currently want to do custom stuff during -# installation such as setting version based on the commit SHA for repo-based -# installs. - - -import codecs -import glob -import os -import re -import stat -import sys -import sysconfig - -import setuptools -from pkg_resources import parse_version -from setuptools import find_packages -from setuptools import setup - -here = os.path.abspath(os.path.dirname(__file__)) -BKC_SETUPTOOLS_VERSION = "59.5.0" - -setuptools_version = parse_version(setuptools.__version__).base_version -if setuptools_version < "43.0.0": - raise RuntimeError( - "To properly install NNCF, please install setuptools>=43.0.0, " - f"while current setuptools version is {setuptools.__version__}. " - f"Recommended version is {BKC_SETUPTOOLS_VERSION}." - ) - -python_version = sys.version_info -if python_version < (3, 8, 0): - print("Only Python >= 3.8.0 is supported") - sys.exit(0) - -version_string = "{}{}".format(sys.version_info[0], sys.version_info[1]) - -is_installing_editable = "develop" in sys.argv -is_building_release = not is_installing_editable and "--release" in sys.argv -if "--release" in sys.argv: - sys.argv.remove("--release") - - -def read(*parts): - with codecs.open(os.path.join(here, *parts), "r") as fp: - return fp.read() - - -def find_version(*file_paths): - version_file = read(*file_paths) - version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M) - if not version_match: - raise RuntimeError("Unable to find version string.") - version_value = version_match.group(1) - if not is_building_release: - if is_installing_editable: - return version_value + ".dev0+editable" - import subprocess # nosec - - dev_version_id = "unknown_version" - try: - repo_root = os.path.dirname(os.path.realpath(__file__)) - dev_version_id = ( - subprocess.check_output(["git", "rev-parse", "--short", "HEAD"], cwd=repo_root) # nosec - .strip() - .decode() - ) - except subprocess.CalledProcessError: - pass - return version_value + f".dev0+{dev_version_id}" - - return version_value - - -INSTALL_REQUIRES = [ - "jsonschema>=3.2.0", - "jstyleson>=0.0.2", - "natsort>=7.1.0", - "networkx>=2.6, <=3.3", - "ninja>=1.10.0.post2, <1.12", - "numpy>=1.19.1, <1.27", - "openvino-telemetry>=2023.2.0", - "packaging>=20.0", - "pandas>=1.1.5,<2.3", - "psutil", - "pydot>=1.4.1, <3.0.0", - "pymoo>=0.6.0.1", - "rich>=13.5.2", - "scikit-learn>=0.24.0", - "scipy>=1.3.2", - "tabulate>=0.9.0", - "tqdm>=4.54.1", -] - -EXTRAS_REQUIRE = { - "plots": [ - "kaleido>=0.2.1", - "matplotlib>=3.3.4, <3.6", - "pillow>=9.0.0", - "plotly-express>=0.4.1", - ], -} - -with open("{}/docs/PyPiPublishing.md".format(here), "r", encoding="utf8") as fh: - long_description = fh.read() - -setup( - name="nncf", - version=find_version(os.path.join(here, "nncf/version.py")), - author="Intel", - author_email="alexander.kozlov@intel.com", - description="Neural Networks Compression Framework", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/openvinotoolkit/nncf", - license="Apache-2.0", - packages=find_packages(exclude=["tests", "tests.*", "examples", "examples.*", "tools", "tools.*"]), - classifiers=[ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: Apache Software License", - "Operating System :: OS Independent", - ], - install_requires=INSTALL_REQUIRES, - extras_require=EXTRAS_REQUIRE, - keywords=[ - "compression", - "quantization", - "sparsity", - "mixed-precision-training", - "quantization-aware-training", - "hawq", - "classification", - "pruning", - "object-detection", - "semantic-segmentation", - "nas", - "nlp", - "bert", - "transformers", - "mmdetection", - ], - include_package_data=True, -) - -path_to_ninja = glob.glob(str(sysconfig.get_paths()["purelib"] + "/ninja*/ninja/data/bin/")) -if path_to_ninja: - path_to_ninja = str(path_to_ninja[0] + "ninja") - if not os.access(path_to_ninja, os.X_OK): - st = os.stat(path_to_ninja) - os.chmod(path_to_ninja, st.st_mode | stat.S_IEXEC) diff --git a/tests/common/conftest.py b/tests/common/conftest.py index 18d72418aaa..96982ce7c4f 100644 --- a/tests/common/conftest.py +++ b/tests/common/conftest.py @@ -10,4 +10,4 @@ # limitations under the License. -from tests.shared.logging import nncf_caplog # noqa: F401 +from tests.cross_fw.shared.logging import nncf_caplog # noqa: F401 diff --git a/tests/common/experimental/test_reducers_and_aggregators.py b/tests/common/experimental/test_reducers_and_aggregators.py index d83405e6f4d..5544a426a54 100644 --- a/tests/common/experimental/test_reducers_and_aggregators.py +++ b/tests/common/experimental/test_reducers_and_aggregators.py @@ -22,6 +22,7 @@ from nncf.common.graph.layer_attributes import Dtype from nncf.common.tensor import NNCFTensor from nncf.experimental.common.tensor_statistics.collectors import AggregationAxes +from nncf.experimental.common.tensor_statistics.collectors import HAWQAggregator from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator from nncf.experimental.common.tensor_statistics.collectors import MeanNoOutliersAggregator @@ -34,6 +35,7 @@ from nncf.experimental.common.tensor_statistics.collectors import PercentileAggregator from nncf.experimental.common.tensor_statistics.collectors import RawReducer from nncf.experimental.common.tensor_statistics.collectors import ShapeAggregator +from nncf.experimental.common.tensor_statistics.collectors import ShapeReducer from nncf.tensor import functions as fns DEFAULT_3D_MEAN_VALUE = [[2503.125, -2493.75, 5009.375], [-4987.5, 7515.625, -7481.25], [10021.875, -9975.0, 12528.125]] @@ -183,7 +185,7 @@ def test_other_reducers(self, reducer_cls, input_data): assert len(reduced_input) == 1 assert fns.allclose(reduced_input[0], tensor_data) - @pytest.mark.parametrize("reducer_cls", [NoopReducer, RawReducer]) + @pytest.mark.parametrize("reducer_cls", [NoopReducer, RawReducer, ShapeReducer]) def test_other_reducers_name_hash_equal(self, reducer_cls): reducers_instances = [reducer_cls() for _ in range(2)] assert hash(reducers_instances[0]) == hash(reducers_instances[1]) @@ -191,6 +193,14 @@ def test_other_reducers_name_hash_equal(self, reducer_cls): assert reducers_instances[0].name == reducers_instances[1].name assert len(set(reducers_instances)) == 1 + @pytest.mark.parametrize("input_data", [np.arange(24).reshape((1, 2, 3, 4)), np.array([1])]) + def test_shape_reducer(self, input_data): + reducer = ShapeReducer() + tensor_data = self.get_nncf_tensor(input_data) + reduced_input = reducer([tensor_data]) + assert len(reduced_input) == 1 + assert all(it[0] == it[1] for it in zip(reduced_input[0], tensor_data.shape)) + @pytest.mark.parametrize( "reducer_name,ref", [ @@ -545,3 +555,18 @@ def product_dict(**kwargs): for reducer, init_hash in zip(reducers_instances, hashes): reducer(test_input) assert hash(reducer) == init_hash + + HAWQ_AGGREGATOR_REFERENCE_VALUES = [ + ([np.arange(10)], 57.0), + ([np.arange(12).reshape((2, 6)), np.arange(24).reshape((4, 6))], 181.92361111111111), + ([np.arange(8 * i).reshape((1, 8, i)) for i in range(1, 5)], 165.61627197265625), + ] + + @pytest.mark.parametrize("inputs,reference_output", HAWQ_AGGREGATOR_REFERENCE_VALUES) + def test_hawq_aggregator(self, inputs, reference_output): + aggregator = HAWQAggregator() + for x in inputs: + aggregator.register_reduced_input(self.get_nncf_tensor(x, Dtype.FLOAT)) + + ret_val = aggregator.aggregate()[0] + assert fns.allclose(ret_val, reference_output) diff --git a/tests/common/experimental/test_statistic_collector.py b/tests/common/experimental/test_statistic_collector.py index 5db5b10addc..36186605032 100644 --- a/tests/common/experimental/test_statistic_collector.py +++ b/tests/common/experimental/test_statistic_collector.py @@ -10,7 +10,7 @@ # limitations under the License. from abc import abstractmethod -from typing import List, Optional +from typing import List, Optional, Tuple import numpy as np import pytest @@ -30,10 +30,16 @@ from nncf.tensor import Tensor +class DummyStatContainer: + @staticmethod + def from_config(config): + return config + + class DummyTensorReducer(TensorReducerBase): def __init__(self, output_name: str, inplace: bool = False, inplace_mock=None): super().__init__(inplace=inplace) - self._output_name = output_name + self.output_name = output_name self._inplace_mock = inplace_mock def _reduce_out_of_place(self, x: List[TensorType]): @@ -42,9 +48,6 @@ def _reduce_out_of_place(self, x: List[TensorType]): def get_inplace_fn(self): return self._inplace_mock - def get_output_names(self, target_node_name: str, port_id: int) -> str: - return [self._output_name] - def _get_processor(self): return None @@ -64,6 +67,13 @@ def _aggregate_impl(self): return self._container[0] +def get_output_info(reducers: List[DummyTensorReducer]) -> List[Tuple[int, List[str]]]: + retval = [] + for reducer in reducers: + retval.append((hash(reducer), [reducer.output_name])) + return retval + + class DummyTensorAggregatorA(DummyTensorAggregator): pass @@ -125,9 +135,7 @@ def test_duplicated_statistics_are_merged(): assert len(collector._aggregators) == 4 assert collector.num_samples == 100 - output_info = collector.get_output_info(None, None) - # Check output info - assert sorted(output_info) == sorted( + output_info = sorted( [(hash(reducer_inplace), ["Dummy_inplace"]), (hash(reducer_a), ["A"]), (hash(reducer), ["Dummy"])] ) @@ -173,8 +181,10 @@ def test_inplace_param(): collector.register_statistic_branch("other", reducer_other, aggregator_other) assert len(collector._reducers) == 3 assert len(collector._aggregators) == 3 - assert collector.get_inplace_fn_info()[0][0] == inplace_op - assert collector.any_stat_out_of_place() + for reducer in collector.reducers: + if reducer.inplace: + assert reducer.get_inplace_fn() == inplace_op + assert any(not reducer.inplace for reducer in collector.reducers) def test_merged_tensor_collector(): @@ -201,7 +211,7 @@ def test_merged_tensor_collector(): for collector in collectors[:-1]: assert collector.aggregators[common_branch_key] is common_aggregator - output_info = merged_collector.get_output_info(None, None) + output_info = get_output_info(merged_collector.reducers) outputs = {"common_input": Tensor(np.array(0))} outputs.update({f"input_{idx + 1}": Tensor(np.array(idx + 1)) for idx, _ in enumerate(collectors[:-1])}) target_inputs = TensorCollector.get_tensor_collector_inputs(outputs, output_info) @@ -243,13 +253,9 @@ class DummyMultipleInpOutTensorReducer(DummyTensorReducer): def _reduce_out_of_place(self, x: List[TensorType]): return x[: self.NUM_OUTPUTS] - def get_output_names(self, target_node_name: str, port_id: int) -> str: - return [f"{target_node_name}_{port_id}_{self._output_name}_{i}" for i in range(self.NUM_INPUTS)] - def test_multiple_branch_reducer(): reducer_output_name = "reducer_output_name" - target_node_name = "target_node_name" collector = TensorCollector() reducer = DummyMultipleInpOutTensorReducer(reducer_output_name) @@ -269,8 +275,7 @@ def test_multiple_branch_reducer(): ] inputs = {name: Tensor(np.array(i)) for i, name in enumerate(ref_output_info[0][1])} - output_info = collector.get_output_info(target_node_name, 0) - assert output_info == ref_output_info + output_info = ref_output_info target_inputs = collector.get_tensor_collector_inputs(inputs, output_info) collector.register_inputs(target_inputs) @@ -303,17 +308,6 @@ def test_register_unnamed_statistics(mocker): assert all(v[0] == inputs_) -def test_wrong_statistic_container_class(): - class BadStatContainer: - pass - - tensor_collector = TensorCollector(BadStatContainer) - tensor_collector.register_statistic_branch("A", DummyTensorReducer("A"), DummyTensorAggregator()) - tensor_collector.register_input_for_all_reducers(Tensor(np.array(1))) - with pytest.raises(nncf.InternalError): - tensor_collector.get_statistics() - - class TemplateTestStatisticCollector: @abstractmethod def get_nncf_tensor(self, value: np.ndarray) -> NNCFTensor: @@ -348,13 +342,12 @@ def test_empty_tensors_register(self, inplace, any_not_empty): stats = collector.get_statistics() assert len(stats) == 1 assert stats["A"] == self.get_nncf_tensor([100]) - return - - assert len(aggregator._container) == 0 - assert aggregator._collected_samples == 0 - stats = collector.get_statistics() - assert len(stats) == 1 - assert stats["A"] is None + else: + assert len(aggregator._container) == 0 + assert aggregator._collected_samples == 0 + stats = collector.get_statistics() + assert len(stats) == 1 + assert stats["A"] is None def test_min_max_stat_building(self): tensor_collector = TensorCollector(MinMaxTensorStatistic) @@ -441,3 +434,48 @@ def test_raw_max_stat_building(self): statistic = tensor_collector.get_statistics() assert isinstance(statistic, RawTensorStatistic) assert statistic.values == 1 + + def test_tensor_collector_cache_and_statistics(self): + # Initialize a single instance of TensorCollector + collector = TensorCollector(DummyStatContainer) + + # Test setting cache + cached_statistics = {"values": [1, 2]} + collector.set_cache(cached_statistics) + assert collector._cached_statistics == cached_statistics, "Cache should match the set value" + assert not collector.enabled, "Collector should be disabled after setting cache" + + # Test clearing cache + collector.clear_cache() + collector.enable() + assert collector._cached_statistics is None, "Cache should be cleared" + + # Test default behavior of get_statistics without cache + collector.register_statistic_branch("container_key", DummyTensorReducer("A"), DummyTensorAggregator()) + collector.register_input_for_all_reducers(Tensor(np.array(1))) + statistics = collector.get_statistics() + assert statistics == {"container_key": Tensor(np.array(1))}, "Statistics should reflect registered input" + + # Test get_statistics with cache + collector.set_cache({"container_key": Tensor(np.array(25))}) + statistics = collector.get_statistics() + assert statistics == {"container_key": Tensor(np.array(25))}, "Statistics should return cached value" + + # Attempt to register new input while cache is set + collector.register_input_for_all_reducers(Tensor(np.array(2))) + statistics = collector.get_statistics() + assert statistics == {"container_key": Tensor(np.array(25))}, "Statistics should still reflect cached value" + + # Clear cache and check behavior + collector.clear_cache() + collector.enable() + empty_stats = {"container_key": None} + statistics = collector.get_statistics() + assert statistics == empty_stats, "Statistics should be empty after clearing cache" + + # Register new input after clearing cache + collector.register_input_for_all_reducers(Tensor(np.array(8))) + statistics = collector.get_statistics() + assert statistics == { + "container_key": Tensor(np.array(8)) + }, "Statistics should reflect the new input after clearing cache" diff --git a/tests/common/graph/test_dot_file_rw.py b/tests/common/graph/test_dot_file_rw.py index f076af33f1a..a97f65d7b11 100644 --- a/tests/common/graph/test_dot_file_rw.py +++ b/tests/common/graph/test_dot_file_rw.py @@ -8,7 +8,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import filecmp + from copy import deepcopy from pathlib import Path @@ -17,8 +17,8 @@ from nncf.common.utils.dot_file_rw import read_dot_graph from nncf.common.utils.dot_file_rw import write_dot_graph -from tests.shared.nx_graph import check_nx_graph -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.nx_graph import check_nx_graph +from tests.cross_fw.shared.paths import TEST_ROOT @pytest.fixture(scope="module") @@ -47,7 +47,9 @@ def test_writing_does_not_modify_original_graph(tmp_path: Path, ref_graph: nx.Di def test_colons_are_replaced_in_written_dot_file(tmp_path: Path, ref_graph: nx.DiGraph): tmp_path_to_graph = tmp_path / "graph.dot" write_dot_graph(ref_graph, tmp_path_to_graph) - assert filecmp.cmp(tmp_path_to_graph, REF_DOT_REPRESENTATION_GRAPH_PATH) + ref = REF_DOT_REPRESENTATION_GRAPH_PATH.read_text() + act = tmp_path_to_graph.read_text() + assert ref == act def test_read_dot_file_gives_graph_with_colons(tmp_path: Path, ref_graph: nx.DiGraph): diff --git a/tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py b/tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py index 2cc0df5bf9c..75477dc2f26 100644 --- a/tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py +++ b/tests/common/hyperparameter_tuner/test_hyperparameter_tuner.py @@ -430,3 +430,45 @@ def test_find_best_combination( combination_score_func = lambda x: scores[x] actual_combination_key = find_best_combination(combinations, combination_score_func, param_settings) assert expected_combination_key == actual_combination_key + + +@pytest.mark.parametrize( + "params, number_considered_combinations", + [ + ( + { + "param_1": [0, 1, 2], + "param_2": [True, False], + }, + # Explanation: + # 2 combinations for default values of each parameter, + # 5 combinations for the sum of values of each parameter. + 2 + 5, + ), + ( + { + "param_1": [0, 1], + "param_2": [2, 3, 8], + "param_3": [4, 5], + }, + # Explanation: + # 3 combinations for default values of each parameter, + # 6 combinations for the sum of values of each parameter. + 3 + 7, + ), + ], +) +def test_number_of_combinations_considered(params, number_considered_combinations): + score_function_call_count = 0 + + def score_f(key): + nonlocal score_function_call_count + score_function_call_count += 1 + return 1 + + combinations = create_combinations(params) + find_best_combination(combinations, score_f, params) + assert number_considered_combinations == score_function_call_count, ( + f"Expected {number_considered_combinations} combinations to be considered, " + f"but got {score_function_call_count}." + ) diff --git a/tests/common/quantization/mock_graphs.py b/tests/common/quantization/mock_graphs.py index db4f187939e..dec54dcd475 100644 --- a/tests/common/quantization/mock_graphs.py +++ b/tests/common/quantization/mock_graphs.py @@ -17,7 +17,6 @@ from nncf.common.graph import NNCFGraph from nncf.common.graph import NNCFNode -from nncf.common.graph import NNCFNodeName from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes from nncf.common.graph.layer_attributes import Dtype @@ -359,7 +358,7 @@ def get_mock_model_graph_with_broken_output_edge_pattern( return get_nncf_graph_from_mock_nx_graph(mock_nx_graph) -def get_ip_graph_for_test(nncf_graph: NNCFGraph, weighted_node_names: List[NNCFNodeName] = None) -> InsertionPointGraph: +def get_ip_graph_for_test(nncf_graph: NNCFGraph) -> InsertionPointGraph: pre_hooks = [] post_hooks = [] for node in nncf_graph.get_all_nodes(): @@ -374,14 +373,8 @@ def get_ip_graph_for_test(nncf_graph: NNCFGraph, weighted_node_names: List[NNCFN ip = PostHookInsertionPoint(node.node_name) post_hooks.append(ip) - weighted_target_points = None - if weighted_node_names is not None: - weighted_target_points = [] - for name in weighted_node_names: - weighted_target_points.append(name) ip_graph = InsertionPointGraph( nncf_graph, - weight_modifiable_node_names=weighted_target_points, allowed_pre_hook_insertion_points=pre_hooks, allowed_post_hook_insertion_points=post_hooks, ) diff --git a/tests/common/quantization/test_passes.py b/tests/common/quantization/test_passes.py index 70d681efa01..ea87dd54e5a 100644 --- a/tests/common/quantization/test_passes.py +++ b/tests/common/quantization/test_passes.py @@ -18,10 +18,10 @@ from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.quantization.passes import find_constant_subgraphs from nncf.quantization.passes import remove_nodes_and_reconnect_graph +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.paths import TEST_ROOT from tests.cross_fw.test_templates.models import NNCFGraphDropoutRemovingCase from tests.cross_fw.test_templates.models import NNCFGraphToTestConstantFiltering -from tests.shared.nx_graph import compare_nx_graph_with_reference -from tests.shared.paths import TEST_ROOT DATA_ROOT = TEST_ROOT / "common" / "data" / "reference_graphs" diff --git a/tests/common/quantization/test_quantizer_propagation_graph.py b/tests/common/quantization/test_quantizer_propagation_graph.py index b957be5b329..219f0e17578 100644 --- a/tests/common/quantization/test_quantizer_propagation_graph.py +++ b/tests/common/quantization/test_quantizer_propagation_graph.py @@ -972,7 +972,8 @@ def _setup_and_propagate_quantizers(self, qpsg: QPSG) -> QPSG: [pq_1, pq_2], [QuantizerConfig()], [None, None], InsertionPointGraph.get_post_hook_node_key("1 /B_0") ) pq_3 = qpsg.add_propagating_quantizer( - [QuantizerConfig(per_channel=True)], InsertionPointGraph.get_pre_hook_node_key("4 /E_0") # sic! + [QuantizerConfig(per_channel=True)], + InsertionPointGraph.get_pre_hook_node_key("4 /E_0"), # sic! ) # pq_3 should be considered redundant w.r.t the upstream per-tensor quantizer paths = get_edge_paths_for_propagation( @@ -1311,55 +1312,55 @@ def create_graph_for_output_quant_as_weights() -> NNCFGraph: MODEL_GRAPH: NNCFGraph = create_graph_for_output_quant_as_weights() -class TestOutputQuantAsWeightsSetup: - class OutputQuantAsWeightsSetupTestStruct(ABC): - operator_node_key_vs_trait_dict: Dict[str, QuantizationTrait] - quantizable_module_node_names_vs_qconfigs: Dict[NNCFNodeName, List[QuantizerConfig]] +class OutputQuantAsWeightsSetupTestStruct(ABC): + operator_node_key_vs_trait_dict: Dict[str, QuantizationTrait] + quantizable_module_node_names_vs_qconfigs: Dict[NNCFNodeName, List[QuantizerConfig]] - def prepare_qpsg_state(self, qpsg: QPSG) -> QPSG: - qpsg = TestQuantizerPropagationStateGraph.mark_nodes_with_traits(qpsg, self.operator_node_key_vs_trait_dict) - return self._setup_and_propagate_quantizers(qpsg) + def prepare_qpsg_state(self, qpsg: QPSG) -> QPSG: + qpsg = TestQuantizerPropagationStateGraph.mark_nodes_with_traits(qpsg, self.operator_node_key_vs_trait_dict) + return self._setup_and_propagate_quantizers(qpsg) - @abstractmethod - def _setup_and_propagate_quantizers(self, qpsg: QPSG) -> QPSG: - pass + @abstractmethod + def _setup_and_propagate_quantizers(self, qpsg: QPSG) -> QPSG: + pass - @abstractmethod - def ref_quantizer_setup(self) -> MultiConfigQuantizerSetup: - pass + @abstractmethod + def ref_quantizer_setup(self) -> MultiConfigQuantizerSetup: + pass - class LinearPropagation(OutputQuantAsWeightsSetupTestStruct): - operator_node_key_vs_trait_dict = { - "5 F/F_0": QuantizationTrait.QUANTIZATION_AGNOSTIC, - "8 G/G_0": QuantizationTrait.INPUTS_QUANTIZABLE, - "2 C/C_0": QuantizationTrait.OUTPUT_QUANTIZATION_AS_WEIGHTS, - } - quantizable_module_node_names_vs_qconfigs = {"C/C_0": [QuantizerConfig()]} - def ref_quantizer_setup(self) -> MultiConfigQuantizerSetup: - setup = MultiConfigQuantizerSetup() - setup.quantization_points[0] = MultiConfigQuantizationPoint( - WeightQuantizationInsertionPoint(target_node_name="C/C_0"), - possible_qconfigs=[QuantizerConfig()], - directly_quantized_operator_node_names=["C/C_0", "G/G_0"], - ) - setup.shared_input_operation_set_groups[0] = {0} - return setup +class LinearPropagation(OutputQuantAsWeightsSetupTestStruct): + operator_node_key_vs_trait_dict = { + "5 F/F_0": QuantizationTrait.QUANTIZATION_AGNOSTIC, + "8 G/G_0": QuantizationTrait.INPUTS_QUANTIZABLE, + "2 C/C_0": QuantizationTrait.OUTPUT_QUANTIZATION_AS_WEIGHTS, + } + quantizable_module_node_names_vs_qconfigs = {"C/C_0": [QuantizerConfig()]} + + def ref_quantizer_setup(self) -> MultiConfigQuantizerSetup: + setup = MultiConfigQuantizerSetup() + setup.quantization_points[0] = MultiConfigQuantizationPoint( + WeightQuantizationInsertionPoint(target_node_name="C/C_0"), + possible_qconfigs=[QuantizerConfig()], + directly_quantized_operator_node_names=["C/C_0", "G/G_0"], + ) + setup.shared_input_operation_set_groups[0] = {0} + return setup + + def _setup_and_propagate_quantizers(self, qpsg: QPSG) -> QPSG: + pq_1 = qpsg.add_propagating_quantizer([QuantizerConfig()], InsertionPointGraph.get_pre_hook_node_key("6 G/G_0")) + paths = get_edge_paths_for_propagation( + qpsg, + InsertionPointGraph.get_post_hook_node_key("2 C/C_0"), + InsertionPointGraph.get_pre_hook_node_key("6 G/G_0"), + ) + path = paths[0] + qpsg.propagate_quantizer_via_path(pq_1, path) + qpsg.mark_act_quantizer_as_dependent_on_weights(pq_1, "2 C/C_0") + return qpsg - def _setup_and_propagate_quantizers(self, qpsg: QPSG) -> QPSG: - pq_1 = qpsg.add_propagating_quantizer( - [QuantizerConfig()], InsertionPointGraph.get_pre_hook_node_key("6 G/G_0") - ) - paths = get_edge_paths_for_propagation( - qpsg, - InsertionPointGraph.get_post_hook_node_key("2 C/C_0"), - InsertionPointGraph.get_pre_hook_node_key("6 G/G_0"), - ) - path = paths[0] - qpsg.propagate_quantizer_via_path(pq_1, path) - qpsg.mark_act_quantizer_as_dependent_on_weights(pq_1, "2 C/C_0") - return qpsg +class TestOutputQuantAsWeightsSetup: class LinearPropagationWithConfigSubspaceSelection(OutputQuantAsWeightsSetupTestStruct): operator_node_key_vs_trait_dict = { "4 D/D_0": QuantizationTrait.QUANTIZATION_AGNOSTIC, @@ -1689,9 +1690,7 @@ def output_quant_as_weights_test_struct(self, request): @pytest.fixture def model_graph_qpsg(self): - ip_graph = get_ip_graph_for_test( - MODEL_GRAPH, weighted_node_names=[node.node_name for node in MODEL_GRAPH.get_all_nodes()] - ) + ip_graph = get_ip_graph_for_test(MODEL_GRAPH) quant_prop_graph = QPSG(ip_graph) return quant_prop_graph @@ -1837,3 +1836,27 @@ def test_create_quantizer_setup_with_output_quant_as_weights_ops( def test_get_weight_and_activation_qconfig_list_intersection(weight_configs, activation_configs, reference_configs): resulted_configs = QPSG._get_weight_and_activation_qconfig_list_intersection(weight_configs, activation_configs) assert resulted_configs == reference_configs + + +class LinearPropagationForRemovalTest(LinearPropagation): + def _setup_and_propagate_quantizers(self, qpsg: QPSG) -> QPSG: + pq_1 = qpsg.add_propagating_quantizer([QuantizerConfig()], InsertionPointGraph.get_pre_hook_node_key("6 G/G_0")) + paths = get_edge_paths_for_propagation( + qpsg, + InsertionPointGraph.get_post_hook_node_key("2 C/C_0"), + InsertionPointGraph.get_pre_hook_node_key("6 G/G_0"), + ) + path = paths[0] + qpsg.propagate_quantizer_via_path(pq_1, path) + qpsg.mark_act_quantizer_as_dependent_on_weights(pq_1, "2 C/C_0") + return qpsg, pq_1 + + +def test_remove_pq_from_pqs_after_weight_dependent_output_quantized_nodes(): + ip_graph = get_ip_graph_for_test(MODEL_GRAPH) + quant_prop_graph = QPSG(ip_graph) + linear_propagation = LinearPropagationForRemovalTest() + quant_prop_graph, pq_1 = linear_propagation.prepare_qpsg_state(quant_prop_graph) + qpsg, pq_1 = linear_propagation._setup_and_propagate_quantizers(quant_prop_graph) + qpsg.remove_propagating_quantizer(pq_1) + assert pq_1 not in qpsg._pqs_after_weight_dependent_output_quantized_nodes diff --git a/tests/common/quantization/test_quantizer_propagation_solver.py b/tests/common/quantization/test_quantizer_propagation_solver.py index 9bb6284651d..313b9181dcc 100644 --- a/tests/common/quantization/test_quantizer_propagation_solver.py +++ b/tests/common/quantization/test_quantizer_propagation_solver.py @@ -29,7 +29,7 @@ from nncf.common.graph.transformations.commands import TargetType from nncf.common.insertion_point_graph import InsertionPointGraph from nncf.common.quantization.quantizer_propagation.graph import QuantizerPropagationStateGraph as QPSG -from nncf.common.quantization.quantizer_propagation.solver import PropagationStrategy +from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationRule from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationSolver from nncf.common.quantization.quantizer_propagation.solver import TransitionStatus from nncf.common.quantization.quantizer_propagation.structs import IgnoreReason @@ -322,24 +322,24 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=8), ] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), ], [QuantizerConfig(num_bits=8), ], [QuantizerConfig(num_bits=8), ] ], ), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8)], branch_qconfig_lists_after_merge=[None, None, None]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8)], branch_qconfig_lists_after_merge=[None, None, None]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8)], branch_qconfig_lists_after_merge=[None, None, @@ -356,7 +356,7 @@ def test_setup_initial_quantizers_sdpa(self): ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), ], @@ -364,20 +364,20 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), ], [QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), ] ]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), ], branch_qconfig_lists_after_merge=[None, None, None, None] ), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], branch_qconfig_lists_after_merge=[None, None, None, None]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], branch_qconfig_lists_after_merge=[None, None, @@ -394,28 +394,28 @@ def test_setup_initial_quantizers_sdpa(self): ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), QuantizerConfig(num_bits=8)], [QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), QuantizerConfig(num_bits=8)], [QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), QuantizerConfig(num_bits=8)], ]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), QuantizerConfig(num_bits=8)], branch_qconfig_lists_after_merge=[None, None, None] ), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), QuantizerConfig(num_bits=8)], branch_qconfig_lists_after_merge=[None, None, None]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC), QuantizerConfig(num_bits=8)], branch_qconfig_lists_after_merge=[None, @@ -432,23 +432,23 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=5), ] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[[QuantizerConfig(num_bits=6), ], [QuantizerConfig(num_bits=4), ], [QuantizerConfig(num_bits=5), ]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[[QuantizerConfig(num_bits=6), ], [QuantizerConfig(num_bits=4), ], [QuantizerConfig(num_bits=5), ]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=6)], branch_qconfig_lists_after_merge=[None, [QuantizerConfig(num_bits=4), ], [QuantizerConfig(num_bits=5), ]]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[[QuantizerConfig(num_bits=6), ], [QuantizerConfig(num_bits=4), ], @@ -463,24 +463,24 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=5), ] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC), ], [QuantizerConfig(num_bits=4), ], [QuantizerConfig(num_bits=5), ]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC), ], [QuantizerConfig(num_bits=4), ], [QuantizerConfig(num_bits=5), ]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC), ], branch_qconfig_lists_after_merge=[None, [QuantizerConfig(num_bits=4), ], [QuantizerConfig(num_bits=5), ]]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC), ], @@ -499,21 +499,21 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=7), ] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=6)], [QuantizerConfig(num_bits=7), ], [QuantizerConfig(num_bits=8), ], [QuantizerConfig(num_bits=7), ]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=6)], [QuantizerConfig(num_bits=7), ], [QuantizerConfig(num_bits=8), ], [QuantizerConfig(num_bits=7), ]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8), ], branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=6)], @@ -521,7 +521,7 @@ def test_setup_initial_quantizers_sdpa(self): None, [QuantizerConfig(num_bits=7), ] ]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=6)], @@ -539,26 +539,26 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=7), QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=7), QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)], branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=7), QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)], None, None ]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC)], branch_qconfig_lists_after_merge=[ None, @@ -575,26 +575,26 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5)], ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6), ], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=7), QuantizerConfig(num_bits=6)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5)], ]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6), ], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=7), QuantizerConfig(num_bits=6)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5)], ]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8), ], branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6), ], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=7), QuantizerConfig(num_bits=6)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5)], ]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6), ], @@ -613,7 +613,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6), ], @@ -621,7 +621,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6), ], @@ -629,7 +629,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6), ], @@ -638,7 +638,7 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)], ]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=6), ], @@ -663,7 +663,7 @@ def test_setup_initial_quantizers_sdpa(self): mode=QuantizationMode.ASYMMETRIC)] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=3)], @@ -671,7 +671,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=3)], @@ -679,7 +679,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=3), ], @@ -688,7 +688,7 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)], ]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=3), ], @@ -708,21 +708,21 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5)] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC), QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5)]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=7, mode=QuantizationMode.ASYMMETRIC), QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5)]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)], @@ -730,7 +730,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=6, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=5)] ]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8), QuantizerConfig(num_bits=4, mode=QuantizationMode.ASYMMETRIC)], @@ -752,7 +752,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC, per_channel=False)] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC, per_channel=True), @@ -761,7 +761,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC, per_channel=False)], [QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC, per_channel=False), QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC, per_channel=False)]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC, per_channel=True), @@ -770,7 +770,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC, per_channel=False)], [QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC, per_channel=False), QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC, per_channel=False)]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[ QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC, per_channel=False), QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC, per_channel=False), @@ -783,7 +783,7 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC, per_channel=False), QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC, per_channel=False)] ]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC, per_channel=False), QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC, per_channel=False)], branch_qconfig_lists_after_merge=[ @@ -802,7 +802,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC)] ], strategy_vs_solution_dict={ - PropagationStrategy.DO_NOT_MERGE_BRANCH_FQS: MergeQConfigSolution( + QuantizerPropagationRule.DO_NOT_MERGE_BRANCHES: MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=4, mode=QuantizationMode.SYMMETRIC), @@ -811,7 +811,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC), QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC)]]), - PropagationStrategy.MERGE_IF_ALL_BRANCH_FQ_OPTIONS_SAME: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_IF_ALL_BRANCHES_SAME : MergeQConfigSolution( merge_qconfig_list=None, branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=4, mode=QuantizationMode.SYMMETRIC), @@ -820,7 +820,7 @@ def test_setup_initial_quantizers_sdpa(self): QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC)], [QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC), QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC)]]), - PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC)], branch_qconfig_lists_after_merge=[ [QuantizerConfig(num_bits=4, mode=QuantizationMode.SYMMETRIC), @@ -830,7 +830,7 @@ def test_setup_initial_quantizers_sdpa(self): [QuantizerConfig(num_bits=8, mode=QuantizationMode.SYMMETRIC), QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC)] ]), - PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT: MergeQConfigSolution( + QuantizerPropagationRule.MERGE_ALL_IN_ONE: MergeQConfigSolution( merge_qconfig_list=[QuantizerConfig(num_bits=8, mode=QuantizationMode.ASYMMETRIC)], branch_qconfig_lists_after_merge=[ None, @@ -846,7 +846,7 @@ def qconfig_merge_test_struct(request): return request.param def test_get_merged_qconfigs(self, qconfig_merge_test_struct: MergeQConfigTestStruct): - for strategy in PropagationStrategy: + for strategy in QuantizerPropagationRule: quant_prop_solver = QuantizerPropagationSolver(propagation_strategy=strategy, run_consistency_checks=True) solution_for_strategy = qconfig_merge_test_struct.strategy_vs_solution_dict[strategy] ref_merge_qconfig_list = solution_for_strategy.merge_qconfig_list @@ -866,7 +866,7 @@ def test_merged_qconfig_list_is_independent_of_branch_qconfig_list_order( self, qconfig_merge_test_struct: MergeQConfigTestStruct ): quant_prop_solver = QuantizerPropagationSolver( - propagation_strategy=PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION + propagation_strategy=QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION ) branch_qconfig_lists_before_merge = qconfig_merge_test_struct.branch_qconfig_lists_before_merge ref_merge_qconfig_list, _ = quant_prop_solver.get_merged_qconfigs_for_downward_branching_case( @@ -1932,7 +1932,7 @@ def test_metatypes_to_ignore(mocker): nncf_graph.add_edge_between_nncf_nodes( nodes[idx - 1].node_id, nodes[idx].node_id, [1, 1, 1, 1], 0, 0, Dtype.FLOAT ) - ip_graph = InsertionPointGraph(nncf_graph=nncf_graph, weight_modifiable_node_names=["A", "B", "C"]) + ip_graph = InsertionPointGraph(nncf_graph=nncf_graph) solver = QuantizerPropagationSolver( metatypes_to_ignore=[IGNORED_METATYPE], diff --git a/tests/post_training/test_tune_range.py b/tests/common/quantization/test_tune_range.py similarity index 100% rename from tests/post_training/test_tune_range.py rename to tests/common/quantization/test_tune_range.py diff --git a/tests/common/sdl/test_config_schema.py b/tests/common/sdl/test_config_schema.py index d3e1183c7af..5abb0b3c89c 100644 --- a/tests/common/sdl/test_config_schema.py +++ b/tests/common/sdl/test_config_schema.py @@ -17,8 +17,8 @@ import pytest from nncf.config import NNCFConfig -from tests.shared.paths import PROJECT_ROOT -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT GOOD_CONFIG_SOURCES = [ PROJECT_ROOT / Path("examples/torch/classification/configs"), diff --git a/tests/common/test_ignored_scope.py b/tests/common/test_ignored_scope.py index 6186e9123b3..e6ba87ddff4 100644 --- a/tests/common/test_ignored_scope.py +++ b/tests/common/test_ignored_scope.py @@ -64,6 +64,10 @@ def __init__(self, conv_metatype, linear_metatype): IgnoredScope(subgraphs=[Subgraph(inputs=["/Linear_1_0"], outputs=["/Linear_3_0"])]), ["/Conv_2_0", "/Linear_1_0", "/Linear_2_0", "/Linear_3_0", "/Marked_Conv_3_0"], ), + ( + IgnoredScope(subgraphs=[Subgraph(inputs=["/Linear_1_0"], outputs=["/Linear_1_0"])]), + ["/Linear_1_0"], + ), ] @@ -78,7 +82,6 @@ def test_ignored_scopes(ignored_scope, ref_ignored_names): IgnoredScope(["/Conv_0_0", "/Conv_1_0", "/Linear_1_0"]), IgnoredScope(patterns=[".*Maarked.*"]), IgnoredScope(types=["wrong_type"]), - IgnoredScope(subgraphs=[Subgraph(inputs=["/Linear_1_0"], outputs=["/Linear_1_0"])]), IgnoredScope(subgraphs=[Subgraph(inputs=["/Linear_3_0"], outputs=["/Linear_1_0"])]), ] diff --git a/tests/common/test_statistics_caching.py b/tests/common/test_statistics_caching.py new file mode 100644 index 00000000000..0093c39545f --- /dev/null +++ b/tests/common/test_statistics_caching.py @@ -0,0 +1,89 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections import deque + +import numpy as np +import pytest + +import nncf +import nncf.common.tensor_statistics.statistics_serializer as statistics_serializer +from nncf.tensor import Tensor +from nncf.tensor.functions import allclose + + +def _compare_dicts(dict1, dict2): + """ + Recursively compares two dictionaries. + Supports comparing numpy arrays and Tensor objects. + """ + if not isinstance(dict1, dict) or not isinstance(dict2, dict): + raise ValueError("Both inputs must be dictionaries") + + if dict1.keys() != dict2.keys(): + return False + + for key in dict1: + val1 = dict1[key] + val2 = dict2[key] + + if isinstance(val1, np.ndarray) and isinstance(val2, np.ndarray): + if not np.array_equal(val1, val2): + return False + elif isinstance(val1, Tensor) and isinstance(val2, Tensor): + if not allclose(val1, val2): + return False + # Recursively compare nested dictionaries + elif isinstance(val1, dict) and isinstance(val2, dict): + if not _compare_dicts(val1, val2): + return False + # Direct comparison for other types + else: + if val1 != val2: + return False + + return True + + +@pytest.fixture +def dummy_statistics(): + """ + Returns a dummy statistics dictionary for testing purposes. + """ + return { + "point_A": {"min": 1, "max": 2}, + "point_B": { + "min_tuple": (1, 2), + "max_dict": {"tensor_1": [10, 10], "tensor_2": deque([1, 2])}, + "tensor_numpy": Tensor(np.ones(shape=(10, 5, 3))), + }, + } + + +def test_dump_and_load_statistics(tmp_path, dummy_statistics): + """ + Tests that dumped statistics can be loaded and match the original. + """ + test_dir = "test_dir" + statistics_serializer.dump_to_dir(dummy_statistics, tmp_path / test_dir) + assert (tmp_path / test_dir).exists(), "Dumped file was not created" + + loaded_statistics, _ = statistics_serializer.load_from_dir(tmp_path / test_dir) + assert _compare_dicts(dummy_statistics, loaded_statistics), "Loaded statistics do not match the original" + + +def test_load_statistics_from_non_existent_dir(): + """ + Tests that attempting to load statistics from a non-existent directory raises an error. + """ + file_path = "non_existent_dir" + with pytest.raises(nncf.ValidationError) as exc_info: + statistics_serializer.load_from_dir(file_path) + assert "The provided directory path does not exist." in str(exc_info) diff --git a/tests/common/test_statistics_serializer.py b/tests/common/test_statistics_serializer.py new file mode 100644 index 00000000000..2ecfc7c0a57 --- /dev/null +++ b/tests/common/test_statistics_serializer.py @@ -0,0 +1,84 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import json +from pathlib import Path + +import pytest + +import nncf +from nncf.common.tensor_statistics.statistics_serializer import dump_to_dir +from nncf.common.tensor_statistics.statistics_serializer import load_from_dir +from nncf.common.tensor_statistics.statistics_serializer import load_metadata +from nncf.common.tensor_statistics.statistics_serializer import sanitize_filename +from nncf.common.tensor_statistics.statistics_serializer import save_metadata + + +def test_sanitize_filename(): + filename = "layer/1_mean/activation" + sanitized = sanitize_filename(filename) + assert sanitized == "layer_1_mean_activation", "Filename was not sanitized correctly" + + +def test_load_metadata(tmp_path): + # Create a metadata file in the temp directory + metadata = {"mapping": {"key1": "value1"}, "metadata": {"model": "test"}} + metadata_file = tmp_path / "statistics_metadata.json" + with open(metadata_file, "w") as f: + json.dump(metadata, f) + + loaded_metadata = load_metadata(tmp_path) + assert loaded_metadata == metadata, "Metadata was not loaded correctly" + + +def test_save_metadata(tmp_path): + metadata = {"mapping": {"key1": "value1"}, "metadata": {"model": "test"}} + save_metadata(metadata, tmp_path) + + metadata_file = tmp_path / "statistics_metadata.json" + assert metadata_file.exists(), "Metadata file was not created" + + with open(metadata_file, "r") as f: + loaded_metadata = json.load(f) + assert loaded_metadata == metadata, "Metadata was not saved correctly" + + +def test_dump_and_load_statistics(tmp_path): + statistics = {"layer/1_mean/activation": [0.1, 0.2, 0.3], "layer/2_variance": [0.05, 0.06, 0.07]} + additional_metadata = {"model": "facebook/opt-125m", "compression": "8-bit"} + + dump_to_dir(statistics, tmp_path, additional_metadata) + + assert len(list(Path(tmp_path).iterdir())) > 0, "No files created during dumping" + + metadata_file = tmp_path / "statistics_metadata.json" + assert metadata_file.exists(), "Metadata file was not created" + + with open(metadata_file, "r") as f: + metadata = json.load(f) + assert "mapping" in metadata, "Mapping is missing in metadata" + assert metadata["metadata"]["model"] == "facebook/opt-125m" + + # Load the statistics and ensure it was loaded correctly + loaded_statistics, loaded_metadata = load_from_dir(tmp_path) + assert "layer/1_mean/activation" in loaded_statistics, "Statistics not loaded correctly" + assert loaded_statistics["layer/1_mean/activation"] == [0.1, 0.2, 0.3] + assert loaded_metadata["model"] == "facebook/opt-125m", "Metadata not loaded correctly" + + +def test_invalid_gzip_file(tmp_path): + # Create a corrupt gzip file in the directory + invalid_file = tmp_path / "invalid_file.gz" + with open(invalid_file, "w") as f: + f.write("This is not a valid gzip file") + + # Expect the load_from_dir to raise an error when trying to load the invalid file + with pytest.raises(nncf.InternalError, match="Error loading statistics"): + load_from_dir(tmp_path) diff --git a/tests/common/test_statistics_validator.py b/tests/common/test_statistics_validator.py new file mode 100644 index 00000000000..8f234f011e6 --- /dev/null +++ b/tests/common/test_statistics_validator.py @@ -0,0 +1,34 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pytest + +import nncf +from nncf.common.tensor_statistics.statistics_validator import validate_backend +from nncf.common.utils.backend import BackendType + + +@pytest.mark.parametrize("backend_value", [BackendType.TORCH, BackendType.ONNX]) +def test_validate_backend(backend_value): + # Test case where backend matches + data = {"backend": backend_value.value} + backend = backend_value + + validate_backend(data, backend) + + with pytest.raises(nncf.ValidationError) as exc_info: + # Test case where backend does not match + validate_backend({"backend": BackendType.ONNX.value}, BackendType.TORCH) + assert "Backend in loaded statistics ONNX does not match to an expected backend Torch." in str(exc_info) + + with pytest.raises(nncf.ValidationError) as exc_info: + # Test case where backend key is missing + validate_backend({}, BackendType.TORCH) + assert "The provided metadata has no information about backend." in str(exc_info) diff --git a/tests/common/test_telemetry.py b/tests/common/test_telemetry.py index 16ee9b503fd..b2962080509 100644 --- a/tests/common/test_telemetry.py +++ b/tests/common/test_telemetry.py @@ -18,10 +18,17 @@ import pytest +from nncf.common.utils.backend import BackendType from nncf.definitions import NNCF_CI_ENV_VAR_NAME from nncf.definitions import NNCF_DEV_ENV_VAR_NAME from nncf.telemetry import TelemetryExtractor from nncf.telemetry import tracked_function +from nncf.telemetry.events import MODEL_BASED_CATEGORY +from nncf.telemetry.events import NNCF_ONNX_CATEGORY +from nncf.telemetry.events import NNCF_OV_CATEGORY +from nncf.telemetry.events import NNCF_PT_CATEGORY +from nncf.telemetry.events import NNCF_PT_FX_CATEGORY +from nncf.telemetry.events import NNCF_TF_CATEGORY from nncf.telemetry.extractors import CollectedEvent from nncf.telemetry.wrapper import NNCFTelemetryStub from nncf.telemetry.wrapper import skip_if_raised @@ -205,3 +212,33 @@ def test_skip_if_raised(): # Incorrect args wrapped(1, 2, 3) assert raises.call_count == 1 + + +@pytest.mark.parametrize( + "backend_type, reference_category", + [ + (BackendType.OPENVINO, NNCF_OV_CATEGORY), + (BackendType.TENSORFLOW, NNCF_TF_CATEGORY), + (BackendType.ONNX, NNCF_ONNX_CATEGORY), + (BackendType.TORCH, NNCF_PT_CATEGORY), + (BackendType.TORCH_FX, NNCF_PT_FX_CATEGORY), + ], +) +def test_model_based_category(backend_type, reference_category, spies, mocker): + send_event_spy, start_session_event_spy, end_session_event_spy = spies + mocker.patch("nncf.telemetry.events.get_backend", return_value=backend_type) + + @tracked_function(category=MODEL_BASED_CATEGORY, extractors=["arg1"]) + def model_based_fn_to_test(model, arg1): + pass + + model_based_fn_to_test(MagicMock(), "arg1_value") + + assert start_session_event_spy.call_count == 1 + assert end_session_event_spy.call_count == 1 + assert send_event_spy.call_count == 1 + + expected_call_args_list = [ + call(event_category=reference_category, event_action="arg1", event_label="arg1_value", event_value=None), + ] + assert send_event_spy.call_args_list == expected_call_args_list diff --git a/tests/common/test_tensor.py b/tests/common/test_tensor.py index 2db57dcd9cb..ed653173c11 100644 --- a/tests/common/test_tensor.py +++ b/tests/common/test_tensor.py @@ -14,7 +14,7 @@ from nncf.tensor import TensorDataType from nncf.tensor.definitions import TensorBackend from nncf.tensor.definitions import TensorDeviceType -from tests.shared.test_templates.template_test_nncf_tensor import TemplateTestNNCFTensorOperators +from tests.cross_fw.test_templates.template_test_nncf_tensor import TemplateTestNNCFTensorOperators class TestNPNNCFTensorOperators(TemplateTestNNCFTensorOperators): diff --git a/tests/cross_fw/examples/.test_durations b/tests/cross_fw/examples/.test_durations new file mode 100644 index 00000000000..1d4a1c57524 --- /dev/null +++ b/tests/cross_fw/examples/.test_durations @@ -0,0 +1,17 @@ +{ + "tests/cross_fw/examples/test_examples.py::test_examples[llm_compression]": 222.974, + "tests/cross_fw/examples/test_examples.py::test_examples[llm_compression_synthetic]": 873.780, + "tests/cross_fw/examples/test_examples.py::test_examples[llm_tune_params]": 1018.932, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_onnx_mobilenet_v2]": 178.509, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_onnx_yolo8_quantize_with_accuracy_control]": 292.766, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_openvino_anomaly_stfpm_quantize_with_accuracy_control]": 443.025, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_openvino_mobilenet_v2_quantize]": 169.789, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_openvino_yolo8_quantize]": 170.593, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_openvino_yolo8_quantize_with_accuracy_control]": 205.533, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_tensorflow_mobilenet_v2]": 149.202, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_torch_mobilenet_v2]": 192.227, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_torch_ssd300_vgg16]": 231.613, + "tests/cross_fw/examples/test_examples.py::test_examples[quantization_aware_training_torch_anomalib]": 478.797, + "tests/cross_fw/examples/test_examples.py::test_examples[quantization_aware_training_torch_resnet18]": 1251.144, + "tests/cross_fw/examples/test_examples.py::test_examples[post_training_quantization_torch_fx_resnet18]": 412.243 +} diff --git a/tests/cross_fw/examples/README.md b/tests/cross_fw/examples/README.md new file mode 100644 index 00000000000..8b2f9001670 --- /dev/null +++ b/tests/cross_fw/examples/README.md @@ -0,0 +1,19 @@ +# Test examples + +## Manual trigger + +To manual run job use GitHub workflow: + +https://github.com/openvinotoolkit/nncf/actions/workflows/examples.yml + +Parameters: + + - `pull_request_number`: The pull request number. + - `pytest_args`: Additional pytest arguments (example `-k llm`) + +## Parallel test + +To set up parallel testing between jobs using `pytest-split` with the option `--splitting-algorithm=least_duration`, +you need to ensure that each test's duration is tracked correctly and stored so that future test runs can use that data +to split the tests efficiently. After run workflow get time frm artifact of job and +add new test to [.test_durations](.test_durations). diff --git a/tests/cross_fw/examples/conftest.py b/tests/cross_fw/examples/conftest.py index fa40af4862f..778db6abe3c 100644 --- a/tests/cross_fw/examples/conftest.py +++ b/tests/cross_fw/examples/conftest.py @@ -31,6 +31,7 @@ def pytest_addoption(parser): parser.addoption( "--ov_version_override", default=None, help="Parameter to set OpenVINO into the env with the version from PyPI" ) + parser.addoption("--data", type=str, default=None, help="Path to test datasets") @pytest.fixture(scope="module") @@ -46,3 +47,8 @@ def is_check_performance(request): @pytest.fixture(scope="module") def ov_version_override(request): return request.config.getoption("--ov_version_override") + + +@pytest.fixture(scope="module") +def data(request): + return request.config.getoption("--data") diff --git a/tests/cross_fw/examples/example_scope.json b/tests/cross_fw/examples/example_scope.json index 3bbc6325ae7..5992dc8c57e 100644 --- a/tests/cross_fw/examples/example_scope.json +++ b/tests/cross_fw/examples/example_scope.json @@ -38,10 +38,11 @@ "backend": "openvino", "requirements": "examples/post_training_quantization/openvino/yolov8/requirements.txt", "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", + "accuracy_tolerance": 0.3, "accuracy_metrics": { - "fp32_mAP": 0.45252755065175254, - "int8_mAP": 0.4561537594798616, - "accuracy_drop": -0.00362620882 + "fp32_mAP": 0.4521154302569843, + "int8_mAP": 0.4599022156047055, + "accuracy_drop": -0.0077867853477212035 }, "performance_metrics": { "fp32_fps": 170.69, @@ -88,19 +89,20 @@ "backend": "onnx", "requirements": "examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt", "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", + "accuracy_tolerance": 0.3, "accuracy_metrics": { - "onnx_fp32_box_mAP": 0.44964819166629366, - "onnx_fp32_mask_mAP": 0.36509415038708726, - "onnx_int8_box_mAP": 0.45071569411799683, - "onnx_int8_mask_mAP": 0.36805097120147484, - "onnx_drop_box_mAP": -0.0010675024517031728, - "onnx_drop_mask_mAP": -0.002956820814387584, - "ov_fp32_box_mAP": 0.44964819083938, + "onnx_fp32_box_mAP": 0.4496481927421265, + "onnx_fp32_mask_mAP": 0.36509979887281135, + "onnx_int8_box_mAP": 0.4509292170587964, + "onnx_int8_mask_mAP": 0.36710935370534864, + "onnx_drop_box_mAP": -0.001281024316669932, + "onnx_drop_mask_mAP": -0.002009554832537286, + "ov_fp32_box_mAP": 0.4496481927421265, "ov_fp32_mask_mAP": 0.36509979887281135, - "ov_int8_box_mAP": 0.4462460126164857, - "ov_int8_mask_mAP": 0.3631361727753598, - "ov_drop_box_mAP": 0.003402178222894292, - "ov_drop_mask_mAP": 0.001963626097451543 + "ov_int8_box_mAP": 0.4494792029705020, + "ov_int8_mask_mAP": 0.3666179206494213, + "ov_drop_box_mAP": 0.000168989771624439, + "ov_drop_mask_mAP": -0.0015181217766099264 }, "performance_metrics": { "ov_fp32_fps": 139.32, @@ -114,8 +116,8 @@ "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", "accuracy_metrics": { "fp32_top1": 0.987770676612854, - "int8_top1": 0.9775795936584473, - "accuracy_drop": 0.010191082954406738 + "int8_top1": 0.9691720008850098, + "accuracy_drop": 0.01859867572784424 }, "performance_metrics": { "fp32_fps": 1703.04, @@ -168,6 +170,21 @@ "model_compression_rate": 3.8631822183889652 } }, + "post_training_quantization_torch_fx_resnet18": { + "backend": "torch", + "requirements": "examples/post_training_quantization/torch_fx/resnet18/requirements.txt", + "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", + "accuracy_tolerance": 0.2, + "accuracy_metrics": { + "fp32_top1": 55.52, + "int8_top1": 55.27 + }, + "performance_metrics": { + "fp32_latency": 3.3447, + "fp32_ov_latency": 1.401, + "int8_latency": 0.6003 + } + }, "quantization_aware_training_torch_resnet18": { "backend": "torch", "requirements": "examples/quantization_aware_training/torch/resnet18/requirements.txt", @@ -211,6 +228,14 @@ "group_size": 128 } }, + "llm_compression_synthetic": { + "backend": "openvino", + "requirements": "examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt", + "cpu": "Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz", + "accuracy_metrics": { + "word_count": 83 + } + }, "quantization_aware_training_torch_anomalib": { "backend": "torch", "requirements": "examples/quantization_aware_training/torch/anomalib/requirements.txt", @@ -218,11 +243,11 @@ "python_version": [3,10,0], "accuracy_tolerance_after_training": 0.02, "accuracy_metrics": { - "fp32_f1score": 0.9919999837875366, + "fp32_f1score": 1.0, "int8_init_f1score": 0.9767441749572754 }, "accuracy_metrics_after_training": { - "int8_f1score": 0.9919999837875366, + "int8_f1score": 1.0, "accuracy_drop": 0.0 }, "performance_metrics": { @@ -236,4 +261,4 @@ "model_compression_rate": 3.7654144877995197 } } -} \ No newline at end of file +} diff --git a/tests/cross_fw/examples/requirements.txt b/tests/cross_fw/examples/requirements.txt index e3c764052fb..70c8ace6604 100644 --- a/tests/cross_fw/examples/requirements.txt +++ b/tests/cross_fw/examples/requirements.txt @@ -1,3 +1,4 @@ -c ../../../constraints.txt pytest pytest-cov +pytest-split diff --git a/tests/cross_fw/examples/run_example.py b/tests/cross_fw/examples/run_example.py index dd87be4ce22..78570c3251a 100644 --- a/tests/cross_fw/examples/run_example.py +++ b/tests/cross_fw/examples/run_example.py @@ -12,10 +12,12 @@ import json import os import sys +import tarfile from argparse import ArgumentParser -from typing import Dict, Tuple +from pathlib import Path +from typing import Dict, Tuple, Union -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT def post_training_quantization_mobilenet_v2(example_root_dir: str) -> Dict[str, float]: @@ -174,6 +176,29 @@ def llm_tune_params() -> Dict[str, float]: return {"awq": bool(awq), "ratio": ratio, "group_size": group_size} +def llm_compression_synthetic() -> Dict[str, float]: + from examples.llm_compression.openvino.tiny_llama_synthetic_data.main import main as llm_compression_synthetic_main + + result = llm_compression_synthetic_main() + + return {"word_count": len(result.split())} + + +def post_training_quantization_torch_fx_resnet18(): + from examples.post_training_quantization.torch_fx.resnet18.main import main as resnet18_main + + # Set manual seed and determenistic cuda mode to make the test determenistic + results = resnet18_main() + + return { + "fp32_top1": float(results[0]), + "int8_top1": float(results[1]), + "fp32_latency": float(results[2]), + "fp32_ov_latency": float(results[3]), + "int8_latency": float(results[4]), + } + + def quantization_aware_training_torch_resnet18(): from examples.quantization_aware_training.torch.resnet18.main import main as resnet18_main @@ -216,9 +241,18 @@ def set_torch_cuda_seed(seed: int = 42): os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" -def quantization_aware_training_torch_anomalib(): +def quantization_aware_training_torch_anomalib(data: Union[str, None]): + from anomalib.data.image import mvtec + + from examples.quantization_aware_training.torch.anomalib.main import DATASET_PATH as dataset_path from examples.quantization_aware_training.torch.anomalib.main import main as anomalib_main + if data is not None and not dataset_path.exists(): + dataset_path.mkdir(parents=True, exist_ok=True) + tar_file_path = Path(data) / mvtec.DOWNLOAD_INFO.url.split("/")[-1] + with tarfile.open(tar_file_path) as tar_file: + tar_file.extractall(dataset_path) + # Set manual seed and determenistic cuda mode to make the test determenistic set_torch_cuda_seed() results = anomalib_main() @@ -240,10 +274,14 @@ def quantization_aware_training_torch_anomalib(): def main(argv): parser = ArgumentParser() parser.add_argument("--name", help="Example name", required=True) + parser.add_argument("--data", help="Path to datasets", default=None, required=False) parser.add_argument("-o", "--output", help="Path to the json file to save example metrics", required=True) args = parser.parse_args(args=argv) - metrics = globals()[args.name]() + if args.name == "quantization_aware_training_torch_anomalib": + metrics = globals()[args.name](args.data) + else: + metrics = globals()[args.name]() with open(args.output, "w", encoding="utf8") as json_file: return json.dump(metrics, json_file) diff --git a/tests/cross_fw/examples/test_examples.py b/tests/cross_fw/examples/test_examples.py index 8466c4d3566..486a20aa45f 100644 --- a/tests/cross_fw/examples/test_examples.py +++ b/tests/cross_fw/examples/test_examples.py @@ -17,14 +17,14 @@ import pytest -from tests.shared.case_collection import skip_if_backend_not_selected -from tests.shared.command import Command -from tests.shared.helpers import create_venv_with_nncf -from tests.shared.helpers import get_pip_executable_with_venv -from tests.shared.helpers import get_python_executable_with_venv -from tests.shared.helpers import load_json -from tests.shared.paths import PROJECT_ROOT -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.case_collection import skip_if_backend_not_selected +from tests.cross_fw.shared.command import Command +from tests.cross_fw.shared.helpers import create_venv_with_nncf +from tests.cross_fw.shared.helpers import get_pip_executable_with_venv +from tests.cross_fw.shared.helpers import get_python_executable_with_venv +from tests.cross_fw.shared.helpers import load_json +from tests.cross_fw.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT EXAMPLE_TEST_ROOT = TEST_ROOT / "cross_fw" / "examples" EXAMPLE_SCOPE_PATH = EXAMPLE_TEST_ROOT / "example_scope.json" @@ -53,7 +53,10 @@ def test_examples( backends_list: List[str], is_check_performance: bool, ov_version_override: str, + data: str, ): + print("\n" + "-" * 64) + print(f"Example name: {example_name}") python_version = sys.version_info example_python_version = tuple(example_params.get("python_version", python_version)) if python_version < example_python_version: @@ -62,39 +65,45 @@ def test_examples( backend = example_params["backend"] skip_if_backend_not_selected(backend, backends_list) venv_path = create_venv_with_nncf(tmp_path, "pip_e_local", "venv", {backend}) + pip_with_venv = get_pip_executable_with_venv(venv_path) if "requirements" in example_params: - pip_with_venv = get_pip_executable_with_venv(venv_path) requirements = PROJECT_ROOT / example_params["requirements"] run_cmd_line = f"{pip_with_venv} install -r {requirements}" subprocess.run(run_cmd_line, check=True, shell=True) if ov_version_override is not None: - pip_with_venv = get_pip_executable_with_venv(venv_path) ov_version_cmd_line = f"{pip_with_venv} install {ov_version_override}" subprocess.run(ov_version_cmd_line, check=True, shell=True) + subprocess.run(f"{pip_with_venv} list", check=True, shell=True) + env = os.environ.copy() env["PYTHONPATH"] = str(PROJECT_ROOT) # need this to be able to import from tests.* in run_example.py + env["ONEDNN_MAX_CPU_ISA"] = "AVX2" # Set ISA to AVX2 to get CPU independent results + env["CUDA_VISIBLE_DEVICES"] = "" # Disable GPU + env["YOLO_VERBOSE"] = "False" # Set ultralytics to quiet mode metrics_file_path = tmp_path / "metrics.json" python_executable_with_venv = get_python_executable_with_venv(venv_path) run_example_py = EXAMPLE_TEST_ROOT / "run_example.py" run_cmd_line = f"{python_executable_with_venv} {run_example_py} --name {example_name} --output {metrics_file_path}" + if data is not None: + run_cmd_line += f" --data {data}" cmd = Command(run_cmd_line, cwd=PROJECT_ROOT, env=env) cmd.run() measured_metrics = load_json(metrics_file_path) - + print(measured_metrics) for name, value in example_params[ACCURACY_METRICS].items(): assert measured_metrics[name] == pytest.approx( value, abs=example_params.get("accuracy_tolerance", ACCURACY_TOLERANCE) - ) + ), f"metric {name}: {measured_metrics[name]} != {value}" if ACCURACY_METRICS_AFTER_TRAINING in example_params: for name, value in example_params[ACCURACY_METRICS_AFTER_TRAINING].items(): assert measured_metrics[name] == pytest.approx( value, abs=example_params.get("accuracy_tolerance_after_training", ACCURACY_TOLERANCE) - ) + ), f"metric {name}: {measured_metrics[name]} != {value}" if MODEL_SIZE_METRICS in example_params: for name, value in example_params[MODEL_SIZE_METRICS].items(): diff --git a/tests/cross_fw/install/test_install.py b/tests/cross_fw/install/test_install.py index 9b1af5b85d0..7d43a71df82 100644 --- a/tests/cross_fw/install/test_install.py +++ b/tests/cross_fw/install/test_install.py @@ -21,12 +21,12 @@ from nncf.common.utils.os import is_linux from nncf.common.utils.os import is_windows from tests.cross_fw.install.conftest import TESTED_BACKENDS -from tests.shared.case_collection import skip_if_backend_not_selected -from tests.shared.helpers import create_venv_with_nncf -from tests.shared.helpers import get_pip_executable_with_venv -from tests.shared.helpers import get_python_executable_with_venv -from tests.shared.paths import PROJECT_ROOT -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.case_collection import skip_if_backend_not_selected +from tests.cross_fw.shared.helpers import create_venv_with_nncf +from tests.cross_fw.shared.helpers import get_pip_executable_with_venv +from tests.cross_fw.shared.helpers import get_python_executable_with_venv +from tests.cross_fw.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT def run_install_checks(venv_path: Path, tmp_path: Path, package_type: str, backend: str, install_type: str): diff --git a/tests/cross_fw/sdl/fuzz/quantize_api.py b/tests/cross_fw/sdl/fuzz/quantize_api.py index d48890eda43..caf12103ca0 100644 --- a/tests/cross_fw/sdl/fuzz/quantize_api.py +++ b/tests/cross_fw/sdl/fuzz/quantize_api.py @@ -11,7 +11,7 @@ import atheris -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT with atheris.instrument_imports(): import nncf diff --git a/tests/cross_fw/shared/__init__.py b/tests/cross_fw/shared/__init__.py new file mode 100644 index 00000000000..2e49d63977d --- /dev/null +++ b/tests/cross_fw/shared/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/shared/case_collection.py b/tests/cross_fw/shared/case_collection.py similarity index 100% rename from tests/shared/case_collection.py rename to tests/cross_fw/shared/case_collection.py diff --git a/tests/shared/command.py b/tests/cross_fw/shared/command.py similarity index 100% rename from tests/shared/command.py rename to tests/cross_fw/shared/command.py diff --git a/tests/shared/config_factory.py b/tests/cross_fw/shared/config_factory.py similarity index 100% rename from tests/shared/config_factory.py rename to tests/cross_fw/shared/config_factory.py diff --git a/tests/shared/data/dataset_definitions.yml b/tests/cross_fw/shared/data/dataset_definitions.yml similarity index 100% rename from tests/shared/data/dataset_definitions.yml rename to tests/cross_fw/shared/data/dataset_definitions.yml diff --git a/tests/shared/datasets.py b/tests/cross_fw/shared/datasets.py similarity index 100% rename from tests/shared/datasets.py rename to tests/cross_fw/shared/datasets.py diff --git a/tests/shared/helpers.py b/tests/cross_fw/shared/helpers.py similarity index 98% rename from tests/shared/helpers.py rename to tests/cross_fw/shared/helpers.py index fb3cf1682cc..1b9b9ef2d66 100644 --- a/tests/shared/helpers.py +++ b/tests/cross_fw/shared/helpers.py @@ -22,8 +22,8 @@ import nncf from nncf.common.utils.os import is_linux from nncf.common.utils.os import is_windows -from tests.shared.paths import GITHUB_REPO_URL -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import GITHUB_REPO_URL +from tests.cross_fw.shared.paths import PROJECT_ROOT TensorType = TypeVar("TensorType") @@ -79,9 +79,9 @@ def create_venv_with_nncf(tmp_path: Path, package_type: str, venv_type: str, bac elif package_type == "pip_git_develop": run_cmd_line = f"{pip_with_venv} install git+{GITHUB_REPO_URL}@develop#egg=nncf" elif package_type == "build_s": - run_cmd_line = f"{python_executable_with_venv} -m build -n -s" + run_cmd_line = f"{python_executable_with_venv} -m build -s" elif package_type == "build_w": - run_cmd_line = f"{python_executable_with_venv} -m build -n -w" + run_cmd_line = f"{python_executable_with_venv} -m build -w" else: raise nncf.ValidationError(f"Invalid package type: {package_type}") diff --git a/tests/shared/install_fixtures.py b/tests/cross_fw/shared/install_fixtures.py similarity index 93% rename from tests/shared/install_fixtures.py rename to tests/cross_fw/shared/install_fixtures.py index cbcf8cea67a..6eeb4e2c228 100644 --- a/tests/shared/install_fixtures.py +++ b/tests/cross_fw/shared/install_fixtures.py @@ -12,7 +12,7 @@ import pytest -from tests.shared.helpers import create_venv_with_nncf +from tests.cross_fw.shared.helpers import create_venv_with_nncf @pytest.fixture(scope="function") diff --git a/tests/shared/isolation_runner.py b/tests/cross_fw/shared/isolation_runner.py similarity index 100% rename from tests/shared/isolation_runner.py rename to tests/cross_fw/shared/isolation_runner.py diff --git a/tests/shared/logging.py b/tests/cross_fw/shared/logging.py similarity index 100% rename from tests/shared/logging.py rename to tests/cross_fw/shared/logging.py diff --git a/tests/shared/metric_thresholds.py b/tests/cross_fw/shared/metric_thresholds.py similarity index 100% rename from tests/shared/metric_thresholds.py rename to tests/cross_fw/shared/metric_thresholds.py diff --git a/tests/shared/nx_graph.py b/tests/cross_fw/shared/nx_graph.py similarity index 100% rename from tests/shared/nx_graph.py rename to tests/cross_fw/shared/nx_graph.py diff --git a/tests/shared/openvino_version.py b/tests/cross_fw/shared/openvino_version.py similarity index 100% rename from tests/shared/openvino_version.py rename to tests/cross_fw/shared/openvino_version.py diff --git a/tests/shared/paths.py b/tests/cross_fw/shared/paths.py similarity index 78% rename from tests/shared/paths.py rename to tests/cross_fw/shared/paths.py index f2e68303453..03860b0163a 100644 --- a/tests/shared/paths.py +++ b/tests/cross_fw/shared/paths.py @@ -12,15 +12,16 @@ import os from pathlib import Path -TEST_ROOT = Path(__file__).absolute().parents[1] +TEST_ROOT = Path(__file__).parent.absolute().parents[1] PROJECT_ROOT = TEST_ROOT.parent.absolute() EXAMPLES_DIR = PROJECT_ROOT / "examples" GITHUB_REPO_URL = "https://github.com/openvinotoolkit/nncf/" -DATASET_DEFINITIONS_PATH = TEST_ROOT / "shared" / "data" / "dataset_definitions.yml" +DATASET_DEFINITIONS_PATH = TEST_ROOT / "cross_fw" / "shared" / "data" / "dataset_definitions.yml" -ROOT_PYTHONPATH_ENV = os.environ.copy().update({"PYTHONPATH": str(PROJECT_ROOT)}) +ROOT_PYTHONPATH_ENV = os.environ.copy() +ROOT_PYTHONPATH_ENV["PYTHONPATH"] = f"{PROJECT_ROOT}:{ROOT_PYTHONPATH_ENV.get('PYTHONPATH', '')}".strip(":") def get_accuracy_aware_checkpoint_dir_path(model_specific_run_dir: Path) -> Path: diff --git a/tests/shared/patterns.py b/tests/cross_fw/shared/patterns.py similarity index 100% rename from tests/shared/patterns.py rename to tests/cross_fw/shared/patterns.py diff --git a/tests/shared/serialization.py b/tests/cross_fw/shared/serialization.py similarity index 100% rename from tests/shared/serialization.py rename to tests/cross_fw/shared/serialization.py diff --git a/tests/cross_fw/test_templates/helpers.py b/tests/cross_fw/test_templates/helpers.py index c25d138fd8c..2bb21dac6d9 100644 --- a/tests/cross_fw/test_templates/helpers.py +++ b/tests/cross_fw/test_templates/helpers.py @@ -19,6 +19,8 @@ from nncf import Dataset from tests.torch.helpers import create_bn from tests.torch.helpers import create_conv +from tests.torch.helpers import create_depthwise_conv +from tests.torch.helpers import create_transpose_conv from tests.torch.helpers import set_torch_seed TTensor = TypeVar("TTensor") @@ -436,3 +438,50 @@ def __init__(self): def forward(self, query, key, value): return nn.functional.scaled_dot_product_attention(query, key, value) + + +class DepthwiseConvTestModel(nn.Module): + INPUT_SIZE = [1, 2, 4, 4] + + def __init__(self): + super().__init__() + with set_torch_seed(): + self.conv = create_depthwise_conv(2, 1, 1, 1) + self.conv.weight.data = torch.randn([2, 1, 1, 1]) + self.conv.bias.data = torch.randn([2]) + + def forward(self, x): + return self.conv(x) + + +class TransposeConvTestModel(nn.Module): + INPUT_SIZE = [1, 1, 3, 3] + + def __init__(self): + super().__init__() + with set_torch_seed(): + self.conv = create_transpose_conv(1, 2, 2, 1, 1, 2) + self.conv.weight.data = torch.randn([1, 2, 2, 2]) + self.conv.bias.data = torch.randn([2]) + + def forward(self, x): + return self.conv(x) + + +class RoPEModel(nn.Module): + INPUT_SIZE = [1, 10] + + def __init__(self): + super().__init__() + with set_torch_seed(): + self.data = torch.randn([5]) + + def forward(self, x): + x = torch.unsqueeze(x, dim=0) + reshape = torch.reshape(self.data, [1, 5, 1]) + x = torch.matmul(reshape, x) + x = torch.transpose(x, 2, 1) + x = torch.cat([x], dim=2) + x1 = x.sin() + x2 = x.cos() + return x1, x2 diff --git a/tests/cross_fw/test_templates/models.py b/tests/cross_fw/test_templates/models.py index 6cc2f0d5c38..c012e93443a 100644 --- a/tests/cross_fw/test_templates/models.py +++ b/tests/cross_fw/test_templates/models.py @@ -412,3 +412,52 @@ def __init__( original_mock_graph = create_mock_graph(nodes, edges) self.nncf_graph = get_nncf_graph_from_mock_nx_graph(original_mock_graph, nncf_graph_cls) + + +class NNCFGraphTransformer: + def __init__( + self, + matmul_metatype, + softmax_metatype, + transpose_metatype, + const_metatype, + mul_metatype, + matmul_layer_weighted_attrs=None, + matmul_layer_non_weighted_attrs=None, + default_layer_attrs=None, + nncf_graph_cls=NNCFGraph, + ): + # softmax((K x Q) * scale) x V.T + nodes = [ + NodeWithType("Input_1", InputNoopMetatype, layer_attributes=default_layer_attrs), + NodeWithType("W_K", const_metatype, layer_attributes=default_layer_attrs), + NodeWithType("W_Q", const_metatype, layer_attributes=default_layer_attrs), + NodeWithType("W_V", const_metatype, layer_attributes=default_layer_attrs), + NodeWithType("K", matmul_metatype, layer_attributes=matmul_layer_weighted_attrs), + NodeWithType("Q", matmul_metatype, layer_attributes=matmul_layer_weighted_attrs), + NodeWithType("V", matmul_metatype, layer_attributes=matmul_layer_weighted_attrs), + NodeWithType("K_Q", matmul_metatype, layer_attributes=matmul_layer_non_weighted_attrs), + NodeWithType("div", mul_metatype, layer_attributes=default_layer_attrs), + NodeWithType("softmax", softmax_metatype, layer_attributes=default_layer_attrs), + NodeWithType("transpose", transpose_metatype, layer_attributes=default_layer_attrs), + NodeWithType("SA_V", matmul_metatype, layer_attributes=matmul_layer_non_weighted_attrs), + NodeWithType("Output_1", OutputNoopMetatype, layer_attributes=default_layer_attrs), + ] + node_edges = [ + ("Input_1", "K"), + ("W_K", "K"), + ("Input_1", "Q"), + ("W_Q", "Q"), + ("Input_1", "V"), + ("W_V", "V"), + ("K", "K_Q"), + ("Q", "K_Q"), + ("K_Q", "div"), + ("div", "softmax"), + ("softmax", "SA_V"), + ("V", "transpose"), + ("transpose", "SA_V"), + ("SA_V", "Output_1"), + ] + original_mock_graph = create_mock_graph(nodes, node_edges) + self.nncf_graph = get_nncf_graph_from_mock_nx_graph(original_mock_graph, nncf_graph_cls) diff --git a/tests/shared/test_templates/template_test_nncf_tensor.py b/tests/cross_fw/test_templates/template_test_nncf_tensor.py similarity index 100% rename from tests/shared/test_templates/template_test_nncf_tensor.py rename to tests/cross_fw/test_templates/template_test_nncf_tensor.py diff --git a/tests/cross_fw/test_templates/test_bias_correction.py b/tests/cross_fw/test_templates/test_bias_correction.py index b154e0870a7..81b638eb900 100644 --- a/tests/cross_fw/test_templates/test_bias_correction.py +++ b/tests/cross_fw/test_templates/test_bias_correction.py @@ -10,7 +10,7 @@ # limitations under the License. from abc import abstractmethod -from typing import Dict, List, Tuple, TypeVar +from typing import Any, Dict, List, Tuple, TypeVar import pytest @@ -22,9 +22,11 @@ from nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization from tests.cross_fw.test_templates.helpers import ConvTestModel +from tests.cross_fw.test_templates.helpers import DepthwiseConvTestModel from tests.cross_fw.test_templates.helpers import MultipleConvTestModel from tests.cross_fw.test_templates.helpers import SplittedModel from tests.cross_fw.test_templates.helpers import StaticDatasetMock +from tests.cross_fw.test_templates.helpers import TransposeConvTestModel TModel = TypeVar("TModel") TTensor = TypeVar("TTensor") @@ -85,7 +87,7 @@ def check_bias(model: TModel, ref_biases: Dict) -> None: """ @staticmethod - def map_references(ref_biases: Dict) -> Dict[str, List]: + def map_references(ref_biases: Dict, model_cls: Any) -> Dict[str, List]: """ Returns backend-specific reference. """ @@ -112,20 +114,6 @@ def remove_fq_from_inputs(model: TModel) -> TModel: Removes quantizer nodes from inputs. """ - @staticmethod - @abstractmethod - def get_ref_path(suffix: str) -> str: - """ - Returns backend-specific reference graph paths. - """ - - @staticmethod - @abstractmethod - def compare_nncf_graphs(model: TModel, ref_path: str) -> None: - """ - Compares backend-specific model with reference graph. - """ - @pytest.fixture() def quantized_test_model(self, tmpdir) -> TModel: model_cls = SplittedModel @@ -153,6 +141,8 @@ def quantized_test_model(self, tmpdir) -> TModel: }, ), (ConvTestModel, {"/conv/Conv": [0.11085186, 1.0017344]}), + (DepthwiseConvTestModel, {"/conv/Conv": [-1.1229, -0.1863]}), + (TransposeConvTestModel, {"/conv/ConvTranspose": [0.66797173, -0.7070703]}), ), ) def test_update_bias(self, model_cls, ref_biases, tmpdir): @@ -163,7 +153,7 @@ def test_update_bias(self, model_cls, ref_biases, tmpdir): graph = NNCFGraphFactory.create(model) quantized_model = quantization_algorithm.apply(model, graph, dataset=dataset) - mapped_ref_biases = self.map_references(ref_biases) + mapped_ref_biases = self.map_references(ref_biases, model_cls) self.check_bias(quantized_model, mapped_ref_biases) def test__get_subgraph_data_for_node(self, quantized_test_model, layer_name, ref_data): diff --git a/tests/cross_fw/test_templates/test_calculate_quantizer_parameters.py b/tests/cross_fw/test_templates/test_calculate_quantizer_parameters.py index 45bb3c65956..791cdfad629 100644 --- a/tests/cross_fw/test_templates/test_calculate_quantizer_parameters.py +++ b/tests/cross_fw/test_templates/test_calculate_quantizer_parameters.py @@ -25,8 +25,8 @@ from nncf.quantization.fake_quantize import FakeQuantizeParameters from nncf.quantization.fake_quantize import calculate_quantizer_parameters from nncf.tensor import functions as fns -from tests.shared.helpers import dump_to_json -from tests.shared.helpers import load_json +from tests.cross_fw.shared.helpers import dump_to_json +from tests.cross_fw.shared.helpers import load_json FQ_CALCULATED_PARAMETERS_PATH = Path(__file__).parent / "fq_params" / "fq_params.json" diff --git a/tests/cross_fw/test_templates/test_min_max.py b/tests/cross_fw/test_templates/test_min_max.py index 880660cddf0..1e83029c0d6 100644 --- a/tests/cross_fw/test_templates/test_min_max.py +++ b/tests/cross_fw/test_templates/test_min_max.py @@ -30,9 +30,9 @@ class TemplateTestMinMaxAlgorithm: @abstractmethod def backend(self) -> MinMaxAlgoBackend: """ - Get backend specific BiasCorrectionAlgoBackend + Get backend specific MinMaxAlgoBackend - :return BiasCorrectionAlgoBackend: Backend specific BiasCorrectionAlgoBackend + :return MinMaxAlgoBackend: Backend specific MinMaxAlgoBackend """ @property @@ -164,7 +164,6 @@ def test_get_channel_axes_matmul_torch(self, weight_shape, ref_axes): Checks MatMul quantization axes in MinMax for Torch. """ matmul_node = NNCFNode({"metatype": self.matmul_metatype}) - matmul_node.layer_attributes = self.get_matmul_node_attrs(None, None, weight_shape) class DummyTargetPoint: input_port_id = 0 diff --git a/tests/cross_fw/test_templates/test_quantizer_config.py b/tests/cross_fw/test_templates/test_quantizer_config.py index a01e9ffdb8e..618007652f9 100644 --- a/tests/cross_fw/test_templates/test_quantizer_config.py +++ b/tests/cross_fw/test_templates/test_quantizer_config.py @@ -16,7 +16,10 @@ import pytest +from nncf import ModelType +from nncf.common.graph.graph import NNCFGraph from nncf.common.graph.patterns import GraphPattern +from nncf.common.graph.patterns.manager import PatternsManager from nncf.common.graph.transformations.commands import TargetType from nncf.common.quantization.quantizer_setup import ActivationQuantizationInsertionPoint from nncf.common.quantization.quantizer_setup import SingleConfigQuantizationPoint @@ -34,6 +37,7 @@ from nncf.experimental.common.tensor_statistics.collectors import MinReducer from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase +from nncf.parameters import TargetDevice from nncf.quantization.advanced_parameters import QuantizationParameters from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization from nncf.quantization.passes import transform_to_inference_graph @@ -48,6 +52,10 @@ class TemplateTestQuantizerConfig: def get_algo_backend(self): pass + @abstractmethod + def get_backend_type(self): + pass + def check_is_min_max_statistic_collector(self, tensor_collector: TensorCollector): aggrs = [aggr.__class__ for aggr in tensor_collector.aggregators.values()] assert len(aggrs) == 2 @@ -63,11 +71,26 @@ def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorColl def get_reduction_axes(self, reducer: TensorReducerBase) -> ReductionAxes: return reducer._reduction_axes + @staticmethod + def _transform_to_inference_graph(nncf_graph: NNCFGraph, min_max_algo: MinMaxQuantization): + return transform_to_inference_graph( + deepcopy(nncf_graph), + min_max_algo._backend_entity.get_start_nodes_for_activation_path_tracing(nncf_graph), + min_max_algo._backend_entity.shapeof_metatypes, + min_max_algo._backend_entity.dropout_metatypes, + min_max_algo._backend_entity.preserved_metatypes, + ) + @abstractmethod @pytest.fixture def single_conv_nncf_graph(self) -> NNCFGraphToTest: pass + @abstractmethod + @pytest.fixture + def transformer_nncf_graph(self) -> NNCFGraphToTest: + pass + @abstractmethod @pytest.fixture def depthwise_conv_nncf_graph(self) -> NNCFGraphToTestDepthwiseConv: @@ -129,13 +152,7 @@ def test_default_quantizer_config(self, single_conv_nncf_graph): min_max_algo = MinMaxQuantization() min_max_algo._backend_entity = self.get_algo_backend() nncf_graph = single_conv_nncf_graph.nncf_graph - inference_nncf_graph = transform_to_inference_graph( - deepcopy(nncf_graph), - min_max_algo._backend_entity.get_start_nodes_for_activation_path_tracing(nncf_graph), - min_max_algo._backend_entity.shapeof_metatypes, - min_max_algo._backend_entity.dropout_metatypes, - min_max_algo._backend_entity.preserved_metatypes, - ) + inference_nncf_graph = self._transform_to_inference_graph(nncf_graph, min_max_algo) q_setup = min_max_algo._get_quantizer_setup( nncf_graph, inference_nncf_graph, hw_patterns=GraphPattern(), ignored_patterns=GraphPattern() ) @@ -184,13 +201,7 @@ def test_quantizer_config_from_ptq_params_for_CPU( ) min_max_algo._backend_entity = self.get_algo_backend() nncf_graph = single_conv_nncf_graph.nncf_graph - inference_nncf_graph = transform_to_inference_graph( - deepcopy(nncf_graph), - min_max_algo._backend_entity.get_start_nodes_for_activation_path_tracing(nncf_graph), - min_max_algo._backend_entity.shapeof_metatypes, - min_max_algo._backend_entity.dropout_metatypes, - min_max_algo._backend_entity.preserved_metatypes, - ) + inference_nncf_graph = self._transform_to_inference_graph(nncf_graph, min_max_algo) if signed_weights is False or signed_activations in [True, False]: # Incompatible with HW CPU config with pytest.raises( ValueError, @@ -227,13 +238,7 @@ def test_depthwise_conv_default_quantizer_config(self, depthwise_conv_nncf_graph min_max_algo = MinMaxQuantization() min_max_algo._backend_entity = self.get_algo_backend() nncf_graph = depthwise_conv_nncf_graph.nncf_graph - inference_nncf_graph = transform_to_inference_graph( - deepcopy(nncf_graph), - min_max_algo._backend_entity.get_start_nodes_for_activation_path_tracing(nncf_graph), - min_max_algo._backend_entity.shapeof_metatypes, - min_max_algo._backend_entity.dropout_metatypes, - min_max_algo._backend_entity.preserved_metatypes, - ) + inference_nncf_graph = self._transform_to_inference_graph(nncf_graph, min_max_algo) q_setup = min_max_algo._get_quantizer_setup( nncf_graph, inference_nncf_graph, hw_patterns=GraphPattern(), ignored_patterns=GraphPattern() ) @@ -253,6 +258,71 @@ def test_depthwise_conv_default_quantizer_config(self, depthwise_conv_nncf_graph if quantization_point.is_activation_quantization_point(): assert quantization_point.qconfig == activation_default_config + REF_TRANSFORMER_SETUP_STATE = { + "quantization_points": { + 4: { + "qip": {"target_node_name": "/K_0", "input_port_id": None}, + "qip_class": "ActivationQuantizationInsertionPoint", + "qconfig": {"num_bits": 8, "mode": "symmetric", "signedness_to_force": None, "per_channel": False}, + "directly_quantized_operator_node_names": ["/K_Q_0"], + }, + 5: { + "qip": {"target_node_name": "/Q_0", "input_port_id": None}, + "qip_class": "ActivationQuantizationInsertionPoint", + "qconfig": {"num_bits": 8, "mode": "symmetric", "signedness_to_force": None, "per_channel": False}, + "directly_quantized_operator_node_names": ["/K_Q_0"], + }, + 6: { + "qip": {"target_node_name": "/Input_1_0", "input_port_id": None}, + "qip_class": "ActivationQuantizationInsertionPoint", + "qconfig": {"num_bits": 8, "mode": "asymmetric", "signedness_to_force": None, "per_channel": False}, + "directly_quantized_operator_node_names": ["/K_0", "/Q_0", "/V_0"], + }, + 8: { + "qip": {"target_node_name": "/K_0"}, + "qip_class": "WeightQuantizationInsertionPoint", + "qconfig": {"num_bits": 8, "mode": "symmetric", "signedness_to_force": True, "per_channel": True}, + "directly_quantized_operator_node_names": ["/K_0"], + }, + 9: { + "qip": {"target_node_name": "/Q_0"}, + "qip_class": "WeightQuantizationInsertionPoint", + "qconfig": {"num_bits": 8, "mode": "symmetric", "signedness_to_force": True, "per_channel": True}, + "directly_quantized_operator_node_names": ["/Q_0"], + }, + 10: { + "qip": {"target_node_name": "/V_0"}, + "qip_class": "WeightQuantizationInsertionPoint", + "qconfig": {"num_bits": 8, "mode": "symmetric", "signedness_to_force": True, "per_channel": True}, + "directly_quantized_operator_node_names": ["/V_0"], + }, + }, + "unified_scale_groups": {}, + "shared_input_operation_set_groups": {0: [4, 5], 1: [8, 9, 10, 6]}, + } + + def test_model_type_transformer_quantization_config(self, transformer_nncf_graph): + min_max_algo = MinMaxQuantization(model_type=ModelType.TRANSFORMER) + min_max_algo._backend_entity = self.get_algo_backend() + nncf_graph = transformer_nncf_graph.nncf_graph + inference_nncf_graph = self._transform_to_inference_graph(nncf_graph, min_max_algo) + hw_patterns = PatternsManager.get_full_hw_pattern_graph( + backend=self.get_backend_type(), device=TargetDevice.ANY, model_type=ModelType.TRANSFORMER + ) + ignored_patterns = PatternsManager.get_full_ignored_pattern_graph( + backend=self.get_backend_type(), device=TargetDevice.ANY, model_type=ModelType.TRANSFORMER + ) + q_setup = min_max_algo._get_quantizer_setup( + nncf_graph, inference_nncf_graph, hw_patterns=hw_patterns, ignored_patterns=ignored_patterns + ) + min_max_algo._apply_model_type_pass(ModelType.TRANSFORMER, q_setup, nncf_graph) + + state = q_setup.get_state() + state["quantization_points"][6]["directly_quantized_operator_node_names"] = sorted( + state["quantization_points"][6]["directly_quantized_operator_node_names"] + ) + assert state == self.REF_TRANSFORMER_SETUP_STATE + @pytest.mark.parametrize( "range_estimator_params", [RangeEstimatorParametersSet.MINMAX, RangeEstimatorParametersSet.MEAN_MINMAX] ) diff --git a/tests/cross_fw/test_templates/test_statistics_caching.py b/tests/cross_fw/test_templates/test_statistics_caching.py new file mode 100644 index 00000000000..1035dc8fcb4 --- /dev/null +++ b/tests/cross_fw/test_templates/test_statistics_caching.py @@ -0,0 +1,102 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from abc import abstractmethod +from pathlib import Path + +import numpy as np +import pytest + +import nncf +from nncf.common.graph.transformations.commands import TargetPoint +from nncf.common.graph.transformations.commands import TargetType +from nncf.common.tensor_statistics.statistic_point import StatisticPoint +from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer +from nncf.common.utils.backend import BackendType +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector +from nncf.experimental.common.tensor_statistics.statistics import MinMaxTensorStatistic +from nncf.tensor import Tensor + + +class TemplateTestStatisticsCaching: + @property + @abstractmethod + def create_target_point(self, target_point_type: TargetType, name: str, port_id: int) -> TargetPoint: + """ + Creates a backend-specific TargetPoint. + + :param target_point_type: The type of target point (e.g., PRE_LAYER_OPERATION). + :param name: The name of the target point. + :param port_id: The port ID for the target point. + :return: A backend-specific TargetPoint. + """ + pass + + @abstractmethod + def get_statistics_aggregator(self): + """ + Returns a statistics aggregator. Must be implemented by subclasses. + + :return: Statistics aggregator instance specific to the backend. + """ + pass + + def _create_dummy_statistic_point(self) -> StatisticPoint: + """ + Creates a dummy statistic point for testing purposes. + + :return: A StatisticPoint object with dummy data. + """ + dummy_t_p = self.create_target_point(TargetType.PRE_LAYER_OPERATION, "dummy_name", 0) + dummy_tensor_collector = TensorCollector() + dummy_tensor_collector._cached_statistics = MinMaxTensorStatistic(Tensor(np.zeros((3))), Tensor(np.ones((3)))) + return StatisticPoint( + target_point=dummy_t_p, tensor_collector=dummy_tensor_collector, algorithm="dummy_algorithm" + ) + + def test_dump_and_load_statistics(self, tmp_path: Path): + """ + Tests the dumping and loading of statistics to and from a file. + + :param tmp_path: The temporary path provided by pytest. + """ + test_dir = "test_dir" + aggregator = self.get_statistics_aggregator() + statistics_points = StatisticPointsContainer() + + dummy_statistic_point = self._create_dummy_statistic_point() + statistics_points.add_statistic_point(dummy_statistic_point) + + aggregator.statistic_points = statistics_points + aggregator.dump_statistics(tmp_path / test_dir) + assert (tmp_path / test_dir).exists(), "Statistics file was not created" + + aggregator.load_statistics_from_dir(tmp_path / test_dir) + + def test_incorrect_backend_statistics_load(self, tmp_path: Path): + """ + Tests the dumping and loading of statistics to and from a file with non matched backends. + + :param tmp_path: The temporary path provided by pytest. + """ + test_file = "test" + aggregator = self.get_statistics_aggregator() + statistics_points = StatisticPointsContainer() + + dummy_statistic_point = self._create_dummy_statistic_point() + statistics_points.add_statistic_point(dummy_statistic_point) + + aggregator.statistic_points = statistics_points + aggregator.dump_statistics(tmp_path / test_file) + assert (tmp_path / test_file).exists(), "Statistics file was not created" + # spoil backend + aggregator.BACKEND = BackendType.TENSORFLOW + with pytest.raises(nncf.ValidationError): + aggregator.load_statistics_from_dir(tmp_path / test_file) diff --git a/tests/cross_fw/test_templates/test_weights_compression_backends.py b/tests/cross_fw/test_templates/test_weights_compression_backends.py new file mode 100644 index 00000000000..fb521fd0726 --- /dev/null +++ b/tests/cross_fw/test_templates/test_weights_compression_backends.py @@ -0,0 +1,77 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import abstractmethod + +import pytest + +from nncf.experimental.common.tensor_statistics.collectors import HAWQAggregator +from nncf.experimental.common.tensor_statistics.collectors import MaxVarianceReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanAbsMaxReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator +from nncf.experimental.common.tensor_statistics.collectors import MeanVarianceReducer +from nncf.experimental.common.tensor_statistics.collectors import NoopReducer +from nncf.experimental.common.tensor_statistics.collectors import TensorCollector + + +class TemplateTestMixedPrecisionAlgoBackend: + @abstractmethod + def get_hawq_with_backend(self, subset_size: int): + """Returns a HAWQ instance of the algorithm.""" + + @abstractmethod + def get_mean_variance_with_backend(self, subset_size: int): + """Returns a Mean Variance instance of the algorithm.""" + + @abstractmethod + def get_max_variance_with_backend(self, subset_size: int): + """Returns a Max Variance instance of the algorithm.""" + + @abstractmethod + def get_mean_max_with_backend(self, subset_size: int): + """Returns a Mean Max instance of the algorithm.""" + + def check_aggregator(self, collector: TensorCollector, expected_aggregator_type, subset_size: int): + assert len(collector.aggregators) == 1, "Collector should have exactly one aggregator." + _, aggregator = collector.aggregators.popitem() + assert isinstance( + aggregator, expected_aggregator_type + ), f"Expected aggregator of type {expected_aggregator_type.__name__}, got {type(aggregator).__name__}." + assert aggregator.num_samples == subset_size, "Aggregator num_samples does not match the provided subset size." + + def check_reducer(self, collector: TensorCollector, expected_reducer_type): + assert len(collector.reducers) == 1 + reducer = collector.reducers.pop() + assert isinstance( + reducer, expected_reducer_type + ), f"Expected reducer of type {expected_reducer_type.__name__}, got {type(reducer).__name__}." + + @pytest.mark.parametrize("subset_size", [1, 10, None]) + @pytest.mark.parametrize( + "algo_func, aggregator_type, reducer_type", + [ + ("get_hawq_with_backend", HAWQAggregator, NoopReducer), + ("get_mean_variance_with_backend", MeanAggregator, MeanVarianceReducer), + ("get_max_variance_with_backend", MeanAggregator, MaxVarianceReducer), + ("get_mean_max_with_backend", MeanAggregator, MeanAbsMaxReducer), + ], + ) + def test_statistic_collector(self, subset_size, algo_func, aggregator_type, reducer_type): + """Test function to validate statistic collectors.""" + algo = getattr(self, algo_func)(subset_size) + collector = algo._get_statistic_collector() + + # Verify the collector instance and properties + assert isinstance(collector, TensorCollector), "Collector is not an instance of TensorCollector." + + # Validate the aggregator and reducer types + self.check_aggregator(collector, aggregator_type, subset_size) + self.check_reducer(collector, reducer_type) diff --git a/tests/onnx/conftest.py b/tests/onnx/conftest.py index f7f53424344..1a057b6d7a4 100644 --- a/tests/onnx/conftest.py +++ b/tests/onnx/conftest.py @@ -11,7 +11,7 @@ import os -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT def pytest_addoption(parser): diff --git a/tests/onnx/data/reference_graphs/original_nncf_graph/synthetic/float64_model.dot b/tests/onnx/data/reference_graphs/original_nncf_graph/synthetic/float64_model.dot index 45ed032523f..56b50cfbbcb 100644 --- a/tests/onnx/data/reference_graphs/original_nncf_graph/synthetic/float64_model.dot +++ b/tests/onnx/data/reference_graphs/original_nncf_graph/synthetic/float64_model.dot @@ -1,11 +1,11 @@ strict digraph { "0 Reciprocal" [id=0, type=Reciprocal]; "1 Cast" [id=1, type=Cast]; -"2 Mul" [id=2, type=Mul]; +"2 Conv1" [id=2, type=Conv]; "3 nncf_model_input_0" [id=3, type=nncf_model_input]; "4 nncf_model_output_0" [id=4, type=nncf_model_output]; "0 Reciprocal" -> "1 Cast" [label="[1, 3, 10, 10]", style=dashed]; -"1 Cast" -> "2 Mul" [label="[1, 3, 10, 10]", style=solid]; -"2 Mul" -> "4 nncf_model_output_0" [label="[1, 3, 10, 10]", style=solid]; +"1 Cast" -> "2 Conv1" [label="[1, 3, 10, 10]", style=solid]; +"2 Conv1" -> "4 nncf_model_output_0" [label="[1, 3, 10, 10]", style=solid]; "3 nncf_model_input_0" -> "0 Reciprocal" [label="[1, 3, 10, 10]", style=dashed]; } diff --git a/tests/onnx/data/reference_graphs/quantization/MaskRCNN-12.dot b/tests/onnx/data/reference_graphs/quantization/MaskRCNN-12.dot index e2a0350705f..c561b783e41 100644 --- a/tests/onnx/data/reference_graphs/quantization/MaskRCNN-12.dot +++ b/tests/onnx/data/reference_graphs/quantization/MaskRCNN-12.dot @@ -1762,2528 +1762,2508 @@ strict digraph { "1760 2525" [id=1760, type=Slice]; "1761 2527" [id=1761, type=Gather]; "1762 2535" [id=1762, type=Sub]; -"1763 QuantizeLinear_2572_1" [id=1763, type=QuantizeLinear]; -"1764 DequantizeLinear_2572_1" [id=1764, type=DequantizeLinear]; -"1765 2537" [id=1765, type=Add]; -"1766 2515" [id=1766, type=Slice]; -"1767 2517" [id=1767, type=Gather]; -"1768 2508" [id=1768, type=Slice]; -"1769 2510" [id=1769, type=Gather]; -"1770 2518" [id=1770, type=Sub]; -"1771 QuantizeLinear_2555_1" [id=1771, type=QuantizeLinear]; -"1772 DequantizeLinear_2555_1" [id=1772, type=DequantizeLinear]; -"1773 2520" [id=1773, type=Add]; -"1774 QuantizeLinear_2574_1" [id=1774, type=QuantizeLinear]; -"1775 DequantizeLinear_2574_1" [id=1775, type=DequantizeLinear]; -"1776 QuantizeLinear_2557_1" [id=1776, type=QuantizeLinear]; -"1777 DequantizeLinear_2557_1" [id=1777, type=DequantizeLinear]; -"1778 2538" [id=1778, type=Mul]; -"1779 QuantizeLinear_2575_1" [id=1779, type=QuantizeLinear]; -"1780 DequantizeLinear_2575_1" [id=1780, type=DequantizeLinear]; -"1781 2539" [id=1781, type=Sqrt]; -"1782 2542" [id=1782, type=Div]; -"1783 QuantizeLinear_2579_1" [id=1783, type=QuantizeLinear]; -"1784 DequantizeLinear_2579_1" [id=1784, type=DequantizeLinear]; -"1785 2543" [id=1785, type=Add]; -"1786 2544" [id=1786, type=Log]; -"1787 2546" [id=1787, type=Div]; -"1788 QuantizeLinear_2583_1" [id=1788, type=QuantizeLinear]; -"1789 DequantizeLinear_2583_1" [id=1789, type=DequantizeLinear]; -"1790 2548" [id=1790, type=Add]; -"1791 QuantizeLinear_2585_1" [id=1791, type=QuantizeLinear]; -"1792 DequantizeLinear_2585_1" [id=1792, type=DequantizeLinear]; -"1793 2549" [id=1793, type=Floor]; -"1794 2550" [id=1794, type=Clip]; -"1795 2551" [id=1795, type=Cast]; -"1796 2553" [id=1796, type=Sub]; -"1797 2555" [id=1797, type=Equal]; -"1798 2557" [id=1798, type=Cast]; -"1799 2558" [id=1799, type=NonZero]; -"1800 2559" [id=1800, type=Transpose]; -"1801 2560" [id=1801, type=Squeeze]; -"1802 2561" [id=1802, type=Cast]; -"1803 2495" [id=1803, type=Slice]; -"1804 2500" [id=1804, type=Slice]; -"1805 2501" [id=1805, type=Shape]; -"1806 2502" [id=1806, type=ConstantOfShape]; -"1807 2503" [id=1807, type=Concat]; -"1808 2562" [id=1808, type=Gather]; -"1809 2568" [id=1809, type=Gather]; -"1810 2564" [id=1810, type=Gather]; -"1811 2565" [id=1811, type=Squeeze]; -"1812 2566" [id=1812, type=Cast]; -"1813 2569" [id=1813, type=RoiAlign]; -"1814 2570" [id=1814, type=Cast]; -"1815 2658" [id=1815, type=Shape]; -"1816 2659" [id=1816, type=Gather]; -"1817 2663" [id=1817, type=Unsqueeze]; -"1818 2655" [id=1818, type=Shape]; -"1819 2656" [id=1819, type=Gather]; -"1820 2662" [id=1820, type=Unsqueeze]; -"1821 2652" [id=1821, type=Shape]; -"1822 2653" [id=1822, type=Gather]; -"1823 2661" [id=1823, type=Unsqueeze]; -"1824 2641" [id=1824, type=Equal]; -"1825 2643" [id=1825, type=Cast]; -"1826 2644" [id=1826, type=NonZero]; -"1827 2645" [id=1827, type=Transpose]; -"1828 2647" [id=1828, type=Reshape]; -"1829 2649" [id=1829, type=Shape]; -"1830 2650" [id=1830, type=Gather]; -"1831 2660" [id=1831, type=Unsqueeze]; -"1832 2664" [id=1832, type=Concat]; -"1833 2665" [id=1833, type=Expand]; -"1834 2666" [id=1834, type=Cast]; -"1835 2632" [id=1835, type=Shape]; -"1836 2633" [id=1836, type=Gather]; -"1837 2637" [id=1837, type=Unsqueeze]; -"1838 2629" [id=1838, type=Shape]; -"1839 2630" [id=1839, type=Gather]; -"1840 2636" [id=1840, type=Unsqueeze]; -"1841 2626" [id=1841, type=Shape]; -"1842 2627" [id=1842, type=Gather]; -"1843 2635" [id=1843, type=Unsqueeze]; -"1844 2623" [id=1844, type=Shape]; -"1845 2624" [id=1845, type=Gather]; -"1846 2634" [id=1846, type=Unsqueeze]; -"1847 2638" [id=1847, type=Concat]; -"1848 2639" [id=1848, type=ConstantOfShape]; -"1849 2667" [id=1849, type=ScatterElements]; -"1850 2572" [id=1850, type=Equal]; -"1851 2574" [id=1851, type=Cast]; -"1852 2575" [id=1852, type=NonZero]; -"1853 2576" [id=1853, type=Transpose]; -"1854 2577" [id=1854, type=Squeeze]; -"1855 2578" [id=1855, type=Cast]; -"1856 2579" [id=1856, type=Gather]; -"1857 2585" [id=1857, type=Gather]; -"1858 2581" [id=1858, type=Gather]; -"1859 2582" [id=1859, type=Squeeze]; -"1860 2583" [id=1860, type=Cast]; -"1861 2586" [id=1861, type=RoiAlign]; -"1862 2587" [id=1862, type=Cast]; -"1863 2686" [id=1863, type=Shape]; -"1864 2687" [id=1864, type=Gather]; -"1865 2691" [id=1865, type=Unsqueeze]; -"1866 2683" [id=1866, type=Shape]; -"1867 2684" [id=1867, type=Gather]; -"1868 2690" [id=1868, type=Unsqueeze]; -"1869 2680" [id=1869, type=Shape]; -"1870 2681" [id=1870, type=Gather]; -"1871 2689" [id=1871, type=Unsqueeze]; -"1872 2669" [id=1872, type=Equal]; -"1873 2671" [id=1873, type=Cast]; -"1874 2672" [id=1874, type=NonZero]; -"1875 2673" [id=1875, type=Transpose]; -"1876 2675" [id=1876, type=Reshape]; -"1877 2677" [id=1877, type=Shape]; -"1878 2678" [id=1878, type=Gather]; -"1879 2688" [id=1879, type=Unsqueeze]; -"1880 2692" [id=1880, type=Concat]; -"1881 2693" [id=1881, type=Expand]; -"1882 2694" [id=1882, type=Cast]; -"1883 2695" [id=1883, type=ScatterElements]; -"1884 2589" [id=1884, type=Equal]; -"1885 2591" [id=1885, type=Cast]; -"1886 2592" [id=1886, type=NonZero]; -"1887 2593" [id=1887, type=Transpose]; -"1888 2594" [id=1888, type=Squeeze]; -"1889 2595" [id=1889, type=Cast]; -"1890 2596" [id=1890, type=Gather]; -"1891 2602" [id=1891, type=Gather]; -"1892 2598" [id=1892, type=Gather]; -"1893 2599" [id=1893, type=Squeeze]; -"1894 2600" [id=1894, type=Cast]; -"1895 2603" [id=1895, type=RoiAlign]; -"1896 2604" [id=1896, type=Cast]; -"1897 2714" [id=1897, type=Shape]; -"1898 2715" [id=1898, type=Gather]; -"1899 2719" [id=1899, type=Unsqueeze]; -"1900 2711" [id=1900, type=Shape]; -"1901 2712" [id=1901, type=Gather]; -"1902 2718" [id=1902, type=Unsqueeze]; -"1903 2708" [id=1903, type=Shape]; -"1904 2709" [id=1904, type=Gather]; -"1905 2717" [id=1905, type=Unsqueeze]; -"1906 2697" [id=1906, type=Equal]; -"1907 2699" [id=1907, type=Cast]; -"1908 2700" [id=1908, type=NonZero]; -"1909 2701" [id=1909, type=Transpose]; -"1910 2703" [id=1910, type=Reshape]; -"1911 2705" [id=1911, type=Shape]; -"1912 2706" [id=1912, type=Gather]; -"1913 2716" [id=1913, type=Unsqueeze]; -"1914 2720" [id=1914, type=Concat]; -"1915 2721" [id=1915, type=Expand]; -"1916 2722" [id=1916, type=Cast]; -"1917 2723" [id=1917, type=ScatterElements]; -"1918 2606" [id=1918, type=Equal]; -"1919 2608" [id=1919, type=Cast]; -"1920 2609" [id=1920, type=NonZero]; -"1921 2610" [id=1921, type=Transpose]; -"1922 2611" [id=1922, type=Squeeze]; -"1923 2612" [id=1923, type=Cast]; -"1924 2613" [id=1924, type=Gather]; -"1925 2619" [id=1925, type=Gather]; -"1926 2615" [id=1926, type=Gather]; -"1927 2616" [id=1927, type=Squeeze]; -"1928 2617" [id=1928, type=Cast]; -"1929 2620" [id=1929, type=RoiAlign]; -"1930 2621" [id=1930, type=Cast]; -"1931 2742" [id=1931, type=Shape]; -"1932 2743" [id=1932, type=Gather]; -"1933 2747" [id=1933, type=Unsqueeze]; -"1934 2739" [id=1934, type=Shape]; -"1935 2740" [id=1935, type=Gather]; -"1936 2746" [id=1936, type=Unsqueeze]; -"1937 2736" [id=1937, type=Shape]; -"1938 2737" [id=1938, type=Gather]; -"1939 2745" [id=1939, type=Unsqueeze]; -"1940 2725" [id=1940, type=Equal]; -"1941 2727" [id=1941, type=Cast]; -"1942 2728" [id=1942, type=NonZero]; -"1943 2729" [id=1943, type=Transpose]; -"1944 2731" [id=1944, type=Reshape]; -"1945 2733" [id=1945, type=Shape]; -"1946 2734" [id=1946, type=Gather]; -"1947 2744" [id=1947, type=Unsqueeze]; -"1948 2748" [id=1948, type=Concat]; -"1949 2749" [id=1949, type=Expand]; -"1950 2750" [id=1950, type=Cast]; -"1951 2751" [id=1951, type=ScatterElements]; -"1952 2757" [id=1952, type=Unsqueeze]; -"1953 QuantizeLinear_2788_1" [id=1953, type=QuantizeLinear]; -"1954 DequantizeLinear_2788_1" [id=1954, type=DequantizeLinear]; -"1955 2753" [id=1955, type=Shape]; -"1956 2754" [id=1956, type=Gather]; -"1957 2756" [id=1957, type=Unsqueeze]; -"1958 2758" [id=1958, type=Concat]; -"1959 2759" [id=1959, type=Reshape]; -"1960 QuantizeLinear_2797_1" [id=1960, type=QuantizeLinear]; -"1961 DequantizeLinear_2797_1" [id=1961, type=DequantizeLinear]; -"1962 2762_MatMul" [id=1962, type=MatMul]; -"1963 2762_Add" [id=1963, type=Add]; -"1964 2763" [id=1964, type=Relu]; -"1965 QuantizeLinear_2800_1" [id=1965, type=QuantizeLinear]; -"1966 DequantizeLinear_2800_1" [id=1966, type=DequantizeLinear]; -"1967 QuantizeLinear_2801_1" [id=1967, type=QuantizeLinear]; -"1968 DequantizeLinear_2801_1" [id=1968, type=DequantizeLinear]; -"1969 2766_MatMul" [id=1969, type=MatMul]; -"1970 2766_Add" [id=1970, type=Add]; -"1971 2767" [id=1971, type=Relu]; -"1972 QuantizeLinear_2804_1" [id=1972, type=QuantizeLinear]; -"1973 DequantizeLinear_2804_1" [id=1973, type=DequantizeLinear]; -"1974 QuantizeLinear_2805_1" [id=1974, type=QuantizeLinear]; -"1975 DequantizeLinear_2805_1" [id=1975, type=DequantizeLinear]; -"1976 2770_MatMul" [id=1976, type=MatMul]; -"1977 2770_Add" [id=1977, type=Add]; -"1978 2774" [id=1978, type=Softmax]; -"1979 2950" [id=1979, type=Shape]; -"1980 2951" [id=1980, type=Gather]; -"1981 2992" [id=1981, type=Unsqueeze]; -"1982 2991" [id=1982, type=Unsqueeze]; -"1983 2993" [id=1983, type=Concat]; -"1984 2955" [id=1984, type=Reshape]; -"1985 2994" [id=1985, type=Reshape]; -"1986 2996" [id=1986, type=Greater]; -"1987 2997" [id=1987, type=Cast]; -"1988 6478" [id=1988, type=Slice]; -"1989 6480" [id=1989, type=Gather]; -"1990 6481" [id=1990, type=Cast]; -"1991 6482" [id=1991, type=NonZero]; -"1992 6483" [id=1992, type=Transpose]; -"1993 6484" [id=1993, type=Squeeze]; -"1994 6487" [id=1994, type=Cast]; -"1995 6486" [id=1995, type=Gather]; -"1996 6488" [id=1996, type=Gather]; -"1997 6497" [id=1997, type=Unsqueeze]; -"1998 6498" [id=1998, type=Unsqueeze]; -"1999 2984" [id=1999, type=Mul]; -"2000 2987" [id=2000, type=Unsqueeze]; -"2001 2986" [id=2001, type=Unsqueeze]; -"2002 2988" [id=2002, type=Concat]; -"2003 QuantizeLinear_2808_1" [id=2003, type=QuantizeLinear]; -"2004 DequantizeLinear_2808_1" [id=2004, type=DequantizeLinear]; -"2005 2773_MatMul" [id=2005, type=MatMul]; -"2006 2773_Add" [id=2006, type=Add]; -"2007 2776" [id=2007, type=Flatten]; -"2008 2947" [id=2008, type=Shape]; -"2009 2775" [id=2009, type=Concat]; -"2010 2777" [id=2010, type=Cast]; -"2011 2806" [id=2011, type=Slice]; -"2012 2808" [id=2012, type=Gather]; -"2013 2799" [id=2013, type=Slice]; -"2014 2801" [id=2014, type=Gather]; -"2015 2809" [id=2015, type=Sub]; -"2016 2811" [id=2016, type=Add]; -"2017 2923" [id=2017, type=Slice]; -"2018 2924" [id=2018, type=Unsqueeze]; -"2019 2872" [id=2019, type=Slice]; -"2020 2877" [id=2020, type=Slice]; -"2021 2879" [id=2021, type=Div]; -"2022 2881" [id=2022, type=Clip]; -"2023 2918" [id=2023, type=Exp]; -"2024 2925" [id=2024, type=Mul]; -"2025 2938" [id=2025, type=Mul]; -"2026 2830" [id=2026, type=Mul]; -"2027 2826" [id=2027, type=Slice]; -"2028 2828" [id=2028, type=Gather]; -"2029 2831" [id=2029, type=Add]; -"2030 2907" [id=2030, type=Slice]; -"2031 2908" [id=2031, type=Unsqueeze]; -"2032 2900" [id=2032, type=Slice]; -"2033 2901" [id=2033, type=Unsqueeze]; -"2034 2848" [id=2034, type=Slice]; -"2035 2853" [id=2035, type=Slice]; -"2036 2855" [id=2036, type=Div]; -"2037 2902" [id=2037, type=Mul]; -"2038 2909" [id=2038, type=Add]; -"2039 2939" [id=2039, type=Add]; -"2040 2941" [id=2040, type=Sub]; -"2041 2945" [id=2041, type=Unsqueeze]; -"2042 2789" [id=2042, type=Slice]; -"2043 2791" [id=2043, type=Gather]; -"2044 2782" [id=2044, type=Slice]; -"2045 2784" [id=2045, type=Gather]; -"2046 2792" [id=2046, type=Sub]; -"2047 2794" [id=2047, type=Add]; -"2048 2915" [id=2048, type=Slice]; -"2049 2916" [id=2049, type=Unsqueeze]; -"2050 2860" [id=2050, type=Slice]; -"2051 2865" [id=2051, type=Slice]; -"2052 2867" [id=2052, type=Div]; -"2053 2880" [id=2053, type=Clip]; -"2054 2910" [id=2054, type=Exp]; -"2055 2917" [id=2055, type=Mul]; -"2056 2933" [id=2056, type=Mul]; -"2057 2820" [id=2057, type=Mul]; -"2058 2816" [id=2058, type=Slice]; -"2059 2818" [id=2059, type=Gather]; -"2060 2821" [id=2060, type=Add]; -"2061 2893" [id=2061, type=Slice]; -"2062 2894" [id=2062, type=Unsqueeze]; -"2063 2886" [id=2063, type=Slice]; -"2064 2887" [id=2064, type=Unsqueeze]; -"2065 2836" [id=2065, type=Slice]; -"2066 2841" [id=2066, type=Slice]; -"2067 2843" [id=2067, type=Div]; -"2068 2888" [id=2068, type=Mul]; -"2069 2895" [id=2069, type=Add]; -"2070 2934" [id=2070, type=Add]; -"2071 2936" [id=2071, type=Sub]; -"2072 2944" [id=2072, type=Unsqueeze]; -"2073 2930" [id=2073, type=Mul]; -"2074 2931" [id=2074, type=Sub]; -"2075 2943" [id=2075, type=Unsqueeze]; -"2076 2927" [id=2076, type=Mul]; -"2077 2928" [id=2077, type=Sub]; -"2078 2942" [id=2078, type=Unsqueeze]; -"2079 2946" [id=2079, type=Concat]; -"2080 2948" [id=2080, type=Reshape]; -"2081 2953" [id=2081, type=Reshape]; -"2082 2971" [id=2082, type=Slice]; -"2083 2976" [id=2083, type=Slice]; -"2084 2977" [id=2084, type=Clip]; -"2085 2979" [id=2085, type=Unsqueeze]; -"2086 2960" [id=2086, type=Slice]; -"2087 2965" [id=2087, type=Slice]; -"2088 2966" [id=2088, type=Clip]; -"2089 2978" [id=2089, type=Unsqueeze]; -"2090 2980" [id=2090, type=Concat]; -"2091 2982" [id=2091, type=Reshape]; 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DequantizeLinear_/features/features.1/block/block.0/block.0.2/Relu_output_0_1" [id=14, type=DequantizeLinear]; "15 /features/features.1/block/block.1/avgpool/GlobalAveragePool" [id=15, type=GlobalAveragePool]; -"16 QuantizeLinear_/features/features.1/block/block.1/avgpool/GlobalAveragePool_output_0_1" [id=16, type=QuantizeLinear]; -"17 DequantizeLinear_/features/features.1/block/block.1/avgpool/GlobalAveragePool_output_0_1" [id=17, type=DequantizeLinear]; -"18 QuantizeLinear_features.1.block.1.fc1.weight_1" [id=18, type=QuantizeLinear]; -"19 DequantizeLinear_features.1.block.1.fc1.weight_1" [id=19, type=DequantizeLinear]; -"20 /features/features.1/block/block.1/fc1/Conv" [id=20, type=Conv]; -"21 /features/features.1/block/block.1/activation/Relu" [id=21, type=Relu]; -"22 QuantizeLinear_/features/features.1/block/block.1/activation/Relu_output_0_1" [id=22, type=QuantizeLinear]; -"23 DequantizeLinear_/features/features.1/block/block.1/activation/Relu_output_0_1" [id=23, type=DequantizeLinear]; -"24 QuantizeLinear_features.1.block.1.fc2.weight_1" [id=24, type=QuantizeLinear]; -"25 DequantizeLinear_features.1.block.1.fc2.weight_1" [id=25, type=DequantizeLinear]; -"26 /features/features.1/block/block.1/fc2/Conv" [id=26, type=Conv]; -"27 /features/features.1/block/block.1/scale_activation/HardSigmoid" [id=27, type=HardSigmoid]; -"28 QuantizeLinear_/features/features.1/block/block.1/scale_activation/HardSigmoid_output_0_1" [id=28, type=QuantizeLinear]; -"29 DequantizeLinear_/features/features.1/block/block.1/scale_activation/HardSigmoid_output_0_1" [id=29, type=DequantizeLinear]; -"30 /features/features.1/block/block.1/Mul" [id=30, type=Mul]; -"31 QuantizeLinear_/features/features.1/block/block.1/Mul_output_0_1" [id=31, type=QuantizeLinear]; -"32 DequantizeLinear_/features/features.1/block/block.1/Mul_output_0_1" [id=32, type=DequantizeLinear]; -"33 QuantizeLinear_onnx^^Conv_427_1" [id=33, label="33 QuantizeLinear_onnx::Conv_427_1", type=QuantizeLinear]; 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type=QuantizeLinear]; -"103 DequantizeLinear_/features/features.4/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=103, type=DequantizeLinear]; -"104 /features/features.4/block/block.2/Mul" [id=104, type=Mul]; -"105 QuantizeLinear_/features/features.4/block/block.2/Mul_output_0_1" [id=105, type=QuantizeLinear]; -"106 DequantizeLinear_/features/features.4/block/block.2/Mul_output_0_1" [id=106, type=DequantizeLinear]; -"107 QuantizeLinear_onnx^^Conv_454_1" [id=107, label="107 QuantizeLinear_onnx::Conv_454_1", type=QuantizeLinear]; -"108 DequantizeLinear_onnx^^Conv_454_1" [id=108, label="108 DequantizeLinear_onnx::Conv_454_1", type=DequantizeLinear]; -"109 /features/features.4/block/block.3/block.3.0/Conv" [id=109, type=Conv]; -"110 QuantizeLinear_/features/features.4/block/block.3/block.3.0/Conv_output_0_1" [id=110, type=QuantizeLinear]; -"111 DequantizeLinear_/features/features.4/block/block.3/block.3.0/Conv_output_0_1" [id=111, type=DequantizeLinear]; -"112 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type=Mul]; -"142 QuantizeLinear_/features/features.5/block/block.2/Mul_output_0_1" [id=142, type=QuantizeLinear]; -"143 DequantizeLinear_/features/features.5/block/block.2/Mul_output_0_1" [id=143, type=DequantizeLinear]; -"144 QuantizeLinear_onnx^^Conv_463_1" [id=144, label="144 QuantizeLinear_onnx::Conv_463_1", type=QuantizeLinear]; -"145 DequantizeLinear_onnx^^Conv_463_1" [id=145, label="145 DequantizeLinear_onnx::Conv_463_1", type=DequantizeLinear]; -"146 /features/features.5/block/block.3/block.3.0/Conv" [id=146, type=Conv]; -"147 QuantizeLinear_/features/features.5/block/block.3/block.3.0/Conv_output_0_1" [id=147, type=QuantizeLinear]; -"148 DequantizeLinear_/features/features.5/block/block.3/block.3.0/Conv_output_0_1" [id=148, type=DequantizeLinear]; -"149 /features/features.5/Add" [id=149, type=Add]; -"150 QuantizeLinear_/features/features.5/Add_output_0_1" [id=150, type=QuantizeLinear]; -"151 DequantizeLinear_/features/features.5/Add_output_0_1" [id=151, type=DequantizeLinear]; 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DequantizeLinear_/features/features.6/Add_output_0_1" [id=191, type=DequantizeLinear]; -"192 QuantizeLinear_onnx^^Conv_475_1" [id=192, label="192 QuantizeLinear_onnx::Conv_475_1", type=QuantizeLinear]; -"193 DequantizeLinear_onnx^^Conv_475_1" [id=193, label="193 DequantizeLinear_onnx::Conv_475_1", type=DequantizeLinear]; -"194 /features/features.7/block/block.0/block.0.0/Conv" [id=194, type=Conv]; -"195 /features/features.7/block/block.0/block.0.2/HardSigmoid" [id=195, type=HardSigmoid]; -"196 /features/features.7/block/block.0/block.0.2/Mul" [id=196, type=Mul]; -"197 QuantizeLinear_/features/features.7/block/block.0/block.0.2/Mul_output_0_1" [id=197, type=QuantizeLinear]; -"198 DequantizeLinear_/features/features.7/block/block.0/block.0.2/Mul_output_0_1" [id=198, type=DequantizeLinear]; -"199 QuantizeLinear_onnx^^Conv_478_1" [id=199, label="199 QuantizeLinear_onnx::Conv_478_1", type=QuantizeLinear]; -"200 DequantizeLinear_onnx^^Conv_478_1" [id=200, label="200 DequantizeLinear_onnx::Conv_478_1", type=DequantizeLinear]; -"201 /features/features.7/block/block.1/block.1.0/Conv" [id=201, type=Conv]; -"202 /features/features.7/block/block.1/block.1.2/HardSigmoid" [id=202, type=HardSigmoid]; -"203 /features/features.7/block/block.1/block.1.2/Mul" [id=203, type=Mul]; -"204 QuantizeLinear_/features/features.7/block/block.1/block.1.2/Mul_output_0_1" [id=204, type=QuantizeLinear]; -"205 DequantizeLinear_/features/features.7/block/block.1/block.1.2/Mul_output_0_1" [id=205, type=DequantizeLinear]; -"206 /features/features.7/block/block.2/avgpool/GlobalAveragePool" [id=206, type=GlobalAveragePool]; -"207 QuantizeLinear_/features/features.7/block/block.2/avgpool/GlobalAveragePool_output_0_1" [id=207, type=QuantizeLinear]; -"208 DequantizeLinear_/features/features.7/block/block.2/avgpool/GlobalAveragePool_output_0_1" [id=208, type=DequantizeLinear]; -"209 QuantizeLinear_features.7.block.2.fc1.weight_1" [id=209, type=QuantizeLinear]; -"210 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DequantizeLinear_/features/features.7/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=220, type=DequantizeLinear]; -"221 /features/features.7/block/block.2/Mul" [id=221, type=Mul]; -"222 QuantizeLinear_/features/features.7/block/block.2/Mul_output_0_1" [id=222, type=QuantizeLinear]; -"223 DequantizeLinear_/features/features.7/block/block.2/Mul_output_0_1" [id=223, type=DequantizeLinear]; -"224 QuantizeLinear_onnx^^Conv_481_1" [id=224, label="224 QuantizeLinear_onnx::Conv_481_1", type=QuantizeLinear]; -"225 DequantizeLinear_onnx^^Conv_481_1" [id=225, label="225 DequantizeLinear_onnx::Conv_481_1", type=DequantizeLinear]; -"226 /features/features.7/block/block.3/block.3.0/Conv" [id=226, type=Conv]; -"227 QuantizeLinear_/features/features.7/block/block.3/block.3.0/Conv_output_0_1" [id=227, type=QuantizeLinear]; -"228 DequantizeLinear_/features/features.7/block/block.3/block.3.0/Conv_output_0_1" [id=228, type=DequantizeLinear]; -"229 QuantizeLinear_onnx^^Conv_484_1" [id=229, label="229 QuantizeLinear_onnx::Conv_484_1", type=QuantizeLinear]; -"230 DequantizeLinear_onnx^^Conv_484_1" [id=230, label="230 DequantizeLinear_onnx::Conv_484_1", type=DequantizeLinear]; -"231 /features/features.8/block/block.0/block.0.0/Conv" [id=231, type=Conv]; -"232 /features/features.8/block/block.0/block.0.2/HardSigmoid" [id=232, type=HardSigmoid]; -"233 /features/features.8/block/block.0/block.0.2/Mul" [id=233, type=Mul]; -"234 QuantizeLinear_/features/features.8/block/block.0/block.0.2/Mul_output_0_1" [id=234, type=QuantizeLinear]; -"235 DequantizeLinear_/features/features.8/block/block.0/block.0.2/Mul_output_0_1" [id=235, type=DequantizeLinear]; -"236 QuantizeLinear_onnx^^Conv_487_1" [id=236, label="236 QuantizeLinear_onnx::Conv_487_1", type=QuantizeLinear]; -"237 DequantizeLinear_onnx^^Conv_487_1" [id=237, label="237 DequantizeLinear_onnx::Conv_487_1", type=DequantizeLinear]; -"238 /features/features.8/block/block.1/block.1.0/Conv" [id=238, type=Conv]; -"239 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/features/features.8/block/block.2/activation/Relu" [id=249, type=Relu]; -"250 QuantizeLinear_/features/features.8/block/block.2/activation/Relu_output_0_1" [id=250, type=QuantizeLinear]; -"251 DequantizeLinear_/features/features.8/block/block.2/activation/Relu_output_0_1" [id=251, type=DequantizeLinear]; -"252 QuantizeLinear_features.8.block.2.fc2.weight_1" [id=252, type=QuantizeLinear]; -"253 DequantizeLinear_features.8.block.2.fc2.weight_1" [id=253, type=DequantizeLinear]; -"254 /features/features.8/block/block.2/fc2/Conv" [id=254, type=Conv]; -"255 /features/features.8/block/block.2/scale_activation/HardSigmoid" [id=255, type=HardSigmoid]; -"256 QuantizeLinear_/features/features.8/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=256, type=QuantizeLinear]; -"257 DequantizeLinear_/features/features.8/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=257, type=DequantizeLinear]; -"258 /features/features.8/block/block.2/Mul" [id=258, type=Mul]; -"259 QuantizeLinear_/features/features.8/block/block.2/Mul_output_0_1" [id=259, type=QuantizeLinear]; -"260 DequantizeLinear_/features/features.8/block/block.2/Mul_output_0_1" [id=260, type=DequantizeLinear]; -"261 QuantizeLinear_onnx^^Conv_490_1" [id=261, label="261 QuantizeLinear_onnx::Conv_490_1", type=QuantizeLinear]; -"262 DequantizeLinear_onnx^^Conv_490_1" [id=262, label="262 DequantizeLinear_onnx::Conv_490_1", type=DequantizeLinear]; -"263 /features/features.8/block/block.3/block.3.0/Conv" [id=263, type=Conv]; -"264 QuantizeLinear_/features/features.8/block/block.3/block.3.0/Conv_output_0_1" [id=264, type=QuantizeLinear]; -"265 DequantizeLinear_/features/features.8/block/block.3/block.3.0/Conv_output_0_1" [id=265, type=DequantizeLinear]; -"266 /features/features.8/Add" [id=266, type=Add]; -"267 QuantizeLinear_/features/features.8/Add_output_0_1" [id=267, type=QuantizeLinear]; -"268 DequantizeLinear_/features/features.8/Add_output_0_1" [id=268, type=DequantizeLinear]; -"269 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[id=278, type=Conv]; -"279 /features/features.9/block/block.1/block.1.2/HardSigmoid" [id=279, type=HardSigmoid]; -"280 /features/features.9/block/block.1/block.1.2/Mul" [id=280, type=Mul]; -"281 QuantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" [id=281, type=QuantizeLinear]; -"282 DequantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" [id=282, type=DequantizeLinear]; -"283 /features/features.9/block/block.2/avgpool/GlobalAveragePool" [id=283, type=GlobalAveragePool]; -"284 QuantizeLinear_/features/features.9/block/block.2/avgpool/GlobalAveragePool_output_0_1" [id=284, type=QuantizeLinear]; -"285 DequantizeLinear_/features/features.9/block/block.2/avgpool/GlobalAveragePool_output_0_1" [id=285, type=DequantizeLinear]; -"286 QuantizeLinear_features.9.block.2.fc1.weight_1" [id=286, type=QuantizeLinear]; -"287 DequantizeLinear_features.9.block.2.fc1.weight_1" [id=287, type=DequantizeLinear]; -"288 /features/features.9/block/block.2/fc1/Conv" [id=288, type=Conv]; -"289 /features/features.9/block/block.2/activation/Relu" [id=289, type=Relu]; -"290 QuantizeLinear_/features/features.9/block/block.2/activation/Relu_output_0_1" [id=290, type=QuantizeLinear]; -"291 DequantizeLinear_/features/features.9/block/block.2/activation/Relu_output_0_1" [id=291, type=DequantizeLinear]; -"292 QuantizeLinear_features.9.block.2.fc2.weight_1" [id=292, type=QuantizeLinear]; -"293 DequantizeLinear_features.9.block.2.fc2.weight_1" [id=293, type=DequantizeLinear]; -"294 /features/features.9/block/block.2/fc2/Conv" [id=294, type=Conv]; -"295 /features/features.9/block/block.2/scale_activation/HardSigmoid" [id=295, type=HardSigmoid]; -"296 QuantizeLinear_/features/features.9/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=296, type=QuantizeLinear]; -"297 DequantizeLinear_/features/features.9/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=297, type=DequantizeLinear]; -"298 /features/features.9/block/block.2/Mul" [id=298, type=Mul]; -"299 QuantizeLinear_/features/features.9/block/block.2/Mul_output_0_1" [id=299, type=QuantizeLinear]; -"300 DequantizeLinear_/features/features.9/block/block.2/Mul_output_0_1" [id=300, type=DequantizeLinear]; -"301 QuantizeLinear_onnx^^Conv_499_1" [id=301, label="301 QuantizeLinear_onnx::Conv_499_1", type=QuantizeLinear]; -"302 DequantizeLinear_onnx^^Conv_499_1" [id=302, label="302 DequantizeLinear_onnx::Conv_499_1", type=DequantizeLinear]; -"303 /features/features.9/block/block.3/block.3.0/Conv" [id=303, type=Conv]; -"304 QuantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" [id=304, type=QuantizeLinear]; -"305 DequantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" [id=305, type=DequantizeLinear]; -"306 QuantizeLinear_onnx^^Conv_502_1" [id=306, label="306 QuantizeLinear_onnx::Conv_502_1", type=QuantizeLinear]; -"307 DequantizeLinear_onnx^^Conv_502_1" [id=307, label="307 DequantizeLinear_onnx::Conv_502_1", type=DequantizeLinear]; -"308 /features/features.10/block/block.0/block.0.0/Conv" [id=308, type=Conv]; -"309 /features/features.10/block/block.0/block.0.2/HardSigmoid" [id=309, type=HardSigmoid]; -"310 /features/features.10/block/block.0/block.0.2/Mul" [id=310, type=Mul]; -"311 QuantizeLinear_/features/features.10/block/block.0/block.0.2/Mul_output_0_1" [id=311, type=QuantizeLinear]; -"312 DequantizeLinear_/features/features.10/block/block.0/block.0.2/Mul_output_0_1" [id=312, type=DequantizeLinear]; -"313 QuantizeLinear_onnx^^Conv_505_1" [id=313, label="313 QuantizeLinear_onnx::Conv_505_1", type=QuantizeLinear]; -"314 DequantizeLinear_onnx^^Conv_505_1" [id=314, label="314 DequantizeLinear_onnx::Conv_505_1", type=DequantizeLinear]; -"315 /features/features.10/block/block.1/block.1.0/Conv" [id=315, type=Conv]; -"316 /features/features.10/block/block.1/block.1.2/HardSigmoid" [id=316, type=HardSigmoid]; -"317 /features/features.10/block/block.1/block.1.2/Mul" [id=317, type=Mul]; -"318 QuantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" [id=318, type=QuantizeLinear]; -"319 DequantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" [id=319, type=DequantizeLinear]; -"320 /features/features.10/block/block.2/avgpool/GlobalAveragePool" [id=320, type=GlobalAveragePool]; -"321 QuantizeLinear_/features/features.10/block/block.2/avgpool/GlobalAveragePool_output_0_1" [id=321, type=QuantizeLinear]; -"322 DequantizeLinear_/features/features.10/block/block.2/avgpool/GlobalAveragePool_output_0_1" [id=322, type=DequantizeLinear]; -"323 QuantizeLinear_features.10.block.2.fc1.weight_1" [id=323, type=QuantizeLinear]; -"324 DequantizeLinear_features.10.block.2.fc1.weight_1" [id=324, type=DequantizeLinear]; -"325 /features/features.10/block/block.2/fc1/Conv" [id=325, type=Conv]; -"326 /features/features.10/block/block.2/activation/Relu" [id=326, type=Relu]; -"327 QuantizeLinear_/features/features.10/block/block.2/activation/Relu_output_0_1" [id=327, type=QuantizeLinear]; -"328 DequantizeLinear_/features/features.10/block/block.2/activation/Relu_output_0_1" [id=328, type=DequantizeLinear]; -"329 QuantizeLinear_features.10.block.2.fc2.weight_1" [id=329, type=QuantizeLinear]; -"330 DequantizeLinear_features.10.block.2.fc2.weight_1" [id=330, type=DequantizeLinear]; -"331 /features/features.10/block/block.2/fc2/Conv" [id=331, type=Conv]; -"332 /features/features.10/block/block.2/scale_activation/HardSigmoid" [id=332, type=HardSigmoid]; -"333 QuantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=333, type=QuantizeLinear]; -"334 DequantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=334, type=DequantizeLinear]; -"335 /features/features.10/block/block.2/Mul" [id=335, type=Mul]; -"336 QuantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" [id=336, type=QuantizeLinear]; -"337 DequantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" [id=337, type=DequantizeLinear]; -"338 QuantizeLinear_onnx^^Conv_508_1" [id=338, label="338 QuantizeLinear_onnx::Conv_508_1", type=QuantizeLinear]; -"339 DequantizeLinear_onnx^^Conv_508_1" [id=339, label="339 DequantizeLinear_onnx::Conv_508_1", type=DequantizeLinear]; -"340 /features/features.10/block/block.3/block.3.0/Conv" [id=340, type=Conv]; -"341 QuantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" [id=341, type=QuantizeLinear]; -"342 DequantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" [id=342, type=DequantizeLinear]; -"343 /features/features.10/Add" [id=343, type=Add]; -"344 QuantizeLinear_/features/features.10/Add_output_0_1" [id=344, type=QuantizeLinear]; -"345 DequantizeLinear_/features/features.10/Add_output_0_1" [id=345, type=DequantizeLinear]; -"346 QuantizeLinear_onnx^^Conv_511_1" [id=346, label="346 QuantizeLinear_onnx::Conv_511_1", type=QuantizeLinear]; -"347 DequantizeLinear_onnx^^Conv_511_1" [id=347, label="347 DequantizeLinear_onnx::Conv_511_1", type=DequantizeLinear]; -"348 /features/features.11/block/block.0/block.0.0/Conv" [id=348, type=Conv]; -"349 /features/features.11/block/block.0/block.0.2/HardSigmoid" [id=349, type=HardSigmoid]; -"350 /features/features.11/block/block.0/block.0.2/Mul" [id=350, type=Mul]; -"351 QuantizeLinear_/features/features.11/block/block.0/block.0.2/Mul_output_0_1" [id=351, type=QuantizeLinear]; -"352 DequantizeLinear_/features/features.11/block/block.0/block.0.2/Mul_output_0_1" [id=352, type=DequantizeLinear]; -"353 QuantizeLinear_onnx^^Conv_514_1" [id=353, label="353 QuantizeLinear_onnx::Conv_514_1", type=QuantizeLinear]; -"354 DequantizeLinear_onnx^^Conv_514_1" [id=354, label="354 DequantizeLinear_onnx::Conv_514_1", type=DequantizeLinear]; -"355 /features/features.11/block/block.1/block.1.0/Conv" [id=355, type=Conv]; -"356 /features/features.11/block/block.1/block.1.2/HardSigmoid" [id=356, type=HardSigmoid]; -"357 /features/features.11/block/block.1/block.1.2/Mul" [id=357, type=Mul]; -"358 QuantizeLinear_/features/features.11/block/block.1/block.1.2/Mul_output_0_1" [id=358, type=QuantizeLinear]; -"359 DequantizeLinear_/features/features.11/block/block.1/block.1.2/Mul_output_0_1" [id=359, type=DequantizeLinear]; -"360 /features/features.11/block/block.2/avgpool/GlobalAveragePool" [id=360, type=GlobalAveragePool]; -"361 QuantizeLinear_/features/features.11/block/block.2/avgpool/GlobalAveragePool_output_0_1" [id=361, type=QuantizeLinear]; -"362 DequantizeLinear_/features/features.11/block/block.2/avgpool/GlobalAveragePool_output_0_1" [id=362, type=DequantizeLinear]; -"363 QuantizeLinear_features.11.block.2.fc1.weight_1" [id=363, type=QuantizeLinear]; -"364 DequantizeLinear_features.11.block.2.fc1.weight_1" [id=364, type=DequantizeLinear]; -"365 /features/features.11/block/block.2/fc1/Conv" [id=365, type=Conv]; -"366 /features/features.11/block/block.2/activation/Relu" [id=366, type=Relu]; -"367 QuantizeLinear_/features/features.11/block/block.2/activation/Relu_output_0_1" [id=367, type=QuantizeLinear]; -"368 DequantizeLinear_/features/features.11/block/block.2/activation/Relu_output_0_1" [id=368, type=DequantizeLinear]; -"369 QuantizeLinear_features.11.block.2.fc2.weight_1" [id=369, type=QuantizeLinear]; -"370 DequantizeLinear_features.11.block.2.fc2.weight_1" [id=370, type=DequantizeLinear]; -"371 /features/features.11/block/block.2/fc2/Conv" [id=371, type=Conv]; -"372 /features/features.11/block/block.2/scale_activation/HardSigmoid" [id=372, type=HardSigmoid]; -"373 QuantizeLinear_/features/features.11/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=373, type=QuantizeLinear]; -"374 DequantizeLinear_/features/features.11/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=374, type=DequantizeLinear]; -"375 /features/features.11/block/block.2/Mul" [id=375, type=Mul]; -"376 QuantizeLinear_/features/features.11/block/block.2/Mul_output_0_1" [id=376, type=QuantizeLinear]; -"377 DequantizeLinear_/features/features.11/block/block.2/Mul_output_0_1" [id=377, type=DequantizeLinear]; -"378 QuantizeLinear_onnx^^Conv_517_1" [id=378, label="378 QuantizeLinear_onnx::Conv_517_1", type=QuantizeLinear]; -"379 DequantizeLinear_onnx^^Conv_517_1" [id=379, label="379 DequantizeLinear_onnx::Conv_517_1", type=DequantizeLinear]; -"380 /features/features.11/block/block.3/block.3.0/Conv" [id=380, type=Conv]; -"381 QuantizeLinear_/features/features.11/block/block.3/block.3.0/Conv_output_0_1" [id=381, type=QuantizeLinear]; -"382 DequantizeLinear_/features/features.11/block/block.3/block.3.0/Conv_output_0_1" [id=382, type=DequantizeLinear]; -"383 /features/features.11/Add" [id=383, type=Add]; -"384 QuantizeLinear_/features/features.11/Add_output_0_1" [id=384, type=QuantizeLinear]; -"385 DequantizeLinear_/features/features.11/Add_output_0_1" [id=385, type=DequantizeLinear]; -"386 QuantizeLinear_onnx^^Conv_520_1" [id=386, label="386 QuantizeLinear_onnx::Conv_520_1", type=QuantizeLinear]; -"387 DequantizeLinear_onnx^^Conv_520_1" [id=387, label="387 DequantizeLinear_onnx::Conv_520_1", type=DequantizeLinear]; -"388 /features/features.12/features.12.0/Conv" [id=388, type=Conv]; -"389 /features/features.12/features.12.2/HardSigmoid" [id=389, type=HardSigmoid]; -"390 /features/features.12/features.12.2/Mul" [id=390, type=Mul]; -"391 QuantizeLinear_/features/features.12/features.12.2/Mul_output_0_1" [id=391, type=QuantizeLinear]; -"392 DequantizeLinear_/features/features.12/features.12.2/Mul_output_0_1" [id=392, type=DequantizeLinear]; -"393 /avgpool/GlobalAveragePool" [id=393, type=GlobalAveragePool]; -"394 QuantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [id=394, type=QuantizeLinear]; -"395 DequantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [id=395, type=DequantizeLinear]; -"396 /Flatten" [id=396, type=Flatten]; -"397 QuantizeLinear_classifier.0.weight_1" [id=397, type=QuantizeLinear]; -"398 DequantizeLinear_classifier.0.weight_1" [id=398, type=DequantizeLinear]; -"399 /classifier/classifier.0/Gemm" [id=399, type=Gemm]; -"400 /classifier/classifier.1/HardSigmoid" [id=400, type=HardSigmoid]; -"401 /classifier/classifier.1/Mul" [id=401, type=Mul]; -"402 QuantizeLinear_/classifier/classifier.1/Mul_output_0_1" [id=402, type=QuantizeLinear]; -"403 DequantizeLinear_/classifier/classifier.1/Mul_output_0_1" [id=403, type=DequantizeLinear]; -"404 QuantizeLinear_classifier.3.weight_1" [id=404, type=QuantizeLinear]; -"405 DequantizeLinear_classifier.3.weight_1" [id=405, type=DequantizeLinear]; -"406 /classifier/classifier.3/Gemm" [id=406, type=Gemm]; -"407 nncf_model_input_0" [id=407, type=nncf_model_input]; -"408 nncf_model_output_0" [id=408, type=nncf_model_output]; +"16 /features/features.1/block/block.1/fc1/Conv" [id=16, type=Conv]; +"17 /features/features.1/block/block.1/activation/Relu" [id=17, type=Relu]; +"18 /features/features.1/block/block.1/fc2/Conv" [id=18, type=Conv]; +"19 /features/features.1/block/block.1/scale_activation/HardSigmoid" [id=19, type=HardSigmoid]; +"20 QuantizeLinear_/features/features.1/block/block.1/scale_activation/HardSigmoid_output_0_1" [id=20, type=QuantizeLinear]; +"21 DequantizeLinear_/features/features.1/block/block.1/scale_activation/HardSigmoid_output_0_1" [id=21, type=DequantizeLinear]; +"22 /features/features.1/block/block.1/Mul" [id=22, type=Mul]; +"23 QuantizeLinear_/features/features.1/block/block.1/Mul_output_0_1" [id=23, type=QuantizeLinear]; +"24 DequantizeLinear_/features/features.1/block/block.1/Mul_output_0_1" [id=24, type=DequantizeLinear]; +"25 QuantizeLinear_onnx^^Conv_427_1" [id=25, label="25 QuantizeLinear_onnx::Conv_427_1", type=QuantizeLinear]; +"26 DequantizeLinear_onnx^^Conv_427_1" [id=26, label="26 DequantizeLinear_onnx::Conv_427_1", type=DequantizeLinear]; +"27 /features/features.1/block/block.2/block.2.0/Conv" [id=27, type=Conv]; +"28 QuantizeLinear_/features/features.1/block/block.2/block.2.0/Conv_output_0_1" [id=28, type=QuantizeLinear]; +"29 DequantizeLinear_/features/features.1/block/block.2/block.2.0/Conv_output_0_1" [id=29, type=DequantizeLinear]; +"30 QuantizeLinear_onnx^^Conv_430_1" [id=30, label="30 QuantizeLinear_onnx::Conv_430_1", type=QuantizeLinear]; +"31 DequantizeLinear_onnx^^Conv_430_1" [id=31, label="31 DequantizeLinear_onnx::Conv_430_1", type=DequantizeLinear]; +"32 /features/features.2/block/block.0/block.0.0/Conv" [id=32, type=Conv]; +"33 /features/features.2/block/block.0/block.0.2/Relu" [id=33, type=Relu]; +"34 QuantizeLinear_/features/features.2/block/block.0/block.0.2/Relu_output_0_1" [id=34, type=QuantizeLinear]; +"35 DequantizeLinear_/features/features.2/block/block.0/block.0.2/Relu_output_0_1" [id=35, type=DequantizeLinear]; +"36 QuantizeLinear_onnx^^Conv_433_1" [id=36, label="36 QuantizeLinear_onnx::Conv_433_1", type=QuantizeLinear]; +"37 DequantizeLinear_onnx^^Conv_433_1" [id=37, label="37 DequantizeLinear_onnx::Conv_433_1", type=DequantizeLinear]; +"38 /features/features.2/block/block.1/block.1.0/Conv" [id=38, type=Conv]; +"39 /features/features.2/block/block.1/block.1.2/Relu" [id=39, type=Relu]; +"40 QuantizeLinear_/features/features.2/block/block.1/block.1.2/Relu_output_0_1" [id=40, type=QuantizeLinear]; +"41 DequantizeLinear_/features/features.2/block/block.1/block.1.2/Relu_output_0_1" [id=41, type=DequantizeLinear]; +"42 QuantizeLinear_onnx^^Conv_436_1" [id=42, label="42 QuantizeLinear_onnx::Conv_436_1", type=QuantizeLinear]; +"43 DequantizeLinear_onnx^^Conv_436_1" [id=43, label="43 DequantizeLinear_onnx::Conv_436_1", type=DequantizeLinear]; +"44 /features/features.2/block/block.2/block.2.0/Conv" [id=44, type=Conv]; +"45 QuantizeLinear_/features/features.2/block/block.2/block.2.0/Conv_output_0_1" [id=45, type=QuantizeLinear]; +"46 DequantizeLinear_/features/features.2/block/block.2/block.2.0/Conv_output_0_1" [id=46, type=DequantizeLinear]; +"47 QuantizeLinear_onnx^^Conv_439_1" [id=47, label="47 QuantizeLinear_onnx::Conv_439_1", type=QuantizeLinear]; +"48 DequantizeLinear_onnx^^Conv_439_1" [id=48, label="48 DequantizeLinear_onnx::Conv_439_1", type=DequantizeLinear]; +"49 /features/features.3/block/block.0/block.0.0/Conv" [id=49, type=Conv]; +"50 /features/features.3/block/block.0/block.0.2/Relu" [id=50, type=Relu]; +"51 QuantizeLinear_/features/features.3/block/block.0/block.0.2/Relu_output_0_1" [id=51, type=QuantizeLinear]; +"52 DequantizeLinear_/features/features.3/block/block.0/block.0.2/Relu_output_0_1" [id=52, type=DequantizeLinear]; +"53 QuantizeLinear_onnx^^Conv_442_1" [id=53, label="53 QuantizeLinear_onnx::Conv_442_1", type=QuantizeLinear]; +"54 DequantizeLinear_onnx^^Conv_442_1" [id=54, label="54 DequantizeLinear_onnx::Conv_442_1", type=DequantizeLinear]; +"55 /features/features.3/block/block.1/block.1.0/Conv" [id=55, type=Conv]; +"56 /features/features.3/block/block.1/block.1.2/Relu" [id=56, type=Relu]; +"57 QuantizeLinear_/features/features.3/block/block.1/block.1.2/Relu_output_0_1" [id=57, type=QuantizeLinear]; +"58 DequantizeLinear_/features/features.3/block/block.1/block.1.2/Relu_output_0_1" [id=58, type=DequantizeLinear]; +"59 QuantizeLinear_onnx^^Conv_445_1" [id=59, label="59 QuantizeLinear_onnx::Conv_445_1", type=QuantizeLinear]; +"60 DequantizeLinear_onnx^^Conv_445_1" [id=60, label="60 DequantizeLinear_onnx::Conv_445_1", type=DequantizeLinear]; +"61 /features/features.3/block/block.2/block.2.0/Conv" [id=61, type=Conv]; +"62 QuantizeLinear_/features/features.3/block/block.2/block.2.0/Conv_output_0_1" [id=62, type=QuantizeLinear]; +"63 DequantizeLinear_/features/features.3/block/block.2/block.2.0/Conv_output_0_1" [id=63, type=DequantizeLinear]; +"64 /features/features.3/Add" [id=64, type=Add]; +"65 QuantizeLinear_/features/features.3/Add_output_0_1" [id=65, type=QuantizeLinear]; +"66 DequantizeLinear_/features/features.3/Add_output_0_1" [id=66, type=DequantizeLinear]; +"67 QuantizeLinear_onnx^^Conv_448_1" [id=67, label="67 QuantizeLinear_onnx::Conv_448_1", type=QuantizeLinear]; +"68 DequantizeLinear_onnx^^Conv_448_1" [id=68, label="68 DequantizeLinear_onnx::Conv_448_1", type=DequantizeLinear]; +"69 /features/features.4/block/block.0/block.0.0/Conv" [id=69, type=Conv]; +"70 /features/features.4/block/block.0/block.0.2/HardSigmoid" [id=70, type=HardSigmoid]; +"71 /features/features.4/block/block.0/block.0.2/Mul" [id=71, type=Mul]; +"72 QuantizeLinear_/features/features.4/block/block.0/block.0.2/Mul_output_0_1" [id=72, type=QuantizeLinear]; +"73 DequantizeLinear_/features/features.4/block/block.0/block.0.2/Mul_output_0_1" [id=73, type=DequantizeLinear]; +"74 QuantizeLinear_onnx^^Conv_451_1" [id=74, label="74 QuantizeLinear_onnx::Conv_451_1", type=QuantizeLinear]; +"75 DequantizeLinear_onnx^^Conv_451_1" [id=75, label="75 DequantizeLinear_onnx::Conv_451_1", type=DequantizeLinear]; +"76 /features/features.4/block/block.1/block.1.0/Conv" [id=76, type=Conv]; +"77 /features/features.4/block/block.1/block.1.2/HardSigmoid" [id=77, type=HardSigmoid]; +"78 /features/features.4/block/block.1/block.1.2/Mul" [id=78, type=Mul]; +"79 QuantizeLinear_/features/features.4/block/block.1/block.1.2/Mul_output_0_1" [id=79, type=QuantizeLinear]; +"80 DequantizeLinear_/features/features.4/block/block.1/block.1.2/Mul_output_0_1" [id=80, type=DequantizeLinear]; +"81 /features/features.4/block/block.2/avgpool/GlobalAveragePool" [id=81, type=GlobalAveragePool]; +"82 /features/features.4/block/block.2/fc1/Conv" [id=82, type=Conv]; +"83 /features/features.4/block/block.2/activation/Relu" [id=83, type=Relu]; +"84 /features/features.4/block/block.2/fc2/Conv" [id=84, type=Conv]; +"85 /features/features.4/block/block.2/scale_activation/HardSigmoid" [id=85, type=HardSigmoid]; +"86 QuantizeLinear_/features/features.4/block/block.2/scale_activation/HardSigmoid_output_0_1" 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DequantizeLinear_/features/features.5/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=116, type=DequantizeLinear]; +"117 /features/features.5/block/block.2/Mul" [id=117, type=Mul]; +"118 QuantizeLinear_/features/features.5/block/block.2/Mul_output_0_1" [id=118, type=QuantizeLinear]; +"119 DequantizeLinear_/features/features.5/block/block.2/Mul_output_0_1" [id=119, type=DequantizeLinear]; +"120 QuantizeLinear_onnx^^Conv_463_1" [id=120, label="120 QuantizeLinear_onnx::Conv_463_1", type=QuantizeLinear]; +"121 DequantizeLinear_onnx^^Conv_463_1" [id=121, label="121 DequantizeLinear_onnx::Conv_463_1", type=DequantizeLinear]; +"122 /features/features.5/block/block.3/block.3.0/Conv" [id=122, type=Conv]; +"123 QuantizeLinear_/features/features.5/block/block.3/block.3.0/Conv_output_0_1" [id=123, type=QuantizeLinear]; +"124 DequantizeLinear_/features/features.5/block/block.3/block.3.0/Conv_output_0_1" [id=124, type=DequantizeLinear]; +"125 /features/features.5/Add" [id=125, type=Add]; 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QuantizeLinear_/features/features.7/block/block.0/block.0.2/Mul_output_0_1" [id=165, type=QuantizeLinear]; +"166 DequantizeLinear_/features/features.7/block/block.0/block.0.2/Mul_output_0_1" [id=166, type=DequantizeLinear]; +"167 QuantizeLinear_onnx^^Conv_478_1" [id=167, label="167 QuantizeLinear_onnx::Conv_478_1", type=QuantizeLinear]; +"168 DequantizeLinear_onnx^^Conv_478_1" [id=168, label="168 DequantizeLinear_onnx::Conv_478_1", type=DequantizeLinear]; +"169 /features/features.7/block/block.1/block.1.0/Conv" [id=169, type=Conv]; +"170 /features/features.7/block/block.1/block.1.2/HardSigmoid" [id=170, type=HardSigmoid]; +"171 /features/features.7/block/block.1/block.1.2/Mul" [id=171, type=Mul]; +"172 QuantizeLinear_/features/features.7/block/block.1/block.1.2/Mul_output_0_1" [id=172, type=QuantizeLinear]; +"173 DequantizeLinear_/features/features.7/block/block.1/block.1.2/Mul_output_0_1" [id=173, type=DequantizeLinear]; +"174 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[id=184, label="184 QuantizeLinear_onnx::Conv_481_1", type=QuantizeLinear]; +"185 DequantizeLinear_onnx^^Conv_481_1" [id=185, label="185 DequantizeLinear_onnx::Conv_481_1", type=DequantizeLinear]; +"186 /features/features.7/block/block.3/block.3.0/Conv" [id=186, type=Conv]; +"187 QuantizeLinear_/features/features.7/block/block.3/block.3.0/Conv_output_0_1" [id=187, type=QuantizeLinear]; +"188 DequantizeLinear_/features/features.7/block/block.3/block.3.0/Conv_output_0_1" [id=188, type=DequantizeLinear]; +"189 QuantizeLinear_onnx^^Conv_484_1" [id=189, label="189 QuantizeLinear_onnx::Conv_484_1", type=QuantizeLinear]; +"190 DequantizeLinear_onnx^^Conv_484_1" [id=190, label="190 DequantizeLinear_onnx::Conv_484_1", type=DequantizeLinear]; +"191 /features/features.8/block/block.0/block.0.0/Conv" [id=191, type=Conv]; +"192 /features/features.8/block/block.0/block.0.2/HardSigmoid" [id=192, type=HardSigmoid]; +"193 /features/features.8/block/block.0/block.0.2/Mul" [id=193, type=Mul]; +"194 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[id=213, label="213 QuantizeLinear_onnx::Conv_490_1", type=QuantizeLinear]; +"214 DequantizeLinear_onnx^^Conv_490_1" [id=214, label="214 DequantizeLinear_onnx::Conv_490_1", type=DequantizeLinear]; +"215 /features/features.8/block/block.3/block.3.0/Conv" [id=215, type=Conv]; +"216 QuantizeLinear_/features/features.8/block/block.3/block.3.0/Conv_output_0_1" [id=216, type=QuantizeLinear]; +"217 DequantizeLinear_/features/features.8/block/block.3/block.3.0/Conv_output_0_1" [id=217, type=DequantizeLinear]; +"218 /features/features.8/Add" [id=218, type=Add]; +"219 QuantizeLinear_/features/features.8/Add_output_0_1" [id=219, type=QuantizeLinear]; +"220 DequantizeLinear_/features/features.8/Add_output_0_1" [id=220, type=DequantizeLinear]; +"221 QuantizeLinear_onnx^^Conv_493_1" [id=221, label="221 QuantizeLinear_onnx::Conv_493_1", type=QuantizeLinear]; +"222 DequantizeLinear_onnx^^Conv_493_1" [id=222, label="222 DequantizeLinear_onnx::Conv_493_1", type=DequantizeLinear]; +"223 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QuantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" [id=233, type=QuantizeLinear]; +"234 DequantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" [id=234, type=DequantizeLinear]; +"235 /features/features.9/block/block.2/avgpool/GlobalAveragePool" [id=235, type=GlobalAveragePool]; +"236 /features/features.9/block/block.2/fc1/Conv" [id=236, type=Conv]; +"237 /features/features.9/block/block.2/activation/Relu" [id=237, type=Relu]; +"238 /features/features.9/block/block.2/fc2/Conv" [id=238, type=Conv]; +"239 /features/features.9/block/block.2/scale_activation/HardSigmoid" [id=239, type=HardSigmoid]; +"240 QuantizeLinear_/features/features.9/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=240, type=QuantizeLinear]; +"241 DequantizeLinear_/features/features.9/block/block.2/scale_activation/HardSigmoid_output_0_1" [id=241, type=DequantizeLinear]; +"242 /features/features.9/block/block.2/Mul" [id=242, type=Mul]; +"243 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QuantizeLinear_/features/features.4/block/block.2/activation/Relu_output_0_1" -> "97 DequantizeLinear_/features/features.4/block/block.2/activation/Relu_output_0_1" [label="[1, 24, 1, 1]", style=dashed]; -"97 DequantizeLinear_/features/features.4/block/block.2/activation/Relu_output_0_1" -> "100 /features/features.4/block/block.2/fc2/Conv" [label="[1, 24, 1, 1]", style=solid]; -"98 QuantizeLinear_features.4.block.2.fc2.weight_1" -> "99 DequantizeLinear_features.4.block.2.fc2.weight_1" [label="[96, 24, 1, 1]", style=dashed]; -"99 DequantizeLinear_features.4.block.2.fc2.weight_1" -> "100 /features/features.4/block/block.2/fc2/Conv" [label="[96, 24, 1, 1]", style=solid]; -"100 /features/features.4/block/block.2/fc2/Conv" -> "101 /features/features.4/block/block.2/scale_activation/HardSigmoid" [label="[1, 96, 1, 1]", style=solid]; -"101 /features/features.4/block/block.2/scale_activation/HardSigmoid" -> "102 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style=solid]; -"126 /features/features.5/block/block.2/avgpool/GlobalAveragePool" -> "127 QuantizeLinear_/features/features.5/block/block.2/avgpool/GlobalAveragePool_output_0_1" [label="[1, 240, 1, 1]", style=solid]; -"127 QuantizeLinear_/features/features.5/block/block.2/avgpool/GlobalAveragePool_output_0_1" -> "128 DequantizeLinear_/features/features.5/block/block.2/avgpool/GlobalAveragePool_output_0_1" [label="[1, 240, 1, 1]", style=dashed]; -"128 DequantizeLinear_/features/features.5/block/block.2/avgpool/GlobalAveragePool_output_0_1" -> "131 /features/features.5/block/block.2/fc1/Conv" [label="[1, 240, 1, 1]", style=solid]; -"129 QuantizeLinear_features.5.block.2.fc1.weight_1" -> "130 DequantizeLinear_features.5.block.2.fc1.weight_1" [label="[64, 240, 1, 1]", style=dashed]; -"130 DequantizeLinear_features.5.block.2.fc1.weight_1" -> "131 /features/features.5/block/block.2/fc1/Conv" [label="[64, 240, 1, 1]", style=solid]; -"131 /features/features.5/block/block.2/fc1/Conv" -> "132 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style=dashed]; -"148 DequantizeLinear_/features/features.5/block/block.3/block.3.0/Conv_output_0_1" -> "149 /features/features.5/Add" [label="[1, 40, 14, 14]", style=solid]; -"149 /features/features.5/Add" -> "150 QuantizeLinear_/features/features.5/Add_output_0_1" [label="[1, 40, 14, 14]", style=solid]; -"150 QuantizeLinear_/features/features.5/Add_output_0_1" -> "151 DequantizeLinear_/features/features.5/Add_output_0_1" [label="[1, 40, 14, 14]", style=dashed]; -"151 DequantizeLinear_/features/features.5/Add_output_0_1" -> "154 /features/features.6/block/block.0/block.0.0/Conv" [label="[1, 40, 14, 14]", style=solid]; -"151 DequantizeLinear_/features/features.5/Add_output_0_1" -> "189 /features/features.6/Add" [label="[1, 40, 14, 14]", style=solid]; -"152 QuantizeLinear_onnx^^Conv_466_1" -> "153 DequantizeLinear_onnx^^Conv_466_1" [label="[240, 40, 1, 1]", style=dashed]; -"153 DequantizeLinear_onnx^^Conv_466_1" -> "154 /features/features.6/block/block.0/block.0.0/Conv" [label="[240, 40, 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/features/features.8/block/block.3/block.3.0/Conv" -> "264 QuantizeLinear_/features/features.8/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 48, 14, 14]", style=solid]; -"264 QuantizeLinear_/features/features.8/block/block.3/block.3.0/Conv_output_0_1" -> "265 DequantizeLinear_/features/features.8/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 48, 14, 14]", style=dashed]; -"265 DequantizeLinear_/features/features.8/block/block.3/block.3.0/Conv_output_0_1" -> "266 /features/features.8/Add" [label="[1, 48, 14, 14]", style=solid]; -"266 /features/features.8/Add" -> "267 QuantizeLinear_/features/features.8/Add_output_0_1" [label="[1, 48, 14, 14]", style=solid]; -"267 QuantizeLinear_/features/features.8/Add_output_0_1" -> "268 DequantizeLinear_/features/features.8/Add_output_0_1" [label="[1, 48, 14, 14]", style=dashed]; -"268 DequantizeLinear_/features/features.8/Add_output_0_1" -> "271 /features/features.9/block/block.0/block.0.0/Conv" [label="[1, 48, 14, 14]", style=solid]; 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QuantizeLinear_/features/features.9/block/block.0/block.0.2/Mul_output_0_1" -> "275 DequantizeLinear_/features/features.9/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 288, 14, 14]", style=dashed]; -"275 DequantizeLinear_/features/features.9/block/block.0/block.0.2/Mul_output_0_1" -> "278 /features/features.9/block/block.1/block.1.0/Conv" [label="[1, 288, 14, 14]", style=solid]; -"276 QuantizeLinear_onnx^^Conv_496_1" -> "277 DequantizeLinear_onnx^^Conv_496_1" [label="[288, 1, 5, 5]", style=dashed]; -"277 DequantizeLinear_onnx^^Conv_496_1" -> "278 /features/features.9/block/block.1/block.1.0/Conv" [label="[288, 1, 5, 5]", style=solid]; -"278 /features/features.9/block/block.1/block.1.0/Conv" -> "279 /features/features.9/block/block.1/block.1.2/HardSigmoid" [label="[1, 288, 7, 7]", style=solid]; -"278 /features/features.9/block/block.1/block.1.0/Conv" -> "280 /features/features.9/block/block.1/block.1.2/Mul" [label="[1, 288, 7, 7]", style=solid]; -"279 /features/features.9/block/block.1/block.1.2/HardSigmoid" -> "280 /features/features.9/block/block.1/block.1.2/Mul" [label="[1, 288, 7, 7]", style=solid]; -"280 /features/features.9/block/block.1/block.1.2/Mul" -> "281 QuantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 288, 7, 7]", style=solid]; -"281 QuantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" -> "282 DequantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 288, 7, 7]", style=dashed]; -"282 DequantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" -> "283 /features/features.9/block/block.2/avgpool/GlobalAveragePool" [label="[1, 288, 7, 7]", style=solid]; -"282 DequantizeLinear_/features/features.9/block/block.1/block.1.2/Mul_output_0_1" -> "298 /features/features.9/block/block.2/Mul" [label="[1, 288, 7, 7]", style=solid]; -"283 /features/features.9/block/block.2/avgpool/GlobalAveragePool" -> "284 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[label="[1, 288, 1, 1]", style=solid]; -"295 /features/features.9/block/block.2/scale_activation/HardSigmoid" -> "296 QuantizeLinear_/features/features.9/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 288, 1, 1]", style=solid]; -"296 QuantizeLinear_/features/features.9/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "297 DequantizeLinear_/features/features.9/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 288, 1, 1]", style=dashed]; -"297 DequantizeLinear_/features/features.9/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "298 /features/features.9/block/block.2/Mul" [label="[1, 288, 1, 1]", style=solid]; -"298 /features/features.9/block/block.2/Mul" -> "299 QuantizeLinear_/features/features.9/block/block.2/Mul_output_0_1" [label="[1, 288, 7, 7]", style=solid]; -"299 QuantizeLinear_/features/features.9/block/block.2/Mul_output_0_1" -> "300 DequantizeLinear_/features/features.9/block/block.2/Mul_output_0_1" [label="[1, 288, 7, 7]", style=dashed]; -"300 DequantizeLinear_/features/features.9/block/block.2/Mul_output_0_1" -> "303 /features/features.9/block/block.3/block.3.0/Conv" [label="[1, 288, 7, 7]", style=solid]; -"301 QuantizeLinear_onnx^^Conv_499_1" -> "302 DequantizeLinear_onnx^^Conv_499_1" [label="[96, 288, 1, 1]", style=dashed]; -"302 DequantizeLinear_onnx^^Conv_499_1" -> "303 /features/features.9/block/block.3/block.3.0/Conv" [label="[96, 288, 1, 1]", style=solid]; -"303 /features/features.9/block/block.3/block.3.0/Conv" -> "304 QuantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=solid]; -"304 QuantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" -> "305 DequantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=dashed]; -"305 DequantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" -> "308 /features/features.10/block/block.0/block.0.0/Conv" [label="[1, 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"317 /features/features.10/block/block.1/block.1.2/Mul" [label="[1, 576, 7, 7]", style=solid]; -"316 /features/features.10/block/block.1/block.1.2/HardSigmoid" -> "317 /features/features.10/block/block.1/block.1.2/Mul" [label="[1, 576, 7, 7]", style=solid]; -"317 /features/features.10/block/block.1/block.1.2/Mul" -> "318 QuantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; -"318 QuantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" -> "319 DequantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; -"319 DequantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" -> "320 /features/features.10/block/block.2/avgpool/GlobalAveragePool" [label="[1, 576, 7, 7]", style=solid]; -"319 DequantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" -> "335 /features/features.10/block/block.2/Mul" [label="[1, 576, 7, 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/features/features.10/block/block.2/fc1/Conv" -> "326 /features/features.10/block/block.2/activation/Relu" [label="[1, 144, 1, 1]", style=solid]; -"326 /features/features.10/block/block.2/activation/Relu" -> "327 QuantizeLinear_/features/features.10/block/block.2/activation/Relu_output_0_1" [label="[1, 144, 1, 1]", style=solid]; -"327 QuantizeLinear_/features/features.10/block/block.2/activation/Relu_output_0_1" -> "328 DequantizeLinear_/features/features.10/block/block.2/activation/Relu_output_0_1" [label="[1, 144, 1, 1]", style=dashed]; -"328 DequantizeLinear_/features/features.10/block/block.2/activation/Relu_output_0_1" -> "331 /features/features.10/block/block.2/fc2/Conv" [label="[1, 144, 1, 1]", style=solid]; -"329 QuantizeLinear_features.10.block.2.fc2.weight_1" -> "330 DequantizeLinear_features.10.block.2.fc2.weight_1" [label="[576, 144, 1, 1]", style=dashed]; -"330 DequantizeLinear_features.10.block.2.fc2.weight_1" -> "331 /features/features.10/block/block.2/fc2/Conv" [label="[576, 144, 1, 1]", style=solid]; -"331 /features/features.10/block/block.2/fc2/Conv" -> "332 /features/features.10/block/block.2/scale_activation/HardSigmoid" [label="[1, 576, 1, 1]", style=solid]; -"332 /features/features.10/block/block.2/scale_activation/HardSigmoid" -> "333 QuantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 576, 1, 1]", style=solid]; -"333 QuantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "334 DequantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 576, 1, 1]", style=dashed]; -"334 DequantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "335 /features/features.10/block/block.2/Mul" [label="[1, 576, 1, 1]", style=solid]; -"335 /features/features.10/block/block.2/Mul" -> "336 QuantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; -"336 QuantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" -> "337 DequantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; -"337 DequantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" -> "340 /features/features.10/block/block.3/block.3.0/Conv" [label="[1, 576, 7, 7]", style=solid]; -"338 QuantizeLinear_onnx^^Conv_508_1" -> "339 DequantizeLinear_onnx^^Conv_508_1" [label="[96, 576, 1, 1]", style=dashed]; -"339 DequantizeLinear_onnx^^Conv_508_1" -> "340 /features/features.10/block/block.3/block.3.0/Conv" [label="[96, 576, 1, 1]", style=solid]; -"340 /features/features.10/block/block.3/block.3.0/Conv" -> "341 QuantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=solid]; -"341 QuantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" -> "342 DequantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=dashed]; -"342 DequantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" -> "343 /features/features.10/Add" [label="[1, 96, 7, 7]", style=solid]; -"343 /features/features.10/Add" -> "344 QuantizeLinear_/features/features.10/Add_output_0_1" [label="[1, 96, 7, 7]", style=solid]; -"344 QuantizeLinear_/features/features.10/Add_output_0_1" -> "345 DequantizeLinear_/features/features.10/Add_output_0_1" [label="[1, 96, 7, 7]", style=dashed]; -"345 DequantizeLinear_/features/features.10/Add_output_0_1" -> "348 /features/features.11/block/block.0/block.0.0/Conv" [label="[1, 96, 7, 7]", style=solid]; -"345 DequantizeLinear_/features/features.10/Add_output_0_1" -> "383 /features/features.11/Add" [label="[1, 96, 7, 7]", style=solid]; -"346 QuantizeLinear_onnx^^Conv_511_1" -> "347 DequantizeLinear_onnx^^Conv_511_1" [label="[576, 96, 1, 1]", style=dashed]; -"347 DequantizeLinear_onnx^^Conv_511_1" -> "348 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[label="[1, 24, 28, 28]", style=dashed]; +"63 DequantizeLinear_/features/features.3/block/block.2/block.2.0/Conv_output_0_1" -> "64 /features/features.3/Add" [label="[1, 24, 28, 28]", style=solid]; +"64 /features/features.3/Add" -> "65 QuantizeLinear_/features/features.3/Add_output_0_1" [label="[1, 24, 28, 28]", style=solid]; +"65 QuantizeLinear_/features/features.3/Add_output_0_1" -> "66 DequantizeLinear_/features/features.3/Add_output_0_1" [label="[1, 24, 28, 28]", style=dashed]; +"66 DequantizeLinear_/features/features.3/Add_output_0_1" -> "69 /features/features.4/block/block.0/block.0.0/Conv" [label="[1, 24, 28, 28]", style=solid]; +"67 QuantizeLinear_onnx^^Conv_448_1" -> "68 DequantizeLinear_onnx^^Conv_448_1" [label="[96, 24, 1, 1]", style=dashed]; +"68 DequantizeLinear_onnx^^Conv_448_1" -> "69 /features/features.4/block/block.0/block.0.0/Conv" [label="[96, 24, 1, 1]", style=solid]; +"69 /features/features.4/block/block.0/block.0.0/Conv" -> "70 /features/features.4/block/block.0/block.0.2/HardSigmoid" [label="[1, 96, 28, 28]", style=solid]; +"69 /features/features.4/block/block.0/block.0.0/Conv" -> "71 /features/features.4/block/block.0/block.0.2/Mul" [label="[1, 96, 28, 28]", style=solid]; +"70 /features/features.4/block/block.0/block.0.2/HardSigmoid" -> "71 /features/features.4/block/block.0/block.0.2/Mul" [label="[1, 96, 28, 28]", style=solid]; +"71 /features/features.4/block/block.0/block.0.2/Mul" -> "72 QuantizeLinear_/features/features.4/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 96, 28, 28]", style=solid]; +"72 QuantizeLinear_/features/features.4/block/block.0/block.0.2/Mul_output_0_1" -> "73 DequantizeLinear_/features/features.4/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 96, 28, 28]", style=dashed]; +"73 DequantizeLinear_/features/features.4/block/block.0/block.0.2/Mul_output_0_1" -> "76 /features/features.4/block/block.1/block.1.0/Conv" [label="[1, 96, 28, 28]", style=solid]; +"74 QuantizeLinear_onnx^^Conv_451_1" -> "75 DequantizeLinear_onnx^^Conv_451_1" [label="[96, 1, 5, 5]", style=dashed]; +"75 DequantizeLinear_onnx^^Conv_451_1" -> "76 /features/features.4/block/block.1/block.1.0/Conv" [label="[96, 1, 5, 5]", style=solid]; +"76 /features/features.4/block/block.1/block.1.0/Conv" -> "77 /features/features.4/block/block.1/block.1.2/HardSigmoid" [label="[1, 96, 14, 14]", style=solid]; +"76 /features/features.4/block/block.1/block.1.0/Conv" -> "78 /features/features.4/block/block.1/block.1.2/Mul" [label="[1, 96, 14, 14]", style=solid]; +"77 /features/features.4/block/block.1/block.1.2/HardSigmoid" -> "78 /features/features.4/block/block.1/block.1.2/Mul" [label="[1, 96, 14, 14]", style=solid]; +"78 /features/features.4/block/block.1/block.1.2/Mul" -> "79 QuantizeLinear_/features/features.4/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 96, 14, 14]", style=solid]; +"79 QuantizeLinear_/features/features.4/block/block.1/block.1.2/Mul_output_0_1" -> "80 DequantizeLinear_/features/features.4/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 96, 14, 14]", style=dashed]; +"80 DequantizeLinear_/features/features.4/block/block.1/block.1.2/Mul_output_0_1" -> "81 /features/features.4/block/block.2/avgpool/GlobalAveragePool" [label="[1, 96, 14, 14]", style=solid]; +"80 DequantizeLinear_/features/features.4/block/block.1/block.1.2/Mul_output_0_1" -> "88 /features/features.4/block/block.2/Mul" [label="[1, 96, 14, 14]", style=solid]; +"81 /features/features.4/block/block.2/avgpool/GlobalAveragePool" -> "82 /features/features.4/block/block.2/fc1/Conv" [label="[1, 96, 1, 1]", style=solid]; +"82 /features/features.4/block/block.2/fc1/Conv" -> "83 /features/features.4/block/block.2/activation/Relu" [label="[1, 24, 1, 1]", style=solid]; +"83 /features/features.4/block/block.2/activation/Relu" -> "84 /features/features.4/block/block.2/fc2/Conv" [label="[1, 24, 1, 1]", style=solid]; +"84 /features/features.4/block/block.2/fc2/Conv" -> "85 /features/features.4/block/block.2/scale_activation/HardSigmoid" [label="[1, 96, 1, 1]", style=solid]; +"85 /features/features.4/block/block.2/scale_activation/HardSigmoid" -> "86 QuantizeLinear_/features/features.4/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 96, 1, 1]", style=solid]; +"86 QuantizeLinear_/features/features.4/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "87 DequantizeLinear_/features/features.4/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 96, 1, 1]", style=dashed]; +"87 DequantizeLinear_/features/features.4/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "88 /features/features.4/block/block.2/Mul" [label="[1, 96, 1, 1]", style=solid]; +"88 /features/features.4/block/block.2/Mul" -> "89 QuantizeLinear_/features/features.4/block/block.2/Mul_output_0_1" [label="[1, 96, 14, 14]", style=solid]; +"89 QuantizeLinear_/features/features.4/block/block.2/Mul_output_0_1" -> "90 DequantizeLinear_/features/features.4/block/block.2/Mul_output_0_1" [label="[1, 96, 14, 14]", style=dashed]; +"90 DequantizeLinear_/features/features.4/block/block.2/Mul_output_0_1" -> "93 /features/features.4/block/block.3/block.3.0/Conv" [label="[1, 96, 14, 14]", style=solid]; +"91 QuantizeLinear_onnx^^Conv_454_1" -> "92 DequantizeLinear_onnx^^Conv_454_1" [label="[40, 96, 1, 1]", style=dashed]; +"92 DequantizeLinear_onnx^^Conv_454_1" -> "93 /features/features.4/block/block.3/block.3.0/Conv" [label="[40, 96, 1, 1]", style=solid]; +"93 /features/features.4/block/block.3/block.3.0/Conv" -> "94 QuantizeLinear_/features/features.4/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 40, 14, 14]", style=solid]; +"94 QuantizeLinear_/features/features.4/block/block.3/block.3.0/Conv_output_0_1" -> "95 DequantizeLinear_/features/features.4/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 40, 14, 14]", style=dashed]; +"95 DequantizeLinear_/features/features.4/block/block.3/block.3.0/Conv_output_0_1" -> "98 /features/features.5/block/block.0/block.0.0/Conv" [label="[1, 40, 14, 14]", style=solid]; +"95 DequantizeLinear_/features/features.4/block/block.3/block.3.0/Conv_output_0_1" -> "125 /features/features.5/Add" [label="[1, 40, 14, 14]", style=solid]; +"96 QuantizeLinear_onnx^^Conv_457_1" -> "97 DequantizeLinear_onnx^^Conv_457_1" [label="[240, 40, 1, 1]", style=dashed]; +"97 DequantizeLinear_onnx^^Conv_457_1" -> "98 /features/features.5/block/block.0/block.0.0/Conv" [label="[240, 40, 1, 1]", style=solid]; +"98 /features/features.5/block/block.0/block.0.0/Conv" -> "99 /features/features.5/block/block.0/block.0.2/HardSigmoid" [label="[1, 240, 14, 14]", style=solid]; +"98 /features/features.5/block/block.0/block.0.0/Conv" -> "100 /features/features.5/block/block.0/block.0.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"99 /features/features.5/block/block.0/block.0.2/HardSigmoid" -> "100 /features/features.5/block/block.0/block.0.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"100 /features/features.5/block/block.0/block.0.2/Mul" -> "101 QuantizeLinear_/features/features.5/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=solid]; +"101 QuantizeLinear_/features/features.5/block/block.0/block.0.2/Mul_output_0_1" -> "102 DequantizeLinear_/features/features.5/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=dashed]; +"102 DequantizeLinear_/features/features.5/block/block.0/block.0.2/Mul_output_0_1" -> "105 /features/features.5/block/block.1/block.1.0/Conv" [label="[1, 240, 14, 14]", style=solid]; +"103 QuantizeLinear_onnx^^Conv_460_1" -> "104 DequantizeLinear_onnx^^Conv_460_1" [label="[240, 1, 5, 5]", style=dashed]; +"104 DequantizeLinear_onnx^^Conv_460_1" -> "105 /features/features.5/block/block.1/block.1.0/Conv" [label="[240, 1, 5, 5]", style=solid]; +"105 /features/features.5/block/block.1/block.1.0/Conv" -> "106 /features/features.5/block/block.1/block.1.2/HardSigmoid" [label="[1, 240, 14, 14]", style=solid]; +"105 /features/features.5/block/block.1/block.1.0/Conv" -> "107 /features/features.5/block/block.1/block.1.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"106 /features/features.5/block/block.1/block.1.2/HardSigmoid" -> "107 /features/features.5/block/block.1/block.1.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"107 /features/features.5/block/block.1/block.1.2/Mul" -> "108 QuantizeLinear_/features/features.5/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=solid]; +"108 QuantizeLinear_/features/features.5/block/block.1/block.1.2/Mul_output_0_1" -> "109 DequantizeLinear_/features/features.5/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=dashed]; +"109 DequantizeLinear_/features/features.5/block/block.1/block.1.2/Mul_output_0_1" -> "110 /features/features.5/block/block.2/avgpool/GlobalAveragePool" [label="[1, 240, 14, 14]", 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1]", style=solid]; +"115 QuantizeLinear_/features/features.5/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "116 DequantizeLinear_/features/features.5/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 240, 1, 1]", style=dashed]; +"116 DequantizeLinear_/features/features.5/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "117 /features/features.5/block/block.2/Mul" [label="[1, 240, 1, 1]", style=solid]; +"117 /features/features.5/block/block.2/Mul" -> "118 QuantizeLinear_/features/features.5/block/block.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=solid]; +"118 QuantizeLinear_/features/features.5/block/block.2/Mul_output_0_1" -> "119 DequantizeLinear_/features/features.5/block/block.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=dashed]; +"119 DequantizeLinear_/features/features.5/block/block.2/Mul_output_0_1" -> "122 /features/features.5/block/block.3/block.3.0/Conv" [label="[1, 240, 14, 14]", style=solid]; +"120 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-> "127 DequantizeLinear_/features/features.5/Add_output_0_1" [label="[1, 40, 14, 14]", style=dashed]; +"127 DequantizeLinear_/features/features.5/Add_output_0_1" -> "130 /features/features.6/block/block.0/block.0.0/Conv" [label="[1, 40, 14, 14]", style=solid]; +"127 DequantizeLinear_/features/features.5/Add_output_0_1" -> "157 /features/features.6/Add" [label="[1, 40, 14, 14]", style=solid]; +"128 QuantizeLinear_onnx^^Conv_466_1" -> "129 DequantizeLinear_onnx^^Conv_466_1" [label="[240, 40, 1, 1]", style=dashed]; +"129 DequantizeLinear_onnx^^Conv_466_1" -> "130 /features/features.6/block/block.0/block.0.0/Conv" [label="[240, 40, 1, 1]", style=solid]; +"130 /features/features.6/block/block.0/block.0.0/Conv" -> "131 /features/features.6/block/block.0/block.0.2/HardSigmoid" [label="[1, 240, 14, 14]", style=solid]; +"130 /features/features.6/block/block.0/block.0.0/Conv" -> "132 /features/features.6/block/block.0/block.0.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"131 /features/features.6/block/block.0/block.0.2/HardSigmoid" -> "132 /features/features.6/block/block.0/block.0.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"132 /features/features.6/block/block.0/block.0.2/Mul" -> "133 QuantizeLinear_/features/features.6/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=solid]; +"133 QuantizeLinear_/features/features.6/block/block.0/block.0.2/Mul_output_0_1" -> "134 DequantizeLinear_/features/features.6/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=dashed]; +"134 DequantizeLinear_/features/features.6/block/block.0/block.0.2/Mul_output_0_1" -> "137 /features/features.6/block/block.1/block.1.0/Conv" [label="[1, 240, 14, 14]", style=solid]; +"135 QuantizeLinear_onnx^^Conv_469_1" -> "136 DequantizeLinear_onnx^^Conv_469_1" [label="[240, 1, 5, 5]", style=dashed]; +"136 DequantizeLinear_onnx^^Conv_469_1" -> "137 /features/features.6/block/block.1/block.1.0/Conv" [label="[240, 1, 5, 5]", style=solid]; +"137 /features/features.6/block/block.1/block.1.0/Conv" -> "138 /features/features.6/block/block.1/block.1.2/HardSigmoid" [label="[1, 240, 14, 14]", style=solid]; +"137 /features/features.6/block/block.1/block.1.0/Conv" -> "139 /features/features.6/block/block.1/block.1.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"138 /features/features.6/block/block.1/block.1.2/HardSigmoid" -> "139 /features/features.6/block/block.1/block.1.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"139 /features/features.6/block/block.1/block.1.2/Mul" -> "140 QuantizeLinear_/features/features.6/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=solid]; +"140 QuantizeLinear_/features/features.6/block/block.1/block.1.2/Mul_output_0_1" -> "141 DequantizeLinear_/features/features.6/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=dashed]; +"141 DequantizeLinear_/features/features.6/block/block.1/block.1.2/Mul_output_0_1" -> "142 /features/features.6/block/block.2/avgpool/GlobalAveragePool" [label="[1, 240, 14, 14]", style=solid]; +"141 DequantizeLinear_/features/features.6/block/block.1/block.1.2/Mul_output_0_1" -> "149 /features/features.6/block/block.2/Mul" [label="[1, 240, 14, 14]", style=solid]; +"142 /features/features.6/block/block.2/avgpool/GlobalAveragePool" -> "143 /features/features.6/block/block.2/fc1/Conv" [label="[1, 240, 1, 1]", style=solid]; +"143 /features/features.6/block/block.2/fc1/Conv" -> "144 /features/features.6/block/block.2/activation/Relu" [label="[1, 64, 1, 1]", style=solid]; +"144 /features/features.6/block/block.2/activation/Relu" -> "145 /features/features.6/block/block.2/fc2/Conv" [label="[1, 64, 1, 1]", style=solid]; +"145 /features/features.6/block/block.2/fc2/Conv" -> "146 /features/features.6/block/block.2/scale_activation/HardSigmoid" [label="[1, 240, 1, 1]", style=solid]; +"146 /features/features.6/block/block.2/scale_activation/HardSigmoid" -> "147 QuantizeLinear_/features/features.6/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 240, 1, 1]", style=solid]; +"147 QuantizeLinear_/features/features.6/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "148 DequantizeLinear_/features/features.6/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 240, 1, 1]", style=dashed]; +"148 DequantizeLinear_/features/features.6/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "149 /features/features.6/block/block.2/Mul" [label="[1, 240, 1, 1]", style=solid]; +"149 /features/features.6/block/block.2/Mul" -> "150 QuantizeLinear_/features/features.6/block/block.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=solid]; +"150 QuantizeLinear_/features/features.6/block/block.2/Mul_output_0_1" -> "151 DequantizeLinear_/features/features.6/block/block.2/Mul_output_0_1" [label="[1, 240, 14, 14]", style=dashed]; +"151 DequantizeLinear_/features/features.6/block/block.2/Mul_output_0_1" -> "154 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DequantizeLinear_onnx^^Conv_499_1" -> "247 /features/features.9/block/block.3/block.3.0/Conv" [label="[96, 288, 1, 1]", style=solid]; +"247 /features/features.9/block/block.3/block.3.0/Conv" -> "248 QuantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=solid]; +"248 QuantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" -> "249 DequantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=dashed]; +"249 DequantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" -> "252 /features/features.10/block/block.0/block.0.0/Conv" [label="[1, 96, 7, 7]", style=solid]; +"249 DequantizeLinear_/features/features.9/block/block.3/block.3.0/Conv_output_0_1" -> "279 /features/features.10/Add" [label="[1, 96, 7, 7]", style=solid]; +"250 QuantizeLinear_onnx^^Conv_502_1" -> "251 DequantizeLinear_onnx^^Conv_502_1" [label="[576, 96, 1, 1]", style=dashed]; +"251 DequantizeLinear_onnx^^Conv_502_1" -> "252 /features/features.10/block/block.0/block.0.0/Conv" [label="[576, 96, 1, 1]", style=solid]; +"252 /features/features.10/block/block.0/block.0.0/Conv" -> "253 /features/features.10/block/block.0/block.0.2/HardSigmoid" [label="[1, 576, 7, 7]", style=solid]; +"252 /features/features.10/block/block.0/block.0.0/Conv" -> "254 /features/features.10/block/block.0/block.0.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"253 /features/features.10/block/block.0/block.0.2/HardSigmoid" -> "254 /features/features.10/block/block.0/block.0.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"254 /features/features.10/block/block.0/block.0.2/Mul" -> "255 QuantizeLinear_/features/features.10/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; +"255 QuantizeLinear_/features/features.10/block/block.0/block.0.2/Mul_output_0_1" -> "256 DequantizeLinear_/features/features.10/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; +"256 DequantizeLinear_/features/features.10/block/block.0/block.0.2/Mul_output_0_1" -> "259 /features/features.10/block/block.1/block.1.0/Conv" [label="[1, 576, 7, 7]", style=solid]; +"257 QuantizeLinear_onnx^^Conv_505_1" -> "258 DequantizeLinear_onnx^^Conv_505_1" [label="[576, 1, 5, 5]", style=dashed]; +"258 DequantizeLinear_onnx^^Conv_505_1" -> "259 /features/features.10/block/block.1/block.1.0/Conv" [label="[576, 1, 5, 5]", style=solid]; +"259 /features/features.10/block/block.1/block.1.0/Conv" -> "260 /features/features.10/block/block.1/block.1.2/HardSigmoid" [label="[1, 576, 7, 7]", style=solid]; +"259 /features/features.10/block/block.1/block.1.0/Conv" -> "261 /features/features.10/block/block.1/block.1.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"260 /features/features.10/block/block.1/block.1.2/HardSigmoid" -> "261 /features/features.10/block/block.1/block.1.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"261 /features/features.10/block/block.1/block.1.2/Mul" -> "262 QuantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; +"262 QuantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" -> "263 DequantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; +"263 DequantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" -> "264 /features/features.10/block/block.2/avgpool/GlobalAveragePool" [label="[1, 576, 7, 7]", style=solid]; +"263 DequantizeLinear_/features/features.10/block/block.1/block.1.2/Mul_output_0_1" -> "271 /features/features.10/block/block.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"264 /features/features.10/block/block.2/avgpool/GlobalAveragePool" -> "265 /features/features.10/block/block.2/fc1/Conv" [label="[1, 576, 1, 1]", style=solid]; +"265 /features/features.10/block/block.2/fc1/Conv" -> "266 /features/features.10/block/block.2/activation/Relu" [label="[1, 144, 1, 1]", style=solid]; +"266 /features/features.10/block/block.2/activation/Relu" -> "267 /features/features.10/block/block.2/fc2/Conv" [label="[1, 144, 1, 1]", style=solid]; +"267 /features/features.10/block/block.2/fc2/Conv" -> "268 /features/features.10/block/block.2/scale_activation/HardSigmoid" [label="[1, 576, 1, 1]", style=solid]; +"268 /features/features.10/block/block.2/scale_activation/HardSigmoid" -> "269 QuantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 576, 1, 1]", style=solid]; +"269 QuantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "270 DequantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 576, 1, 1]", style=dashed]; +"270 DequantizeLinear_/features/features.10/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "271 /features/features.10/block/block.2/Mul" [label="[1, 576, 1, 1]", style=solid]; +"271 /features/features.10/block/block.2/Mul" -> "272 QuantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; +"272 QuantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" -> "273 DequantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; +"273 DequantizeLinear_/features/features.10/block/block.2/Mul_output_0_1" -> "276 /features/features.10/block/block.3/block.3.0/Conv" [label="[1, 576, 7, 7]", style=solid]; +"274 QuantizeLinear_onnx^^Conv_508_1" -> "275 DequantizeLinear_onnx^^Conv_508_1" [label="[96, 576, 1, 1]", style=dashed]; +"275 DequantizeLinear_onnx^^Conv_508_1" -> "276 /features/features.10/block/block.3/block.3.0/Conv" [label="[96, 576, 1, 1]", style=solid]; +"276 /features/features.10/block/block.3/block.3.0/Conv" -> "277 QuantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=solid]; +"277 QuantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" -> "278 DequantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=dashed]; +"278 DequantizeLinear_/features/features.10/block/block.3/block.3.0/Conv_output_0_1" -> "279 /features/features.10/Add" [label="[1, 96, 7, 7]", style=solid]; +"279 /features/features.10/Add" -> "280 QuantizeLinear_/features/features.10/Add_output_0_1" [label="[1, 96, 7, 7]", style=solid]; +"280 QuantizeLinear_/features/features.10/Add_output_0_1" -> "281 DequantizeLinear_/features/features.10/Add_output_0_1" [label="[1, 96, 7, 7]", style=dashed]; +"281 DequantizeLinear_/features/features.10/Add_output_0_1" -> "284 /features/features.11/block/block.0/block.0.0/Conv" [label="[1, 96, 7, 7]", style=solid]; +"281 DequantizeLinear_/features/features.10/Add_output_0_1" -> "311 /features/features.11/Add" [label="[1, 96, 7, 7]", style=solid]; +"282 QuantizeLinear_onnx^^Conv_511_1" -> "283 DequantizeLinear_onnx^^Conv_511_1" [label="[576, 96, 1, 1]", style=dashed]; +"283 DequantizeLinear_onnx^^Conv_511_1" -> "284 /features/features.11/block/block.0/block.0.0/Conv" [label="[576, 96, 1, 1]", style=solid]; +"284 /features/features.11/block/block.0/block.0.0/Conv" -> "285 /features/features.11/block/block.0/block.0.2/HardSigmoid" [label="[1, 576, 7, 7]", style=solid]; +"284 /features/features.11/block/block.0/block.0.0/Conv" -> "286 /features/features.11/block/block.0/block.0.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"285 /features/features.11/block/block.0/block.0.2/HardSigmoid" -> "286 /features/features.11/block/block.0/block.0.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"286 /features/features.11/block/block.0/block.0.2/Mul" -> "287 QuantizeLinear_/features/features.11/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; +"287 QuantizeLinear_/features/features.11/block/block.0/block.0.2/Mul_output_0_1" -> "288 DequantizeLinear_/features/features.11/block/block.0/block.0.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; +"288 DequantizeLinear_/features/features.11/block/block.0/block.0.2/Mul_output_0_1" -> "291 /features/features.11/block/block.1/block.1.0/Conv" [label="[1, 576, 7, 7]", style=solid]; +"289 QuantizeLinear_onnx^^Conv_514_1" -> "290 DequantizeLinear_onnx^^Conv_514_1" [label="[576, 1, 5, 5]", style=dashed]; +"290 DequantizeLinear_onnx^^Conv_514_1" -> "291 /features/features.11/block/block.1/block.1.0/Conv" [label="[576, 1, 5, 5]", style=solid]; +"291 /features/features.11/block/block.1/block.1.0/Conv" -> "292 /features/features.11/block/block.1/block.1.2/HardSigmoid" [label="[1, 576, 7, 7]", style=solid]; +"291 /features/features.11/block/block.1/block.1.0/Conv" -> "293 /features/features.11/block/block.1/block.1.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"292 /features/features.11/block/block.1/block.1.2/HardSigmoid" -> "293 /features/features.11/block/block.1/block.1.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"293 /features/features.11/block/block.1/block.1.2/Mul" -> "294 QuantizeLinear_/features/features.11/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; +"294 QuantizeLinear_/features/features.11/block/block.1/block.1.2/Mul_output_0_1" -> "295 DequantizeLinear_/features/features.11/block/block.1/block.1.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; +"295 DequantizeLinear_/features/features.11/block/block.1/block.1.2/Mul_output_0_1" -> "296 /features/features.11/block/block.2/avgpool/GlobalAveragePool" [label="[1, 576, 7, 7]", style=solid]; +"295 DequantizeLinear_/features/features.11/block/block.1/block.1.2/Mul_output_0_1" -> "303 /features/features.11/block/block.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"296 /features/features.11/block/block.2/avgpool/GlobalAveragePool" -> "297 /features/features.11/block/block.2/fc1/Conv" [label="[1, 576, 1, 1]", style=solid]; +"297 /features/features.11/block/block.2/fc1/Conv" -> "298 /features/features.11/block/block.2/activation/Relu" [label="[1, 144, 1, 1]", style=solid]; +"298 /features/features.11/block/block.2/activation/Relu" -> "299 /features/features.11/block/block.2/fc2/Conv" [label="[1, 144, 1, 1]", style=solid]; +"299 /features/features.11/block/block.2/fc2/Conv" -> "300 /features/features.11/block/block.2/scale_activation/HardSigmoid" [label="[1, 576, 1, 1]", style=solid]; +"300 /features/features.11/block/block.2/scale_activation/HardSigmoid" -> "301 QuantizeLinear_/features/features.11/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 576, 1, 1]", style=solid]; +"301 QuantizeLinear_/features/features.11/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "302 DequantizeLinear_/features/features.11/block/block.2/scale_activation/HardSigmoid_output_0_1" [label="[1, 576, 1, 1]", style=dashed]; +"302 DequantizeLinear_/features/features.11/block/block.2/scale_activation/HardSigmoid_output_0_1" -> "303 /features/features.11/block/block.2/Mul" [label="[1, 576, 1, 1]", style=solid]; +"303 /features/features.11/block/block.2/Mul" -> "304 QuantizeLinear_/features/features.11/block/block.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; +"304 QuantizeLinear_/features/features.11/block/block.2/Mul_output_0_1" -> "305 DequantizeLinear_/features/features.11/block/block.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; +"305 DequantizeLinear_/features/features.11/block/block.2/Mul_output_0_1" -> "308 /features/features.11/block/block.3/block.3.0/Conv" [label="[1, 576, 7, 7]", style=solid]; +"306 QuantizeLinear_onnx^^Conv_517_1" -> "307 DequantizeLinear_onnx^^Conv_517_1" [label="[96, 576, 1, 1]", style=dashed]; +"307 DequantizeLinear_onnx^^Conv_517_1" -> "308 /features/features.11/block/block.3/block.3.0/Conv" [label="[96, 576, 1, 1]", style=solid]; +"308 /features/features.11/block/block.3/block.3.0/Conv" -> "309 QuantizeLinear_/features/features.11/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=solid]; +"309 QuantizeLinear_/features/features.11/block/block.3/block.3.0/Conv_output_0_1" -> "310 DequantizeLinear_/features/features.11/block/block.3/block.3.0/Conv_output_0_1" [label="[1, 96, 7, 7]", style=dashed]; +"310 DequantizeLinear_/features/features.11/block/block.3/block.3.0/Conv_output_0_1" -> "311 /features/features.11/Add" [label="[1, 96, 7, 7]", style=solid]; +"311 /features/features.11/Add" -> "312 QuantizeLinear_/features/features.11/Add_output_0_1" [label="[1, 96, 7, 7]", style=solid]; +"312 QuantizeLinear_/features/features.11/Add_output_0_1" -> "313 DequantizeLinear_/features/features.11/Add_output_0_1" [label="[1, 96, 7, 7]", style=dashed]; +"313 DequantizeLinear_/features/features.11/Add_output_0_1" -> "316 /features/features.12/features.12.0/Conv" [label="[1, 96, 7, 7]", style=solid]; +"314 QuantizeLinear_onnx^^Conv_520_1" -> "315 DequantizeLinear_onnx^^Conv_520_1" [label="[576, 96, 1, 1]", style=dashed]; +"315 DequantizeLinear_onnx^^Conv_520_1" -> "316 /features/features.12/features.12.0/Conv" [label="[576, 96, 1, 1]", style=solid]; +"316 /features/features.12/features.12.0/Conv" -> "317 /features/features.12/features.12.2/HardSigmoid" [label="[1, 576, 7, 7]", style=solid]; +"316 /features/features.12/features.12.0/Conv" -> "318 /features/features.12/features.12.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"317 /features/features.12/features.12.2/HardSigmoid" -> "318 /features/features.12/features.12.2/Mul" [label="[1, 576, 7, 7]", style=solid]; +"318 /features/features.12/features.12.2/Mul" -> "319 QuantizeLinear_/features/features.12/features.12.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=solid]; +"319 QuantizeLinear_/features/features.12/features.12.2/Mul_output_0_1" -> "320 DequantizeLinear_/features/features.12/features.12.2/Mul_output_0_1" [label="[1, 576, 7, 7]", style=dashed]; +"320 DequantizeLinear_/features/features.12/features.12.2/Mul_output_0_1" -> "321 /avgpool/GlobalAveragePool" [label="[1, 576, 7, 7]", style=solid]; +"321 /avgpool/GlobalAveragePool" -> "322 QuantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [label="[1, 576, 1, 1]", style=solid]; +"322 QuantizeLinear_/avgpool/GlobalAveragePool_output_0_1" -> "323 DequantizeLinear_/avgpool/GlobalAveragePool_output_0_1" [label="[1, 576, 1, 1]", style=dashed]; +"323 DequantizeLinear_/avgpool/GlobalAveragePool_output_0_1" -> "324 /Flatten" [label="[1, 576, 1, 1]", style=solid]; +"324 /Flatten" -> "327 /classifier/classifier.0/Gemm" [label="[1, 576]", style=solid]; +"325 QuantizeLinear_classifier.0.weight_1" -> "326 DequantizeLinear_classifier.0.weight_1" [label="[1024, 576]", style=dashed]; +"326 DequantizeLinear_classifier.0.weight_1" -> "327 /classifier/classifier.0/Gemm" [label="[1024, 576]", style=solid]; +"327 /classifier/classifier.0/Gemm" -> "328 /classifier/classifier.1/HardSigmoid" [label="[1, 1024]", style=solid]; +"327 /classifier/classifier.0/Gemm" -> "329 /classifier/classifier.1/Mul" [label="[1, 1024]", style=solid]; +"328 /classifier/classifier.1/HardSigmoid" -> "329 /classifier/classifier.1/Mul" [label="[1, 1024]", style=solid]; +"329 /classifier/classifier.1/Mul" -> "330 QuantizeLinear_/classifier/classifier.1/Mul_output_0_1" [label="[1, 1024]", style=solid]; +"330 QuantizeLinear_/classifier/classifier.1/Mul_output_0_1" -> "331 DequantizeLinear_/classifier/classifier.1/Mul_output_0_1" [label="[1, 1024]", style=dashed]; +"331 DequantizeLinear_/classifier/classifier.1/Mul_output_0_1" -> "334 /classifier/classifier.3/Gemm" [label="[1, 1024]", style=solid]; +"332 QuantizeLinear_classifier.3.weight_1" -> "333 DequantizeLinear_classifier.3.weight_1" [label="[1000, 1024]", style=dashed]; +"333 DequantizeLinear_classifier.3.weight_1" -> "334 /classifier/classifier.3/Gemm" [label="[1000, 1024]", style=solid]; +"334 /classifier/classifier.3/Gemm" -> "336 nncf_model_output_0" [label="[1, 1000]", style=solid]; +"335 nncf_model_input_0" -> "0 QuantizeLinear_input.1_1" [label="[1, 3, 224, 224]", style=solid]; } diff --git a/tests/onnx/data/reference_graphs/quantization/synthetic/float64_model.dot b/tests/onnx/data/reference_graphs/quantization/synthetic/float64_model.dot index 12356eee1a5..3a52bb7d31f 100644 --- a/tests/onnx/data/reference_graphs/quantization/synthetic/float64_model.dot +++ b/tests/onnx/data/reference_graphs/quantization/synthetic/float64_model.dot @@ -3,13 +3,17 @@ strict digraph { "1 Cast" [id=1, type=Cast]; "2 QuantizeLinear_Cast_Y_1" [id=2, type=QuantizeLinear]; "3 DequantizeLinear_Cast_Y_1" [id=3, type=DequantizeLinear]; -"4 Mul" [id=4, type=Mul]; -"5 nncf_model_input_0" [id=5, type=nncf_model_input]; -"6 nncf_model_output_0" [id=6, type=nncf_model_output]; +"4 QuantizeLinear_Conv1_W_1" [id=4, type=QuantizeLinear]; +"5 DequantizeLinear_Conv1_W_1" [id=5, type=DequantizeLinear]; +"6 Conv1" [id=6, type=Conv]; +"7 nncf_model_input_0" [id=7, type=nncf_model_input]; +"8 nncf_model_output_0" [id=8, type=nncf_model_output]; "0 Reciprocal" -> "1 Cast" [label="[1, 3, 10, 10]", style=dashed]; "1 Cast" -> "2 QuantizeLinear_Cast_Y_1" [label="[1, 3, 10, 10]", style=solid]; "2 QuantizeLinear_Cast_Y_1" -> "3 DequantizeLinear_Cast_Y_1" [label="[1, 3, 10, 10]", style=dashed]; -"3 DequantizeLinear_Cast_Y_1" -> "4 Mul" [label="[1, 3, 10, 10]", style=solid]; -"4 Mul" -> "6 nncf_model_output_0" [label="[1, 3, 10, 10]", style=solid]; -"5 nncf_model_input_0" -> "0 Reciprocal" [label="[1, 3, 10, 10]", style=dashed]; +"3 DequantizeLinear_Cast_Y_1" -> "6 Conv1" [label="[1, 3, 10, 10]", style=solid]; +"4 QuantizeLinear_Conv1_W_1" -> "5 DequantizeLinear_Conv1_W_1" [label="[32, 3, 3, 3]", style=dashed]; +"5 DequantizeLinear_Conv1_W_1" -> "6 Conv1" [label="[32, 3, 3, 3]", style=solid]; +"6 Conv1" -> "8 nncf_model_output_0" [label="[1, 3, 10, 10]", style=solid]; +"7 nncf_model_input_0" -> "0 Reciprocal" [label="[1, 3, 10, 10]", style=dashed]; } diff --git a/tests/onnx/data/reference_graphs/quantization/synthetic/rope_model.dot b/tests/onnx/data/reference_graphs/quantization/synthetic/rope_model.dot new file mode 100644 index 00000000000..84ddf95e3de --- /dev/null +++ b/tests/onnx/data/reference_graphs/quantization/synthetic/rope_model.dot @@ -0,0 +1,23 @@ +strict digraph { +"0 unsqueeze" [id=0, type=Unsqueeze]; +"1 cast" [id=1, type=Cast]; +"2 reshape" [id=2, type=Reshape]; +"3 matmul" [id=3, type=MatMul]; +"4 transpose" [id=4, type=Transpose]; +"5 concat" [id=5, type=Concat]; +"6 sin" [id=6, type=Sin]; +"7 cos" [id=7, type=Cos]; +"8 nncf_model_input_0" [id=8, type=nncf_model_input]; +"9 nncf_model_output_0" [id=9, type=nncf_model_output]; +"10 nncf_model_output_1" [id=10, type=nncf_model_output]; +"0 unsqueeze" -> "1 cast" [label="[1, 10, 1]", style=dashed]; +"1 cast" -> "3 matmul" [label="[1, 10, 1]", style=solid]; +"2 reshape" -> "3 matmul" [label="[1, 5]", style=solid]; +"3 matmul" -> "4 transpose" [label="[1, 10, 5]", style=solid]; +"4 transpose" -> "5 concat" [label="[1, 5, 10]", style=solid]; +"5 concat" -> "6 sin" [label="[1, 5, 10]", style=solid]; +"5 concat" -> "7 cos" [label="[1, 5, 10]", style=solid]; +"6 sin" -> "9 nncf_model_output_0" [label="[1, 5, 10]", style=solid]; +"7 cos" -> "10 nncf_model_output_1" [label="[1, 5, 10]", style=solid]; +"8 nncf_model_input_0" -> "0 unsqueeze" [label="[1, 10]", style=dashed]; +} diff --git a/tests/onnx/models.py b/tests/onnx/models.py index 0f8c5f2adc9..6ff99dced56 100644 --- a/tests/onnx/models.py +++ b/tests/onnx/models.py @@ -954,8 +954,6 @@ class Float64InputMulModel(ONNXReferenceModel): def __init__(self): input_shape = [1, 3, 10, 10] model_input_name = "X" - model_mul_op_name = "Mul" - model_output_name = "Y" model_reciprocal_op_name = "Reciprocal" model_cast_op_name = "Cast" model_cast_output = "Cast_Y" @@ -983,21 +981,37 @@ def __init__(self): name=tensor_name, tensor_array=tensor, data_type=onnx.TensorProto.FLOAT ) - mul_node = onnx.helper.make_node( - name=model_mul_op_name, - op_type="Mul", - inputs=[model_cast_output, tensor_name], - outputs=[model_output_name], + conv_output_node_name = "Conv1_Y" + conv_in_channels, conv_out_channels, conv1_kernel_shape = 3, 32, (3, 3) + rng = get_random_generator() + conv_W = rng.uniform(0, 1, (conv_out_channels, conv_in_channels, *conv1_kernel_shape)).astype(np.float32) + conv_B = rng.uniform(0, 1, conv_out_channels).astype(np.float32) + + conv_W_initializer_tensor_name = "Conv1_W" + conv_W_initializer_tensor = create_initializer_tensor( + name=conv_W_initializer_tensor_name, tensor_array=conv_W, data_type=onnx.TensorProto.FLOAT + ) + conv_B_initializer_tensor_name = "Conv1_B" + conv_B_initializer_tensor = create_initializer_tensor( + name=conv_B_initializer_tensor_name, tensor_array=conv_B, data_type=onnx.TensorProto.FLOAT ) - Y = onnx.helper.make_tensor_value_info(model_output_name, onnx.TensorProto.FLOAT, input_shape) + conv_node = onnx.helper.make_node( + name="Conv1", + op_type="Conv", + inputs=[model_cast_output, conv_W_initializer_tensor_name, conv_B_initializer_tensor_name], + outputs=[conv_output_node_name], + kernel_shape=conv1_kernel_shape, + ) + + Y = onnx.helper.make_tensor_value_info(conv_output_node_name, onnx.TensorProto.FLOAT, input_shape) graph_def = onnx.helper.make_graph( - nodes=[reciprocal_node, cast_node, mul_node], + nodes=[reciprocal_node, cast_node, conv_node], name="Float64Net", inputs=[X], outputs=[Y], - initializer=[initializer_tensor], + initializer=[initializer_tensor, conv_W_initializer_tensor, conv_B_initializer_tensor], ) op = onnx.OperatorSetIdProto() @@ -1743,3 +1757,126 @@ def __init__(self): model = onnx.helper.make_model(graph_def, opset_imports=[op]) onnx.checker.check_model(model) super().__init__(model, [input_shape], "unified_embedding_model.dot") + + +class RoPEModel(ONNXReferenceModel): + def __init__(self): + rng = np.random.default_rng(seed=0) + + input_shape = [1, 10] + input_name = "model_in" + model_in = onnx.helper.make_tensor_value_info(input_name, onnx.TensorProto.INT64, input_shape) + + output_shape = [1, 5, 10] + cos_out_name = "cos_out" + cos_out = onnx.helper.make_tensor_value_info(cos_out_name, onnx.TensorProto.FLOAT, output_shape) + sin_out_name = "sin_out" + sin_out = onnx.helper.make_tensor_value_info(sin_out_name, onnx.TensorProto.FLOAT, output_shape) + + unsqueeze_out_name = "un_out" + unsqueeze_tensor_name = "un_tensor" + unsqueeze_tensor = create_initializer_tensor( + name=unsqueeze_tensor_name, tensor_array=np.int64([2]), data_type=onnx.TensorProto.INT64 + ) + unsqueeze_node = onnx.helper.make_node( + name="unsqueeze", + op_type="Unsqueeze", + inputs=[input_name, unsqueeze_tensor_name], + outputs=[unsqueeze_out_name], + ) + + cast_out_name = "cast_out" + cast_node = onnx.helper.make_node( + name="cast", + op_type="Cast", + to=onnx.TensorProto.FLOAT, + inputs=[unsqueeze_out_name], + outputs=[cast_out_name], + ) + + reshape_shape_name = "re_shape" + reshape_shape = create_initializer_tensor( + name=reshape_shape_name, + tensor_array=np.array([1, 5]).astype(np.int64), + data_type=onnx.TensorProto.INT64, + ) + reshape_tensor_name = "re_tensor" + reshape_tensor = create_initializer_tensor( + name=reshape_tensor_name, + tensor_array=rng.uniform(0, 1, (5)).astype(np.float32), + data_type=onnx.TensorProto.FLOAT, + ) + reshape_out_name = "re_out" + reshape_node = onnx.helper.make_node( + name="reshape", + op_type="Reshape", + inputs=[reshape_tensor_name, reshape_shape_name], + outputs=[reshape_out_name], + ) + + matmul_out_name = "mm_out" + matmul_node = onnx.helper.make_node( + name="matmul", + op_type="MatMul", + inputs=[cast_out_name, reshape_out_name], + outputs=[matmul_out_name], + ) + + transpose_out_name = "trans_out" + transpose_node = onnx.helper.make_node( + name="transpose", + op_type="Transpose", + inputs=[matmul_out_name], + outputs=[transpose_out_name], + perm=[0, 2, 1], + ) + + concat_out_name = "concat_out" + concat_node = onnx.helper.make_node( + name="concat", + op_type="Concat", + inputs=[transpose_out_name], + outputs=[concat_out_name], + axis=-1, + ) + + sin_node = onnx.helper.make_node( + name="sin", + op_type="Sin", + inputs=[concat_out_name], + outputs=[sin_out_name], + ) + + cos_node = onnx.helper.make_node( + name="cos", + op_type="Cos", + inputs=[concat_out_name], + outputs=[cos_out_name], + ) + + graph_def = onnx.helper.make_graph( + nodes=[ + unsqueeze_node, + cast_node, + reshape_node, + matmul_node, + transpose_node, + concat_node, + sin_node, + cos_node, + ], + name="RoPEModel", + inputs=[model_in], + outputs=[sin_out, cos_out], + initializer=[ + unsqueeze_tensor, + reshape_tensor, + reshape_shape, + ], + ) + + op = onnx.OperatorSetIdProto() + op.version = OPSET_VERSION + model = onnx.helper.make_model(graph_def, opset_imports=[op]) + onnx.checker.check_model(model) + super().__init__(model, [input_shape], "rope_model.dot") diff --git a/tests/onnx/quantization/common.py b/tests/onnx/quantization/common.py index dc23ba342d1..18f36b29ee4 100644 --- a/tests/onnx/quantization/common.py +++ b/tests/onnx/quantization/common.py @@ -26,11 +26,11 @@ from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization from nncf.quantization.fake_quantize import FakeQuantizeParameters from nncf.tensor import Tensor +from tests.cross_fw.shared.nx_graph import check_nx_graph +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.paths import TEST_ROOT from tests.onnx.common import get_random_generator from tests.onnx.opset_converter import convert_opset_version -from tests.shared.nx_graph import check_nx_graph -from tests.shared.nx_graph import compare_nx_graph_with_reference -from tests.shared.paths import TEST_ROOT REFERENCE_GRAPHS_TEST_ROOT = "data/reference_graphs/quantization" diff --git a/tests/onnx/quantization/test_bias_correction.py b/tests/onnx/quantization/test_bias_correction.py index 40cdf0dc8e1..8ffe20c6102 100644 --- a/tests/onnx/quantization/test_bias_correction.py +++ b/tests/onnx/quantization/test_bias_correction.py @@ -22,11 +22,12 @@ from nncf.onnx.graph.node_utils import get_bias_value from nncf.quantization.algorithms.bias_correction.onnx_backend import ONNXBiasCorrectionAlgoBackend from tests.cross_fw.test_templates.helpers import ConvTestModel +from tests.cross_fw.test_templates.helpers import DepthwiseConvTestModel from tests.cross_fw.test_templates.helpers import MultipleConvTestModel from tests.cross_fw.test_templates.helpers import SplittedModel +from tests.cross_fw.test_templates.helpers import TransposeConvTestModel from tests.cross_fw.test_templates.test_bias_correction import TemplateTestBCAlgorithm from tests.onnx.quantization.common import compare_nncf_graph -from tests.shared.paths import TEST_ROOT def get_data_from_node(model: onnx.ModelProto, node_name: str): @@ -69,10 +70,6 @@ def remove_fq_from_inputs(model: onnx.ModelProto) -> onnx.ModelProto: graph = GraphConverter.create_nncf_graph(model) return remove_fq_from_inputs(model, graph) - @staticmethod - def get_ref_path(suffix: str) -> str: - return TEST_ROOT / "onnx" / "data" / "reference_graphs" / "quantization" / "subgraphs" / f"{suffix}.dot" - @staticmethod def compare_nncf_graphs(model: onnx.ModelProto, ref_path: str) -> None: return compare_nncf_graph(model, ref_path) @@ -216,6 +213,8 @@ def test__get_subgraph_data_for_node(self, quantized_test_model, layer_name, ref }, ), (ConvTestModel, {("/conv/Conv", 0): ("nncf_model_input_0", 0)}), + (DepthwiseConvTestModel, {("/conv/Conv", 0): ("nncf_model_input_0", 0)}), + (TransposeConvTestModel, {("/conv/ConvTranspose", 0): ("nncf_model_input_0", 0)}), ), ) def test_verify_collected_stat_inputs_map(self, model_cls, ref_stat_inputs_map, tmpdir): diff --git a/tests/onnx/quantization/test_graphs.py b/tests/onnx/quantization/test_graphs.py index 9eccc145b42..ecdcf1dd747 100644 --- a/tests/onnx/quantization/test_graphs.py +++ b/tests/onnx/quantization/test_graphs.py @@ -11,7 +11,9 @@ import pytest +from nncf.parameters import ModelType from tests.onnx.models import ALL_SYNTHETIC_MODELS +from tests.onnx.models import RoPEModel from tests.onnx.quantization.common import compare_nncf_graph from tests.onnx.quantization.common import min_max_quantize_model from tests.onnx.quantization.common import mock_collect_statistics @@ -23,3 +25,13 @@ def test_synthetic_models_graph(model_cls_to_test, mocker): model_to_test = model_cls_to_test() quantized_model = min_max_quantize_model(model_to_test.onnx_model) compare_nncf_graph(quantized_model, "synthetic/" + model_to_test.path_ref_graph) + + +@pytest.mark.parametrize("model_cls_to_test", [RoPEModel]) +def test_synthetic_models_graph_transformer(model_cls_to_test, mocker): + mock_collect_statistics(mocker) + model_to_test = model_cls_to_test() + quantized_model = min_max_quantize_model( + model_to_test.onnx_model, quantization_params={"model_type": ModelType.TRANSFORMER} + ) + compare_nncf_graph(quantized_model, "synthetic/" + model_to_test.path_ref_graph) diff --git a/tests/onnx/quantization/test_qdq_params_calculation.py b/tests/onnx/quantization/test_qdq_params_calculation.py index 00d8bd73a52..9968706ecbd 100644 --- a/tests/onnx/quantization/test_qdq_params_calculation.py +++ b/tests/onnx/quantization/test_qdq_params_calculation.py @@ -18,6 +18,8 @@ from nncf.onnx.graph.onnx_helper import get_tensor_value from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.advanced_parameters import OverflowFix +from tests.cross_fw.shared.helpers import compare_stats +from tests.cross_fw.shared.helpers import load_json from tests.onnx.conftest import ONNX_TEST_ROOT from tests.onnx.models import EmbeddingModel from tests.onnx.models import GEMMTransposeWeightModel @@ -28,8 +30,6 @@ from tests.onnx.models import ReshapeWeightModel from tests.onnx.models import WeightSharingModel from tests.onnx.quantization.common import min_max_quantize_model -from tests.shared.helpers import compare_stats -from tests.shared.helpers import load_json REFERENCE_SCALES_DIR = ONNX_TEST_ROOT / "data" / "reference_scales" diff --git a/tests/onnx/quantization/test_quantizer_config.py b/tests/onnx/quantization/test_quantizer_config.py index b82318b34db..1ff5563f30f 100644 --- a/tests/onnx/quantization/test_quantizer_config.py +++ b/tests/onnx/quantization/test_quantizer_config.py @@ -11,14 +11,21 @@ import pytest +from nncf.common.utils.backend import BackendType from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXAddLayerMetatype +from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXConstantMetatype from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXConvolutionMetatype from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXDepthwiseConvolutionMetatype +from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXMatMulMetatype +from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXMulLayerMetatype +from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXSoftmaxMetatype +from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXTransposeMetatype from nncf.onnx.graph.nncf_graph_builder import ONNXLayerAttributes from nncf.quantization.algorithms.min_max.onnx_backend import ONNXMinMaxAlgoBackend from tests.cross_fw.test_templates.models import NNCFGraphToTest from tests.cross_fw.test_templates.models import NNCFGraphToTestDepthwiseConv from tests.cross_fw.test_templates.models import NNCFGraphToTestSumAggregation +from tests.cross_fw.test_templates.models import NNCFGraphTransformer from tests.cross_fw.test_templates.test_quantizer_config import TemplateTestQuantizerConfig @@ -26,6 +33,9 @@ class TestQuantizerConfig(TemplateTestQuantizerConfig): def get_algo_backend(self): return ONNXMinMaxAlgoBackend() + def get_backend_type(self): + return BackendType.ONNX + @pytest.fixture def single_conv_nncf_graph(self) -> NNCFGraphToTest: conv_layer_attrs = ONNXLayerAttributes(weight_attrs={1: {"shape": [4, 4, 4, 4]}}, bias_attrs={}) @@ -59,3 +69,16 @@ def conv_sum_aggregation_nncf_graph(self) -> NNCFGraphToTestSumAggregation: output_layer_attrs=ONNXLayerAttributes(), const_layer_attrs=ONNXLayerAttributes(), ) + + @pytest.fixture + def transformer_nncf_graph(self) -> NNCFGraphToTest: + return NNCFGraphTransformer( + matmul_metatype=ONNXMatMulMetatype, + softmax_metatype=ONNXSoftmaxMetatype, + mul_metatype=ONNXMulLayerMetatype, + const_metatype=ONNXConstantMetatype, + transpose_metatype=ONNXTransposeMetatype, + matmul_layer_weighted_attrs=ONNXLayerAttributes({"name": "edge_name", "shape": (1, 1, 1, 1)}), + matmul_layer_non_weighted_attrs=ONNXLayerAttributes(), + default_layer_attrs=ONNXLayerAttributes(), + ) diff --git a/tests/onnx/quantization/test_reducers_and_aggregators.py b/tests/onnx/quantization/test_reducers_and_aggregators.py index bd4aae88879..6a7b7273391 100644 --- a/tests/onnx/quantization/test_reducers_and_aggregators.py +++ b/tests/onnx/quantization/test_reducers_and_aggregators.py @@ -15,14 +15,14 @@ import pytest from nncf.common.graph.layer_attributes import Dtype -from nncf.onnx.statistics.collectors import ONNXAbsMaxReducer -from nncf.onnx.statistics.collectors import ONNXAbsQuantileReducer -from nncf.onnx.statistics.collectors import ONNXBatchMeanReducer -from nncf.onnx.statistics.collectors import ONNXMaxReducer -from nncf.onnx.statistics.collectors import ONNXMeanPerChanelReducer -from nncf.onnx.statistics.collectors import ONNXMeanReducer -from nncf.onnx.statistics.collectors import ONNXMinReducer -from nncf.onnx.statistics.collectors import ONNXQuantileReducer +from nncf.experimental.common.tensor_statistics.collectors import AbsMaxReducer +from nncf.experimental.common.tensor_statistics.collectors import AbsQuantileReducer +from nncf.experimental.common.tensor_statistics.collectors import BatchMeanReducer +from nncf.experimental.common.tensor_statistics.collectors import MaxReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanPerChReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanReducer +from nncf.experimental.common.tensor_statistics.collectors import MinReducer +from nncf.experimental.common.tensor_statistics.collectors import QuantileReducer from nncf.tensor import Tensor from tests.common.experimental.test_reducers_and_aggregators import TemplateTestReducersAggregators @@ -39,14 +39,14 @@ def get_nncf_tensor(self, x: np.array, dtype: Optional[Dtype] = None): @pytest.fixture(scope="module") def reducers(self): return { - "min": ONNXMinReducer, - "max": ONNXMaxReducer, - "abs_max": ONNXAbsMaxReducer, - "mean": ONNXMeanReducer, - "quantile": ONNXQuantileReducer, - "abs_quantile": ONNXAbsQuantileReducer, - "batch_mean": ONNXBatchMeanReducer, - "mean_per_ch": ONNXMeanPerChanelReducer, + "min": MinReducer, + "max": MaxReducer, + "abs_max": AbsMaxReducer, + "mean": MeanReducer, + "quantile": QuantileReducer, + "abs_quantile": AbsQuantileReducer, + "batch_mean": BatchMeanReducer, + "mean_per_ch": MeanPerChReducer, } def all_close(self, val, ref) -> bool: diff --git a/tests/onnx/test_e2e_ptq.py b/tests/onnx/test_e2e_ptq.py index bfd58ab0878..0ca8eeedfc6 100644 --- a/tests/onnx/test_e2e_ptq.py +++ b/tests/onnx/test_e2e_ptq.py @@ -26,9 +26,9 @@ from yattag import indent from nncf.common.logging.logger import nncf_logger +from tests.cross_fw.shared.paths import DATASET_DEFINITIONS_PATH +from tests.cross_fw.shared.paths import PROJECT_ROOT from tests.onnx.conftest import ONNX_TEST_ROOT -from tests.shared.paths import DATASET_DEFINITIONS_PATH -from tests.shared.paths import PROJECT_ROOT BG_COLOR_GREEN_HEX = "ccffcc" BG_COLOR_YELLOW_HEX = "ffffcc" diff --git a/tests/onnx/test_nncf_graph_builder.py b/tests/onnx/test_nncf_graph_builder.py index 8194f6858fb..88b8a6c896f 100644 --- a/tests/onnx/test_nncf_graph_builder.py +++ b/tests/onnx/test_nncf_graph_builder.py @@ -18,14 +18,14 @@ from nncf.onnx.graph.model_transformer import ONNXModelTransformer from nncf.onnx.graph.nncf_graph_builder import GraphConverter +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.paths import TEST_ROOT from tests.onnx.conftest import ONNX_TEST_ROOT from tests.onnx.models import ALL_SYNTHETIC_MODELS from tests.onnx.models import OneConvolutionalModel from tests.onnx.opset_converter import convert_opset_version from tests.onnx.quantization.common import ModelToTest from tests.onnx.weightless_model import load_model_topology_with_zeros_weights -from tests.shared.nx_graph import compare_nx_graph_with_reference -from tests.shared.paths import TEST_ROOT REFERENCE_GRAPHS_DIR = ONNX_TEST_ROOT / "data" / "reference_graphs" / "original_nncf_graph" diff --git a/tests/onnx/test_pattern_manager.py b/tests/onnx/test_pattern_manager.py index 39ca671bf3f..46f5a6366a8 100644 --- a/tests/onnx/test_pattern_manager.py +++ b/tests/onnx/test_pattern_manager.py @@ -12,8 +12,8 @@ from nncf.common.graph.patterns import HWFusedPatternNames from nncf.common.graph.patterns import IgnoredPatternNames from nncf.common.utils.backend import BackendType -from tests.shared.patterns import check_hw_patterns -from tests.shared.patterns import check_ignored_patterns +from tests.cross_fw.shared.patterns import check_hw_patterns +from tests.cross_fw.shared.patterns import check_ignored_patterns IGNORING_HW_PATTERN_REASONS = { HWFusedPatternNames.L2_NORM: "Not relevant for ONNX.", diff --git a/tests/onnx/test_statistics_caching.py b/tests/onnx/test_statistics_caching.py new file mode 100644 index 00000000000..e1224e7b5f3 --- /dev/null +++ b/tests/onnx/test_statistics_caching.py @@ -0,0 +1,22 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nncf.common.graph.transformations.commands import TargetType +from nncf.onnx.graph.transformations.commands import ONNXTargetPoint +from nncf.onnx.statistics.aggregator import ONNXStatisticsAggregator +from tests.cross_fw.test_templates.test_statistics_caching import TemplateTestStatisticsCaching + + +class TestStatisticsCaching(TemplateTestStatisticsCaching): + def create_target_point(self, target_point_type: TargetType, name: str, port_id: int) -> ONNXTargetPoint: + return ONNXTargetPoint(target_type=target_point_type, target_node_name=name, port_id=port_id) + + def get_statistics_aggregator(self): + return ONNXStatisticsAggregator(None) diff --git a/tests/onnx/test_telemetry.py b/tests/onnx/test_telemetry.py index 33c839e282e..851b3b0cdb2 100644 --- a/tests/onnx/test_telemetry.py +++ b/tests/onnx/test_telemetry.py @@ -9,9 +9,9 @@ # See the License for the specific language governing permissions and # limitations under the License. import nncf +from tests.cross_fw.shared.datasets import MockDataset +from tests.cross_fw.shared.helpers import telemetry_send_event_test_driver from tests.onnx.models import LinearModel -from tests.shared.datasets import MockDataset -from tests.shared.helpers import telemetry_send_event_test_driver def test_telemetry_is_sent_via_quantize(mocker): diff --git a/tests/openvino/conftest.py b/tests/openvino/conftest.py index 6452c50aa26..5bd2f6eb825 100644 --- a/tests/openvino/conftest.py +++ b/tests/openvino/conftest.py @@ -13,10 +13,10 @@ import pytest -from tests.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS -from tests.shared.case_collection import skip_marked_cases_if_options_not_specified -from tests.shared.install_fixtures import tmp_venv_with_nncf # noqa: F401 -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS +from tests.cross_fw.shared.case_collection import skip_marked_cases_if_options_not_specified +from tests.cross_fw.shared.install_fixtures import tmp_venv_with_nncf # noqa: F401 +from tests.cross_fw.shared.paths import TEST_ROOT def pytest_addoption(parser): diff --git a/tests/openvino/native/common.py b/tests/openvino/native/common.py index 8e4fa7673c8..8ad23c8e9e9 100644 --- a/tests/openvino/native/common.py +++ b/tests/openvino/native/common.py @@ -20,9 +20,9 @@ from nncf import Dataset from nncf.openvino.graph.nncf_graph_builder import GraphConverter +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.openvino_version import get_openvino_version from tests.openvino.conftest import OPENVINO_NATIVE_TEST_ROOT -from tests.shared.nx_graph import compare_nx_graph_with_reference -from tests.shared.openvino_version import get_openvino_version def convert_torch_model(model: torch.nn.Module, input_shape: Tuple[int], tmp_path: Path) -> ov.Model: diff --git a/tests/openvino/native/data/2024.3/reference_graphs/quantized/GroupNormalizationModel.dot b/tests/openvino/native/data/2024.3/reference_graphs/quantized/GroupNormalizationModel.dot new file mode 100644 index 00000000000..69886afd0d7 --- /dev/null +++ b/tests/openvino/native/data/2024.3/reference_graphs/quantized/GroupNormalizationModel.dot @@ -0,0 +1,59 @@ +strict digraph { +"0 Input_1" [id=0, type=Parameter]; +"1 Input_1/fq_output_0" [id=1, type=FakeQuantize]; +"2 Conv" [id=2, type=Convolution]; +"3 Conv_Add" [id=3, type=Add]; +"4 Conv_Add/fq_output_0" [id=4, type=FakeQuantize]; +"5 GroupNormalization_9" [id=5, type=GroupNormalization]; +"6 Relu" [id=6, type=Relu]; +"7 Mul" [id=7, type=Multiply]; +"8 Add" [id=8, type=Add]; +"9 Result" [id=9, type=Result]; +"10 Add/Constant_13" [id=10, type=Constant]; +"11 Mul/Constant_11" [id=11, type=Constant]; +"12 Constant_8" [id=12, type=Constant]; +"13 Constant_7" [id=13, type=Constant]; +"14 Conv_Add/fq_output_0/output_high" [id=14, type=Constant]; +"15 Conv_Add/fq_output_0/output_low" [id=15, type=Constant]; +"16 Conv_Add/fq_output_0/input_high" [id=16, type=Constant]; +"17 Conv_Add/fq_output_0/input_low" [id=17, type=Constant]; +"18 Bias" [id=18, type=Constant]; +"19 Conv/fq_weights_1" [id=19, type=FakeQuantize]; +"20 Conv/fq_weights_1/output_high" [id=20, type=Constant]; +"21 Conv/fq_weights_1/output_low" [id=21, type=Constant]; +"22 Conv/fq_weights_1/input_high" [id=22, type=Constant]; +"23 Conv/fq_weights_1/input_low" [id=23, type=Constant]; +"24 Conv/Constant_2" [id=24, type=Constant]; +"25 Input_1/fq_output_0/output_high" [id=25, type=Constant]; +"26 Input_1/fq_output_0/output_low" [id=26, type=Constant]; +"27 Input_1/fq_output_0/input_high" [id=27, type=Constant]; +"28 Input_1/fq_output_0/input_low" [id=28, type=Constant]; +"0 Input_1" -> "1 Input_1/fq_output_0" [label="[1, 2, 3, 4, 4]", style=solid]; +"1 Input_1/fq_output_0" -> "2 Conv" [label="[1, 2, 3, 4, 4]", style=solid]; +"2 Conv" -> "3 Conv_Add" [label="[1, 4, 1, 2, 2]", style=solid]; +"3 Conv_Add" -> "4 Conv_Add/fq_output_0" [label="[1, 4, 3, 2, 2]", style=solid]; +"4 Conv_Add/fq_output_0" -> "5 GroupNormalization_9" [label="[1, 4, 3, 2, 2]", style=solid]; +"5 GroupNormalization_9" -> "6 Relu" [label="[1, 4, 3, 2, 2]", style=solid]; +"6 Relu" -> "7 Mul" [label="[1, 4, 3, 2, 2]", style=solid]; +"7 Mul" -> "8 Add" [label="[1, 4, 3, 2, 2]", style=solid]; +"8 Add" -> "9 Result" [label="[1, 4, 3, 2, 2]", style=solid]; +"10 Add/Constant_13" -> "8 Add" [label="[1, 4, 1, 1, 1]", style=solid]; +"11 Mul/Constant_11" -> "7 Mul" [label="[1, 4, 1, 1, 1]", style=solid]; +"12 Constant_8" -> "5 GroupNormalization_9" [label="[4]", style=solid]; +"13 Constant_7" -> "5 GroupNormalization_9" [label="[4]", style=solid]; +"14 Conv_Add/fq_output_0/output_high" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; +"15 Conv_Add/fq_output_0/output_low" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; +"16 Conv_Add/fq_output_0/input_high" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; +"17 Conv_Add/fq_output_0/input_low" -> "4 Conv_Add/fq_output_0" [label="[]", style=solid]; +"18 Bias" -> "3 Conv_Add" [label="[1, 1, 3, 1, 1]", style=solid]; +"19 Conv/fq_weights_1" -> "2 Conv" [label="[4, 2, 3, 3, 3]", style=solid]; +"20 Conv/fq_weights_1/output_high" -> "19 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; +"21 Conv/fq_weights_1/output_low" -> "19 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; +"22 Conv/fq_weights_1/input_high" -> "19 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; +"23 Conv/fq_weights_1/input_low" -> "19 Conv/fq_weights_1" [label="[4, 1, 1, 1, 1]", style=solid]; +"24 Conv/Constant_2" -> "19 Conv/fq_weights_1" [label="[4, 2, 3, 3, 3]", style=solid]; +"25 Input_1/fq_output_0/output_high" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"26 Input_1/fq_output_0/output_low" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"27 Input_1/fq_output_0/input_high" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +"28 Input_1/fq_output_0/input_low" -> "1 Input_1/fq_output_0" [label="[]", style=solid]; +} diff --git a/tests/openvino/native/data/2024.3/reference_graphs/quantized/LSTMModel.dot b/tests/openvino/native/data/2024.3/reference_graphs/quantized/LSTMModel.dot new file mode 100644 index 00000000000..cd0f967ad7e --- /dev/null +++ b/tests/openvino/native/data/2024.3/reference_graphs/quantized/LSTMModel.dot @@ -0,0 +1,200 @@ +strict digraph { +"0 Input" [id=0, type=Parameter]; +"1 ReadValue_1" [id=1, type=ReadValue]; +"2 Input/fq_output_0" [id=2, type=FakeQuantize]; +"3 Add" [id=3, type=Add]; +"4 Squeeze_1" [id=4, type=Squeeze]; +"5 Add/fq_output_0" [id=5, type=FakeQuantize]; +"6 MatMul_1" [id=6, type=MatMul]; +"7 MatMul_2" [id=7, type=MatMul]; +"8 MatMul_1/fq_output_0" [id=8, type=FakeQuantize]; +"9 MatMul_2/fq_output_0" [id=9, type=FakeQuantize]; +"10 Add_1" [id=10, type=Add]; +"11 Split_14" [id=11, type=Split]; +"12 Sigmoid_1" [id=12, type=Sigmoid]; +"13 Sigmoid_2" [id=13, type=Sigmoid]; +"14 Sigmoid_3" [id=14, type=Sigmoid]; +"15 Tanh_1" [id=15, type=Tanh]; +"16 Multiply_1" [id=16, type=Multiply]; +"17 Sigmoid_2/fq_output_0" [id=17, type=FakeQuantize]; +"18 Sigmoid_3/fq_output_0" [id=18, type=FakeQuantize]; +"19 Tanh_1/fq_output_0" [id=19, type=FakeQuantize]; +"20 Multiply_1/fq_output_0" [id=20, type=FakeQuantize]; +"21 Multiply_2" [id=21, type=Multiply]; +"22 Multiply_3" [id=22, type=Multiply]; +"23 Add_2" [id=23, type=Add]; +"24 Multiply_2/fq_output_0" [id=24, type=FakeQuantize]; +"25 Assign_25" [id=25, type=Assign]; +"26 MatMul_3/fq_input_0" [id=26, type=FakeQuantize]; +"27 Tanh_2" [id=27, type=Tanh]; +"28 MatMul_3" [id=28, type=MatMul]; +"29 Tanh_2/fq_output_0" [id=29, type=FakeQuantize]; +"30 Result" [id=30, type=Result]; +"31 Tanh_2/fq_output_0/output_high" [id=31, type=Constant]; +"32 Tanh_2/fq_output_0/output_low" [id=32, type=Constant]; +"33 Tanh_2/fq_output_0/input_high" [id=33, type=Constant]; +"34 Tanh_2/fq_output_0/input_low" [id=34, type=Constant]; +"35 Multiply_2/fq_output_0/output_high" [id=35, type=Constant]; +"36 Multiply_2/fq_output_0/output_low" [id=36, type=Constant]; +"37 Multiply_2/fq_output_0/input_high" [id=37, type=Constant]; +"38 Multiply_2/fq_output_0/input_low" [id=38, type=Constant]; +"39 Sigmoid_2/fq_output_0/output_high" [id=39, type=Constant]; +"40 Sigmoid_2/fq_output_0/output_low" [id=40, type=Constant]; +"41 Sigmoid_2/fq_output_0/input_high" [id=41, type=Constant]; +"42 Sigmoid_2/fq_output_0/input_low" [id=42, type=Constant]; +"43 Constant_13" [id=43, type=Constant]; +"44 MatMul_2/fq_output_0/output_high" [id=44, type=Constant]; +"45 MatMul_2/fq_output_0/output_low" [id=45, type=Constant]; +"46 MatMul_2/fq_output_0/input_high" [id=46, type=Constant]; +"47 MatMul_2/fq_output_0/input_low" [id=47, type=Constant]; +"48 MatMul_2/fq_weights_1" [id=48, type=FakeQuantize]; +"49 MatMul_2/fq_weights_1/output_high" [id=49, type=Constant]; +"50 MatMul_2/fq_weights_1/output_low" [id=50, type=Constant]; +"51 MatMul_2/fq_weights_1/input_high" [id=51, type=Constant]; +"52 MatMul_2/fq_weights_1/input_low" [id=52, type=Constant]; +"53 MatMul_2/Constant_10" [id=53, type=Constant]; +"54 Add/fq_output_0/output_high" [id=54, type=Constant]; +"55 Add/fq_output_0/output_low" [id=55, type=Constant]; +"56 Add/fq_output_0/input_high" [id=56, type=Constant]; +"57 Add/fq_output_0/input_low" [id=57, type=Constant]; +"58 Add/Constant_8" [id=58, type=Constant]; +"59 ReadValue_1/Constant_6" [id=59, type=Constant]; +"60 MatMul_1/fq_output_0/output_high" [id=60, type=Constant]; +"61 MatMul_1/fq_output_0/output_low" [id=61, type=Constant]; +"62 MatMul_1/fq_output_0/input_high" [id=62, type=Constant]; +"63 MatMul_1/fq_output_0/input_low" [id=63, type=Constant]; +"64 MatMul_1/fq_weights_1" [id=64, type=FakeQuantize]; +"65 MatMul_1/fq_weights_1/output_high" [id=65, type=Constant]; +"66 MatMul_1/fq_weights_1/output_low" [id=66, type=Constant]; +"67 MatMul_1/fq_weights_1/input_high" [id=67, type=Constant]; +"68 MatMul_1/fq_weights_1/input_low" [id=68, type=Constant]; +"69 MatMul_1/Constant_4" [id=69, type=Constant]; +"70 Squeeze_1/Constant_2" [id=70, type=Constant]; +"71 Input/fq_output_0/output_high" [id=71, type=Constant]; +"72 Input/fq_output_0/output_low" [id=72, type=Constant]; +"73 Input/fq_output_0/input_high" [id=73, type=Constant]; +"74 Input/fq_output_0/input_low" [id=74, type=Constant]; +"75 Tanh_1/fq_output_0/output_high" [id=75, type=Constant]; +"76 Tanh_1/fq_output_0/output_low" [id=76, type=Constant]; +"77 Tanh_1/fq_output_0/input_high" [id=77, type=Constant]; +"78 Tanh_1/fq_output_0/input_low" [id=78, type=Constant]; +"79 Multiply_1/fq_output_0/output_high" [id=79, type=Constant]; +"80 Multiply_1/fq_output_0/output_low" [id=80, type=Constant]; +"81 Multiply_1/fq_output_0/input_high" [id=81, type=Constant]; +"82 Multiply_1/fq_output_0/input_low" [id=82, type=Constant]; +"83 Multiply_1/Constant_19" [id=83, type=Constant]; +"84 Sigmoid_3/fq_output_0/output_high" [id=84, type=Constant]; +"85 Sigmoid_3/fq_output_0/output_low" [id=85, type=Constant]; +"86 Sigmoid_3/fq_output_0/input_high" [id=86, type=Constant]; +"87 Sigmoid_3/fq_output_0/input_low" [id=87, type=Constant]; +"88 MatMul_3/fq_weights_1" [id=88, type=FakeQuantize]; +"89 MatMul_3/fq_weights_1/output_high" [id=89, type=Constant]; +"90 MatMul_3/fq_weights_1/output_low" [id=90, type=Constant]; +"91 MatMul_3/fq_weights_1/input_high" [id=91, type=Constant]; +"92 MatMul_3/fq_weights_1/input_low" [id=92, type=Constant]; +"93 MatMul_3/Constant_26" [id=93, type=Constant]; +"94 MatMul_3/fq_input_0/output_high" [id=94, type=Constant]; +"95 MatMul_3/fq_input_0/output_low" [id=95, type=Constant]; +"96 MatMul_3/fq_input_0/input_high" [id=96, type=Constant]; +"97 MatMul_3/fq_input_0/input_low" [id=97, type=Constant]; +"0 Input" -> "2 Input/fq_output_0" [label="[1, 1, 128]", style=solid]; +"1 ReadValue_1" -> "3 Add" [label="[1, 64]", style=solid]; +"2 Input/fq_output_0" -> "4 Squeeze_1" [label="[1, 1, 128]", style=solid]; +"3 Add" -> "5 Add/fq_output_0" [label="[1, 64]", style=solid]; +"4 Squeeze_1" -> "6 MatMul_1" [label="[1, 128]", style=solid]; +"5 Add/fq_output_0" -> "7 MatMul_2" [label="[1, 64]", style=solid]; +"6 MatMul_1" -> "8 MatMul_1/fq_output_0" [label="[1, 256]", style=solid]; +"7 MatMul_2" -> "9 MatMul_2/fq_output_0" [label="[1, 256]", style=solid]; +"8 MatMul_1/fq_output_0" -> "10 Add_1" [label="[1, 256]", style=solid]; +"9 MatMul_2/fq_output_0" -> "10 Add_1" [label="[1, 256]", style=solid]; +"10 Add_1" -> "11 Split_14" [label="[1, 256]", style=solid]; +"11 Split_14" -> "12 Sigmoid_1" [label="[1, 64]", style=solid]; +"11 Split_14" -> "13 Sigmoid_2" [label="[1, 64]", style=solid]; +"11 Split_14" -> "14 Sigmoid_3" [label="[1, 64]", style=solid]; +"11 Split_14" -> "15 Tanh_1" [label="[1, 64]", style=solid]; +"12 Sigmoid_1" -> "16 Multiply_1" [label="[1, 64]", style=solid]; +"13 Sigmoid_2" -> "17 Sigmoid_2/fq_output_0" [label="[1, 64]", style=solid]; +"14 Sigmoid_3" -> "18 Sigmoid_3/fq_output_0" [label="[1, 64]", style=solid]; +"15 Tanh_1" -> "19 Tanh_1/fq_output_0" [label="[1, 64]", style=solid]; +"16 Multiply_1" -> "20 Multiply_1/fq_output_0" [label="[1, 64]", style=solid]; +"17 Sigmoid_2/fq_output_0" -> "21 Multiply_2" [label="[1, 64]", style=solid]; +"18 Sigmoid_3/fq_output_0" -> "22 Multiply_3" [label="[1, 64]", style=solid]; +"19 Tanh_1/fq_output_0" -> "21 Multiply_2" [label="[1, 64]", style=solid]; +"20 Multiply_1/fq_output_0" -> "23 Add_2" [label="[1, 64]", style=solid]; +"21 Multiply_2" -> "24 Multiply_2/fq_output_0" [label="[1, 64]", style=solid]; +"22 Multiply_3" -> "25 Assign_25" [label="[1, 64]", style=solid]; +"22 Multiply_3" -> "26 MatMul_3/fq_input_0" [label="[1, 64]", style=solid]; +"23 Add_2" -> "27 Tanh_2" [label="[1, 64]", style=solid]; +"24 Multiply_2/fq_output_0" -> "23 Add_2" [label="[1, 64]", style=solid]; +"26 MatMul_3/fq_input_0" -> "28 MatMul_3" [label="[1, 64]", style=solid]; +"27 Tanh_2" -> "29 Tanh_2/fq_output_0" [label="[1, 64]", style=solid]; +"28 MatMul_3" -> "30 Result" [label="[1, 128]", style=solid]; +"29 Tanh_2/fq_output_0" -> "22 Multiply_3" [label="[1, 64]", style=solid]; +"31 Tanh_2/fq_output_0/output_high" -> "29 Tanh_2/fq_output_0" [label="[]", style=solid]; +"32 Tanh_2/fq_output_0/output_low" -> "29 Tanh_2/fq_output_0" [label="[]", style=solid]; +"33 Tanh_2/fq_output_0/input_high" -> "29 Tanh_2/fq_output_0" [label="[]", style=solid]; +"34 Tanh_2/fq_output_0/input_low" -> "29 Tanh_2/fq_output_0" [label="[]", style=solid]; +"35 Multiply_2/fq_output_0/output_high" -> "24 Multiply_2/fq_output_0" [label="[]", style=solid]; +"36 Multiply_2/fq_output_0/output_low" -> "24 Multiply_2/fq_output_0" [label="[]", style=solid]; +"37 Multiply_2/fq_output_0/input_high" -> "24 Multiply_2/fq_output_0" [label="[]", style=solid]; +"38 Multiply_2/fq_output_0/input_low" -> "24 Multiply_2/fq_output_0" [label="[]", style=solid]; +"39 Sigmoid_2/fq_output_0/output_high" -> "17 Sigmoid_2/fq_output_0" [label="[]", style=solid]; +"40 Sigmoid_2/fq_output_0/output_low" -> "17 Sigmoid_2/fq_output_0" [label="[]", style=solid]; +"41 Sigmoid_2/fq_output_0/input_high" -> "17 Sigmoid_2/fq_output_0" [label="[]", style=solid]; +"42 Sigmoid_2/fq_output_0/input_low" -> "17 Sigmoid_2/fq_output_0" [label="[]", style=solid]; +"43 Constant_13" -> "11 Split_14" [label="[]", style=dashed]; +"44 MatMul_2/fq_output_0/output_high" -> "9 MatMul_2/fq_output_0" [label="[]", style=solid]; +"45 MatMul_2/fq_output_0/output_low" -> "9 MatMul_2/fq_output_0" [label="[]", style=solid]; +"46 MatMul_2/fq_output_0/input_high" -> "9 MatMul_2/fq_output_0" 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/basenet/basenet.37/Conv/WithoutBiases/fq_weights_1" -> "78 /basenet/basenet.37/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"327 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1/output_high" -> "326 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"328 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1/output_low" -> "326 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"329 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1/input_high" -> "326 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"330 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1/input_low" -> "326 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"331 onnx^^Conv_435" -> "326 /basenet/basenet.37/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"332 /basenet/basenet.36/Relu/fq_output_0/output_high" -> "74 /basenet/basenet.36/Relu/fq_output_0" [label="[]", style=solid]; +"333 /basenet/basenet.36/Relu/fq_output_0/output_low" -> "74 /basenet/basenet.36/Relu/fq_output_0" [label="[]", style=solid]; +"334 /basenet/basenet.36/Relu/fq_output_0/input_high" -> "74 /basenet/basenet.36/Relu/fq_output_0" [label="[]", style=solid]; +"335 /basenet/basenet.36/Relu/fq_output_0/input_low" -> "74 /basenet/basenet.36/Relu/fq_output_0" [label="[]", style=solid]; +"336 Reshape_233" -> "68 /basenet/basenet.34/Conv" [label="[1, 512, 1, 1]", style=solid]; +"337 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1" -> "66 /basenet/basenet.34/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"338 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1/output_high" -> "337 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"339 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1/output_low" -> "337 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"340 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1/input_high" -> "337 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"341 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1/input_low" -> "337 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"342 onnx^^Conv_432" -> "337 /basenet/basenet.34/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"343 /basenet/basenet.32/Relu/fq_output_0/output_high" -> "63 /basenet/basenet.32/Relu/fq_output_0" [label="[]", style=solid]; +"344 /basenet/basenet.32/Relu/fq_output_0/output_low" -> "63 /basenet/basenet.32/Relu/fq_output_0" [label="[]", style=solid]; +"345 /basenet/basenet.32/Relu/fq_output_0/input_high" -> "63 /basenet/basenet.32/Relu/fq_output_0" [label="[]", style=solid]; +"346 /basenet/basenet.32/Relu/fq_output_0/input_low" -> "63 /basenet/basenet.32/Relu/fq_output_0" [label="[]", style=solid]; +"347 Reshape_218" -> "61 /basenet/basenet.30/Conv" [label="[1, 512, 1, 1]", style=solid]; +"348 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1" -> "60 /basenet/basenet.30/Conv/WithoutBiases" [label="[512, 512, 3, 3]", style=solid]; +"349 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1/output_high" -> "348 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"350 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1/output_low" -> "348 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"351 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1/input_high" -> "348 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"352 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1/input_low" -> "348 /basenet/basenet.30/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"353 onnx^^Conv_429" -> "348 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style=solid]; +"361 /basenet/basenet.27/Conv/WithoutBiases/fq_weights_1/output_low" -> "359 /basenet/basenet.27/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"362 /basenet/basenet.27/Conv/WithoutBiases/fq_weights_1/input_high" -> "359 /basenet/basenet.27/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"363 /basenet/basenet.27/Conv/WithoutBiases/fq_weights_1/input_low" -> "359 /basenet/basenet.27/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"364 onnx^^Conv_426" -> "359 /basenet/basenet.27/Conv/WithoutBiases/fq_weights_1" [label="[512, 512, 3, 3]", style=solid]; +"365 /basenet/basenet.26/Relu/fq_output_0/output_high" -> "55 /basenet/basenet.26/Relu/fq_output_0" [label="[]", style=solid]; +"366 /basenet/basenet.26/Relu/fq_output_0/output_low" -> "55 /basenet/basenet.26/Relu/fq_output_0" [label="[]", style=solid]; +"367 /basenet/basenet.26/Relu/fq_output_0/input_high" -> "55 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/basenet/basenet.24/Conv/WithoutBiases/fq_weights_1/input_low" -> "370 /basenet/basenet.24/Conv/WithoutBiases/fq_weights_1" [label="[512, 1, 1, 1]", style=solid]; +"375 onnx^^Conv_423" -> "370 /basenet/basenet.24/Conv/WithoutBiases/fq_weights_1" [label="[512, 256, 3, 3]", style=solid]; +"376 /basenet/basenet.22/Relu/fq_output_0/output_high" -> "50 /basenet/basenet.22/Relu/fq_output_0" [label="[]", style=solid]; +"377 /basenet/basenet.22/Relu/fq_output_0/output_low" -> "50 /basenet/basenet.22/Relu/fq_output_0" [label="[]", style=solid]; +"378 /basenet/basenet.22/Relu/fq_output_0/input_high" -> "50 /basenet/basenet.22/Relu/fq_output_0" [label="[]", style=solid]; +"379 /basenet/basenet.22/Relu/fq_output_0/input_low" -> "50 /basenet/basenet.22/Relu/fq_output_0" [label="[]", style=solid]; +"380 Reshape_175" -> "48 /basenet/basenet.20/Conv" [label="[1, 256, 1, 1]", style=solid]; +"381 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1" -> "47 /basenet/basenet.20/Conv/WithoutBiases" [label="[256, 256, 3, 3]", style=solid]; +"382 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1/output_high" -> "381 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"383 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1/output_low" -> "381 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"384 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1/input_high" -> "381 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"385 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1/input_low" -> "381 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1" [label="[256, 1, 1, 1]", style=solid]; +"386 onnx^^Conv_420" -> "381 /basenet/basenet.20/Conv/WithoutBiases/fq_weights_1" [label="[256, 256, 3, 3]", style=solid]; +"387 /basenet/basenet.19/Relu/fq_output_0/output_high" -> "46 /basenet/basenet.19/Relu/fq_output_0" [label="[]", style=solid]; +"388 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"77 /detection_head/heads.0/loc/Conv" [label="[1, 16, 1, 1]", style=solid]; +"583 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1" -> "73 /detection_head/heads.0/loc/Conv/WithoutBiases" [label="[16, 512, 3, 3]", style=solid]; +"584 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1/output_high" -> "583 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"585 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1/output_low" -> "583 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"586 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1/input_high" -> "583 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"587 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1/input_low" -> "583 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1" [label="[16, 1, 1, 1]", style=solid]; +"588 detection_head.heads.0.loc.weight" -> "583 /detection_head/heads.0/loc/Conv/WithoutBiases/fq_weights_1" [label="[16, 512, 3, 3]", style=solid]; +} diff --git a/tests/openvino/native/models.py b/tests/openvino/native/models.py index 77ec443274f..95f079a8800 100644 --- a/tests/openvino/native/models.py +++ b/tests/openvino/native/models.py @@ -601,27 +601,33 @@ def _create_ov_model(self, input_name) -> ov.Model: @SYNTHETIC_MODELS.register() class IntegerModel(OVReferenceModel): - def _create_ov_model(self): - input_1 = opset.parameter([1, 7, 1], name="Input") + def _create_ov_model(self, dim1=1, dim2=7, dim3=6, max_input_value=2, add_batch_dimension=False): + input_1 = opset.parameter([dim1, dim2, dim1], name="Input") convert_1 = opset.convert(input_1, destination_type="i64", name="Convert_1") - gather_1 = opset.gather(convert_1, 2, axis=0, batch_dims=0) + gather_1 = opset.gather(convert_1, 0, axis=0, batch_dims=0) gather_1.set_friendly_name("Gather_1") - gather_2_data = opset.constant(self._rng.random((3, 6)), dtype=np.float32, name="gather_2_data") + gather_2_data = opset.constant( + self._rng.random((max_input_value + 1, dim3)), dtype=np.float32, name="gather_2_data" + ) gather_2 = opset.gather(gather_2_data, gather_1, axis=0, batch_dims=0) gather_2.set_friendly_name("Gather_2") gather_3 = opset.gather(gather_2, 2, axis=0, batch_dims=0) + if add_batch_dimension: + gather_3 = opset.unsqueeze(gather_3, 0) gather_3.set_friendly_name("Gather_3") - matmul_1_data = opset.constant(self._rng.random((6, 6)), dtype=np.float32, name="matmul_1_data") + matmul_1_data = opset.constant(self._rng.random((dim3, dim3)), dtype=np.float32, name="matmul_1_data") matmul_1 = opset.matmul(gather_3, matmul_1_data, transpose_a=False, transpose_b=True, name="MatMul_1") gather_4 = opset.gather(input_1, 0, axis=2, batch_dims=0) + if add_batch_dimension: + gather_4 = opset.unsqueeze(gather_4, 0) gather_4.set_friendly_name("Gather_4") - matmul_2_data = opset.constant(self._rng.random((6, 7)), dtype=np.float32, name="matmul_2_data") + matmul_2_data = opset.constant(self._rng.random((dim3, dim2)), dtype=np.float32, name="matmul_2_data") matmul_2 = opset.matmul(gather_4, matmul_2_data, transpose_a=False, transpose_b=True, name="MatMul_2") add_1 = opset.add(matmul_1, matmul_2, name="Add_1") @@ -792,7 +798,7 @@ class SequentialMatmulModel(OVReferenceModel): """ def _create_ov_model(self): - input_node = opset.parameter([3, 3], name="Input_1") + input_node = opset.parameter([1, 3, 3], name="Input_1") main_values = [10000, 1000, 1, 10, 10000] last_node = input_node @@ -820,7 +826,7 @@ def _create_ov_model(self, weights_dtype: Optional[ov.Type] = None, activation_d weights_dtype = ov.Type.f32 if weights_dtype is None else weights_dtype activation_dtype = ov.Type.f32 if activation_dtype is None else activation_dtype - input_node = opset.parameter([3, 3], dtype=activation_dtype, name="Input_1") + input_node = opset.parameter([1, 3, 3], dtype=activation_dtype, name="Input_1") weights_data = np.eye(3) * 255 current_weights = opset.constant(weights_data, dtype=weights_dtype, name="weights") if weights_dtype != activation_dtype: @@ -949,8 +955,8 @@ def get_weights(weights_data, is_int8, name): ) return (qw - zp) * scale - def _create_ov_model(self, is_int8=False): - input_node = opset.parameter([8, 8], name="Input_1") + def _create_ov_model(self, n_extra_dims: int = 1, is_int8=False): + input_node = opset.parameter([1] * n_extra_dims + [-1, 8], name="Input_1") weights_data1 = np.arange(0, 64).reshape(8, 8) weights_data1[:] = 2.0 @@ -998,7 +1004,7 @@ class AWQActMatmulModel(OVReferenceModel): """ def _create_ov_model(self, is_int8=False, with_multiply=False, n_layers=8): - input_node = opset.parameter([8, 8], name="Input_1") + input_node = opset.parameter([1, 8, 8], name="Input_1") weights_data = np.arange(0, 64).reshape(8, 8) - 32 weights = AWQMatmulModel.get_weights(weights_data, is_int8, name="weights_emb") out_node = opset.matmul(input_node, weights, transpose_a=False, transpose_b=True, name="MatMul_emb") @@ -1017,7 +1023,7 @@ def _create_ov_model(self, is_int8=False, with_multiply=False, n_layers=8): alpha = np.array([1.5], dtype=np.float32) alpha = opset.constant(alpha, dtype=np.float32) lambda_value = np.array([1.5], dtype=np.float32) - lambda_value = opset.constant(alpha, dtype=np.float32) + lambda_value = opset.constant(lambda_value, dtype=np.float32) node2 = opset.selu(mm2, alpha, lambda_value, name=f"SeLU_{i}") node_multiply = opset.multiply(node1, node2, name=f"Multiply_{i}") @@ -1102,7 +1108,7 @@ def _create_ov_model(self, stateful=True): result.get_output_tensor(0).set_names(set(["Result"])) model = ov.Model(results=[result], sinks=[node], parameters=[input_data], name="TestModel") else: - bias = opset.constant(init_val, data_type) + bias = opset.constant(np.zeros(input_shape), data_type) add = opset.add(input_data, bias, name="Add") scale_val = opset.constant(np.ones(input_shape), data_type) scale = opset.multiply(add, scale_val, name="Scale") @@ -1156,3 +1162,26 @@ def _create_ov_model(self): result = opset.result(conv, name="Result") model = ov.Model([result], [input]) return model + + +class RoPEModel(OVReferenceModel): + def _create_ov_model(self): + position_ids = opset.parameter([1, 10], name="position_ids") + + unsqueeze = opset.unsqueeze(position_ids, 0, name="unsqueeze") + convert = opset.convert(unsqueeze, ov.Type.f32, name="convert") + + broadcast_data = self._rng.random((1, 5, 1)).astype(np.float32) + broadcast_shape = [1, 5, 1] + broadcast = opset.broadcast(broadcast_data, broadcast_shape, name="broadcast") + + matmul = opset.matmul(broadcast, convert, transpose_a=False, transpose_b=False, name="MatMul") + transpose = opset.transpose(matmul, [0, 2, 1], name="transpose") + concat = opset.concat([transpose], axis=0, name="concat") + sin = opset.sin(concat, name="sin") + cos = opset.cos(concat, name="cos") + sin_result = opset.result(sin, name="sin_result") + cos_result = opset.result(cos, name="cos_result") + + model = ov.Model([sin_result, cos_result], [position_ids]) + return model diff --git a/tests/openvino/native/quantization/test_fq_params_calculation.py b/tests/openvino/native/quantization/test_fq_params_calculation.py index ce6fe1d356c..3053330cc77 100644 --- a/tests/openvino/native/quantization/test_fq_params_calculation.py +++ b/tests/openvino/native/quantization/test_fq_params_calculation.py @@ -21,6 +21,8 @@ from nncf.parameters import QuantizationMode from nncf.quantization.advanced_parameters import OverflowFix from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization +from tests.cross_fw.shared.helpers import compare_stats +from tests.cross_fw.shared.helpers import load_json from tests.openvino.native.common import convert_torch_model from tests.openvino.native.common import get_actual_reference_for_current_openvino from tests.openvino.native.common import get_dataset_for_test @@ -32,8 +34,6 @@ from tests.openvino.native.models import UnifiedScalesModel from tests.openvino.native.models import WeightsModel from tests.openvino.native.models import get_torch_model_info -from tests.shared.helpers import compare_stats -from tests.shared.helpers import load_json REFERENCE_SCALES_DIR = Path("reference_scales") diff --git a/tests/openvino/native/quantization/test_gptq.py b/tests/openvino/native/quantization/test_gptq.py index ad19990eac0..d8274580389 100644 --- a/tests/openvino/native/quantization/test_gptq.py +++ b/tests/openvino/native/quantization/test_gptq.py @@ -22,6 +22,10 @@ from nncf.quantization.algorithms.weight_compression.gptq import GPTQ from nncf.tensor.tensor import Tensor +# Modification Notes: +# This unit test is adapted from: +# https://github.com/AutoGPTQ/AutoGPTQ/blob/main/auto_gptq/quantization/quantizer.py + def quantize(x, scale, zero, minq, maxq): if maxq < 0: diff --git a/tests/openvino/native/quantization/test_graphs.py b/tests/openvino/native/quantization/test_graphs.py index bc72efd7912..7dc3c94c081 100644 --- a/tests/openvino/native/quantization/test_graphs.py +++ b/tests/openvino/native/quantization/test_graphs.py @@ -40,6 +40,7 @@ from tests.openvino.native.models import IfModel from tests.openvino.native.models import IfModel_2 from tests.openvino.native.models import MatmulSoftmaxMatmulBlock +from tests.openvino.native.models import RoPEModel from tests.openvino.native.models import ScaledDotProductAttentionModel from tests.openvino.native.models import get_torch_model_info from tests.openvino.native.quantization.test_fq_params_calculation import quantize_model @@ -96,7 +97,7 @@ def test_real_models_fq_placement(model_name_params, tmp_path): compare_nncf_graphs(quantized_model, path_ref_graph) -@pytest.mark.parametrize("model_creator_func", [MatmulSoftmaxMatmulBlock]) +@pytest.mark.parametrize("model_creator_func", [MatmulSoftmaxMatmulBlock, RoPEModel]) def test_transformer_models_fq_placement(model_creator_func, tmp_path): model = model_creator_func() quantized_model = quantize_model( diff --git a/tests/openvino/native/quantization/test_min_max.py b/tests/openvino/native/quantization/test_min_max.py index fcccf04bf5f..9f849be4adf 100644 --- a/tests/openvino/native/quantization/test_min_max.py +++ b/tests/openvino/native/quantization/test_min_max.py @@ -43,7 +43,7 @@ def create_target_point(self, target_point_type: TargetType, name: str, port_id: class TestOVGetTargetPointShape(TemplateTestGetTargetPointShape, TestOVMinMaxAlgorithm): def get_nncf_graph(self, weight_port_id: int, weight_shape: Tuple[int]) -> NNCFGraph: - conv_layer_attrs = OVLayerAttributes({weight_port_id: {"name": "dummy", "shape": weight_shape}}) + conv_layer_attrs = OVLayerAttributes({weight_port_id: {"name": "dummy", "shape": weight_shape, "dtype": "f32"}}) return NNCFGraphToTest(OVConvolutionMetatype, conv_layer_attrs).nncf_graph @@ -58,7 +58,7 @@ def matmul_metatype(self): @staticmethod def get_conv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> OVLayerAttributes: - constant_attributes = {weight_port_id: {"name": "dummy", "shape": weight_shape}} + constant_attributes = {weight_port_id: {"name": "dummy", "shape": weight_shape, "dtype": "f32"}} return OVLayerAttributes(constant_attributes, {}, {}) @staticmethod @@ -69,7 +69,7 @@ def get_depthwiseconv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) def get_matmul_node_attrs( weight_port_id: int, transpose_weight: Tuple[int], weight_shape: Tuple[int] ) -> OVLayerAttributes: - constant_attributes = {weight_port_id: {"name": "dummy", "shape": weight_shape}} + constant_attributes = {weight_port_id: {"name": "dummy", "shape": weight_shape, "dtype": "f32"}} constant_attributes[weight_port_id]["transpose"] = transpose_weight return OVLayerAttributes(constant_attributes, {}, {}) diff --git a/tests/openvino/native/quantization/test_ptq_params.py b/tests/openvino/native/quantization/test_ptq_params.py index e0200ec07f1..92097c68b41 100644 --- a/tests/openvino/native/quantization/test_ptq_params.py +++ b/tests/openvino/native/quantization/test_ptq_params.py @@ -136,7 +136,7 @@ def test_params(self): @pytest.fixture( params=[ (IgnoredScope(), 1, 1), - (IgnoredScope(["MatMul"]), 1, 0), + (IgnoredScope(["MatMul"]), 0, 0), (IgnoredScope(["Add"]), 1, 1), (IgnoredScope(["MatMul", "Add"]), 0, 0), ] diff --git a/tests/openvino/native/quantization/test_quantize_api.py b/tests/openvino/native/quantization/test_quantize_api.py index ddbb6612d96..b272553c985 100644 --- a/tests/openvino/native/quantization/test_quantize_api.py +++ b/tests/openvino/native/quantization/test_quantize_api.py @@ -17,7 +17,7 @@ import nncf from nncf import Dataset -from tests.shared.datasets import MockDataset +from tests.cross_fw.shared.datasets import MockDataset INPUT_SHAPE = [2, 1, 1, 1] @@ -32,6 +32,6 @@ def get_mock_model() -> Model: def test_non_positive_subset_size(): model_to_test = get_mock_model() - with pytest.raises(ValueError) as e: + with pytest.raises(nncf.ValidationError) as e: nncf.quantize(model_to_test, Dataset(MockDataset(INPUT_SHAPE)), subset_size=0) assert "Subset size must be positive." in e.info diff --git a/tests/openvino/native/quantization/test_quantizer_config.py b/tests/openvino/native/quantization/test_quantizer_config.py index 020ef951009..1be390fbce9 100644 --- a/tests/openvino/native/quantization/test_quantizer_config.py +++ b/tests/openvino/native/quantization/test_quantizer_config.py @@ -11,14 +11,21 @@ import pytest +from nncf.common.utils.backend import BackendType from nncf.openvino.graph.layer_attributes import OVLayerAttributes +from nncf.openvino.graph.metatypes.openvino_metatypes import OVConstantMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVConvolutionMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVDepthwiseConvolutionMetatype +from nncf.openvino.graph.metatypes.openvino_metatypes import OVMatMulMetatype +from nncf.openvino.graph.metatypes.openvino_metatypes import OVMultiplyMetatype +from nncf.openvino.graph.metatypes.openvino_metatypes import OVSoftmaxMetatype from nncf.openvino.graph.metatypes.openvino_metatypes import OVSumMetatype +from nncf.openvino.graph.metatypes.openvino_metatypes import OVTransposeMetatype from nncf.quantization.algorithms.min_max.openvino_backend import OVMinMaxAlgoBackend from tests.cross_fw.test_templates.models import NNCFGraphToTest from tests.cross_fw.test_templates.models import NNCFGraphToTestDepthwiseConv from tests.cross_fw.test_templates.models import NNCFGraphToTestSumAggregation +from tests.cross_fw.test_templates.models import NNCFGraphTransformer from tests.cross_fw.test_templates.test_quantizer_config import TemplateTestQuantizerConfig @@ -26,9 +33,12 @@ class TestQuantizerConfig(TemplateTestQuantizerConfig): def get_algo_backend(self): return OVMinMaxAlgoBackend() + def get_backend_type(self): + return BackendType.OPENVINO + @pytest.fixture def single_conv_nncf_graph(self) -> NNCFGraphToTest: - conv_layer_attrs = OVLayerAttributes({0: {"name": "dummy", "shape": (4, 4, 4, 4)}}) + conv_layer_attrs = OVLayerAttributes({0: {"name": "dummy", "shape": (4, 4, 4, 4), "dtype": "f32"}}) return NNCFGraphToTest(OVConvolutionMetatype, conv_layer_attrs) @pytest.fixture @@ -37,5 +47,16 @@ def depthwise_conv_nncf_graph(self): @pytest.fixture def conv_sum_aggregation_nncf_graph(self) -> NNCFGraphToTestSumAggregation: - conv_layer_attrs = OVLayerAttributes({0: {"name": "dummy", "shape": (4, 4, 4, 4)}}) + conv_layer_attrs = OVLayerAttributes({0: {"name": "dummy", "shape": (4, 4, 4, 4), "dtype": "f32"}}) return NNCFGraphToTestSumAggregation(OVConvolutionMetatype, OVSumMetatype, conv_layer_attrs) + + @pytest.fixture + def transformer_nncf_graph(self) -> NNCFGraphToTest: + return NNCFGraphTransformer( + matmul_metatype=OVMatMulMetatype, + softmax_metatype=OVSoftmaxMetatype, + mul_metatype=OVMultiplyMetatype, + const_metatype=OVConstantMetatype, + transpose_metatype=OVTransposeMetatype, + matmul_layer_weighted_attrs=OVLayerAttributes({}), + ) diff --git a/tests/openvino/native/quantization/test_reducers_and_aggregators.py b/tests/openvino/native/quantization/test_reducers_and_aggregators.py index 3ff3876800d..3a4e1e5bada 100644 --- a/tests/openvino/native/quantization/test_reducers_and_aggregators.py +++ b/tests/openvino/native/quantization/test_reducers_and_aggregators.py @@ -12,6 +12,8 @@ from typing import Any, List, Optional, Tuple import numpy as np +import openvino as ov +import openvino.runtime.opset13 as opset import pytest from nncf.common.graph.layer_attributes import Dtype @@ -19,15 +21,27 @@ from nncf.openvino.statistics.collectors import OVAbsQuantileReducer from nncf.openvino.statistics.collectors import OVBatchMeanReducer from nncf.openvino.statistics.collectors import OVMaxReducer +from nncf.openvino.statistics.collectors import OVMaxVarianceReducer +from nncf.openvino.statistics.collectors import OVMeanAbsMaxReducer from nncf.openvino.statistics.collectors import OVMeanPerChanelReducer from nncf.openvino.statistics.collectors import OVMeanReducer +from nncf.openvino.statistics.collectors import OVMeanVarianceReducer from nncf.openvino.statistics.collectors import OVMinReducer from nncf.openvino.statistics.collectors import OVQuantileReducer +from nncf.openvino.statistics.collectors import OVShapeReducer from nncf.tensor import Tensor from tests.common.experimental.test_reducers_and_aggregators import TemplateTestReducersAggregators class TestReducersAggregators(TemplateTestReducersAggregators): + MIXED_PRECISION_REDUCERS_REF_VALUES = [ + (OVMeanVarianceReducer, (0, 1), np.array([695.375])), + (OVMeanVarianceReducer, None, np.array([707.1875])), + (OVMaxVarianceReducer, (0, 1), np.array([710.25])), + (OVMaxVarianceReducer, None, np.array([707.1875])), + (OVMeanAbsMaxReducer, (0, 1), np.array([87.0])), + (OVMeanAbsMaxReducer, None, np.array([94.0])), + ] def get_nncf_tensor(self, x: np.array, dtype: Optional[Dtype] = None): if dtype is Dtype.INTEGER: @@ -59,3 +73,34 @@ def squeeze_tensor(self, ref_tensor: List[Any], axes: Optional[Tuple[int]] = Non def cast_tensor(self, tensor, dtype: Dtype): return tensor + + @pytest.mark.parametrize("reducer_cls,reduction_axes,ref_value", MIXED_PRECISION_REDUCERS_REF_VALUES) + def test_mixed_precision_reducers(self, reducer_cls, reduction_axes, ref_value): + input_ = np.arange(2 * 4 * 8).reshape(2, 4, 8) + input_[:, :2] *= 2 + + reducer = reducer_cls(reduction_axes=reduction_axes, inplace=True) + inplace_fn = reducer.get_inplace_fn() + + ov_model_input = opset.parameter(input_.shape) + ov_model_output = inplace_fn(ov_model_input, 0, "reducer_output") + ov_model = ov.Model([ov_model_output], [ov_model_input]) + compiled_ov_model = ov.compile_model(ov_model) + + reducer_output = compiled_ov_model(input_)[0] + assert np.allclose(reducer_output, ref_value) + + @pytest.mark.parametrize( + "input_", [np.arange(2), np.arange(2 * 4 * 8).reshape(8, 8), np.arange(2 * 4 * 8).reshape(2, 4, 8)] + ) + def test_inplace_shape_reducer(self, input_): + reducer = OVShapeReducer() + inplace_fn = reducer.get_inplace_fn() + + ov_model_input = opset.parameter(input_.shape) + ov_model_output = inplace_fn(ov_model_input, 0, "reducer_output") + ov_model = ov.Model([ov_model_output], [ov_model_input]) + compiled_ov_model = ov.compile_model(ov_model) + + reducer_output = compiled_ov_model(input_)[0] + assert all([it[0] == it[1] for it in zip(input_.shape, reducer_output)]) diff --git a/tests/openvino/native/quantization/test_weights_compression.py b/tests/openvino/native/quantization/test_weights_compression.py index c51cf667ca2..541b8a386e5 100644 --- a/tests/openvino/native/quantization/test_weights_compression.py +++ b/tests/openvino/native/quantization/test_weights_compression.py @@ -20,15 +20,18 @@ from attr import dataclass from openvino.runtime import opset13 as opset +import nncf from nncf import CompressWeightsMode from nncf import SensitivityMetric from nncf.common.utils.debug import nncf_debug from nncf.data.dataset import Dataset -from nncf.errors import ValidationError from nncf.experimental.common.tensor_statistics.collectors import AggregatorBase from nncf.openvino.graph.node_utils import get_const_value +from nncf.parameters import BackupMode from nncf.quantization import compress_weights +from nncf.quantization.advanced_parameters import AdvancedCompressionParameters from nncf.quantization.advanced_parameters import AdvancedCompressionParameters as CompressionParams +from nncf.quantization.advanced_parameters import AdvancedGPTQParameters as GPTQParams from nncf.quantization.advanced_parameters import AdvancedLoraCorrectionParameters as LoraParams from nncf.quantization.algorithms.weight_compression.config import WeightCompressionConfig from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters @@ -40,6 +43,10 @@ from nncf.quantization.algorithms.weight_compression.weight_lowering import reshape_weight_for_grouped_quantization from nncf.scopes import IgnoredScope from nncf.tensor import Tensor +from nncf.tensor import TensorDataType +from tests.cross_fw.shared.helpers import compare_stats +from tests.cross_fw.shared.helpers import dump_to_json +from tests.cross_fw.shared.helpers import load_json from tests.openvino.native.common import get_actual_reference_for_current_openvino from tests.openvino.native.models import AWQActMatmulModel from tests.openvino.native.models import AWQMatmulModel @@ -52,9 +59,6 @@ from tests.openvino.native.models import SequentialMatmulModel from tests.openvino.native.models import WeightsModel from tests.openvino.native.quantization.test_fq_params_calculation import REFERENCE_SCALES_DIR -from tests.shared.helpers import compare_stats -from tests.shared.helpers import dump_to_json -from tests.shared.helpers import load_json TEST_MODELS = { IntegerModel: ["matmul_2_data", "gather_2_data", "matmul_1_data"], @@ -76,23 +80,29 @@ class LMLinearModel(OVReferenceModel): - HIDDEN_DIM = 16 OUTPUT_DIM = 32 - INPUT_SHAPE = [24, HIDDEN_DIM] # [SeqLen, HiddenDim] + HIDDEN_DIM = 16 + INPUT_SHAPE = [1, 24, HIDDEN_DIM] # [B, SeqLen, HiddenDim] - def _create_ov_model(self, transpose_b: bool = True): - input_1 = opset.parameter(self.INPUT_SHAPE, name="Input") + def _create_ov_model(self, transpose_b: bool = True, transpose_a=False, input_shape=None): + self._input_shape = self.INPUT_SHAPE if input_shape is None else input_shape + hdim_axis = -2 if transpose_a else -1 + self._hidden_dim = self._input_shape[hdim_axis] + input_1 = opset.parameter(self._input_shape, name="Input") weight_shape = self.get_weight_shape(transpose_b) data = self._rng.random(weight_shape).astype(np.float32) - matmul = opset.matmul(input_1, data, transpose_a=False, transpose_b=transpose_b, name="MatMul") + matmul = opset.matmul(input_1, data, transpose_a=transpose_a, transpose_b=transpose_b, name="MatMul") result = opset.result(matmul, name="Result") result.get_output_tensor(0).set_names(set(["Result"])) model = ov.Model([result], [input_1]) return model - @classmethod - def get_weight_shape(cls, transpose_b: bool = True): - return [cls.OUTPUT_DIM, cls.HIDDEN_DIM] if transpose_b else [cls.HIDDEN_DIM, cls.OUTPUT_DIM] + @property + def hidden_dim(self): + return self._hidden_dim + + def get_weight_shape(self, transpose_b: bool = True): + return [self.OUTPUT_DIM, self.hidden_dim] if transpose_b else [self.hidden_dim, self.OUTPUT_DIM] def get_next_node(node): @@ -276,7 +286,7 @@ def test_compare_compressed_weights(mode, group_size, check_fn_per_node_map): ) def test_mixed_precision(mode, all_layers, ratio, ref_ids, mocker): model = SequentialMatmulModel().ov_model - dataset = Dataset([np.ones([3, 3]), np.arange(9).reshape(3, 3)]) + dataset = Dataset([np.ones([1, 3, 3]), np.arange(9).reshape(1, 3, 3)]) compressed_model = compress_weights( model, mode=CompressWeightsMode.NF4, @@ -295,8 +305,20 @@ def test_mixed_precision(mode, all_layers, ratio, ref_ids, mocker): @pytest.mark.parametrize("metric", DATA_BASED_SENSITIVITY_METRICS) def test_gather_in_4_bit_if_all_layers_with_data(metric): - model = IntegerModel().ov_model - dataset = Dataset([np.arange(7).reshape(1, 7, 1)]) + dim1 = 2 # sequence length dimension + dim2 = 7 + max_input_value = 6 + model = IntegerModel(dim1=dim1, dim2=dim2, max_input_value=max_input_value, add_batch_dimension=True).ov_model + + input_shape = (dim1, dim2, dim1) + n_inputs = input_shape[0] * input_shape[1] * input_shape[2] + n_copies = int(np.ceil(n_inputs / (max_input_value + 1))) + # Rolling is needed to ensure non-zero variance + input_ = [np.roll(np.arange(max_input_value + 1), i + 1) for i in range(n_copies)] + input_ = np.hstack(input_)[:n_inputs] + input_ = input_.reshape(input_shape) + + dataset = Dataset([input_]) compressed_model = compress_weights( model, mode=CompressWeightsMode.INT4_SYM, @@ -389,7 +411,7 @@ def test_gather_can_be_4_bit_if_all_layers_without_data(): @pytest.mark.parametrize("metric", ALL_SENSITIVITY_METRICS) def test_gather_in_8_bit_if_not_all_layers(metric): - model = IntegerModel().ov_model + model = IntegerModel(add_batch_dimension=True).ov_model dataset = Dataset([np.ones([1, 7, 1])]) compressed_model = compress_weights( model, @@ -410,10 +432,10 @@ def test_gather_in_8_bit_if_not_all_layers(metric): MAX_BASELINE_SCORE = 1 / np.finfo(np.float32).eps NON_ZERO_ROW = [-4, 1, 2] -ACTIVATION = np.array([NON_ZERO_ROW, [0, 0, 0], [0, 0, 0]]) -MAX_VAR = 3.555555 # np.max(np.var(ACTIVATION, 0)) -MEAN_VAR = 1.555555 # np.mean(np.var(ACTIVATION, 0)) -MEAN_MAX = 2.333333 # np.mean(np.max(np.abs(ACTIVATION), 0)) +ACTIVATION = np.array([[NON_ZERO_ROW, [0, 0, 0], [0, 0, 0]]]) +MAX_VAR = 3.555555 # np.max(np.var(ACTIVATION, 1)) +MEAN_VAR = 1.555555 # np.mean(np.var(ACTIVATION, 1)) +MEAN_MAX = 2.333333 # np.mean(np.max(np.abs(ACTIVATION), 1)) HESSIAN_TRACE = (16 + 1 + 4) * 2 / 9 # sum(i*i for i in NON_ZERO_ROW) * 2 / ACTIVATION.size @@ -680,12 +702,12 @@ def test_calculate_scale_per_group(desc: CalculateScaleDesc): def test_raise_error_for_many_axes(): - with pytest.raises(RuntimeError): + with pytest.raises(nncf.UnsupportedModelError): reshape_weight_for_grouped_quantization(WEIGHTS_2x4, reduction_axes=(0, 1), group_size=1) def test_raise_error_channel_size_is_not_divisible_by_group_size(): - with pytest.raises(ValidationError): + with pytest.raises(nncf.UnsupportedModelError): reshape_weight_for_grouped_quantization(WEIGHTS_2x4, reduction_axes=(0,), group_size=3) @@ -703,34 +725,67 @@ def test_raise_error_channel_size_is_not_divisible_by_group_size(): {"lora_correction": True}, {"gptq": True}, {"awq": True}, + {"backup_mode": BackupMode.NONE}, + {"backup_mode": BackupMode.INT8_ASYM}, + {"backup_mode": BackupMode.INT8_SYM}, + {"advanced_parameters": AdvancedCompressionParameters(statistics_path="anything")}, ), ) def test_raise_error_with_unsupported_params_for_int8(mode, params): - with pytest.raises(AttributeError): + with pytest.raises(nncf.ParameterNotSupportedError): compress_weights(ov.Model([], []), mode=mode, **params) -@pytest.mark.parametrize("mode", INT4_MODES) +@pytest.mark.parametrize("mode", INT4_NF4_MODES) @pytest.mark.parametrize( "params", ({"dataset": "anything", "lora_correction": True, "gptq": True},), ) def test_raise_error_with_unsupported_params_for_int4(mode, params): - with pytest.raises(AttributeError): + with pytest.raises(nncf.ValidationError): compress_weights(ov.Model([], []), mode=mode, **params) +@pytest.mark.parametrize( + "algo", + ( + "lora_correction", + "awq", + "scale_estimation", + "gptq", + ), +) +def test_raise_error_with_unsupported_params_for_e2m1(algo): + with pytest.raises(nncf.ParameterNotSupportedError): + compress_weights(ov.Model([], []), dataset="anything", mode=CompressWeightsMode.E2M1, **{algo: True}) + + +@pytest.mark.parametrize("mode", INT4_NF4_MODES) +@pytest.mark.parametrize( + "algo", + ( + "lora_correction", + "awq", + "scale_estimation", + "gptq", + ), +) +def test_raise_error_with_unsupported_params_for_empty_dataset(mode, algo): + with pytest.raises(nncf.ParameterNotSupportedError): + compress_weights(ov.Model([], []), dataset=None, mode=mode, **{algo: True}) + + @pytest.mark.parametrize("mode", INT4_NF4_MODES) @pytest.mark.parametrize("metric", DATA_BASED_SENSITIVITY_METRICS) def test_raise_error_with_data_metric_and_without_dataset(mode, metric): model = IntegerModel().ov_model - with pytest.raises(AttributeError): + with pytest.raises(nncf.ValidationError): compress_weights(model, mode=mode, sensitivity_metric=metric, group_size=-1, ratio=0.8) @pytest.mark.parametrize("mode", INT4_NF4_MODES) def test_call_max_var_criterion_with_dataset_by_default(mocker, mode): - model = IntegerModel().ov_model + model = IntegerModel(add_batch_dimension=True).ov_model dataset = Dataset([np.ones([1, 7, 1])]) criterion_cls = MIXED_PRECISION_CRITERIA.get(SensitivityMetric.MAX_ACTIVATION_VARIANCE) scores_spy = mocker.spy(criterion_cls, "_calc_sensitivity") @@ -743,18 +798,18 @@ def test_call_max_var_criterion_with_dataset_by_default(mocker, mode): @pytest.mark.parametrize("mode", INT4_MODES) def test_call_max_var_criterion_with_dataset_by_default_awq(mode): model = AWQMatmulModel().ov_model - dataset = Dataset([np.ones([8, 8])]) + dataset = Dataset([np.ones([1, 8, 8])]) compress_weights(model, mode=mode, ratio=1.0, group_size=2, dataset=dataset, awq=True) -@pytest.mark.parametrize("mode", INT4_MODES) +@pytest.mark.parametrize("mode", INT4_NF4_MODES) @pytest.mark.parametrize("with_multiply", (True, False)) def test_call_max_var_criterion_with_dataset_by_default_awq_act_matmul(mode, with_multiply): n_layers = 8 n_awq_target = n_layers - 1 # first MatMul is always int8 model = AWQActMatmulModel(with_multiply=with_multiply, n_layers=n_layers).ov_model - dataset = Dataset([np.ones([8, 8])]) + dataset = Dataset([np.ones([1, 8, 8])]) compress_weights(model, mode=mode, ratio=1.0, group_size=2, dataset=dataset, awq=True) @@ -765,18 +820,18 @@ def test_call_max_var_criterion_with_dataset_by_default_awq_act_matmul(mode, wit assert awq_num == n_awq_target -@pytest.mark.parametrize("mode", INT4_MODES) +@pytest.mark.parametrize("mode", INT4_NF4_MODES) def test_call_max_var_criterion_with_dataset_awq_for_compressed_model(mode): model = AWQMatmulModel(is_int8=True).ov_model - dataset = Dataset([np.ones([8, 8])]) + dataset = Dataset([np.ones([1, 8, 8])]) compress_weights(model, mode=mode, ratio=1.0, group_size=2, dataset=dataset, awq=True) -@pytest.mark.parametrize("mode", INT4_MODES) +@pytest.mark.parametrize("mode", INT4_NF4_MODES) def test_call_max_var_criterion_with_dataset_awq_neg_group_size(mode): model = AWQMatmulModel().ov_model - dataset = Dataset([np.ones([8, 8])]) + dataset = Dataset([np.ones([1, 8, 8])]) compress_weights(model, mode=mode, ratio=1.0, group_size=-1, dataset=dataset, awq=True) @@ -831,25 +886,78 @@ def test_compression_for_different_dtypes(activation_dtype, weight_dtype): check_compressed_matmul_subgraph(scale_multiply_node, activation_dtype, weight_dtype) -DATASET_SIZE = 129 +DATASET_SIZE = 5 @pytest.mark.parametrize( - ("subset_size", "ref_size"), + ("dataset_size", "subset_size", "ref_size"), ( - (1, 1), - (5, 5), - (130, DATASET_SIZE), + (DATASET_SIZE, 1, 1), + (DATASET_SIZE, DATASET_SIZE, DATASET_SIZE), + (DATASET_SIZE, DATASET_SIZE + 1, DATASET_SIZE), ), ) -def test_valid_subset_size(mocker, subset_size, ref_size): +@pytest.mark.parametrize( + ("compression_args", "multiplier_of_calls"), + [ + ({"mode": CompressWeightsMode.INT4_ASYM, "ratio": 1}, 0), # data-free, no reducers + ({"mode": CompressWeightsMode.INT4_ASYM, "ratio": 1, "awq": True}, 2), # mean & shape reducer for AWQ + ( + {"mode": CompressWeightsMode.INT4_ASYM, "ratio": 0.5, "awq": True}, + 3, + ), # 2 - for AWQ + 1 - for Mixed Precision + ( + { + "mode": CompressWeightsMode.INT4_ASYM, + "ratio": 0.5, + "sensitivity_metric": nncf.SensitivityMetric.HESSIAN_INPUT_ACTIVATION, + }, + 1, + ), # 1 reducer for mixed precision + ( + { + "mode": CompressWeightsMode.INT4_ASYM, + "ratio": 0.5, + "sensitivity_metric": nncf.SensitivityMetric.MEAN_ACTIVATION_VARIANCE, + }, + 1, + ), # 1 reducer for mixed precision + ( + { + "mode": CompressWeightsMode.INT4_ASYM, + "ratio": 0.5, + "sensitivity_metric": nncf.SensitivityMetric.MAX_ACTIVATION_VARIANCE, + }, + 1, + ), # 1 reducer for mixed precision + ( + { + "mode": CompressWeightsMode.INT4_ASYM, + "ratio": 0.5, + "sensitivity_metric": nncf.SensitivityMetric.MEAN_ACTIVATION_MAGNITUDE, + }, + 1, + ), # 1 reducer for mixed precision + ( + { + "mode": CompressWeightsMode.INT4_ASYM, + "ratio": 0.5, + "sensitivity_metric": nncf.SensitivityMetric.WEIGHT_QUANTIZATION_ERROR, + }, + 0, + ), # 0 - data-free method + ], +) +def test_number_of_reduced_statistics_for_subset_size( + mocker, dataset_size, subset_size, ref_size, compression_args, multiplier_of_calls +): model = IdentityMatmul().ov_model - dataset = Dataset([ACTIVATION] * DATASET_SIZE) + dataset = Dataset([ACTIVATION] * dataset_size) stats_spy = mocker.spy(AggregatorBase, "register_reduced_input") - compress_weights(model, mode=CompressWeightsMode.INT4_ASYM, ratio=0.5, dataset=dataset, subset_size=subset_size) + compress_weights(model, dataset=dataset, subset_size=subset_size, **compression_args) - assert stats_spy.call_count == ref_size + assert stats_spy.call_count == ref_size * multiplier_of_calls def test_default_subset_value(): @@ -857,11 +965,11 @@ def test_default_subset_value(): assert default_value == 128 -@pytest.mark.parametrize("subset_size", (-1, 0, None)) +@pytest.mark.parametrize("subset_size", (-1, 0)) def test_invalid_subset_size(subset_size): model = IdentityMatmul().ov_model dataset = Dataset([ACTIVATION]) - with pytest.raises(ValueError): + with pytest.raises(nncf.ValidationError): compress_weights(model, mode=CompressWeightsMode.INT4_ASYM, ratio=0.5, dataset=dataset, subset_size=subset_size) @@ -875,26 +983,41 @@ def test_duplicate_names_generation(): op_names.add(name) -@pytest.mark.parametrize("mode", INT4_MODES) -def test_call_max_var_criterion_with_dataset_by_default_scale_estimation(mode): +@pytest.mark.parametrize( + ("mode", "compressed_weight_dtype"), + ( + (CompressWeightsMode.INT4_SYM, TensorDataType.int8), + (CompressWeightsMode.INT4_ASYM, TensorDataType.uint8), + (CompressWeightsMode.NF4, TensorDataType.float32), + ), +) +def test_call_max_var_criterion_with_dataset_by_default_scale_estimation(mode, compressed_weight_dtype, mocker): model = AWQMatmulModel().ov_model - dataset = Dataset([np.ones([8, 8])]) + dataset = Dataset([np.ones([1, 8, 8])]) + from nncf.quantization.algorithms.weight_compression import scale_estimation + from nncf.quantization.algorithms.weight_compression.algorithm import ScaleEstimation + + se_spy = mocker.spy(ScaleEstimation, "apply") + tzm_spy = mocker.spy(scale_estimation, "get_target_zero_mask") compress_weights(model, mode=mode, ratio=1.0, group_size=2, dataset=dataset, scale_estimation=True) + assert se_spy.call_count == 1 + assert tzm_spy.call_args_list[0][0][0].dtype == compressed_weight_dtype -@pytest.mark.parametrize("mode", INT4_MODES) + +@pytest.mark.parametrize("mode", INT4_NF4_MODES) def test_call_max_var_criterion_with_dataset_scale_estimation_for_compressed_model(mode): model = AWQMatmulModel(is_int8=True).ov_model - dataset = Dataset([np.ones([8, 8])]) + dataset = Dataset([np.ones([1, 8, 8])]) compress_weights(model, mode=mode, ratio=1.0, group_size=2, dataset=dataset, scale_estimation=True) -@pytest.mark.parametrize("mode", INT4_MODES) +@pytest.mark.parametrize("mode", INT4_NF4_MODES) def test_call_max_var_criterion_with_dataset_scale_estimation_neg_group_size(mode): model = AWQMatmulModel().ov_model - dataset = Dataset([np.ones([8, 8])]) + dataset = Dataset([np.ones([1, 8, 8])]) compress_weights(model, mode=mode, ratio=1.0, group_size=-1, dataset=dataset, scale_estimation=True) @@ -902,7 +1025,7 @@ def test_call_max_var_criterion_with_dataset_scale_estimation_neg_group_size(mod @pytest.mark.parametrize("mode", INT4_NF4_MODES) def test_call_gptq(mode): model = AWQMatmulModel().ov_model - dataset = Dataset([np.ones([8, 8])]) + dataset = Dataset([np.ones([1, 8, 8])]) compress_weights(model, mode=mode, ratio=1.0, group_size=2, dataset=dataset, gptq=True) @@ -932,7 +1055,7 @@ def test_call_gptq(mode): ) def test_mixed_precision_e2m1(mode, all_layers, ratio, ref_ids): model = SequentialMatmulModel().ov_model - dataset = Dataset([np.ones([3, 3]), np.arange(9).reshape(3, 3)]) + dataset = Dataset([np.ones([1, 3, 3]), np.arange(9).reshape(3, 3)]) compressed_model = compress_weights( model, mode=CompressWeightsMode.E2M1, @@ -1004,11 +1127,58 @@ def test_compressed_weighs_range(mode, data): assert np.allclose(np.abs(compressed_weighs.data), np.abs(w.data)) +@pytest.mark.parametrize( + ("config", "precompute_scale", "precompute_zero_point", "raises"), + [ + (WeightCompressionConfig(CompressWeightsMode.INT8_ASYM), False, False, False), + (WeightCompressionConfig(CompressWeightsMode.INT8_ASYM), True, True, False), + (WeightCompressionConfig(CompressWeightsMode.INT8_ASYM), True, False, True), + (WeightCompressionConfig(CompressWeightsMode.INT8_ASYM), False, True, True), + (WeightCompressionConfig(CompressWeightsMode.INT4_ASYM), False, False, False), + (WeightCompressionConfig(CompressWeightsMode.INT4_ASYM), True, True, False), + (WeightCompressionConfig(CompressWeightsMode.INT4_ASYM), True, False, True), + (WeightCompressionConfig(CompressWeightsMode.INT4_ASYM), False, True, True), + (WeightCompressionConfig(CompressWeightsMode.INT8_SYM), True, False, False), + (WeightCompressionConfig(CompressWeightsMode.INT8_SYM), False, False, False), + (WeightCompressionConfig(CompressWeightsMode.INT4_SYM), True, False, False), + (WeightCompressionConfig(CompressWeightsMode.INT4_SYM), False, False, False), + ], +) +def test_int_quantization_with_precomputed_parameters(config, precompute_scale, precompute_zero_point, raises): + is_asym = config.mode in [CompressWeightsMode.INT4_ASYM, CompressWeightsMode.INT8_ASYM] + + precomputed_scale, precomputed_zero_point = None, None + weight = Tensor(((np.arange(11) - 5) / 10).astype(np.float32)[:, None]) + if precompute_scale: + precomputed_scale = Tensor(-((np.arange(11) - 5) / 100).astype(np.float32)[:, None]) + if precompute_zero_point: + precomputed_zero_point = Tensor(np.arange(11).astype(np.int32)[:, None]) + + if raises: + with pytest.raises(ValueError) as exc_info: + _, scale, zero_point = do_int_quantization(weight, -1, config, precomputed_scale, precomputed_zero_point) + assert exc_info.value == ( + "If precomputed quantization parameters are provided, both scale and zero point " + "are required for asymmetric quantization." + ) + return + else: + _, scale, zero_point = do_int_quantization(weight, -1, config, precomputed_scale, precomputed_zero_point) + + if precompute_scale: + assert np.allclose(scale.data, precomputed_scale.data) + if is_asym: + if precompute_zero_point: + assert np.allclose(zero_point.data, precomputed_zero_point.data) + else: + assert zero_point is None + + @pytest.mark.parametrize("mode", INT4_NF4_MODES) def test_call_max_var_criterion_with_dataset_gptq_neg_group_size(mode): model = AWQMatmulModel().ov_model sz = 8 - dataset = Dataset([np.ones([sz, sz])]) + dataset = Dataset([np.ones([1, sz, sz])]) compressed_model = compress_weights(model, mode=mode, ratio=1.0, group_size=-1, dataset=dataset, gptq=True) @@ -1018,6 +1188,45 @@ def test_call_max_var_criterion_with_dataset_gptq_neg_group_size(mode): assert op.get_shape() == [sz, 1] +@pytest.mark.parametrize("mode", INT4_MODES) +def test_one_dimentional_samples(mode): + model = AWQMatmulModel().ov_model + sz = 8 + n_samples = 10 + dataset = Dataset([np.ones([1, i + 1, sz]) for i in range(n_samples)]) + + compressed_model = compress_weights(model, mode=mode, ratio=1.0, group_size=-1, dataset=dataset, awq=True) + + for op in compressed_model.get_ordered_ops(): + op_name = op.get_friendly_name() + if op.get_type_name() == "Constant" and ("/zero_point" in op_name or "/scale" in op_name): + assert op.get_shape() == [sz, 1] + + +def test_awq_with_ignored_scope(): + model = AWQMatmulModel().ov_model + sz = 8 + n_samples = 10 + dataset = Dataset([np.ones([1, i + 1, sz]) for i in range(n_samples)]) + + compressed_model = compress_weights( + model, + mode=CompressWeightsMode.INT4_ASYM, + ratio=1.0, + group_size=-1, + dataset=dataset, + awq=True, + ignored_scope=IgnoredScope(names=["MatMul_6"]), + ) + + act_num = 0 + num_compressed = 8 + for op in compressed_model.get_ops(): + if op.get_type_name() == "Constant" and op.get_element_type() == ov.Type.u4: + act_num += 1 + assert act_num == num_compressed + + def get_shape_for_second_input(op_with_weights: ov.Node) -> List[int]: return list(op_with_weights.inputs()[1].get_shape()) @@ -1028,11 +1237,12 @@ def get_shape_for_second_input(op_with_weights: ov.Node) -> List[int]: ) def test_lora_adapters_in_the_graph(params, transpose_b): advanced_parameters = CompressionParams() if params is None else CompressionParams(lora_correction_params=params) - model = LMLinearModel(transpose_b=transpose_b).ov_model - dataset = Dataset(np.ones(inp.shape) for inp in model.inputs) + model = LMLinearModel(transpose_b=transpose_b) + ov_model = model.ov_model + dataset = Dataset(np.ones(inp.shape) for inp in ov_model.inputs) compressed_model = compress_weights( - model, + ov_model, mode=CompressWeightsMode.INT4_SYM, ratio=1.0, group_size=8, @@ -1048,8 +1258,8 @@ def test_lora_adapters_in_the_graph(params, transpose_b): next_node = target_input.get_node() assert next_node.type_info.name == "MatMul" shape = get_shape_for_second_input(next_node) - if shape != LMLinearModel.get_weight_shape(transpose_b): - assert shape == [advanced_parameters.lora_correction_params.adapter_rank, LMLinearModel.HIDDEN_DIM] + if shape != model.get_weight_shape(transpose_b): + assert shape == [advanced_parameters.lora_correction_params.adapter_rank, model.hidden_dim] node = get_next_node(next_node) assert node.type_info.name == "MatMul" assert get_shape_for_second_input(node) == [ @@ -1076,9 +1286,9 @@ def test_lora_adapters_in_the_graph(params, transpose_b): def test_lora_adapters_reduce_noise(zero_seed, mode, apply_regularization, is_per_channel, mocker, tmp_path): mocker.patch("nncf.quantization.algorithms.weight_compression.lora_correction.DEBUG_LOG_DIR", str(tmp_path)) - model_cls = LMLinearModel - group_size = -1 if is_per_channel else model_cls.HIDDEN_DIM // 2 - model = model_cls().ov_model + model = LMLinearModel() + group_size = -1 if is_per_channel else model.hidden_dim // 2 + model = model.ov_model n_iters = 1 ie = ov.Core() input_data = [np.ones(inp.shape) for inp in model.inputs] @@ -1095,7 +1305,7 @@ def test_lora_adapters_reduce_noise(zero_seed, mode, apply_regularization, is_pe int4_out = next(iter(int4_out.values())) noise_before = np.mean(np.abs(fp32_out - int4_out)) - model = model_cls().ov_model + model = LMLinearModel().ov_model with nncf_debug(): int4_model = compress_weights( @@ -1173,8 +1383,9 @@ def test_compression_with_lora_for_different_dtypes(activation_dtype, weight_dty def test_compression_with_lora_with_subset_size(mocker): subset_size = 2 dataset_size = 4 - model = LMLinearModel().ov_model - input_data = [np.ones(inp.shape) for inp in model.inputs] * dataset_size + model = LMLinearModel() + ov_model = model.ov_model + input_data = [np.ones(inp.shape) for inp in ov_model.inputs] * dataset_size dataset = Dataset(input_data) from nncf.quantization.algorithms.weight_compression import lora_correction @@ -1182,7 +1393,7 @@ def test_compression_with_lora_with_subset_size(mocker): get_stats_spy = mocker.spy(lora_correction, "process_stats") compress_weights( - model, + ov_model, mode=CompressWeightsMode.INT4_SYM, ratio=1.0, group_size=8, @@ -1198,14 +1409,14 @@ def test_compression_with_lora_with_subset_size(mocker): get_stats_spy.assert_called_once() s, X = get_stats_spy.spy_return - assert X.shape == (LMLinearModel.HIDDEN_DIM, subset_size) - assert s.shape == (LMLinearModel.HIDDEN_DIM,) + assert X.shape == (model.hidden_dim, subset_size) + assert s.shape == (model.hidden_dim,) def test_lora_with_mixed_precision(): model = AWQMatmulModel().ov_model sz = 8 - dataset = Dataset([np.ones([sz, sz])]) + dataset = Dataset([np.ones([1, sz, sz])]) compressed_model = compress_weights( model, mode=CompressWeightsMode.INT4_ASYM, ratio=0.5, group_size=-1, dataset=dataset, lora_correction=True @@ -1214,3 +1425,174 @@ def test_lora_with_mixed_precision(): op_name = op.get_friendly_name() if op.get_type_name() == "Constant" and ("/zero_point" in op_name or "/scale" in op_name): assert op.get_shape() == [sz, 1] + + +@pytest.mark.parametrize("backup_mode", [BackupMode.NONE, BackupMode.INT8_ASYM, BackupMode.INT8_SYM]) +def test_data_free_compression_with_backup_mode(backup_mode): + model = AWQMatmulModel().ov_model + compressed_model = compress_weights( + model, + mode=CompressWeightsMode.NF4, + ratio=0.7, + group_size=-1, + backup_mode=backup_mode, + ) + act_num = 0 + num_compressed = 3 + if backup_mode == BackupMode.INT8_ASYM: + backup_ov_mode = ov.Type.u8 + elif backup_mode == BackupMode.INT8_SYM: + backup_ov_mode = ov.Type.i8 + else: + backup_ov_mode = ov.Type.f32 + for op in compressed_model.get_ops(): + if op.get_type_name() == "Constant": + if op.get_element_type() == ov.Type.nf4: + act_num += 1 + elif "/scale" in op.get_friendly_name(): + assert op.get_element_type() == ov.Type.f16 + else: + assert op.get_element_type() == backup_ov_mode + assert act_num == num_compressed + + +@pytest.mark.parametrize("backup_mode", [BackupMode.NONE, BackupMode.INT8_ASYM, BackupMode.INT8_SYM]) +@pytest.mark.parametrize( + ("params", "num_compressed"), + ( + ({"all_layers": True}, 8), + ({"all_layers": False}, 6), + ({"sensitivity_metric": SensitivityMetric.HESSIAN_INPUT_ACTIVATION}, 6), + ({"sensitivity_metric": SensitivityMetric.MEAN_ACTIVATION_VARIANCE}, 6), + ({"sensitivity_metric": SensitivityMetric.MAX_ACTIVATION_VARIANCE}, 6), + ({"sensitivity_metric": SensitivityMetric.MEAN_ACTIVATION_MAGNITUDE}, 6), + ({"scale_estimation": True}, 6), + ({"lora_correction": True}, 6), + ({"gptq": True}, 6), + ({"awq": True}, 6), + ), +) +def test_data_based_compression_with_backup_mode(backup_mode, params, num_compressed): + model = AWQMatmulModel().ov_model + sz = 8 + n_samples = 10 + dataset = Dataset([np.ones([1, i + 1, sz]) for i in range(n_samples)]) + + compressed_model = compress_weights( + model, + mode=CompressWeightsMode.INT4_ASYM, + ratio=0.8, + group_size=-1, + dataset=dataset, + backup_mode=backup_mode, + **params, + ) + act_num = 0 + if backup_mode == BackupMode.INT8_ASYM: + backup_ov_mode = ov.Type.u8 + elif backup_mode == BackupMode.INT8_SYM: + backup_ov_mode = ov.Type.i8 + else: + backup_ov_mode = ov.Type.f32 + for op in compressed_model.get_ops(): + if op.get_type_name() == "Constant": + if op.get_element_type() == ov.Type.u4: + act_num += 1 + elif "/scale" in op.get_friendly_name(): + assert op.get_element_type() == ov.Type.f16 + elif "_lora_" in op.get_friendly_name(): + assert op.get_element_type() == ov.Type.u8 + else: + assert op.get_element_type() == backup_ov_mode + assert act_num == num_compressed + + +@pytest.mark.parametrize("n_extra_dims", [0, 1, 2]) +def test_data_aware_algo_with_different_activation_dimensions(n_extra_dims): + model = AWQMatmulModel(n_extra_dims=n_extra_dims).ov_model + dataset = Dataset([np.ones([1] * n_extra_dims + [8, 8])]) + compress_weights( + model, + mode=CompressWeightsMode.INT4_ASYM, + group_size=-1, + dataset=dataset, + awq=True, + ratio=0.5, + sensitivity_metric=SensitivityMetric.MEAN_ACTIVATION_MAGNITUDE, + ) + + +@pytest.mark.parametrize( + "input_shape", + [ + LMLinearModel.INPUT_SHAPE, + [3, 5, 16], + [1, 1, 16], + ], +) +@pytest.mark.parametrize( + "kwargs", + [ + dict(scale_estimation=True), + dict(lora_correction=True), + dict( + gptq=True, + scale_estimation=True, + advanced_parameters=CompressionParams(gptq_params=GPTQParams(subset_size=2)), + ), + dict( + awq=True, + gptq=True, + scale_estimation=True, + advanced_parameters=CompressionParams(gptq_params=GPTQParams(subset_size=2)), + ), + ], +) +def test_compression_with_different_algo_combinations(input_shape, kwargs): + dataset_size = 4 + model = LMLinearModel(input_shape=input_shape).ov_model + input_data = [np.ones(inp.shape) for inp in model.inputs] * dataset_size + dataset = Dataset(input_data) + + compress_weights( + model, + mode=CompressWeightsMode.INT4_SYM, + ratio=1.0, + group_size=8, + subset_size=2, + dataset=dataset, + all_layers=True, + **kwargs, + ) + + +@pytest.mark.parametrize( + "kwargs", + [ + dict(scale_estimation=True), + dict(lora_correction=True), + dict( + gptq=True, + awq=True, + scale_estimation=True, + advanced_parameters=CompressionParams(gptq_params=GPTQParams(subset_size=2)), + ), + ], +) +def test_compression_with_transposed_activations(kwargs): + dataset_size = 4 + model = LMLinearModel(transpose_a=True, transpose_b=False).ov_model + input_data = [np.ones(inp.shape) for inp in model.inputs] * dataset_size + dataset = Dataset(input_data) + + with pytest.raises(nncf.UnsupportedModelError): + compress_weights( + model, + mode=CompressWeightsMode.INT4_SYM, + ratio=1.0, + group_size=8, + subset_size=2, + dataset=dataset, + all_layers=True, + **kwargs, + ) diff --git a/tests/openvino/native/quantization/test_weights_compression_backends.py b/tests/openvino/native/quantization/test_weights_compression_backends.py new file mode 100644 index 00000000000..ab0271a6a4f --- /dev/null +++ b/tests/openvino/native/quantization/test_weights_compression_backends.py @@ -0,0 +1,39 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nncf.quantization.algorithms.weight_compression.mixed_precision import HAWQCriterion +from nncf.quantization.algorithms.weight_compression.mixed_precision import MaxVarianceCriterion +from nncf.quantization.algorithms.weight_compression.mixed_precision import MeanMaxCriterion +from nncf.quantization.algorithms.weight_compression.mixed_precision import MeanVarianceCriterion +from nncf.quantization.algorithms.weight_compression.openvino_backend import OVMixedPrecisionAlgoBackend +from tests.cross_fw.test_templates.test_weights_compression_backends import TemplateTestMixedPrecisionAlgoBackend +from tests.openvino.native.models import IdentityMatmul + + +class TestOVMixedPrecisionAlgoBackend(TemplateTestMixedPrecisionAlgoBackend): + def get_hawq_with_backend(self, subset_size): + hawq = HAWQCriterion(None, None, subset_size=subset_size) + hawq._backend_entity = OVMixedPrecisionAlgoBackend(IdentityMatmul().ov_model) + return hawq + + def get_mean_variance_with_backend(self, subset_size: int): + mean_variance = MeanVarianceCriterion(None, None, subset_size=subset_size) + mean_variance._backend_entity = OVMixedPrecisionAlgoBackend(IdentityMatmul().ov_model) + return mean_variance + + def get_max_variance_with_backend(self, subset_size: int): + max_variance = MaxVarianceCriterion(None, None, subset_size=subset_size) + max_variance._backend_entity = OVMixedPrecisionAlgoBackend(IdentityMatmul().ov_model) + return max_variance + + def get_mean_max_with_backend(self, subset_size: int): + mean_max_variance = MeanMaxCriterion(None, None, subset_size=subset_size) + mean_max_variance._backend_entity = OVMixedPrecisionAlgoBackend(IdentityMatmul().ov_model) + return mean_max_variance diff --git a/tests/openvino/native/quantization/test_weights_compression_statistics_caching.py b/tests/openvino/native/quantization/test_weights_compression_statistics_caching.py new file mode 100644 index 00000000000..538643075ee --- /dev/null +++ b/tests/openvino/native/quantization/test_weights_compression_statistics_caching.py @@ -0,0 +1,170 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from copy import deepcopy +from functools import partial +from itertools import product +from typing import Tuple + +import datasets +import openvino as ov +from optimum.intel.openvino import OVModelForCausalLM +from transformers import AutoTokenizer + +import nncf +from nncf.quantization.advanced_parameters import AdvancedCompressionParameters +from nncf.scopes import IgnoredScope + +MODEL_ID = "hf-internal-testing/tiny-random-OPTForCausalLM" +DEFAULT_RATIO = 0.4 +DEFAULT_GROUP_SIZE = 4 +DEFAULT_SENSITIVITY = nncf.SensitivityMetric.HESSIAN_INPUT_ACTIVATION +DEFAULT_IGNORED_SCOPE = IgnoredScope() +DEFAULT_SUBSET_SIZE = 4 +DEFAULT_MODE = nncf.CompressWeightsMode.INT4_ASYM + + +def create_transform_fn(model: OVModelForCausalLM, tokenizer: AutoTokenizer): + def transform_fn(data, model=model, tokenizer=tokenizer): + tokenized_text = tokenizer(data["text"], return_tensors="np") + input_ids = tokenized_text["input_ids"] + inputs = {"input_ids": input_ids, "attention_mask": tokenized_text["attention_mask"]} + + batch_size = input_ids.shape[0] + if hasattr(model, "key_value_input_names"): + for input_name in model.key_value_input_names: + model_inputs = model.model.input(input_name) + shape = model_inputs.get_partial_shape() + shape[0] = batch_size + shape[2 if shape[2].is_dynamic else 1] = 0 + inputs[input_name] = ov.Tensor(model_inputs.get_element_type(), shape.get_shape()) + return inputs + + return transform_fn + + +def _setup_model_and_dataset(model_id: str) -> Tuple[OVModelForCausalLM, nncf.Dataset]: + dataset = datasets.load_dataset("wikitext", "wikitext-2-raw-v1", split="test") + tokenizer = AutoTokenizer.from_pretrained(model_id) + model = OVModelForCausalLM.from_pretrained(model_id, export=True, load_in_8bit=False, compile=False, stateful=False) + transform_fn = create_transform_fn(model, tokenizer) + quantization_dataset = nncf.Dataset(dataset, partial(transform_fn)) + return model, quantization_dataset + + +def _test_basic_configurations(model, quantization_dataset, tmp_path, subset_size, mode) -> int: + awq_options = [True, False] + group_size_options = [1, 4] + ratio_options = [0.4, 0.8] + sensitivity_metrics = [ + nncf.SensitivityMetric.HESSIAN_INPUT_ACTIVATION, + nncf.SensitivityMetric.MAX_ACTIVATION_VARIANCE, + ] + ignored_scopes = [IgnoredScope(), IgnoredScope(types=["MatMul"])] + + load_count = 0 + for awq, group_size, ratio, sensitivity, scope in product( + awq_options, group_size_options, ratio_options, sensitivity_metrics, ignored_scopes + ): + print(f"Testing: AWQ={awq}, Group={group_size}, Ratio={ratio}, Metric={sensitivity}, Scope={scope}") + nncf.compress_weights( + deepcopy(model.model), + mode=mode, + dataset=quantization_dataset, + ratio=ratio, + awq=awq, + gptq=False, + group_size=group_size, + scale_estimation=False, + subset_size=subset_size, + sensitivity_metric=sensitivity, + ignored_scope=scope, + lora_correction=False, + advanced_parameters=AdvancedCompressionParameters(statistics_path=tmp_path / "statistics"), + ) + load_count += 1 + return load_count + + +def _test_advanced_gptq_scale_estimation(model, quantization_dataset, tmp_path, subset_size, mode) -> int: + load_count = 0 + for gptq, scale_est in product([True, False], [True, False]): + print(f"Testing: AWQ=True, GPTQ={gptq}, Scale={scale_est}, LoRA=False") + nncf.compress_weights( + deepcopy(model.model), + mode=mode, + dataset=quantization_dataset, + ratio=DEFAULT_RATIO, + awq=True, + gptq=gptq, + group_size=DEFAULT_GROUP_SIZE, + scale_estimation=scale_est, + subset_size=subset_size, + sensitivity_metric=DEFAULT_SENSITIVITY, + ignored_scope=DEFAULT_IGNORED_SCOPE, + lora_correction=False, + advanced_parameters=AdvancedCompressionParameters(statistics_path=tmp_path / "statistics"), + ) + load_count += 1 + return load_count + + +def _test_advanced_lora_scale_estimation(model, quantization_dataset, tmp_path, subset_size, mode) -> int: + load_count = 0 + for scale_est, lora_corr in product([True, False], [True, False]): + print(f"Testing: AWQ=True, GPTQ=False, Scale={scale_est}, LoRA={lora_corr}") + nncf.compress_weights( + deepcopy(model.model), + mode=mode, + dataset=quantization_dataset, + ratio=DEFAULT_RATIO, + awq=True, + gptq=False, + group_size=DEFAULT_GROUP_SIZE, + scale_estimation=scale_est, + subset_size=subset_size, + sensitivity_metric=DEFAULT_SENSITIVITY, + ignored_scope=DEFAULT_IGNORED_SCOPE, + lora_correction=lora_corr, + advanced_parameters=AdvancedCompressionParameters(statistics_path=tmp_path / "statistics"), + ) + load_count += 1 + return load_count + + +def test_weight_compression_statistics_caching(tmp_path, mocker): + """ + Tests the weight compression process, focusing on the statistics caching mechanism. + Ensures that: + - Statistics are collected once. + - Statistics are loaded according to the number of configurations tested. + - Statistics are dumped once. + :param tmp_path: Temporary directory path for storing statistics. + :param mocker: Mocking utility to spy on function calls. + """ + from nncf.openvino.statistics.aggregator import OVStatisticsAggregator + + collect_spy = mocker.spy(OVStatisticsAggregator, "collect_statistics") + load_spy = mocker.spy(OVStatisticsAggregator, "load_statistics_from_dir") + dump_spy = mocker.spy(OVStatisticsAggregator, "dump_statistics") + + model_id = MODEL_ID + subset_size = DEFAULT_SUBSET_SIZE + mode = DEFAULT_MODE + model, quantization_dataset = _setup_model_and_dataset(model_id) + + load_count = 0 + load_count += _test_basic_configurations(model, quantization_dataset, tmp_path, subset_size, mode) + load_count += _test_advanced_gptq_scale_estimation(model, quantization_dataset, tmp_path, subset_size, mode) + load_count += _test_advanced_lora_scale_estimation(model, quantization_dataset, tmp_path, subset_size, mode) + + assert collect_spy.call_count == 1, "Statistics should be collected only once." + assert load_spy.call_count == load_count, f"Expected {load_count} load calls, found {load_spy.call_count}." + assert dump_spy.call_count == 1, "Statistics should be dumped only once." diff --git a/tests/openvino/native/test_bias_correction.py b/tests/openvino/native/test_bias_correction.py index e90ad6c92f0..711c831facd 100644 --- a/tests/openvino/native/test_bias_correction.py +++ b/tests/openvino/native/test_bias_correction.py @@ -9,8 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from pathlib import Path -from typing import Dict, List +from typing import Any, Dict, List import numpy as np import openvino as ov @@ -23,14 +22,17 @@ from nncf.openvino.graph.node_utils import get_bias_value from nncf.quantization.algorithms.bias_correction.openvino_backend import OVBiasCorrectionAlgoBackend from tests.cross_fw.test_templates.helpers import ConvTestModel +from tests.cross_fw.test_templates.helpers import DepthwiseConvTestModel from tests.cross_fw.test_templates.helpers import MultipleConvTestModel from tests.cross_fw.test_templates.helpers import SplittedModel +from tests.cross_fw.test_templates.helpers import TransposeConvTestModel from tests.cross_fw.test_templates.test_bias_correction import TemplateTestBCAlgorithm from tests.openvino.native.common import compare_nncf_graphs -from tests.openvino.native.common import get_actual_reference_for_current_openvino class TestOVBCAlgorithm(TemplateTestBCAlgorithm): + TRANSPOSE_CONV_NAME = "/conv/ConvTranspose/WithoutBiases" + @staticmethod def list_to_backend_type(data: List) -> np.ndarray: return np.array(data) @@ -44,6 +46,10 @@ def backend_specific_model(model: torch.nn.Module, tmp_dir: str): onnx_path = f"{tmp_dir}/model.onnx" torch.onnx.export(model, torch.rand(model.INPUT_SIZE), onnx_path, opset_version=13, input_names=["input.1"]) ov_model = ov.convert_model(onnx_path) + if isinstance(model, TransposeConvTestModel): + for node in ov_model.get_ops(): + if node.get_type_name() == "ConvolutionBackpropData": + node.set_friendly_name(TestOVBCAlgorithm.TRANSPOSE_CONV_NAME) return ov_model @staticmethod @@ -59,7 +65,7 @@ def transform_fn(data_item): return transform_fn @staticmethod - def map_references(ref_biases: Dict) -> Dict[str, List]: + def map_references(ref_biases: Dict, model_cls: Any) -> Dict[str, List]: mapping = {f"{name}/WithoutBiases": val for name, val in ref_biases.items()} return mapping @@ -68,12 +74,6 @@ def remove_fq_from_inputs(model: ov.Model) -> ov.Model: graph = GraphConverter.create_nncf_graph(model) return remove_fq_from_inputs(model, graph) - @staticmethod - def get_ref_path(suffix: str) -> str: - return get_actual_reference_for_current_openvino( - Path("reference_graphs") / "quantized" / "subgraphs" / f"{suffix}.dot" - ) - @staticmethod def compare_nncf_graphs(model: ov.Model, ref_path: str) -> None: return compare_nncf_graphs(model, ref_path) @@ -214,6 +214,8 @@ def test__get_subgraph_data_for_node(self, quantized_test_model, layer_name, ref }, ), (ConvTestModel, {("/conv/Conv/WithoutBiases", 0): ("input.1", 0)}), + (DepthwiseConvTestModel, {("/conv/Conv/WithoutBiases", 0): ("input.1", 0)}), + (TransposeConvTestModel, {(TRANSPOSE_CONV_NAME, 0): ("input.1", 0)}), ), ) def test_verify_collected_stat_inputs_map(self, model_cls, ref_stat_inputs_map, tmpdir): diff --git a/tests/openvino/native/test_layer_attributes.py b/tests/openvino/native/test_layer_attributes.py index 95497cf1a12..23127ee7074 100644 --- a/tests/openvino/native/test_layer_attributes.py +++ b/tests/openvino/native/test_layer_attributes.py @@ -166,7 +166,7 @@ class LayerAttributesTestCase: (1, 3, 3, 3), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (4, 3, 2, 1)}}, + {1: {"name": "Const", "shape": (4, 3, 2, 1), "dtype": "f32"}}, ConvolutionLayerAttributes( weight_requires_grad=False, in_channels=3, @@ -193,7 +193,7 @@ class LayerAttributesTestCase: (1, 3, 3, 3), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (4, 3, 1, 1)}}, + {1: {"name": "Const", "shape": (4, 3, 1, 1), "dtype": "f32"}}, ConvolutionLayerAttributes( weight_requires_grad=False, in_channels=3, @@ -220,7 +220,7 @@ class LayerAttributesTestCase: (1, 3, 3, 3), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (3, 3, 1, 1, 1)}}, + {1: {"name": "Const", "shape": (3, 3, 1, 1, 1), "dtype": "f32"}}, ConvolutionLayerAttributes( weight_requires_grad=False, in_channels=1, @@ -248,7 +248,7 @@ class LayerAttributesTestCase: (1, 10, 3, 3), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (5, 10, 2, 1, 1)}}, + {1: {"name": "Const", "shape": (5, 10, 2, 1, 1), "dtype": "f32"}}, ConvolutionLayerAttributes( weight_requires_grad=False, in_channels=2, @@ -276,7 +276,7 @@ class LayerAttributesTestCase: (1, 3, 3, 3), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (3, 4, 2, 1)}}, + {1: {"name": "Const", "shape": (3, 4, 2, 1), "dtype": "f32"}}, ConvolutionLayerAttributes( weight_requires_grad=False, in_channels=3, @@ -303,7 +303,7 @@ class LayerAttributesTestCase: (1, 3, 3, 3), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (3, 1, 3, 1, 1)}}, + {1: {"name": "Const", "shape": (3, 1, 3, 1, 1), "dtype": "f32"}}, ConvolutionLayerAttributes( weight_requires_grad=False, in_channels=1, @@ -335,7 +335,7 @@ class LayerAttributesTestCase: (1, 3, 4), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (1, 4), "transpose": True}}, + {1: {"name": "Const", "shape": (1, 4), "dtype": "f32", "transpose": True}}, LinearLayerAttributes( weight_requires_grad=False, in_features=4, @@ -352,7 +352,7 @@ class LayerAttributesTestCase: (1, 3, 4), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (3, 1), "transpose": False}}, + {1: {"name": "Const", "shape": (3, 1), "dtype": "f32", "transpose": False}}, LinearLayerAttributes( weight_requires_grad=False, in_features=3, @@ -369,7 +369,7 @@ class LayerAttributesTestCase: (1, 3, 4), 1, OVLayerAttributes( - {0: {"name": "Const", "shape": (3, 1), "transpose": True}}, + {0: {"name": "Const", "shape": (3, 1), "dtype": "f32", "transpose": True}}, LinearLayerAttributes( weight_requires_grad=False, in_features=3, @@ -386,7 +386,7 @@ class LayerAttributesTestCase: (1, 3, 4), 1, OVLayerAttributes( - {0: {"name": "Const", "shape": (1, 4), "transpose": False}}, + {0: {"name": "Const", "shape": (1, 4), "dtype": "f32", "transpose": False}}, LinearLayerAttributes( weight_requires_grad=False, in_features=4, @@ -403,7 +403,7 @@ class LayerAttributesTestCase: (1, 3, 4), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (4,), "transpose": False}}, + {1: {"name": "Const", "shape": (4,), "dtype": "f32", "transpose": False}}, LinearLayerAttributes( weight_requires_grad=False, in_features=4, @@ -425,7 +425,7 @@ class LayerAttributesTestCase: (1, 3, 4, 5), 0, OVLayerAttributes( - {1: {"name": "Const", "shape": (1, 1, 1, 1)}}, + {1: {"name": "Const", "shape": (1, 1, 1, 1), "dtype": "f32"}}, GenericWeightedLayerAttributes(False, weight_shape=(1, 1, 1, 1)), {}, ), @@ -438,10 +438,10 @@ class LayerAttributesTestCase: 0, OVLayerAttributes( { - 1: {"name": "hs", "shape": (2, 1, 4)}, - 2: {"name": "cs", "shape": (2, 1, 4)}, - 4: {"name": "w", "shape": (1, 16, 4)}, - 5: {"name": "r", "shape": (1, 16, 4)}, + 1: {"name": "hs", "shape": (2, 1, 4), "dtype": "f32"}, + 2: {"name": "cs", "shape": (2, 1, 4), "dtype": "f32"}, + 4: {"name": "w", "shape": (1, 16, 4), "dtype": "f32"}, + 5: {"name": "r", "shape": (1, 16, 4), "dtype": "f32"}, }, None, {}, diff --git a/tests/openvino/native/test_model_transformer.py b/tests/openvino/native/test_model_transformer.py index e4bb876e76a..1d21f495b5a 100644 --- a/tests/openvino/native/test_model_transformer.py +++ b/tests/openvino/native/test_model_transformer.py @@ -25,9 +25,13 @@ from nncf.openvino.graph.node_utils import get_const_value from nncf.openvino.graph.node_utils import get_inplace_batch_mean_op from nncf.openvino.graph.node_utils import get_inplace_max_op +from nncf.openvino.graph.node_utils import get_inplace_max_var_op +from nncf.openvino.graph.node_utils import get_inplace_mean_max_op from nncf.openvino.graph.node_utils import get_inplace_mean_op from nncf.openvino.graph.node_utils import get_inplace_mean_per_ch +from nncf.openvino.graph.node_utils import get_inplace_mean_var_op from nncf.openvino.graph.node_utils import get_inplace_min_op +from nncf.openvino.graph.node_utils import get_inplace_shape_op from nncf.openvino.graph.node_utils import get_result_node_name from nncf.openvino.graph.transformations.commands import OVBiasCorrectionCommand from nncf.openvino.graph.transformations.commands import OVBiasInsertionCommand @@ -152,6 +156,34 @@ def __str__(self) -> str: InplaceOpTestCase("mean", (1, 2), get_inplace_mean_op, ["ReduceMean"], [(1, 2)]), InplaceOpTestCase("max", (1, 2), lambda r: get_inplace_max_op(r, False), ["ReduceMax"], [(1, 2)]), InplaceOpTestCase("abs_max", (1, 2), lambda r: get_inplace_max_op(r, True), ["Abs", "ReduceMax"], [None, (1, 2)]), + InplaceOpTestCase( + "mean_var", + (1, 2), + lambda r: get_inplace_mean_var_op(r), + ["ReduceMean", "SquaredDifference", "ReduceMean", "ReduceMean"], + [(1, 2), None, (1, 2), (0, 1, 2, 3)], + ), + InplaceOpTestCase( + "max_var", + (1, 2), + lambda r: get_inplace_max_var_op(r), + ["ReduceMean", "SquaredDifference", "ReduceMean", "ReduceMax"], + [(1, 2), None, (1, 2), (0, 1, 2, 3)], + ), + InplaceOpTestCase( + "mean_max", + (1, 2), + lambda r: get_inplace_mean_max_op(r, False), + ["ReduceMax", "ReduceMean"], + [(1, 2), (0, 1, 2, 3)], + ), + InplaceOpTestCase( + "mean_max", + (1, 2), + lambda r: get_inplace_mean_max_op(r, True), + ["Abs", "ReduceMax", "ReduceMean"], + [None, (1, 2), (0, 1, 2, 3)], + ), # Calculated reduce shape InplaceOpTestCase("min", None, get_inplace_min_op, ["ReduceMin"], [(0, 1, 2, 3)]), InplaceOpTestCase("mean", None, get_inplace_mean_op, ["ReduceMean"], [(0, 1, 2, 3)]), @@ -159,6 +191,41 @@ def __str__(self) -> str: InplaceOpTestCase( "abs_max", None, lambda r: get_inplace_max_op(r, True), ["Abs", "ReduceMax"], [None, (0, 1, 2, 3)] ), + InplaceOpTestCase( + "mean_var", + None, + lambda r: get_inplace_mean_var_op(r), + ["ReduceMean", "SquaredDifference", "ReduceMean"], + [(0, 1, 2, 3), None, (0, 1, 2, 3)], + ), + InplaceOpTestCase( + "max_var", + None, + lambda r: get_inplace_max_var_op(r), + ["ReduceMean", "SquaredDifference", "ReduceMean"], + [(0, 1, 2, 3), None, (0, 1, 2, 3)], + ), + InplaceOpTestCase( + "mean_max", + None, + lambda r: get_inplace_mean_max_op(r, False), + ["ReduceMax"], + [(0, 1, 2, 3)], + ), + InplaceOpTestCase( + "mean_max", + None, + lambda r: get_inplace_mean_max_op(r, True), + ["Abs", "ReduceMax"], + [None, (0, 1, 2, 3)], + ), + InplaceOpTestCase( + "shape", + None, + lambda r: get_inplace_shape_op(), + ["ShapeOf"], + [None], + ), # Batch mean and mean per ch operations InplaceOpTestCase("batch_mean", None, lambda r: get_inplace_batch_mean_op(), ["ReduceMean"], [0]), InplaceOpTestCase("mean_per_ch", 1, get_inplace_mean_per_ch, ["Reshape", "ReduceMean"], [(1, 3, 16), (0, 2)]), @@ -184,6 +251,30 @@ def __str__(self) -> str: InplaceOpTestCase("mean", (), get_inplace_mean_op, ["ReduceMean"], [()]), InplaceOpTestCase("max", (), lambda r: get_inplace_max_op(r, False), ["ReduceMax"], [()]), InplaceOpTestCase("abs_max", (), lambda r: get_inplace_max_op(r, True), ["Abs", "ReduceMax"], [None, ()]), + InplaceOpTestCase( + "mean_var", + (), + lambda r: get_inplace_mean_var_op(r), + ["ReduceMean", "SquaredDifference", "ReduceMean", "ReduceMean"], + [(), None, (), (0, 1, 2, 3)], + ), + InplaceOpTestCase( + "max_var", + (), + lambda r: get_inplace_max_var_op(r), + ["ReduceMean", "SquaredDifference", "ReduceMean", "ReduceMax"], + [(), None, (), (0, 1, 2, 3)], + ), + InplaceOpTestCase( + "mean_max", (), lambda r: get_inplace_mean_max_op(r, False), ["ReduceMax", "ReduceMean"], [(), (0, 1, 2, 3)] + ), + InplaceOpTestCase( + "mean_max", + (), + lambda r: get_inplace_mean_max_op(r, True), + ["Abs", "ReduceMax", "ReduceMean"], + [None, (), (0, 1, 2, 3)], + ), ] @@ -204,7 +295,7 @@ def get_node_by_name(model: ov.Model, name: str): def check_inplace_op(target_node, ref_types, ref_vals, inplace_branches_num, output_port_id): next_nodes = get_next_nodes(target_node, output_port_id) first_inplace_op = [node for node in next_nodes if node.type_info.name == ref_types[0]] - assert len(first_inplace_op) == inplace_branches_num + assert len(set(first_inplace_op)) == inplace_branches_num node = first_inplace_op[0] for t, ref_val in zip(ref_types, ref_vals): assert node.type_info.name == t diff --git a/tests/openvino/native/test_node_utils.py b/tests/openvino/native/test_node_utils.py index 69ddcc8d146..fd962d6938b 100644 --- a/tests/openvino/native/test_node_utils.py +++ b/tests/openvino/native/test_node_utils.py @@ -77,29 +77,29 @@ def test_is_node_with_bias(model_to_create, is_with_bias, node_name): @pytest.mark.parametrize( - "weights_port_id, transpose, shape, expected_channel_axes", + "weights_port_id, transpose, shape, dtype, expected_channel_axes", [ - (0, False, (1,), []), - (0, True, (1,), []), - (1, False, (1,), []), - (1, True, (1,), []), - (0, False, (1, 1), [0]), - (0, True, (1, 1), [1]), - (1, False, (1, 1), [1]), - (1, True, (1, 1), [0]), - (0, False, (1, 1, 1, 1), [0, 1, 2]), - (0, True, (1, 1, 1, 1), [0, 1, 3]), - (1, False, (1, 1, 1, 1), [0, 1, 3]), - (1, True, (1, 1, 1, 1), [0, 1, 2]), + (0, False, (1,), "f32", []), + (0, True, (1,), "f32", []), + (1, False, (1,), "f32", []), + (1, True, (1,), "f32", []), + (0, False, (1, 1), "f32", [0]), + (0, True, (1, 1), "f32", [1]), + (1, False, (1, 1), "f32", [1]), + (1, True, (1, 1), "f32", [0]), + (0, False, (1, 1, 1, 1), "f32", [0, 1, 2]), + (0, True, (1, 1, 1, 1), "f32", [0, 1, 3]), + (1, False, (1, 1, 1, 1), "f32", [0, 1, 3]), + (1, True, (1, 1, 1, 1), "f32", [0, 1, 2]), ], ) -def test_get_weight_channel_axes_for_matmul(weights_port_id, transpose, shape, expected_channel_axes): +def test_get_weight_channel_axes_for_matmul(weights_port_id, transpose, shape, dtype, expected_channel_axes): input_1 = opset.parameter([1, 1], name="Input", dtype=np.float32) constant_1 = opset.constant(np.ones(shape).astype(np.float32)) inputs_ = (input_1, constant_1) if weights_port_id == 1 else (constant_1, input_1) matmul_1 = opset.matmul(*inputs_, transpose_a=transpose, transpose_b=transpose, name="MatMul") - constant_attrs = {weights_port_id: {"transpose": transpose, "shape": shape}} + constant_attrs = {weights_port_id: {"transpose": transpose, "shape": shape, "dtype": dtype}} attributes = { NNCFNode.ID_NODE_ATTR: 0, NNCFNode.NODE_NAME_ATTR: "test", diff --git a/tests/openvino/native/test_pattern_manager.py b/tests/openvino/native/test_pattern_manager.py index a0f0bc38dd5..49be5efa99f 100644 --- a/tests/openvino/native/test_pattern_manager.py +++ b/tests/openvino/native/test_pattern_manager.py @@ -10,8 +10,8 @@ # limitations under the License. from nncf.common.graph.patterns import HWFusedPatternNames from nncf.common.utils.backend import BackendType -from tests.shared.patterns import check_hw_patterns -from tests.shared.patterns import check_ignored_patterns +from tests.cross_fw.shared.patterns import check_hw_patterns +from tests.cross_fw.shared.patterns import check_ignored_patterns IGNORING_HW_PATTERN_REASONS = { HWFusedPatternNames.L2_NORM: "Not relevant for OpenVINO.", diff --git a/tests/openvino/native/test_statistics_caching.py b/tests/openvino/native/test_statistics_caching.py new file mode 100644 index 00000000000..15000d0bba7 --- /dev/null +++ b/tests/openvino/native/test_statistics_caching.py @@ -0,0 +1,22 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nncf.common.graph.transformations.commands import TargetType +from nncf.openvino.graph.transformations.commands import OVTargetPoint +from nncf.openvino.statistics.aggregator import OVStatisticsAggregator +from tests.cross_fw.test_templates.test_statistics_caching import TemplateTestStatisticsCaching + + +class TestStatisticsCaching(TemplateTestStatisticsCaching): + def create_target_point(self, target_point_type: TargetType, name: str, port_id: int) -> OVTargetPoint: + return OVTargetPoint(target_point_type, name, port_id) + + def get_statistics_aggregator(self): + return OVStatisticsAggregator(None) diff --git a/tests/openvino/requirements.txt b/tests/openvino/requirements.txt index 9a74f4d74d8..9f6a390a1d5 100644 --- a/tests/openvino/requirements.txt +++ b/tests/openvino/requirements.txt @@ -12,3 +12,7 @@ virtualenv addict>=2.4.0 timm==0.9.2 efficientnet_pytorch==0.7.1 +datasets==3.0.1 +transformers==4.45.2 +optimum-intel==1.20.0 +optimum==1.23.1 diff --git a/tests/openvino/test_telemetry.py b/tests/openvino/test_telemetry.py index ed8e634abba..991e07f283e 100644 --- a/tests/openvino/test_telemetry.py +++ b/tests/openvino/test_telemetry.py @@ -16,8 +16,8 @@ import nncf from nncf import Dataset -from tests.shared.datasets import MockDataset -from tests.shared.helpers import telemetry_send_event_test_driver +from tests.cross_fw.shared.datasets import MockDataset +from tests.cross_fw.shared.helpers import telemetry_send_event_test_driver INPUT_SHAPE = [2, 1, 1, 1] diff --git a/tests/openvino/tools/calibrate.py b/tests/openvino/tools/calibrate.py index a044bf8f269..c0542180223 100644 --- a/tests/openvino/tools/calibrate.py +++ b/tests/openvino/tools/calibrate.py @@ -14,6 +14,8 @@ import json import multiprocessing import os +import platform +import subprocess from argparse import ArgumentParser from collections import OrderedDict from collections import defaultdict @@ -26,6 +28,7 @@ import numpy as np import openvino.runtime as ov +import pkg_resources from config import Config from openvino.runtime import Dimension from openvino.runtime import PartialShape @@ -41,6 +44,7 @@ import nncf from nncf.common.deprecation import warning_deprecated +from nncf.common.logging import nncf_logger from nncf.common.logging.logger import set_log_file from nncf.common.quantization.structs import QuantizationPreset from nncf.common.quantization.structs import QuantizationScheme @@ -1076,6 +1080,67 @@ def update_nncf_algorithms_config(nncf_algorithms_config: Dict[str, Dict[str, An print(f"Updated subset_size value for {nncf_method} method to {new_subset_size} ") +class EnvInfo: + + @staticmethod + def print_info() -> None: + """ + Prints NNCF version, python version and CPU model name. + """ + python_version = EnvInfo._get_python_version() + nncf_version = EnvInfo._get_nncf_version() + cpu_model = EnvInfo._get_cpu_model() + + nncf_logger.info(f"Python version: {python_version}") + nncf_logger.info(f"NNCF version: {nncf_version}") + nncf_logger.info(f"CPU model: {cpu_model}") + + @staticmethod + def _get_nncf_version() -> str: + try: + version = pkg_resources.get_distribution("nncf").version + except pkg_resources.DistributionNotFound: + version = "Unknown" + return version + + @staticmethod + def _get_python_version() -> str: + return platform.python_version() + + @staticmethod + def _get_cpu_model() -> str: + """ + Returns CPU device name. + """ + + def _get_cpu_model_name_linux_os() -> str: + try: + output = subprocess.check_output("lscpu", shell=True) + for line in output.decode().splitlines(): + if "Model name:" in line: + return line.split(":")[1].strip() + return "Unknown" + except Exception: + return "Unknown" + + def _get_cpu_model_name_windows() -> str: + try: + output = subprocess.check_output("wmic cpu get name", shell=True) + return output.decode().splitlines()[1].strip() + except Exception: + return "Unknown" + + os_name = platform.system() + + cpu_model = "Unknown" + if os_name == "Linux": + cpu_model = _get_cpu_model_name_linux_os() + elif os_name == "Windows": + cpu_model = _get_cpu_model_name_windows() + + return cpu_model + + def main(): args = parse_args() if args.impl is not None: @@ -1095,6 +1160,8 @@ def main(): output_dir = os.path.join(args.output_dir, "optimized") os.makedirs(output_dir, exist_ok=True) + EnvInfo.print_info() + algo_name_to_method_map = { "quantize": quantize_model, "quantize_with_accuracy_control": quantize_model_with_accuracy_control, diff --git a/tests/post_training/data/ptq_reference_data.yaml b/tests/post_training/data/ptq_reference_data.yaml index 4bacf77ff2e..94f70f3a931 100644 --- a/tests/post_training/data/ptq_reference_data.yaml +++ b/tests/post_training/data/ptq_reference_data.yaml @@ -38,20 +38,28 @@ torchvision/resnet18_backend_CUDA_TORCH: metric_value: 0.69152 torchvision/resnet18_backend_FX_TORCH: metric_value: 0.6946 +torchvision/mobilenet_v3_small_BC_backend_FP32: + metric_value: 0.6766 +torchvision/mobilenet_v3_small_BC_backend_OV: + metric_value: 0.6669 +torchvision/mobilenet_v3_small_BC_backend_ONNX: + metric_value: 0.6679 +torchvision/mobilenet_v3_small_BC_backend_FX_TORCH: + metric_value: 0.6679 torchvision/vit_b_16_backend_FP32: metric_value: 0.8107 torchvision/vit_b_16_backend_OV: metric_value: 0.80948 torchvision/vit_b_16_backend_FX_TORCH: - metric_value: 0.80702 + metric_value: 0.80922 torchvision/swin_v2_s_backend_FP32: metric_value: 0.83712 torchvision/swin_v2_s_backend_OV: metric_value: 0.83638 torchvision/swin_v2_s_backend_FX_TORCH: - metric_value: 0.8296 + metric_value: 0.8360 timm/crossvit_9_240_backend_CUDA_TORCH: - metric_value: 0.689 + metric_value: 0.7275 timm/crossvit_9_240_backend_FP32: metric_value: 0.73982 timm/crossvit_9_240_backend_ONNX: @@ -71,7 +79,7 @@ timm/darknet53_backend_OV: timm/darknet53_backend_TORCH: metric_value: 0.79094 timm/deit3_small_patch16_224_backend_CUDA_TORCH: - metric_value: 0.76816 + metric_value: 0.81246 timm/deit3_small_patch16_224_backend_FP32: metric_value: 0.81358 timm/deit3_small_patch16_224_backend_ONNX: @@ -81,7 +89,7 @@ timm/deit3_small_patch16_224_backend_OV: timm/deit3_small_patch16_224_backend_TORCH: metric_value: 0.81274 timm/dla34_backend_CUDA_TORCH: - metric_value: 0.73978 + metric_value: 0.74272 timm/dla34_backend_FP32: metric_value: 0.74628 timm/dla34_backend_ONNX: @@ -91,7 +99,7 @@ timm/dla34_backend_OV: timm/dla34_backend_TORCH: metric_value: 0.74256 timm/dpn68_backend_CUDA_TORCH: - metric_value: 0.75492 + metric_value: 0.75786 timm/dpn68_backend_FP32: metric_value: 0.76342 timm/dpn68_backend_ONNX: @@ -107,7 +115,7 @@ timm/efficientnet_b0_BC_backend_ONNX: timm/efficientnet_b0_BC_backend_OV: metric_value: 0.77166 timm/efficientnet_b0_backend_CUDA_TORCH: - metric_value: 0.768 + metric_value: 0.77124 timm/efficientnet_b0_backend_FP32: metric_value: 0.77698 timm/efficientnet_b0_backend_ONNX: @@ -117,7 +125,7 @@ timm/efficientnet_b0_backend_OV: timm/efficientnet_b0_backend_TORCH: metric_value: 0.77042 timm/efficientnet_lite0_backend_CUDA_TORCH: - metric_value: 0.74686 + metric_value: 0.75162 timm/efficientnet_lite0_backend_FP32: metric_value: 0.75496 timm/efficientnet_lite0_backend_ONNX: @@ -127,7 +135,7 @@ timm/efficientnet_lite0_backend_OV: timm/efficientnet_lite0_backend_TORCH: metric_value: 0.7517 timm/hrnet_w18_backend_CUDA_TORCH: - metric_value: 0.76712 + metric_value: 0.77178 timm/hrnet_w18_backend_FP32: metric_value: 0.78124 timm/hrnet_w18_backend_ONNX: @@ -137,7 +145,7 @@ timm/hrnet_w18_backend_OV: timm/hrnet_w18_backend_TORCH: metric_value: 0.7722 timm/inception_resnet_v2_backend_CUDA_TORCH: - metric_value: 0.80024 + metric_value: 0.80334 timm/inception_resnet_v2_backend_FP32: metric_value: 0.80448 timm/inception_resnet_v2_backend_ONNX: @@ -147,7 +155,7 @@ timm/inception_resnet_v2_backend_OV: timm/inception_resnet_v2_backend_TORCH: metric_value: 0.80334 timm/levit_128_backend_CUDA_TORCH: - metric_value: 0.7324 + metric_value: 0.77812 timm/levit_128_backend_FP32: metric_value: 0.78474 timm/levit_128_backend_ONNX: @@ -163,7 +171,7 @@ timm/mobilenetv2_050_BC_backend_ONNX: timm/mobilenetv2_050_BC_backend_OV: metric_value: 0.65332 timm/mobilenetv2_050_backend_CUDA_TORCH: - metric_value: 0.64278 + metric_value: 0.6534 timm/mobilenetv2_050_backend_FP32: metric_value: 0.6594 timm/mobilenetv2_050_backend_ONNX: @@ -173,7 +181,7 @@ timm/mobilenetv2_050_backend_OV: timm/mobilenetv2_050_backend_TORCH: metric_value: 0.65334 timm/mobilenetv3_small_050_backend_CUDA_TORCH: - metric_value: 0.41888 + metric_value: 0.4267 timm/mobilenetv3_small_050_backend_FP32: metric_value: 0.57906 timm/mobilenetv3_small_050_backend_ONNX: @@ -189,7 +197,7 @@ timm/mobilenetv3_small_050_BC_backend_ONNX: timm/mobilenetv3_small_050_BC_backend_OV: metric_value: 0.5655 timm/regnetx_002_backend_CUDA_TORCH: - metric_value: 0.67452 + metric_value: 0.68596 timm/regnetx_002_backend_FP32: metric_value: 0.68756 timm/regnetx_002_backend_ONNX: @@ -199,7 +207,7 @@ timm/regnetx_002_backend_OV: timm/regnetx_002_backend_TORCH: metric_value: 0.68576 timm/resnest14d_backend_CUDA_TORCH: - metric_value: 0.74176 + metric_value: 0.74898 timm/resnest14d_backend_FP32: metric_value: 0.75516 timm/resnest14d_backend_ONNX: @@ -231,7 +239,7 @@ timm/tf_inception_v3_backend_OV: timm/tf_inception_v3_backend_TORCH: metric_value: 0.77586 timm/vgg11_backend_CUDA_TORCH: - metric_value: 0.6809 + metric_value: 0.688 timm/vgg11_backend_FP32: metric_value: 0.6904 timm/vgg11_backend_ONNX: @@ -241,7 +249,7 @@ timm/vgg11_backend_OV: timm/vgg11_backend_TORCH: metric_value: 0.6879 timm/visformer_small_backend_CUDA_TORCH: - metric_value: 0.77728 + metric_value: 0.81612 timm/visformer_small_backend_FP32: metric_value: 0.82098 timm/visformer_small_backend_ONNX: diff --git a/tests/post_training/data/ptq_reference_data_2024.5.yaml b/tests/post_training/data/ptq_reference_data_2024.5.yaml new file mode 100644 index 00000000000..998fc76f889 --- /dev/null +++ b/tests/post_training/data/ptq_reference_data_2024.5.yaml @@ -0,0 +1,2 @@ +torchvision/mobilenet_v3_small_BC_backend_OV: + metric_value: 0.6681 diff --git a/tests/post_training/data/wc_reference_data.yaml b/tests/post_training/data/wc_reference_data.yaml index f42b379208e..b3c2cc44d20 100644 --- a/tests/post_training/data/wc_reference_data.yaml +++ b/tests/post_training/data/wc_reference_data.yaml @@ -7,32 +7,40 @@ tinyllama_data_aware_backend_OV: num_int4: 94 num_int8: 124 tinyllama_data_aware_awq_stateful_backend_OV: - metric_value: 0.85616 + metric_value: 0.85571 num_int4: 94 num_int8: 124 tinyllama_data_aware_awq_scale_estimation_backend_OV: - metric_value: 0.85502 + metric_value: 0.86355 num_int4: 94 num_int8: 124 tinyllama_data_aware_awq_scale_estimation_stateful_backend_OV: - metric_value: 0.85502 + metric_value: 0.86355 num_int4: 94 num_int8: 124 tinyllama_int8_data_free_backend_TORCH: metric_value: 0.95624 num_int4: 0 num_int8: 312 -tinyllama_data_aware_gptq_backend_OV: - metric_value: 0.87134 +tinyllama_data_aware_gptq_scale_estimation_stateful_backend_OV: + metric_value: 0.86697 num_int4: 94 num_int8: 124 metrics_xfail_reason: "Issue-148819" tinyllama_scale_estimation_per_channel_backend_OV: - metric_value: 0.81389 + metric_value: 0.80798 num_int4: 188 num_int8: 124 - metrics_xfail_reason: "Issue-148819" tinyllama_data_aware_lora_stateful_backend_OV: metric_value: 0.83446 num_int4: 94 - num_int8: 500 \ No newline at end of file + num_int8: 500 +tinyllama_NF4_scale_estimation_stateful_per_channel_backend_OV: + metric_value: 0.87942 + num_int4: 11 + num_int8: 290 + metrics_xfail_reason: "Issue-148819" +tinyllama_awq_backup_mode_none_backend_OV: + metric_value: 0.84793 + num_int4: 208 + num_int8: 0 diff --git a/tests/post_training/data/wc_reference_data_2024.4.yaml b/tests/post_training/data/wc_reference_data_2024.4.yaml deleted file mode 100644 index 02131f283fa..00000000000 --- a/tests/post_training/data/wc_reference_data_2024.4.yaml +++ /dev/null @@ -1,22 +0,0 @@ -tinyllama_data_aware_awq_stateful_backend_OV: - metric_value: 0.85571 - num_int4: 94 - num_int8: 124 -tinyllama_data_aware_awq_scale_estimation_backend_OV: - metric_value: 0.86355 - num_int4: 94 - num_int8: 124 -tinyllama_data_aware_awq_scale_estimation_stateful_backend_OV: - metric_value: 0.86355 - num_int4: 94 - num_int8: 124 -tinyllama_data_aware_gptq_backend_OV: - metric_value: 0.86521 - num_int4: 94 - num_int8: 124 - metrics_xfail_reason: "Issue-148819" -tinyllama_scale_estimation_per_channel_backend_OV: - metric_value: 0.80853 - num_int4: 188 - num_int8: 124 - metrics_xfail_reason: "Issue-148819" \ No newline at end of file diff --git a/tests/post_training/data/wc_reference_data_2024.5.yaml b/tests/post_training/data/wc_reference_data_2024.5.yaml index b321c426cb6..e55a9f03dcd 100644 --- a/tests/post_training/data/wc_reference_data_2024.5.yaml +++ b/tests/post_training/data/wc_reference_data_2024.5.yaml @@ -1,10 +1,34 @@ -tinyllama_data_aware_gptq_backend_OV: - metric_value: 0.81664 +tinyllama_NF4_scale_estimation_stateful_per_channel_backend_OV: + metric_value: 0.88663 + num_int4: 11 + num_int8: 290 + metrics_xfail_reason: "Issue-148819" +tinyllama_int4_data_free_backend_TORCH: + metric_value: 0.73873 + num_int4: 114 + num_int8: 84 +tinyllama_awq_backup_mode_none_backend_OV: + metric_value: 0.84783 + num_int4: 208 + num_int8: 0 +tinyllama_data_aware_awq_scale_estimation_backend_OV: + metric_value: 0.85502 + num_int4: 94 + num_int8: 124 +tinyllama_data_aware_awq_scale_estimation_stateful_backend_OV: + metric_value: 0.85502 + num_int4: 94 + num_int8: 124 +tinyllama_data_aware_awq_stateful_backend_OV: + metric_value: 0.85616 + num_int4: 94 + num_int8: 124 +tinyllama_data_aware_gptq_scale_estimation_stateful_backend_OV: + metric_value: 0.86503 num_int4: 94 num_int8: 124 metrics_xfail_reason: "Issue-148819" tinyllama_scale_estimation_per_channel_backend_OV: - metric_value: 0.80798 + metric_value: 0.81389 num_int4: 188 - num_int8: 124 - metrics_xfail_reason: "Issue-148819" \ No newline at end of file + num_int8: 124 \ No newline at end of file diff --git a/tests/post_training/experimental/sparsify_activations/pipelines.py b/tests/post_training/experimental/sparsify_activations/pipelines.py index 82da57caa86..cb12f3b08f2 100644 --- a/tests/post_training/experimental/sparsify_activations/pipelines.py +++ b/tests/post_training/experimental/sparsify_activations/pipelines.py @@ -30,8 +30,8 @@ from nncf.experimental.torch.sparsify_activations import sparsify_activations from nncf.experimental.torch.sparsify_activations.sparsify_activations_impl import SparsifyActivationsAlgoBackend from nncf.experimental.torch.sparsify_activations.torch_backend import PTSparsifyActivationsAlgoBackend -from nncf.torch.quantization.layers import AsymmetricWeightsDecompressor -from nncf.torch.quantization.layers import SymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor from tests.post_training.pipelines.base import LIMIT_LENGTH_OF_STATUS from tests.post_training.pipelines.base import PT_BACKENDS from tests.post_training.pipelines.base import BackendType @@ -133,7 +133,7 @@ def count_compressed_nodes_from_ir(model: ov.Model) -> SANumCompressNodes: for i in range(node.get_output_size()): if node.get_output_element_type(i).get_type_name() in ["i8", "u8"]: num_int8 += 1 - if node.get_output_element_type(i).get_type_name() in ["i4", "u4"]: + if node.get_output_element_type(i).get_type_name() in ["i4", "u4", "nf4"]: num_int4 += 1 num_sparse_activations = count_sparsifier_patterns_in_ov(model) @@ -267,7 +267,7 @@ def save_compressed_model(self): if self.backend == BackendType.CUDA_TORCH: self.model_hf.float() for module in self.model_hf.nncf.modules(): - if isinstance(module, (AsymmetricWeightsDecompressor, SymmetricWeightsDecompressor)): + if isinstance(module, (INT8AsymmetricWeightsDecompressor, INT8SymmetricWeightsDecompressor)): module.result_dtype = torch.float32 export_from_model( self.model_hf, self.output_model_dir, stateful=False, compression_option="fp32", device="cuda" diff --git a/tests/post_training/model_scope.py b/tests/post_training/model_scope.py index ccaaec8e7c6..54b49d63a21 100644 --- a/tests/post_training/model_scope.py +++ b/tests/post_training/model_scope.py @@ -15,6 +15,7 @@ import nncf from nncf import ModelType from nncf import QuantizationPreset +from nncf.parameters import BackupMode from nncf.parameters import CompressWeightsMode from nncf.parameters import SensitivityMetric from nncf.quantization.advanced_parameters import AdvancedCompressionParameters @@ -89,6 +90,17 @@ "backends": [BackendType.FX_TORCH, BackendType.TORCH, BackendType.CUDA_TORCH, BackendType.OV, BackendType.ONNX], "batch_size": 128, }, + { + "reported_name": "torchvision/mobilenet_v3_small_BC", + "model_id": "mobilenet_v3_small", + "pipeline_cls": ImageClassificationTorchvision, + "compression_params": { + "fast_bias_correction": False, + "preset": QuantizationPreset.MIXED, + }, + "backends": [BackendType.FX_TORCH, BackendType.OV, BackendType.ONNX], + "batch_size": 128, + }, { "reported_name": "torchvision/vit_b_16", "model_id": "vit_b_16", @@ -430,7 +442,19 @@ "backends": [BackendType.TORCH], }, { - "reported_name": "tinyllama_data_aware_gptq", + "reported_name": "tinyllama_int4_data_free", + "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b", + "pipeline_cls": LMWeightCompression, + "compression_params": { + "group_size": 64, + "ratio": 0.8, + "mode": CompressWeightsMode.INT4_SYM, + "sensitivity_metric": SensitivityMetric.WEIGHT_QUANTIZATION_ERROR, + }, + "backends": [BackendType.TORCH], + }, + { + "reported_name": "tinyllama_data_aware_gptq_scale_estimation_stateful", "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b", "pipeline_cls": LMWeightCompression, "compression_params": { @@ -438,7 +462,28 @@ "ratio": 0.8, "mode": CompressWeightsMode.INT4_SYM, "gptq": True, + "scale_estimation": True, + "advanced_parameters": AdvancedCompressionParameters( + scale_estimation_params=AdvancedScaleEstimationParameters(32, 5, 10, 1.0) + ), + }, + "params": {"is_stateful": True}, + "backends": [BackendType.OV], + }, + { + "reported_name": "tinyllama_NF4_scale_estimation_stateful_per_channel", + "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b", + "pipeline_cls": LMWeightCompression, + "compression_params": { + "group_size": -1, + "ratio": 0.1, + "mode": CompressWeightsMode.NF4, + "scale_estimation": True, + "advanced_parameters": AdvancedCompressionParameters( + scale_estimation_params=AdvancedScaleEstimationParameters(32, 5, 10, 1.0) + ), }, + "params": {"is_stateful": True}, "backends": [BackendType.OV], }, { @@ -471,6 +516,21 @@ "params": {"is_stateful": True}, "backends": [BackendType.OV], }, + { + "reported_name": "tinyllama_awq_backup_mode_none", + "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b", + "pipeline_cls": LMWeightCompression, + "compression_params": { + "group_size": 64, + "ratio": 0.8, + "all_layers": True, + "backup_mode": BackupMode.NONE, + "mode": CompressWeightsMode.INT4_ASYM, + "awq": True, + "ignored_scope": nncf.IgnoredScope(types=["Gather"]), + }, + "backends": [BackendType.OV], + }, ] diff --git a/tests/post_training/pipelines/base.py b/tests/post_training/pipelines/base.py index 3657848063c..41e9bcf17e4 100644 --- a/tests/post_training/pipelines/base.py +++ b/tests/post_training/pipelines/base.py @@ -30,7 +30,7 @@ import nncf from nncf import TargetDevice -from tests.shared.command import Command +from tests.cross_fw.shared.command import Command from tools.memory_monitor import MemoryType from tools.memory_monitor import MemoryUnit from tools.memory_monitor import memory_monitor_context @@ -418,6 +418,9 @@ def save_compressed_model(self) -> None: ov.serialize(ov_model, self.path_compressed_ir) elif self.backend in OV_BACKENDS: self.path_compressed_ir = self.output_model_dir / "model.xml" + from openvino._offline_transformations import apply_moc_transformations + + apply_moc_transformations(self.compressed_model, cf=True) ov.serialize(self.compressed_model, str(self.path_compressed_ir)) def get_num_compressed(self) -> None: @@ -439,7 +442,7 @@ def get_num_compressed(self) -> None: for i in range(node.get_output_size()): if node.get_output_element_type(i).get_type_name() in ["i8", "u8"]: num_int8 += 1 - if node.get_output_element_type(i).get_type_name() in ["i4", "u4"]: + if node.get_output_element_type(i).get_type_name() in ["i4", "u4", "nf4"]: num_int4 += 1 self.run_info.num_compress_nodes.num_int8 = num_int8 diff --git a/tests/post_training/pipelines/image_classification_torchvision.py b/tests/post_training/pipelines/image_classification_torchvision.py index c42aa9ab1bb..b675bd6e814 100644 --- a/tests/post_training/pipelines/image_classification_torchvision.py +++ b/tests/post_training/pipelines/image_classification_torchvision.py @@ -9,6 +9,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +from dataclasses import dataclass +from typing import Any, Callable, Tuple + import numpy as np import onnx import openvino as ov @@ -22,29 +25,50 @@ from tests.post_training.pipelines.image_classification_base import ImageClassificationBase +def _capture_pre_autograd_module(model: torch.nn.Module, args: Tuple[Any, ...]) -> torch.fx.GraphModule: + return capture_pre_autograd_graph(model, args) + + +def _export_graph_module(model: torch.nn.Module, args: Tuple[Any, ...]) -> torch.fx.GraphModule: + return torch.export.export(model, args).module() + + +@dataclass +class VisionModelParams: + weights: models.WeightsEnum + export_fn: Callable[[torch.nn.Module, Tuple[Any, ...]], torch.fx.GraphModule] + export_torch_before_ov_convert: bool = False + + class ImageClassificationTorchvision(ImageClassificationBase): """Pipeline for Image Classification model from torchvision repository""" - models_vs_imagenet_weights = { - models.resnet18: models.ResNet18_Weights.DEFAULT, - models.mobilenet_v3_small: models.MobileNet_V3_Small_Weights.DEFAULT, - models.vit_b_16: models.ViT_B_16_Weights.DEFAULT, - models.swin_v2_s: models.Swin_V2_S_Weights.DEFAULT, + models_vs_model_params = { + models.resnet18: VisionModelParams(models.ResNet18_Weights.DEFAULT, _capture_pre_autograd_module), + models.mobilenet_v3_small: VisionModelParams( + models.MobileNet_V3_Small_Weights.DEFAULT, _capture_pre_autograd_module + ), + models.vit_b_16: VisionModelParams( + models.ViT_B_16_Weights.DEFAULT, _export_graph_module, export_torch_before_ov_convert=True + ), + models.swin_v2_s: VisionModelParams( + models.Swin_V2_S_Weights.DEFAULT, _export_graph_module, export_torch_before_ov_convert=True + ), } def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.model_weights: models.WeightsEnum = None + self.model_params: VisionModelParams = None self.input_name: str = None def prepare_model(self) -> None: model_cls = models.__dict__.get(self.model_id) - self.model_weights = self.models_vs_imagenet_weights[model_cls] - model = model_cls(weights=self.model_weights) + self.model_params = self.models_vs_model_params[model_cls] + model = model_cls(weights=self.model_params.weights) model.eval() default_input_size = [self.batch_size, 3, 224, 224] - self.dummy_tensor = self.model_weights.transforms()(torch.rand(default_input_size)) + self.dummy_tensor = self.model_params.weights.transforms()(torch.rand(default_input_size)) self.static_input_size = list(self.dummy_tensor.shape) self.input_size = self.static_input_size.copy() @@ -54,7 +78,7 @@ def prepare_model(self) -> None: if self.backend == BackendType.FX_TORCH: with torch.no_grad(): with disable_patching(): - self.model = capture_pre_autograd_graph(model, (self.dummy_tensor,)) + self.model = self.model_params.export_fn(model, (self.dummy_tensor,)) elif self.backend in PT_BACKENDS: self.model = model @@ -73,6 +97,9 @@ def prepare_model(self) -> None: elif self.backend in [BackendType.OV, BackendType.FP32]: with torch.no_grad(): + if self.model_params.export_torch_before_ov_convert: + with disable_patching(): + model = torch.export.export(model, (self.dummy_tensor,)) self.model = ov.convert_model(model, example_input=self.dummy_tensor, input=self.input_size) self.input_name = list(inp.get_any_name() for inp in self.model.inputs)[0] @@ -103,7 +130,7 @@ def _dump_model_fp32(self) -> None: ov.serialize(self.model, self.fp32_model_dir / "model_fp32.xml") def prepare_preprocessor(self) -> None: - self.transform = self.model_weights.transforms() + self.transform = self.model_params.weights.transforms() def get_transform_calibration_fn(self): if self.backend in [BackendType.FX_TORCH] + PT_BACKENDS: diff --git a/tests/post_training/pipelines/lm_weight_compression.py b/tests/post_training/pipelines/lm_weight_compression.py index 757232f4b63..3c45d9de636 100644 --- a/tests/post_training/pipelines/lm_weight_compression.py +++ b/tests/post_training/pipelines/lm_weight_compression.py @@ -28,10 +28,10 @@ from whowhatbench import Evaluator import nncf +from tests.cross_fw.shared.paths import TEST_ROOT from tests.post_training.pipelines.base import BackendType from tests.post_training.pipelines.base import BaseTestPipeline from tests.post_training.pipelines.base import StatsFromOutput -from tests.shared.paths import TEST_ROOT from tools.memory_monitor import MemoryType from tools.memory_monitor import MemoryUnit from tools.memory_monitor import memory_monitor_context @@ -227,7 +227,7 @@ def get_num_compressed(self) -> None: for i in range(node.get_output_size()): if node.get_output_element_type(i).get_type_name() in ["i8", "u8"]: num_int8 += 1 - if node.get_output_element_type(i).get_type_name() in ["i4", "u4"]: + if node.get_output_element_type(i).get_type_name() in ["i4", "u4", "nf4"]: num_int4 += 1 self.run_info.num_compress_nodes.num_int8 = num_int8 diff --git a/tests/post_training/requirements.txt b/tests/post_training/requirements.txt index 9a972694614..93103066aab 100644 --- a/tests/post_training/requirements.txt +++ b/tests/post_training/requirements.txt @@ -18,3 +18,4 @@ tensorflow-io==0.32.0 timm==0.9.2 transformers==4.38.2 whowhatbench @ git+https://github.com/andreyanufr/who_what_benchmark@456d3584ce628f6c8605f37cd9a3ab2db1ebf933 +datasets==2.21.0 diff --git a/tests/post_training/test_quantize_conformance.py b/tests/post_training/test_quantize_conformance.py index d5217fc0762..20504a8b086 100644 --- a/tests/post_training/test_quantize_conformance.py +++ b/tests/post_training/test_quantize_conformance.py @@ -23,12 +23,12 @@ from packaging import version import nncf +from tests.cross_fw.shared.openvino_version import get_openvino_version from tests.post_training.model_scope import PTQ_TEST_CASES from tests.post_training.model_scope import WC_TEST_CASES from tests.post_training.pipelines.base import BackendType from tests.post_training.pipelines.base import BaseTestPipeline from tests.post_training.pipelines.base import RunInfo -from tests.shared.openvino_version import get_openvino_version DATA_ROOT = Path(__file__).parent / "data" @@ -106,7 +106,10 @@ def ref_data_correction(data: Dict, file_name: str): with file_path.open() as f: correction_data = yaml.safe_load(f) for m_name, c_data in correction_data.items(): - data[m_name].update(c_data) + if m_name in data: + data[m_name].update(c_data) + else: + data[m_name] = c_data print(f"Applied correction file {file_path}") return data @@ -125,17 +128,18 @@ def fixture_wc_reference_data(): path_reference = DATA_ROOT / "wc_reference_data.yaml" with path_reference.open() as f: data = yaml.safe_load(f) - fp32_test_cases = defaultdict(dict) - for test_case_name in data: - if "atol" not in data[test_case_name]: - data[test_case_name]["atol"] = 1e-5 - reported_name = test_case_name.split("_backend_")[0] - fp32_case_name = f"{reported_name}_backend_FP32" - fp32_test_cases[fp32_case_name]["metric_value"] = 1 - if "atol" not in fp32_test_cases[fp32_case_name]: - fp32_test_cases[fp32_case_name]["atol"] = 1e-10 - data.update(fp32_test_cases) - return ref_data_correction(data, "wc_reference_data") + data = ref_data_correction(data, "wc_reference_data") + fp32_test_cases = defaultdict(dict) + for test_case_name in data: + if "atol" not in data[test_case_name]: + data[test_case_name]["atol"] = 1e-5 + reported_name = test_case_name.split("_backend_")[0] + fp32_case_name = f"{reported_name}_backend_FP32" + fp32_test_cases[fp32_case_name]["metric_value"] = 1 + if "atol" not in fp32_test_cases[fp32_case_name]: + fp32_test_cases[fp32_case_name]["atol"] = 1e-10 + data.update(fp32_test_cases) + return data @pytest.fixture(scope="session", name="ptq_result_data") @@ -343,7 +347,7 @@ def test_weight_compression( start_time = time.perf_counter() try: if test_case_name not in wc_reference_data: - raise RuntimeError(f"{test_case_name} is not defined in `wc_reference_data` fixture") + pytest.skip(f"{test_case_name} is not defined in `wc_reference_data` fixture") test_model_param = WC_TEST_CASES[test_case_name] maybe_skip_test_case(test_model_param, run_fp32_backend, run_torch_cuda_backend, batch_size) pipeline_cls = test_model_param["pipeline_cls"] diff --git a/tests/shared/README.md b/tests/shared/README.md deleted file mode 100644 index b8a0d7bd262..00000000000 --- a/tests/shared/README.md +++ /dev/null @@ -1,4 +0,0 @@ -This folder contains test code that is meant to be reused in more than one backend-specific (or agnostic) test subfolder, such as reusable fixtures, parsers, comparators etc. -No test cases themselves should be stored here. - -For test cases that validate NNCF's common, framework-agnostic code, see the "common" folder. diff --git a/tests/tensorflow/conftest.py b/tests/tensorflow/conftest.py index d3ce687c0af..46792578c61 100644 --- a/tests/tensorflow/conftest.py +++ b/tests/tensorflow/conftest.py @@ -12,8 +12,8 @@ import pytest -from tests.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS -from tests.shared.case_collection import skip_marked_cases_if_options_not_specified +from tests.cross_fw.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS +from tests.cross_fw.shared.case_collection import skip_marked_cases_if_options_not_specified try: import tensorflow as tf diff --git a/tests/tensorflow/data/reference_graphs/2.11/quantized/w_sym_ch_a_asym_t/mask_rcnn.dot b/tests/tensorflow/data/reference_graphs/2.11/quantized/w_sym_ch_a_asym_t/mask_rcnn.dot index 331582f6ba4..fb851c21741 100644 --- a/tests/tensorflow/data/reference_graphs/2.11/quantized/w_sym_ch_a_asym_t/mask_rcnn.dot +++ b/tests/tensorflow/data/reference_graphs/2.11/quantized/w_sym_ch_a_asym_t/mask_rcnn.dot @@ -2558,4960 +2558,4360 @@ strict digraph { "2556 maskrcnn/tf.math.exp_2/Exp" [id=2556, op=Exp]; "2557 maskrcnn/tf.math.exp_1/Exp" [id=2557, op=Exp]; "2558 maskrcnn/tf.math.exp/Exp" [id=2558, op=Exp]; -"2559 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" [id=2559, op=Placeholder]; -"2560 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/Abs/ReadVariableOp" [id=2560, op=ReadVariableOp]; -"2561 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/Abs" [id=2561, op=Abs]; -"2562 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/add/y" [id=2562, op=Const]; -"2563 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maskrcnn/tf.compat.v1.gather_nd_1/GatherNd" [id=19791, op=GatherNd]; +"19792 maskrcnn/tf.math.sigmoid_5/Sigmoid" [id=19792, op=Sigmoid]; +"19793 Identity" [id=19793, op=Identity]; +"19794 Identity_1" [id=19794, op=Identity]; +"19795 Identity_2" [id=19795, op=Identity]; +"19796 Identity_3" [id=19796, op=Identity]; +"19797 Identity_4" [id=19797, op=Identity]; +"19798 Identity_5" [id=19798, op=Identity]; +"19799 Identity_6" [id=19799, op=Identity]; +"19800 Identity_7" [id=19800, op=Identity]; +"19801 Identity_8" [id=19801, op=Identity]; +"19802 Identity_9" [id=19802, op=Identity]; +"19803 Identity_10" [id=19803, op=Identity]; +"19804 Identity_11" [id=19804, op=Identity]; +"19805 Identity_12" [id=19805, op=Identity]; +"19806 Identity_13" [id=19806, op=Identity]; +"19807 Identity_14" [id=19807, op=Identity]; +"19808 Identity_15" [id=19808, op=Identity]; +"19809 Identity_16" [id=19809, op=Identity]; "0 args_0" -> "11 maskrcnn/image/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"1 args_0_1" -> "2988 maskrcnn/image_info/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"1 args_0_1" -> "2748 maskrcnn/image_info/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2 maskrcnn/image/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3 maskrcnn/image/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; "3 maskrcnn/image/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4 maskrcnn/image/fake_quantize/AsymmQuant/Abs"; "4 maskrcnn/image/fake_quantize/AsymmQuant/Abs" -> "6 maskrcnn/image/fake_quantize/AsymmQuant/add"; @@ -9893,8 +9293,8 @@ strict digraph { "2213 maskrcnn/p6-bn/fake_quantize/AsymmQuant/add_1" -> "2215 maskrcnn/p6-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2214 maskrcnn/p6-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2215 maskrcnn/p6-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2215 maskrcnn/p6-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2267 maskrcnn/rpn/Conv2D"; -"2215 maskrcnn/p6-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5040 maskrcnn/tf.reshape_18/Reshape"; -"2215 maskrcnn/p6-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7171 maskrcnn/tf.reshape_47/Reshape"; +"2215 maskrcnn/p6-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4600 maskrcnn/tf.reshape_18/Reshape"; +"2215 maskrcnn/p6-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6571 maskrcnn/tf.reshape_47/Reshape"; "2216 maskrcnn/p5-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2217 maskrcnn/p5-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; "2217 maskrcnn/p5-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2218 maskrcnn/p5-bn/fake_quantize/AsymmQuant/Abs"; "2218 maskrcnn/p5-bn/fake_quantize/AsymmQuant/Abs" -> "2220 maskrcnn/p5-bn/fake_quantize/AsymmQuant/add"; @@ -9906,8 +9306,8 @@ strict digraph { "2223 maskrcnn/p5-bn/fake_quantize/AsymmQuant/add_1" -> "2225 maskrcnn/p5-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2224 maskrcnn/p5-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2225 maskrcnn/p5-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2225 maskrcnn/p5-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2280 maskrcnn/rpn/Conv2D_1"; -"2225 maskrcnn/p5-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5038 maskrcnn/tf.reshape_16/Reshape"; -"2225 maskrcnn/p5-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7169 maskrcnn/tf.reshape_45/Reshape"; +"2225 maskrcnn/p5-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4598 maskrcnn/tf.reshape_16/Reshape"; +"2225 maskrcnn/p5-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6569 maskrcnn/tf.reshape_45/Reshape"; "2226 maskrcnn/p4-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2227 maskrcnn/p4-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; "2227 maskrcnn/p4-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2228 maskrcnn/p4-bn/fake_quantize/AsymmQuant/Abs"; "2228 maskrcnn/p4-bn/fake_quantize/AsymmQuant/Abs" -> "2230 maskrcnn/p4-bn/fake_quantize/AsymmQuant/add"; @@ -9919,8 +9319,8 @@ strict digraph { "2233 maskrcnn/p4-bn/fake_quantize/AsymmQuant/add_1" -> "2235 maskrcnn/p4-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2234 maskrcnn/p4-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2235 maskrcnn/p4-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2235 maskrcnn/p4-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2292 maskrcnn/rpn/Conv2D_2"; -"2235 maskrcnn/p4-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5036 maskrcnn/tf.reshape_14/Reshape"; -"2235 maskrcnn/p4-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7167 maskrcnn/tf.reshape_43/Reshape"; +"2235 maskrcnn/p4-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4596 maskrcnn/tf.reshape_14/Reshape"; +"2235 maskrcnn/p4-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6567 maskrcnn/tf.reshape_43/Reshape"; "2236 maskrcnn/p3-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2237 maskrcnn/p3-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; "2237 maskrcnn/p3-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2238 maskrcnn/p3-bn/fake_quantize/AsymmQuant/Abs"; "2238 maskrcnn/p3-bn/fake_quantize/AsymmQuant/Abs" -> "2240 maskrcnn/p3-bn/fake_quantize/AsymmQuant/add"; @@ -9932,8 +9332,8 @@ strict digraph { "2243 maskrcnn/p3-bn/fake_quantize/AsymmQuant/add_1" -> "2245 maskrcnn/p3-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2244 maskrcnn/p3-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2245 maskrcnn/p3-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2245 maskrcnn/p3-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2304 maskrcnn/rpn/Conv2D_3"; -"2245 maskrcnn/p3-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5034 maskrcnn/tf.reshape_12/Reshape"; -"2245 maskrcnn/p3-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7165 maskrcnn/tf.reshape_41/Reshape"; +"2245 maskrcnn/p3-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4594 maskrcnn/tf.reshape_12/Reshape"; +"2245 maskrcnn/p3-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6565 maskrcnn/tf.reshape_41/Reshape"; "2246 maskrcnn/p2-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2247 maskrcnn/p2-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; "2247 maskrcnn/p2-bn/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2248 maskrcnn/p2-bn/fake_quantize/AsymmQuant/Abs"; "2248 maskrcnn/p2-bn/fake_quantize/AsymmQuant/Abs" -> "2250 maskrcnn/p2-bn/fake_quantize/AsymmQuant/add"; @@ -9945,8 +9345,8 @@ strict digraph { "2253 maskrcnn/p2-bn/fake_quantize/AsymmQuant/add_1" -> "2255 maskrcnn/p2-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2254 maskrcnn/p2-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2255 maskrcnn/p2-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2255 maskrcnn/p2-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2316 maskrcnn/rpn/Conv2D_4"; -"2255 maskrcnn/p2-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5032 maskrcnn/tf.reshape_10/Reshape"; -"2255 maskrcnn/p2-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7163 maskrcnn/tf.reshape_39/Reshape"; +"2255 maskrcnn/p2-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4592 maskrcnn/tf.reshape_10/Reshape"; +"2255 maskrcnn/p2-bn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6563 maskrcnn/tf.reshape_39/Reshape"; "2256 maskrcnn/rpn/SymmQuant/Abs/ReadVariableOp/resource" -> "2257 maskrcnn/rpn/SymmQuant/Abs/ReadVariableOp"; "2256 maskrcnn/rpn/SymmQuant/Abs/ReadVariableOp/resource" -> "2272 maskrcnn/rpn/SymmQuant_1/Abs/ReadVariableOp"; "2256 maskrcnn/rpn/SymmQuant/Abs/ReadVariableOp/resource" -> "2284 maskrcnn/rpn/SymmQuant_2/Abs/ReadVariableOp"; @@ -10043,7 +9443,7 @@ strict digraph { "2327 maskrcnn/rpn/fake_quantize_4/AsymmQuant/add_1" -> "2329 maskrcnn/rpn/fake_quantize_4/AsymmQuant/FakeQuantWithMinMaxVars"; "2328 maskrcnn/rpn/fake_quantize_4/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2329 maskrcnn/rpn/fake_quantize_4/AsymmQuant/FakeQuantWithMinMaxVars"; "2329 maskrcnn/rpn/fake_quantize_4/AsymmQuant/FakeQuantWithMinMaxVars" -> "2381 maskrcnn/rpn-box/Conv2D"; -"2329 maskrcnn/rpn/fake_quantize_4/AsymmQuant/FakeQuantWithMinMaxVars" -> "3806 maskrcnn/rpn-class/Conv2D"; +"2329 maskrcnn/rpn/fake_quantize_4/AsymmQuant/FakeQuantWithMinMaxVars" -> "3491 maskrcnn/rpn-class/Conv2D"; "2330 maskrcnn/rpn/fake_quantize_3/AsymmQuant/Abs/ReadVariableOp/resource" -> "2331 maskrcnn/rpn/fake_quantize_3/AsymmQuant/Abs/ReadVariableOp"; "2331 maskrcnn/rpn/fake_quantize_3/AsymmQuant/Abs/ReadVariableOp" -> "2332 maskrcnn/rpn/fake_quantize_3/AsymmQuant/Abs"; "2332 maskrcnn/rpn/fake_quantize_3/AsymmQuant/Abs" -> "2334 maskrcnn/rpn/fake_quantize_3/AsymmQuant/add"; @@ -10055,7 +9455,7 @@ strict digraph { "2337 maskrcnn/rpn/fake_quantize_3/AsymmQuant/add_1" -> "2339 maskrcnn/rpn/fake_quantize_3/AsymmQuant/FakeQuantWithMinMaxVars"; "2338 maskrcnn/rpn/fake_quantize_3/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2339 maskrcnn/rpn/fake_quantize_3/AsymmQuant/FakeQuantWithMinMaxVars"; "2339 maskrcnn/rpn/fake_quantize_3/AsymmQuant/FakeQuantWithMinMaxVars" -> "2393 maskrcnn/rpn-box/Conv2D_1"; -"2339 maskrcnn/rpn/fake_quantize_3/AsymmQuant/FakeQuantWithMinMaxVars" -> "3818 maskrcnn/rpn-class/Conv2D_1"; +"2339 maskrcnn/rpn/fake_quantize_3/AsymmQuant/FakeQuantWithMinMaxVars" -> "3503 maskrcnn/rpn-class/Conv2D_1"; "2340 maskrcnn/rpn/fake_quantize_2/AsymmQuant/Abs/ReadVariableOp/resource" -> "2341 maskrcnn/rpn/fake_quantize_2/AsymmQuant/Abs/ReadVariableOp"; "2341 maskrcnn/rpn/fake_quantize_2/AsymmQuant/Abs/ReadVariableOp" -> "2342 maskrcnn/rpn/fake_quantize_2/AsymmQuant/Abs"; "2342 maskrcnn/rpn/fake_quantize_2/AsymmQuant/Abs" -> "2344 maskrcnn/rpn/fake_quantize_2/AsymmQuant/add"; @@ -10067,7 +9467,7 @@ strict digraph { "2347 maskrcnn/rpn/fake_quantize_2/AsymmQuant/add_1" -> "2349 maskrcnn/rpn/fake_quantize_2/AsymmQuant/FakeQuantWithMinMaxVars"; "2348 maskrcnn/rpn/fake_quantize_2/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2349 maskrcnn/rpn/fake_quantize_2/AsymmQuant/FakeQuantWithMinMaxVars"; "2349 maskrcnn/rpn/fake_quantize_2/AsymmQuant/FakeQuantWithMinMaxVars" -> "2404 maskrcnn/rpn-box/Conv2D_2"; -"2349 maskrcnn/rpn/fake_quantize_2/AsymmQuant/FakeQuantWithMinMaxVars" -> "3829 maskrcnn/rpn-class/Conv2D_2"; +"2349 maskrcnn/rpn/fake_quantize_2/AsymmQuant/FakeQuantWithMinMaxVars" -> "3514 maskrcnn/rpn-class/Conv2D_2"; "2350 maskrcnn/rpn/fake_quantize_1/AsymmQuant/Abs/ReadVariableOp/resource" -> "2351 maskrcnn/rpn/fake_quantize_1/AsymmQuant/Abs/ReadVariableOp"; "2351 maskrcnn/rpn/fake_quantize_1/AsymmQuant/Abs/ReadVariableOp" -> "2352 maskrcnn/rpn/fake_quantize_1/AsymmQuant/Abs"; "2352 maskrcnn/rpn/fake_quantize_1/AsymmQuant/Abs" -> "2354 maskrcnn/rpn/fake_quantize_1/AsymmQuant/add"; @@ -10079,7 +9479,7 @@ strict digraph { "2357 maskrcnn/rpn/fake_quantize_1/AsymmQuant/add_1" -> "2359 maskrcnn/rpn/fake_quantize_1/AsymmQuant/FakeQuantWithMinMaxVars"; "2358 maskrcnn/rpn/fake_quantize_1/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2359 maskrcnn/rpn/fake_quantize_1/AsymmQuant/FakeQuantWithMinMaxVars"; "2359 maskrcnn/rpn/fake_quantize_1/AsymmQuant/FakeQuantWithMinMaxVars" -> "2415 maskrcnn/rpn-box/Conv2D_3"; -"2359 maskrcnn/rpn/fake_quantize_1/AsymmQuant/FakeQuantWithMinMaxVars" -> "3840 maskrcnn/rpn-class/Conv2D_3"; +"2359 maskrcnn/rpn/fake_quantize_1/AsymmQuant/FakeQuantWithMinMaxVars" -> "3525 maskrcnn/rpn-class/Conv2D_3"; "2360 maskrcnn/rpn/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2361 maskrcnn/rpn/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; "2361 maskrcnn/rpn/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2362 maskrcnn/rpn/fake_quantize/AsymmQuant/Abs"; "2362 maskrcnn/rpn/fake_quantize/AsymmQuant/Abs" -> "2364 maskrcnn/rpn/fake_quantize/AsymmQuant/add"; @@ -10091,7 +9491,7 @@ strict digraph { "2367 maskrcnn/rpn/fake_quantize/AsymmQuant/add_1" -> "2369 maskrcnn/rpn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2368 maskrcnn/rpn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2369 maskrcnn/rpn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; "2369 maskrcnn/rpn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2426 maskrcnn/rpn-box/Conv2D_4"; -"2369 maskrcnn/rpn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3851 maskrcnn/rpn-class/Conv2D_4"; +"2369 maskrcnn/rpn/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3536 maskrcnn/rpn-class/Conv2D_4"; "2370 maskrcnn/rpn-box/SymmQuant/Abs/ReadVariableOp/resource" -> "2371 maskrcnn/rpn-box/SymmQuant/Abs/ReadVariableOp"; "2370 maskrcnn/rpn-box/SymmQuant/Abs/ReadVariableOp/resource" -> "2385 maskrcnn/rpn-box/SymmQuant_1/Abs/ReadVariableOp"; "2370 maskrcnn/rpn-box/SymmQuant/Abs/ReadVariableOp/resource" -> "2396 maskrcnn/rpn-box/SymmQuant_2/Abs/ReadVariableOp"; @@ -10124,7 +9524,7 @@ strict digraph { "2382 maskrcnn/rpn-box/BiasAdd/ReadVariableOp/resource" -> "2427 maskrcnn/rpn-box/BiasAdd_4/ReadVariableOp"; "2383 maskrcnn/rpn-box/BiasAdd/ReadVariableOp" -> "2384 maskrcnn/rpn-box/BiasAdd"; "2384 maskrcnn/rpn-box/BiasAdd" -> "2438 maskrcnn/tf.reshape_9/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"2384 maskrcnn/rpn-box/BiasAdd" -> "20404 Identity_11"; +"2384 maskrcnn/rpn-box/BiasAdd" -> "19804 Identity_11"; "2385 maskrcnn/rpn-box/SymmQuant_1/Abs/ReadVariableOp" -> "2386 maskrcnn/rpn-box/SymmQuant_1/Abs"; "2386 maskrcnn/rpn-box/SymmQuant_1/Abs" -> "2388 maskrcnn/rpn-box/SymmQuant_1/add"; "2387 maskrcnn/rpn-box/SymmQuant_1/add/y" -> "2388 maskrcnn/rpn-box/SymmQuant_1/add"; @@ -10137,7 +9537,7 @@ strict digraph { "2393 maskrcnn/rpn-box/Conv2D_1" -> "2395 maskrcnn/rpn-box/BiasAdd_1"; "2394 maskrcnn/rpn-box/BiasAdd_1/ReadVariableOp" -> "2395 maskrcnn/rpn-box/BiasAdd_1"; "2395 maskrcnn/rpn-box/BiasAdd_1" -> "2448 maskrcnn/tf.reshape_7/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"2395 maskrcnn/rpn-box/BiasAdd_1" -> "20403 Identity_10"; +"2395 maskrcnn/rpn-box/BiasAdd_1" -> "19803 Identity_10"; "2396 maskrcnn/rpn-box/SymmQuant_2/Abs/ReadVariableOp" -> "2397 maskrcnn/rpn-box/SymmQuant_2/Abs"; "2397 maskrcnn/rpn-box/SymmQuant_2/Abs" -> "2399 maskrcnn/rpn-box/SymmQuant_2/add"; "2398 maskrcnn/rpn-box/SymmQuant_2/add/y" -> "2399 maskrcnn/rpn-box/SymmQuant_2/add"; @@ -10150,7 +9550,7 @@ strict digraph { "2404 maskrcnn/rpn-box/Conv2D_2" -> "2406 maskrcnn/rpn-box/BiasAdd_2"; "2405 maskrcnn/rpn-box/BiasAdd_2/ReadVariableOp" -> "2406 maskrcnn/rpn-box/BiasAdd_2"; "2406 maskrcnn/rpn-box/BiasAdd_2" -> "2458 maskrcnn/tf.reshape_5/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"2406 maskrcnn/rpn-box/BiasAdd_2" -> "20402 Identity_9"; +"2406 maskrcnn/rpn-box/BiasAdd_2" -> "19802 Identity_9"; "2407 maskrcnn/rpn-box/SymmQuant_3/Abs/ReadVariableOp" -> "2408 maskrcnn/rpn-box/SymmQuant_3/Abs"; "2408 maskrcnn/rpn-box/SymmQuant_3/Abs" -> "2410 maskrcnn/rpn-box/SymmQuant_3/add"; "2409 maskrcnn/rpn-box/SymmQuant_3/add/y" -> "2410 maskrcnn/rpn-box/SymmQuant_3/add"; @@ -10163,7 +9563,7 @@ strict digraph { "2415 maskrcnn/rpn-box/Conv2D_3" -> "2417 maskrcnn/rpn-box/BiasAdd_3"; "2416 maskrcnn/rpn-box/BiasAdd_3/ReadVariableOp" -> "2417 maskrcnn/rpn-box/BiasAdd_3"; "2417 maskrcnn/rpn-box/BiasAdd_3" -> "2468 maskrcnn/tf.reshape_3/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"2417 maskrcnn/rpn-box/BiasAdd_3" -> "20401 Identity_8"; +"2417 maskrcnn/rpn-box/BiasAdd_3" -> "19801 Identity_8"; "2418 maskrcnn/rpn-box/SymmQuant_4/Abs/ReadVariableOp" -> "2419 maskrcnn/rpn-box/SymmQuant_4/Abs"; "2419 maskrcnn/rpn-box/SymmQuant_4/Abs" -> "2421 maskrcnn/rpn-box/SymmQuant_4/add"; "2420 maskrcnn/rpn-box/SymmQuant_4/add/y" -> "2421 maskrcnn/rpn-box/SymmQuant_4/add"; @@ -10176,7 +9576,7 @@ strict digraph { "2426 maskrcnn/rpn-box/Conv2D_4" -> "2428 maskrcnn/rpn-box/BiasAdd_4"; "2427 maskrcnn/rpn-box/BiasAdd_4/ReadVariableOp" -> "2428 maskrcnn/rpn-box/BiasAdd_4"; "2428 maskrcnn/rpn-box/BiasAdd_4" -> "2478 maskrcnn/tf.reshape_1/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"2428 maskrcnn/rpn-box/BiasAdd_4" -> "20400 Identity_7"; +"2428 maskrcnn/rpn-box/BiasAdd_4" -> "19800 Identity_7"; "2429 maskrcnn/tf.reshape_9/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp/resource" -> "2430 maskrcnn/tf.reshape_9/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp"; "2430 maskrcnn/tf.reshape_9/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp" -> "2431 maskrcnn/tf.reshape_9/fake_quantize_I0/AsymmQuant/Abs"; "2431 maskrcnn/tf.reshape_9/fake_quantize_I0/AsymmQuant/Abs" -> "2433 maskrcnn/tf.reshape_9/fake_quantize_I0/AsymmQuant/add"; @@ -10235,28 +9635,28 @@ strict digraph { "2479 maskrcnn/tf.reshape_9/Reshape/shape" -> "2480 maskrcnn/tf.reshape_9/Reshape"; "2480 maskrcnn/tf.reshape_9/Reshape" -> "2492 maskrcnn/tf.__operators__.getitem_20/strided_slice"; "2480 maskrcnn/tf.reshape_9/Reshape" -> "2496 maskrcnn/tf.__operators__.getitem_19/strided_slice"; -"2480 maskrcnn/tf.reshape_9/Reshape" -> "2572 maskrcnn/tf.__operators__.getitem_18/strided_slice"; -"2480 maskrcnn/tf.reshape_9/Reshape" -> "2586 maskrcnn/tf.__operators__.getitem_17/strided_slice"; +"2480 maskrcnn/tf.reshape_9/Reshape" -> "2564 maskrcnn/tf.__operators__.getitem_18/strided_slice"; +"2480 maskrcnn/tf.reshape_9/Reshape" -> "2570 maskrcnn/tf.__operators__.getitem_17/strided_slice"; "2481 maskrcnn/tf.reshape_7/Reshape/shape" -> "2482 maskrcnn/tf.reshape_7/Reshape"; "2482 maskrcnn/tf.reshape_7/Reshape" -> "2500 maskrcnn/tf.__operators__.getitem_16/strided_slice"; "2482 maskrcnn/tf.reshape_7/Reshape" -> "2504 maskrcnn/tf.__operators__.getitem_15/strided_slice"; -"2482 maskrcnn/tf.reshape_7/Reshape" -> "2600 maskrcnn/tf.__operators__.getitem_14/strided_slice"; -"2482 maskrcnn/tf.reshape_7/Reshape" -> "2614 maskrcnn/tf.__operators__.getitem_13/strided_slice"; +"2482 maskrcnn/tf.reshape_7/Reshape" -> "2576 maskrcnn/tf.__operators__.getitem_14/strided_slice"; +"2482 maskrcnn/tf.reshape_7/Reshape" -> "2582 maskrcnn/tf.__operators__.getitem_13/strided_slice"; "2483 maskrcnn/tf.reshape_5/Reshape/shape" -> "2484 maskrcnn/tf.reshape_5/Reshape"; "2484 maskrcnn/tf.reshape_5/Reshape" -> "2508 maskrcnn/tf.__operators__.getitem_12/strided_slice"; "2484 maskrcnn/tf.reshape_5/Reshape" -> "2512 maskrcnn/tf.__operators__.getitem_11/strided_slice"; -"2484 maskrcnn/tf.reshape_5/Reshape" -> "2628 maskrcnn/tf.__operators__.getitem_10/strided_slice"; -"2484 maskrcnn/tf.reshape_5/Reshape" -> "2642 maskrcnn/tf.__operators__.getitem_9/strided_slice"; +"2484 maskrcnn/tf.reshape_5/Reshape" -> "2588 maskrcnn/tf.__operators__.getitem_10/strided_slice"; +"2484 maskrcnn/tf.reshape_5/Reshape" -> "2594 maskrcnn/tf.__operators__.getitem_9/strided_slice"; "2485 maskrcnn/tf.reshape_3/Reshape/shape" -> "2486 maskrcnn/tf.reshape_3/Reshape"; "2486 maskrcnn/tf.reshape_3/Reshape" -> "2516 maskrcnn/tf.__operators__.getitem_8/strided_slice"; "2486 maskrcnn/tf.reshape_3/Reshape" -> "2520 maskrcnn/tf.__operators__.getitem_7/strided_slice"; -"2486 maskrcnn/tf.reshape_3/Reshape" -> "2656 maskrcnn/tf.__operators__.getitem_6/strided_slice"; -"2486 maskrcnn/tf.reshape_3/Reshape" -> "2670 maskrcnn/tf.__operators__.getitem_5/strided_slice"; +"2486 maskrcnn/tf.reshape_3/Reshape" -> "2600 maskrcnn/tf.__operators__.getitem_6/strided_slice"; +"2486 maskrcnn/tf.reshape_3/Reshape" -> "2606 maskrcnn/tf.__operators__.getitem_5/strided_slice"; "2487 maskrcnn/tf.reshape_1/Reshape/shape" -> "2488 maskrcnn/tf.reshape_1/Reshape"; "2488 maskrcnn/tf.reshape_1/Reshape" -> "2524 maskrcnn/tf.__operators__.getitem_4/strided_slice"; "2488 maskrcnn/tf.reshape_1/Reshape" -> "2528 maskrcnn/tf.__operators__.getitem_3/strided_slice"; -"2488 maskrcnn/tf.reshape_1/Reshape" -> "2684 maskrcnn/tf.__operators__.getitem_2/strided_slice"; -"2488 maskrcnn/tf.reshape_1/Reshape" -> "2698 maskrcnn/tf.__operators__.getitem_1/strided_slice"; +"2488 maskrcnn/tf.reshape_1/Reshape" -> "2612 maskrcnn/tf.__operators__.getitem_2/strided_slice"; +"2488 maskrcnn/tf.reshape_1/Reshape" -> "2618 maskrcnn/tf.__operators__.getitem_1/strided_slice"; "2489 maskrcnn/tf.__operators__.getitem_20/strided_slice/stack" -> "2492 maskrcnn/tf.__operators__.getitem_20/strided_slice"; "2490 maskrcnn/tf.__operators__.getitem_20/strided_slice/stack_1" -> "2492 maskrcnn/tf.__operators__.getitem_20/strided_slice"; "2491 maskrcnn/tf.__operators__.getitem_20/strided_slice/stack_2" -> "2492 maskrcnn/tf.__operators__.getitem_20/strided_slice"; @@ -10317,5513 +9717,4853 @@ strict digraph { "2546 maskrcnn/tf.math.minimum_1/Minimum" -> "2557 maskrcnn/tf.math.exp_1/Exp"; "2547 maskrcnn/tf.math.minimum/Minimum/y" -> "2548 maskrcnn/tf.math.minimum/Minimum"; "2548 maskrcnn/tf.math.minimum/Minimum" -> "2558 maskrcnn/tf.math.exp/Exp"; -"2549 maskrcnn/tf.math.exp_9/Exp" -> "2568 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2550 maskrcnn/tf.math.exp_8/Exp" -> "2582 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2551 maskrcnn/tf.math.exp_7/Exp" -> "2596 maskrcnn/tf.math.exp_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2552 maskrcnn/tf.math.exp_6/Exp" -> "2610 maskrcnn/tf.math.exp_6/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2553 maskrcnn/tf.math.exp_5/Exp" -> "2624 maskrcnn/tf.math.exp_5/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2554 maskrcnn/tf.math.exp_4/Exp" -> "2638 maskrcnn/tf.math.exp_4/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2555 maskrcnn/tf.math.exp_3/Exp" -> "2652 maskrcnn/tf.math.exp_3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2556 maskrcnn/tf.math.exp_2/Exp" -> "2666 maskrcnn/tf.math.exp_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2557 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maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/ReadVariableOp"; -"2564 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2567 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"2565 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/ReadVariableOp" -> "2566 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/add_1"; -"2566 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/add_1" -> "2568 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2567 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2568 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2568 maskrcnn/tf.math.exp_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2700 maskrcnn/tf.math.multiply_27/Mul"; -"2569 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack" -> "2572 maskrcnn/tf.__operators__.getitem_18/strided_slice"; -"2570 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack_1" -> "2572 maskrcnn/tf.__operators__.getitem_18/strided_slice"; -"2571 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack_2" -> "2572 maskrcnn/tf.__operators__.getitem_18/strided_slice"; -"2572 maskrcnn/tf.__operators__.getitem_18/strided_slice" -> "2702 maskrcnn/tf.math.multiply_25/Mul"; -"2573 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2574 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"2574 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2575 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/Abs"; -"2575 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/Abs" -> "2577 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/add"; -"2576 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/add/y" -> "2577 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/add"; -"2577 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/add" -> "2580 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/add_1"; -"2578 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2579 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/ReadVariableOp"; -"2578 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2581 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"2579 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/ReadVariableOp" -> "2580 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/add_1"; -"2580 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/add_1" -> "2582 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2581 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2582 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"2582 maskrcnn/tf.math.exp_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2704 maskrcnn/tf.math.multiply_26/Mul"; -"2583 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maskrcnn/tf.__operators__.add_32/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3038 maskrcnn/tf.__operators__.add_32/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"3038 maskrcnn/tf.__operators__.add_32/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3191 maskrcnn/tf.math.subtract_25/Sub"; -"3039 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3040 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"3040 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3041 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/Abs"; -"3041 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/Abs" -> "3043 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/add"; -"3042 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/add/y" -> "3043 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/add"; -"3043 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/add" -> "3046 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maskrcnn/tf.math.subtract_41/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4094 maskrcnn/tf.math.multiply_31/Mul"; -"4093 maskrcnn/tf.math.subtract_41/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4174 maskrcnn/tf.math.truediv_3/truediv"; -"4094 maskrcnn/tf.math.multiply_31/Mul" -> "4104 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4095 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4096 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4096 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4097 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/Abs"; -"4097 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/Abs" -> "4099 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/add"; -"4098 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/add/y" -> "4099 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/add"; -"4099 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/add" -> "4102 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/add_1"; -"4100 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4101 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/ReadVariableOp"; -"4100 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4103 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4101 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/ReadVariableOp" -> "4102 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/add_1"; -"4102 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/add_1" -> "4104 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4103 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4104 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4104 maskrcnn/tf.math.multiply_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4105 maskrcnn/tf.math.sqrt/Sqrt"; -"4105 maskrcnn/tf.math.sqrt/Sqrt" -> "4115 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4106 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4107 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4107 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4108 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/Abs"; -"4108 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/Abs" -> "4110 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/add"; -"4109 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/add/y" -> "4110 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/add"; -"4110 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/add" -> "4113 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/add_1"; -"4111 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4112 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/ReadVariableOp"; -"4111 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4114 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4112 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/ReadVariableOp" -> "4113 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/add_1"; -"4113 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/add_1" -> "4115 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4114 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4115 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4115 maskrcnn/tf.math.sqrt/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4117 maskrcnn/tf.math.truediv/truediv"; -"4116 maskrcnn/tf.math.truediv/truediv/y" -> "4117 maskrcnn/tf.math.truediv/truediv"; -"4117 maskrcnn/tf.math.truediv/truediv" -> "4118 maskrcnn/tf.math.log/Log"; -"4118 maskrcnn/tf.math.log/Log" -> "4128 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4119 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4120 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4120 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4121 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/Abs"; -"4121 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/Abs" -> "4123 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/add"; -"4122 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/add/y" -> "4123 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/add"; -"4123 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/add" -> "4126 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/add_1"; -"4124 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4125 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/ReadVariableOp"; -"4124 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4127 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4125 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/ReadVariableOp" -> "4126 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/add_1"; -"4126 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/add_1" -> "4128 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4127 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4128 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4128 maskrcnn/tf.math.log/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4130 maskrcnn/tf.compat.v1.floor_div/FloorDiv"; -"4129 maskrcnn/tf.compat.v1.floor_div/FloorDiv/y" -> "4130 maskrcnn/tf.compat.v1.floor_div/FloorDiv"; -"4130 maskrcnn/tf.compat.v1.floor_div/FloorDiv" -> "4140 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4131 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4132 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4132 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4133 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/Abs"; -"4133 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/Abs" -> "4135 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/add"; -"4134 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/add/y" -> "4135 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/add"; -"4135 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/add" -> "4138 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/add_1"; -"4136 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4137 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/ReadVariableOp"; -"4136 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4139 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4137 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/ReadVariableOp" -> "4138 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/add_1"; -"4138 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/add_1" -> "4140 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4139 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4140 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4140 maskrcnn/tf.compat.v1.floor_div/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4142 maskrcnn/tf.__operators__.add_39/AddV2"; -"4141 maskrcnn/tf.__operators__.add_39/y" -> "4142 maskrcnn/tf.__operators__.add_39/AddV2"; -"4142 maskrcnn/tf.__operators__.add_39/AddV2" -> "4143 maskrcnn/tf.cast_20/Cast"; -"4143 maskrcnn/tf.cast_20/Cast" -> "4145 maskrcnn/tf.math.maximum_5/Maximum"; -"4144 maskrcnn/tf.math.maximum_5/Maximum/y" -> "4145 maskrcnn/tf.math.maximum_5/Maximum"; -"4145 maskrcnn/tf.math.maximum_5/Maximum" -> "4147 maskrcnn/tf.math.minimum_15/Minimum"; -"4147 maskrcnn/tf.math.minimum_15/Minimum" -> "4148 maskrcnn/tf.cast_21/Cast"; -"4147 maskrcnn/tf.math.minimum_15/Minimum" -> "4177 maskrcnn/tf.math.subtract_42/Sub"; -"4148 maskrcnn/tf.cast_21/Cast" -> "4158 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4149 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4150 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4150 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4151 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/Abs"; -"4151 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/Abs" -> "4153 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/add"; -"4152 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/add/y" -> "4153 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/add"; -"4153 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/add" -> "4156 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/add_1"; -"4154 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4155 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/ReadVariableOp"; -"4154 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4157 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4155 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/ReadVariableOp" -> "4156 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/add_1"; -"4156 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/add_1" -> "4158 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4157 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4158 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4158 maskrcnn/tf.cast_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4160 maskrcnn/tf.math.pow/Pow"; -"4160 maskrcnn/tf.math.pow/Pow" -> "4170 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4161 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4162 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4162 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4163 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/Abs"; -"4163 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/Abs" -> "4165 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/add"; -"4164 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/add/y" -> "4165 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/add"; -"4165 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/add" -> "4168 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/add_1"; -"4166 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4167 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/ReadVariableOp"; -"4166 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4169 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4167 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/ReadVariableOp" -> "4168 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/add_1"; -"4168 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/add_1" -> "4170 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4169 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4170 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4170 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4172 maskrcnn/tf.expand_dims_12/ExpandDims"; -"4170 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4173 maskrcnn/tf.math.truediv_2/truediv"; -"4170 maskrcnn/tf.math.pow/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4174 maskrcnn/tf.math.truediv_3/truediv"; -"4171 maskrcnn/tf.expand_dims_12/ExpandDims/dim" -> "4172 maskrcnn/tf.expand_dims_12/ExpandDims"; -"4172 maskrcnn/tf.expand_dims_12/ExpandDims" -> "4175 maskrcnn/tf.math.truediv_1/truediv"; -"4173 maskrcnn/tf.math.truediv_2/truediv" -> "4187 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4174 maskrcnn/tf.math.truediv_3/truediv" -> "4197 maskrcnn/tf.math.truediv_3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4175 maskrcnn/tf.math.truediv_1/truediv" -> "4207 maskrcnn/tf.math.truediv_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4176 maskrcnn/tf.math.subtract_42/Sub/y" -> "4177 maskrcnn/tf.math.subtract_42/Sub"; -"4177 maskrcnn/tf.math.subtract_42/Sub" -> "4208 maskrcnn/tf.cast_23/Cast"; -"4177 maskrcnn/tf.math.subtract_42/Sub" -> "4957 maskrcnn/tf.compat.v1.gather_1/GatherV2"; -"4177 maskrcnn/tf.math.subtract_42/Sub" -> "4996 maskrcnn/tf.compat.v1.gather/GatherV2"; -"4178 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4179 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4179 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4180 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/Abs"; -"4180 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/Abs" -> "4182 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/add"; -"4181 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/add/y" -> "4182 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/add"; -"4182 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/add" -> "4185 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/add_1"; -"4183 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4184 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/ReadVariableOp"; -"4183 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4186 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4184 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/ReadVariableOp" -> "4185 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/add_1"; -"4185 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/add_1" -> "4187 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4186 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4187 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4187 maskrcnn/tf.math.truediv_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4216 maskrcnn/tf.expand_dims_14/ExpandDims"; -"4188 maskrcnn/tf.math.truediv_3/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4189 maskrcnn/tf.math.truediv_3/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4189 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maskrcnn/tf.__operators__.getitem_46/strided_slice"; -"4542 maskrcnn/tf.__operators__.getitem_46/strided_slice/stack_1" -> "4544 maskrcnn/tf.__operators__.getitem_46/strided_slice"; -"4543 maskrcnn/tf.__operators__.getitem_46/strided_slice/stack_2" -> "4544 maskrcnn/tf.__operators__.getitem_46/strided_slice"; -"4544 maskrcnn/tf.__operators__.getitem_46/strided_slice" -> "4649 maskrcnn/tf.__operators__.add_49/AddV2"; -"4545 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4546 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4546 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4547 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs"; -"4547 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs" -> "4549 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add"; -"4548 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add/y" -> "4549 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maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4554 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4649 maskrcnn/tf.__operators__.add_49/AddV2"; -"4555 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack" -> "4558 maskrcnn/tf.__operators__.getitem_42/strided_slice"; -"4556 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack_1" -> "4558 maskrcnn/tf.__operators__.getitem_42/strided_slice"; -"4557 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack_2" -> "4558 maskrcnn/tf.__operators__.getitem_42/strided_slice"; -"4558 maskrcnn/tf.__operators__.getitem_42/strided_slice" -> "4648 maskrcnn/tf.__operators__.add_47/AddV2"; -"4559 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4560 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4560 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4561 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maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/add_1" -> "4568 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4567 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4568 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4568 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4648 maskrcnn/tf.__operators__.add_47/AddV2"; -"4569 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack" -> "4572 maskrcnn/tf.__operators__.getitem_38/strided_slice"; -"4570 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack_1" -> "4572 maskrcnn/tf.__operators__.getitem_38/strided_slice"; -"4571 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack_2" -> "4572 maskrcnn/tf.__operators__.getitem_38/strided_slice"; -"4572 maskrcnn/tf.__operators__.getitem_38/strided_slice" -> "4647 maskrcnn/tf.__operators__.add_45/AddV2"; -"4573 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maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4579 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/ReadVariableOp" -> "4580 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add_1"; -"4580 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add_1" -> "4582 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4581 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4582 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4582 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4647 maskrcnn/tf.__operators__.add_45/AddV2"; -"4583 maskrcnn/tf.__operators__.getitem_34/strided_slice/stack" -> "4586 maskrcnn/tf.__operators__.getitem_34/strided_slice"; -"4584 maskrcnn/tf.__operators__.getitem_34/strided_slice/stack_1" -> "4586 maskrcnn/tf.__operators__.getitem_34/strided_slice"; -"4585 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maskrcnn/tf.stack_7/stack" -> "4870 maskrcnn/tf.math.subtract_45/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"4653 maskrcnn/tf.__operators__.getitem_52/strided_slice/stack" -> "4656 maskrcnn/tf.__operators__.getitem_52/strided_slice"; -"4654 maskrcnn/tf.__operators__.getitem_52/strided_slice/stack_1" -> "4656 maskrcnn/tf.__operators__.getitem_52/strided_slice"; -"4655 maskrcnn/tf.__operators__.getitem_52/strided_slice/stack_2" -> "4656 maskrcnn/tf.__operators__.getitem_52/strided_slice"; -"4656 maskrcnn/tf.__operators__.getitem_52/strided_slice" -> "4761 maskrcnn/tf.__operators__.add_52/AddV2"; -"4657 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4658 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4658 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4659 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs"; -"4659 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs" -> "4661 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add"; -"4660 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add/y" -> "4661 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add"; -"4661 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add" -> "4664 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add_1"; -"4662 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4663 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/ReadVariableOp"; -"4662 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4665 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4663 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/ReadVariableOp" -> "4664 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add_1"; -"4664 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add_1" -> "4666 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4665 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4666 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4666 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4761 maskrcnn/tf.__operators__.add_52/AddV2"; -"4667 maskrcnn/tf.__operators__.getitem_48/strided_slice/stack" -> "4670 maskrcnn/tf.__operators__.getitem_48/strided_slice"; -"4668 maskrcnn/tf.__operators__.getitem_48/strided_slice/stack_1" -> "4670 maskrcnn/tf.__operators__.getitem_48/strided_slice"; -"4669 maskrcnn/tf.__operators__.getitem_48/strided_slice/stack_2" -> "4670 maskrcnn/tf.__operators__.getitem_48/strided_slice"; -"4670 maskrcnn/tf.__operators__.getitem_48/strided_slice" -> "4760 maskrcnn/tf.__operators__.add_50/AddV2"; -"4671 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4672 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4672 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4673 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs"; -"4673 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs" -> "4675 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/add"; -"4674 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/add/y" -> "4675 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/add"; -"4675 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/add" -> "4678 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/add_1"; -"4676 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4677 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/ReadVariableOp"; -"4676 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4679 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4677 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/ReadVariableOp" -> "4678 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/add_1"; -"4678 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/add_1" -> "4680 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4679 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4680 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4680 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4760 maskrcnn/tf.__operators__.add_50/AddV2"; -"4681 maskrcnn/tf.__operators__.getitem_44/strided_slice/stack" -> "4684 maskrcnn/tf.__operators__.getitem_44/strided_slice"; -"4682 maskrcnn/tf.__operators__.getitem_44/strided_slice/stack_1" -> "4684 maskrcnn/tf.__operators__.getitem_44/strided_slice"; -"4683 maskrcnn/tf.__operators__.getitem_44/strided_slice/stack_2" -> "4684 maskrcnn/tf.__operators__.getitem_44/strided_slice"; -"4684 maskrcnn/tf.__operators__.getitem_44/strided_slice" -> "4759 maskrcnn/tf.__operators__.add_48/AddV2"; -"4685 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4686 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4686 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4687 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/Abs"; -"4687 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/Abs" -> "4689 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/add"; -"4688 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/add/y" -> "4689 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/add"; -"4689 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/add" -> "4692 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/add_1"; -"4690 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4691 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/ReadVariableOp"; -"4690 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4693 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4691 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/ReadVariableOp" -> "4692 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/add_1"; -"4692 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/add_1" -> "4694 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4693 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4694 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4694 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4759 maskrcnn/tf.__operators__.add_48/AddV2"; -"4695 maskrcnn/tf.__operators__.getitem_40/strided_slice/stack" -> "4698 maskrcnn/tf.__operators__.getitem_40/strided_slice"; -"4696 maskrcnn/tf.__operators__.getitem_40/strided_slice/stack_1" -> "4698 maskrcnn/tf.__operators__.getitem_40/strided_slice"; -"4697 maskrcnn/tf.__operators__.getitem_40/strided_slice/stack_2" -> "4698 maskrcnn/tf.__operators__.getitem_40/strided_slice"; -"4698 maskrcnn/tf.__operators__.getitem_40/strided_slice" -> "4758 maskrcnn/tf.__operators__.add_46/AddV2"; -"4699 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4700 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4700 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4701 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/Abs"; -"4701 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/Abs" -> "4703 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/add"; -"4702 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/add/y" -> "4703 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/add"; -"4703 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/add" -> "4706 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/add_1"; -"4704 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4705 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/ReadVariableOp"; -"4704 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4707 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4705 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/ReadVariableOp" -> "4706 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/add_1"; -"4706 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/add_1" -> "4708 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4707 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4708 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4708 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4758 maskrcnn/tf.__operators__.add_46/AddV2"; -"4709 maskrcnn/tf.__operators__.getitem_36/strided_slice/stack" -> "4712 maskrcnn/tf.__operators__.getitem_36/strided_slice"; -"4710 maskrcnn/tf.__operators__.getitem_36/strided_slice/stack_1" -> "4712 maskrcnn/tf.__operators__.getitem_36/strided_slice"; -"4711 maskrcnn/tf.__operators__.getitem_36/strided_slice/stack_2" -> "4712 maskrcnn/tf.__operators__.getitem_36/strided_slice"; -"4712 maskrcnn/tf.__operators__.getitem_36/strided_slice" -> "4757 maskrcnn/tf.__operators__.add_44/AddV2"; -"4713 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4714 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4714 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4715 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/Abs"; -"4715 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/Abs" -> "4717 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/add"; -"4716 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/add/y" -> "4717 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/add"; -"4717 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/add" -> "4720 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/add_1"; -"4718 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4719 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/ReadVariableOp"; -"4718 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4721 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4719 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/ReadVariableOp" -> "4720 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/add_1"; -"4720 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/add_1" -> "4722 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4721 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4722 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4722 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4757 maskrcnn/tf.__operators__.add_44/AddV2"; -"4723 maskrcnn/tf.__operators__.getitem_32/strided_slice/stack" -> "4726 maskrcnn/tf.__operators__.getitem_32/strided_slice"; -"4724 maskrcnn/tf.__operators__.getitem_32/strided_slice/stack_1" -> "4726 maskrcnn/tf.__operators__.getitem_32/strided_slice"; -"4725 maskrcnn/tf.__operators__.getitem_32/strided_slice/stack_2" -> "4726 maskrcnn/tf.__operators__.getitem_32/strided_slice"; -"4726 maskrcnn/tf.__operators__.getitem_32/strided_slice" -> "4756 maskrcnn/tf.__operators__.add_42/AddV2"; -"4727 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4728 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4728 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4729 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/Abs"; -"4729 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/Abs" -> "4731 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/add"; -"4730 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/add/y" -> "4731 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/add"; -"4731 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/add" -> "4734 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/add_1"; -"4732 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4733 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/ReadVariableOp"; -"4732 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4735 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"4733 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/ReadVariableOp" -> "4734 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/add_1"; -"4734 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/add_1" -> "4736 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4735 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4736 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"4736 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4756 maskrcnn/tf.__operators__.add_42/AddV2"; -"4737 maskrcnn/tf.__operators__.getitem_28/strided_slice/stack" -> "4740 maskrcnn/tf.__operators__.getitem_28/strided_slice"; -"4738 maskrcnn/tf.__operators__.getitem_28/strided_slice/stack_1" -> "4740 maskrcnn/tf.__operators__.getitem_28/strided_slice"; -"4739 maskrcnn/tf.__operators__.getitem_28/strided_slice/stack_2" -> "4740 maskrcnn/tf.__operators__.getitem_28/strided_slice"; -"4740 maskrcnn/tf.__operators__.getitem_28/strided_slice" -> "4755 maskrcnn/tf.__operators__.add_40/AddV2"; -"4741 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4742 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"4742 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4743 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/Abs"; -"4743 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/Abs" -> "4745 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/add"; -"4744 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/add/y" -> "4745 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/add"; -"4745 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/add" -> "4748 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/add_1"; -"4746 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4747 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/ReadVariableOp"; -"4746 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/ReadVariableOp/resource" 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maskrcnn/tf.__operators__.add_66/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5569 maskrcnn/tf.__operators__.add_66/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5569 maskrcnn/tf.__operators__.add_66/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5589 maskrcnn/tf.math.subtract_56/Sub"; -"5570 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5571 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5571 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5572 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/Abs"; -"5572 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/Abs" -> "5574 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/add"; -"5573 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/add/y" -> "5574 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/add"; -"5574 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/add" -> "5577 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/add_1"; -"5575 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5576 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/ReadVariableOp"; -"5575 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5578 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5576 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/ReadVariableOp" -> "5577 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/add_1"; -"5577 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/add_1" -> "5579 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5578 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5579 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5579 maskrcnn/tf.__operators__.add_65/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5591 maskrcnn/tf.math.subtract_55/Sub"; -"5580 maskrcnn/tf.math.subtract_60/Sub/y" -> "5581 maskrcnn/tf.math.subtract_60/Sub"; -"5581 maskrcnn/tf.math.subtract_60/Sub" -> "5631 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5582 maskrcnn/tf.math.subtract_59/Sub/y" -> "5583 maskrcnn/tf.math.subtract_59/Sub"; -"5583 maskrcnn/tf.math.subtract_59/Sub" -> "5621 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5584 maskrcnn/tf.math.subtract_58/Sub/y" -> "5585 maskrcnn/tf.math.subtract_58/Sub"; -"5585 maskrcnn/tf.math.subtract_58/Sub" -> "5611 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5586 maskrcnn/tf.math.subtract_57/Sub/y" -> "5587 maskrcnn/tf.math.subtract_57/Sub"; -"5587 maskrcnn/tf.math.subtract_57/Sub" -> "5601 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5588 maskrcnn/tf.math.subtract_56/Sub/y" -> "5589 maskrcnn/tf.math.subtract_56/Sub"; -"5589 maskrcnn/tf.math.subtract_56/Sub" -> "5651 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5590 maskrcnn/tf.math.subtract_55/Sub/y" -> "5591 maskrcnn/tf.math.subtract_55/Sub"; -"5591 maskrcnn/tf.math.subtract_55/Sub" -> "5641 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5592 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5593 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5593 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5594 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/Abs"; -"5594 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/Abs" -> "5596 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/add"; -"5595 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/add/y" -> "5596 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/add"; -"5596 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/add" -> "5599 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/add_1"; -"5597 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5598 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/ReadVariableOp"; -"5597 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5600 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5598 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/ReadVariableOp" -> "5599 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/add_1"; -"5599 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/add_1" -> "5601 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5600 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5601 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5601 maskrcnn/tf.math.subtract_57/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5654 maskrcnn/tf.stack_15/stack"; -"5602 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5603 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5603 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5604 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/Abs"; -"5604 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/Abs" -> "5606 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/add"; -"5605 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/add/y" -> "5606 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/add"; -"5606 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/add" -> "5609 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/add_1"; -"5607 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5608 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/ReadVariableOp"; -"5607 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5610 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5608 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/ReadVariableOp" -> "5609 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/add_1"; -"5609 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/add_1" -> "5611 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5610 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5611 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5611 maskrcnn/tf.math.subtract_58/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5654 maskrcnn/tf.stack_15/stack"; -"5612 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5613 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5613 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5614 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/Abs"; -"5614 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/Abs" -> "5616 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/add"; -"5615 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/add/y" -> "5616 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/add"; -"5616 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/add" -> "5619 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/add_1"; -"5617 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5618 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/ReadVariableOp"; -"5617 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5620 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5618 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/ReadVariableOp" -> "5619 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/add_1"; -"5619 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/add_1" -> "5621 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5620 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5621 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5621 maskrcnn/tf.math.subtract_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5654 maskrcnn/tf.stack_15/stack"; -"5622 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5623 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5623 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5624 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/Abs"; -"5624 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/Abs" -> "5626 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/add"; -"5625 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/add/y" -> "5626 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/add"; -"5626 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/add" -> "5629 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/add_1"; -"5627 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5628 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/ReadVariableOp"; -"5627 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5630 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5628 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/ReadVariableOp" -> "5629 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/add_1"; -"5629 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/add_1" -> "5631 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5630 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5631 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5631 maskrcnn/tf.math.subtract_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5654 maskrcnn/tf.stack_15/stack"; -"5632 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5633 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5633 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5634 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/Abs"; -"5634 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/Abs" -> "5636 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/add"; -"5635 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/add/y" -> "5636 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/add"; -"5636 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/add" -> "5639 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/add_1"; -"5637 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5638 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/ReadVariableOp"; -"5637 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5640 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5638 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/ReadVariableOp" -> "5639 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/add_1"; -"5639 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/add_1" -> "5641 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5640 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5641 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5641 maskrcnn/tf.math.subtract_55/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5653 maskrcnn/tf.concat_13/concat"; -"5642 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5643 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5643 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5644 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/Abs"; -"5644 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/Abs" -> "5646 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/add"; -"5645 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/add/y" -> "5646 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/add"; -"5646 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/add" -> "5649 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/add_1"; -"5647 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5648 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/ReadVariableOp"; -"5647 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5650 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5648 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/ReadVariableOp" -> "5649 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/add_1"; -"5649 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/add_1" -> "5651 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5650 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5651 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5651 maskrcnn/tf.math.subtract_56/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5653 maskrcnn/tf.concat_13/concat"; -"5652 maskrcnn/tf.concat_13/concat/axis" -> "5653 maskrcnn/tf.concat_13/concat"; -"5653 maskrcnn/tf.concat_13/concat" -> "5655 maskrcnn/tf.math.minimum_22/Minimum"; -"5654 maskrcnn/tf.stack_15/stack" -> "5655 maskrcnn/tf.math.minimum_22/Minimum"; -"5655 maskrcnn/tf.math.minimum_22/Minimum" -> "5665 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5656 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5657 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5657 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5658 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/Abs"; -"5658 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/Abs" -> "5660 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/add"; -"5659 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/add/y" -> "5660 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/add"; -"5660 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/add" -> "5663 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/add_1"; -"5661 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5662 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/ReadVariableOp"; -"5661 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5664 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5662 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/ReadVariableOp" -> "5663 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/add_1"; -"5663 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/add_1" -> "5665 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5664 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5665 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5665 maskrcnn/tf.math.minimum_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5669 maskrcnn/tf.math.maximum_8/Maximum"; -"5666 maskrcnn/tf.math.subtract_61/Sub/y" -> "5667 maskrcnn/tf.math.subtract_61/Sub"; -"5667 maskrcnn/tf.math.subtract_61/Sub" -> "5681 maskrcnn/tf.stack_16/stack"; -"5668 maskrcnn/tf.math.maximum_8/Maximum/y" -> "5669 maskrcnn/tf.math.maximum_8/Maximum"; -"5669 maskrcnn/tf.math.maximum_8/Maximum" -> "5679 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5670 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5671 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5671 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5672 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/Abs"; -"5672 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/Abs" -> "5674 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/add"; -"5673 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/add/y" -> "5674 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/add"; -"5674 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/add" -> "5677 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/add_1"; -"5675 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5676 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/ReadVariableOp"; -"5675 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5678 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5676 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/ReadVariableOp" -> "5677 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/add_1"; -"5677 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/add_1" -> "5679 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5678 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5679 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5679 maskrcnn/tf.math.maximum_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5682 maskrcnn/tf.reshape_38/Reshape"; -"5681 maskrcnn/tf.stack_16/stack" -> "5682 maskrcnn/tf.reshape_38/Reshape"; -"5682 maskrcnn/tf.reshape_38/Reshape" -> "5684 maskrcnn/tf.math.reduce_max/Max"; -"5682 maskrcnn/tf.reshape_38/Reshape" -> "5695 maskrcnn/tf.math.truediv_24/truediv"; -"5683 maskrcnn/tf.math.reduce_max/Const" -> "5684 maskrcnn/tf.math.reduce_max/Max"; -"5684 maskrcnn/tf.math.reduce_max/Max" -> "5694 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5685 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5686 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5686 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5687 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/Abs"; -"5687 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/Abs" -> "5689 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/add"; -"5688 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/add/y" -> "5689 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/add"; -"5689 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/add" -> "5692 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/add_1"; -"5690 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5691 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/ReadVariableOp"; -"5690 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5693 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5691 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/ReadVariableOp" -> "5692 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/add_1"; -"5692 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/add_1" -> "5694 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5693 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5694 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5694 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5695 maskrcnn/tf.math.truediv_24/truediv"; -"5694 maskrcnn/tf.math.reduce_max/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5714 maskrcnn/tf.math.multiply_59/Mul"; -"5695 maskrcnn/tf.math.truediv_24/truediv" -> "5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression"; -"5696 maskrcnn/tf.slice/Slice/begin" -> "5698 maskrcnn/tf.slice/Slice"; -"5697 maskrcnn/tf.slice/Slice/size" -> "5698 maskrcnn/tf.slice/Slice"; -"5698 maskrcnn/tf.slice/Slice" -> "5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression"; -"5699 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/iou_threshold" -> "5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression"; -"5700 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/score_threshold" -> "5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression"; -"5701 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/Const" -> "5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression"; -"5702 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression/max_output_size_per_class" -> "5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression"; -"5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression" -> "5713 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression" -> "7373 maskrcnn/tf.cast_32/Cast"; -"5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression" -> "20398 Identity_5"; -"5703 maskrcnn/tf.image.combined_non_max_suppression_5/combined_non_max_suppression/CombinedNonMaxSuppression" -> "20399 Identity_6"; -"5704 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp/resource" -> "5705 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp"; -"5705 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp" -> "5706 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/Abs"; -"5706 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/Abs" -> "5708 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/add"; -"5707 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/add/y" -> "5708 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/add"; -"5708 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/add" -> "5711 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/add_1"; -"5709 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/ReadVariableOp/resource" -> "5710 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/ReadVariableOp"; -"5709 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/ReadVariableOp/resource" -> "5712 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5710 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/ReadVariableOp" -> "5711 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/add_1"; -"5711 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/add_1" -> "5713 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"5712 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5713 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; -"5713 maskrcnn/tf.math.multiply_59/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars" -> "5714 maskrcnn/tf.math.multiply_59/Mul"; -"5714 maskrcnn/tf.math.multiply_59/Mul" -> "5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5715 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5716 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5716 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5717 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/Abs"; -"5717 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/Abs" -> "5719 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/add"; -"5718 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/add/y" -> "5719 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/add"; -"5719 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/add" -> "5722 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/add_1"; -"5720 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5721 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/ReadVariableOp"; -"5720 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5723 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5721 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/ReadVariableOp" -> "5722 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/add_1"; -"5722 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/add_1" -> "5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5723 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5728 maskrcnn/tf.__operators__.getitem_72/strided_slice"; -"5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5732 maskrcnn/tf.__operators__.getitem_73/strided_slice"; -"5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5736 maskrcnn/tf.__operators__.getitem_74/strided_slice"; -"5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5740 maskrcnn/tf.__operators__.getitem_75/strided_slice"; -"5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5844 maskrcnn/tf.math.truediv_26/truediv"; -"5724 maskrcnn/tf.math.multiply_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "20395 Identity_2"; -"5725 maskrcnn/tf.__operators__.getitem_72/strided_slice/stack" -> "5728 maskrcnn/tf.__operators__.getitem_72/strided_slice"; -"5726 maskrcnn/tf.__operators__.getitem_72/strided_slice/stack_1" -> "5728 maskrcnn/tf.__operators__.getitem_72/strided_slice"; -"5727 maskrcnn/tf.__operators__.getitem_72/strided_slice/stack_2" -> "5728 maskrcnn/tf.__operators__.getitem_72/strided_slice"; -"5728 maskrcnn/tf.__operators__.getitem_72/strided_slice" -> "5741 maskrcnn/tf.math.subtract_62/Sub"; -"5729 maskrcnn/tf.__operators__.getitem_73/strided_slice/stack" -> "5732 maskrcnn/tf.__operators__.getitem_73/strided_slice"; -"5730 maskrcnn/tf.__operators__.getitem_73/strided_slice/stack_1" -> "5732 maskrcnn/tf.__operators__.getitem_73/strided_slice"; -"5731 maskrcnn/tf.__operators__.getitem_73/strided_slice/stack_2" -> "5732 maskrcnn/tf.__operators__.getitem_73/strided_slice"; -"5732 maskrcnn/tf.__operators__.getitem_73/strided_slice" -> "5741 maskrcnn/tf.math.subtract_62/Sub"; -"5733 maskrcnn/tf.__operators__.getitem_74/strided_slice/stack" -> "5736 maskrcnn/tf.__operators__.getitem_74/strided_slice"; -"5734 maskrcnn/tf.__operators__.getitem_74/strided_slice/stack_1" -> "5736 maskrcnn/tf.__operators__.getitem_74/strided_slice"; -"5735 maskrcnn/tf.__operators__.getitem_74/strided_slice/stack_2" -> "5736 maskrcnn/tf.__operators__.getitem_74/strided_slice"; -"5736 maskrcnn/tf.__operators__.getitem_74/strided_slice" -> "5742 maskrcnn/tf.math.subtract_63/Sub"; -"5737 maskrcnn/tf.__operators__.getitem_75/strided_slice/stack" -> "5740 maskrcnn/tf.__operators__.getitem_75/strided_slice"; -"5738 maskrcnn/tf.__operators__.getitem_75/strided_slice/stack_1" -> "5740 maskrcnn/tf.__operators__.getitem_75/strided_slice"; -"5739 maskrcnn/tf.__operators__.getitem_75/strided_slice/stack_2" -> "5740 maskrcnn/tf.__operators__.getitem_75/strided_slice"; -"5740 maskrcnn/tf.__operators__.getitem_75/strided_slice" -> "5742 maskrcnn/tf.math.subtract_63/Sub"; -"5741 maskrcnn/tf.math.subtract_62/Sub" -> "5752 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5742 maskrcnn/tf.math.subtract_63/Sub" -> "5762 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5743 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5744 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5744 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5745 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/Abs"; -"5745 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/Abs" -> "5747 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add"; -"5746 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add/y" -> "5747 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add"; -"5747 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add" -> "5750 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add_1"; -"5748 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5749 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/ReadVariableOp"; -"5748 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5751 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5749 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/ReadVariableOp" -> "5750 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add_1"; -"5750 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add_1" -> "5752 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5751 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5752 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5752 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5763 maskrcnn/tf.math.multiply_60/Mul"; -"5752 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5842 maskrcnn/tf.math.truediv_27/truediv"; -"5753 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5754 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5754 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5755 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs"; -"5755 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs" -> "5757 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/add"; -"5756 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/add/y" -> "5757 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/add"; -"5757 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/add" -> "5760 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/add_1"; -"5758 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5759 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/ReadVariableOp"; -"5758 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5761 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5759 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/ReadVariableOp" -> "5760 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/add_1"; -"5760 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/add_1" -> "5762 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5761 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5762 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5762 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5763 maskrcnn/tf.math.multiply_60/Mul"; -"5762 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5843 maskrcnn/tf.math.truediv_28/truediv"; -"5763 maskrcnn/tf.math.multiply_60/Mul" -> "5773 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5764 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5765 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5765 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5766 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs"; -"5766 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs" -> "5768 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add"; -"5767 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add/y" -> "5768 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add"; -"5768 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add" -> "5771 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add_1"; -"5769 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5770 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/ReadVariableOp"; -"5769 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5772 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"5770 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/ReadVariableOp" -> "5771 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add_1"; -"5771 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add_1" -> "5773 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5772 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5773 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5773 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5774 maskrcnn/tf.math.sqrt_1/Sqrt"; -"5774 maskrcnn/tf.math.sqrt_1/Sqrt" -> "5784 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"5775 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5776 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"5776 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5777 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs"; -"5777 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs" -> "5779 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add"; -"5778 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add/y" -> "5779 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add"; -"5779 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add" -> "5782 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maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"7248 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/ReadVariableOp" -> "7249 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add_1"; -"7249 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add_1" -> "7251 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7250 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "7251 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7251 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7263 maskrcnn/mask-conv-l1/Conv2D"; -"7252 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp/resource" -> "7253 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp"; -"7253 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp" -> "7254 maskrcnn/mask-conv-l1/SymmQuant/Abs"; -"7254 maskrcnn/mask-conv-l1/SymmQuant/Abs" -> "7256 maskrcnn/mask-conv-l1/SymmQuant/add"; -"7255 maskrcnn/mask-conv-l1/SymmQuant/add/y" -> "7256 maskrcnn/mask-conv-l1/SymmQuant/add"; -"7256 maskrcnn/mask-conv-l1/SymmQuant/add" -> "7259 maskrcnn/mask-conv-l1/SymmQuant/mul"; -"7256 maskrcnn/mask-conv-l1/SymmQuant/add" -> "7262 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7257 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp/resource" -> "7258 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp"; -"7258 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp" -> "7259 maskrcnn/mask-conv-l1/SymmQuant/mul"; -"7259 maskrcnn/mask-conv-l1/SymmQuant/mul" -> "7262 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7260 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "7261 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; -"7261 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "7262 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7262 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "7263 maskrcnn/mask-conv-l1/Conv2D"; -"7263 maskrcnn/mask-conv-l1/Conv2D" -> "7266 maskrcnn/mask-conv-l1/BiasAdd"; -"7264 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp/resource" -> "7265 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp"; -"7265 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp" -> "7266 maskrcnn/mask-conv-l1/BiasAdd"; -"7266 maskrcnn/mask-conv-l1/BiasAdd" -> "7267 maskrcnn/mask-conv-l1/Relu"; -"7267 maskrcnn/mask-conv-l1/Relu" -> "7277 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7268 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "7269 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"7269 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "7270 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs"; -"7270 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs" -> "7272 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add"; -"7271 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add/y" -> "7272 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add"; -"7272 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add" -> "7275 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add_1"; -"7273 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "7274 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/ReadVariableOp"; -"7273 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "7276 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"7274 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/ReadVariableOp" -> "7275 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add_1"; -"7275 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add_1" -> "7277 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7276 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "7277 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7277 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7289 maskrcnn/mask-conv-l2/Conv2D"; -"7278 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp/resource" -> "7279 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp"; -"7279 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp" -> "7280 maskrcnn/mask-conv-l2/SymmQuant/Abs"; -"7280 maskrcnn/mask-conv-l2/SymmQuant/Abs" -> "7282 maskrcnn/mask-conv-l2/SymmQuant/add"; -"7281 maskrcnn/mask-conv-l2/SymmQuant/add/y" -> "7282 maskrcnn/mask-conv-l2/SymmQuant/add"; -"7282 maskrcnn/mask-conv-l2/SymmQuant/add" -> "7285 maskrcnn/mask-conv-l2/SymmQuant/mul"; -"7282 maskrcnn/mask-conv-l2/SymmQuant/add" -> "7288 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7283 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp/resource" -> "7284 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp"; -"7284 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp" -> "7285 maskrcnn/mask-conv-l2/SymmQuant/mul"; -"7285 maskrcnn/mask-conv-l2/SymmQuant/mul" -> "7288 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7286 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "7287 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; -"7287 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "7288 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7288 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "7289 maskrcnn/mask-conv-l2/Conv2D"; -"7289 maskrcnn/mask-conv-l2/Conv2D" -> "7292 maskrcnn/mask-conv-l2/BiasAdd"; -"7290 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp/resource" -> "7291 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp"; -"7291 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp" -> "7292 maskrcnn/mask-conv-l2/BiasAdd"; -"7292 maskrcnn/mask-conv-l2/BiasAdd" -> "7293 maskrcnn/mask-conv-l2/Relu"; -"7293 maskrcnn/mask-conv-l2/Relu" -> "7303 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7294 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "7295 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"7295 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "7296 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs"; -"7296 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs" -> "7298 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add"; -"7297 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add/y" -> "7298 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add"; -"7298 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add" -> "7301 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add_1"; -"7299 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "7300 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/ReadVariableOp"; -"7299 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "7302 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"7300 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/ReadVariableOp" -> "7301 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add_1"; -"7301 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add_1" -> "7303 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7302 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "7303 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7303 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7315 maskrcnn/mask-conv-l3/Conv2D"; -"7304 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp/resource" -> "7305 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp"; -"7305 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp" -> "7306 maskrcnn/mask-conv-l3/SymmQuant/Abs"; -"7306 maskrcnn/mask-conv-l3/SymmQuant/Abs" -> "7308 maskrcnn/mask-conv-l3/SymmQuant/add"; -"7307 maskrcnn/mask-conv-l3/SymmQuant/add/y" -> "7308 maskrcnn/mask-conv-l3/SymmQuant/add"; -"7308 maskrcnn/mask-conv-l3/SymmQuant/add" -> "7311 maskrcnn/mask-conv-l3/SymmQuant/mul"; -"7308 maskrcnn/mask-conv-l3/SymmQuant/add" -> "7314 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7309 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp/resource" -> "7310 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp"; -"7310 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp" -> "7311 maskrcnn/mask-conv-l3/SymmQuant/mul"; -"7311 maskrcnn/mask-conv-l3/SymmQuant/mul" -> "7314 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7312 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "7313 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; -"7313 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "7314 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7314 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "7315 maskrcnn/mask-conv-l3/Conv2D"; -"7315 maskrcnn/mask-conv-l3/Conv2D" -> "7318 maskrcnn/mask-conv-l3/BiasAdd"; -"7316 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp/resource" -> "7317 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp"; -"7317 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp" -> "7318 maskrcnn/mask-conv-l3/BiasAdd"; -"7318 maskrcnn/mask-conv-l3/BiasAdd" -> "7319 maskrcnn/mask-conv-l3/Relu"; -"7319 maskrcnn/mask-conv-l3/Relu" -> "7329 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7320 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "7321 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"7321 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "7322 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs"; -"7322 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs" -> "7324 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add"; -"7323 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add/y" -> "7324 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add"; -"7324 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add" -> "7327 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add_1"; -"7325 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "7326 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/ReadVariableOp"; -"7325 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "7328 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"7326 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/ReadVariableOp" -> "7327 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add_1"; -"7327 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add_1" -> "7329 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7328 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "7329 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7329 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7358 maskrcnn/conv5-mask/conv2d_transpose"; -"7330 maskrcnn/conv5-mask/transpose/ReadVariableOp/resource" -> "7331 maskrcnn/conv5-mask/transpose/ReadVariableOp"; -"7331 maskrcnn/conv5-mask/transpose/ReadVariableOp" -> "7333 maskrcnn/conv5-mask/transpose"; -"7332 maskrcnn/conv5-mask/transpose/perm" -> "7333 maskrcnn/conv5-mask/transpose"; -"7333 maskrcnn/conv5-mask/transpose" -> "7342 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7334 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp/resource" -> "7335 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp"; -"7335 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp" -> "7336 maskrcnn/conv5-mask/SymmQuant/Abs"; -"7336 maskrcnn/conv5-mask/SymmQuant/Abs" -> "7338 maskrcnn/conv5-mask/SymmQuant/add"; -"7337 maskrcnn/conv5-mask/SymmQuant/add/y" -> "7338 maskrcnn/conv5-mask/SymmQuant/add"; -"7338 maskrcnn/conv5-mask/SymmQuant/add" -> "7341 maskrcnn/conv5-mask/SymmQuant/mul"; -"7338 maskrcnn/conv5-mask/SymmQuant/add" -> "7342 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7339 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp/resource" -> "7340 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp"; -"7340 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp" -> "7341 maskrcnn/conv5-mask/SymmQuant/mul"; -"7341 maskrcnn/conv5-mask/SymmQuant/mul" -> "7342 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7342 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "7344 maskrcnn/conv5-mask/transpose_1"; -"7343 maskrcnn/conv5-mask/transpose_1/perm" -> "7344 maskrcnn/conv5-mask/transpose_1"; -"7344 maskrcnn/conv5-mask/transpose_1" -> "7358 maskrcnn/conv5-mask/conv2d_transpose"; -"7345 maskrcnn/conv5-mask/Shape" -> "7349 maskrcnn/conv5-mask/strided_slice"; -"7346 maskrcnn/conv5-mask/strided_slice/stack" -> "7349 maskrcnn/conv5-mask/strided_slice"; -"7347 maskrcnn/conv5-mask/strided_slice/stack_1" -> "7349 maskrcnn/conv5-mask/strided_slice"; -"7348 maskrcnn/conv5-mask/strided_slice/stack_2" -> "7349 maskrcnn/conv5-mask/strided_slice"; -"7349 maskrcnn/conv5-mask/strided_slice" -> "7353 maskrcnn/conv5-mask/stack"; -"7350 maskrcnn/conv5-mask/stack/1" -> "7353 maskrcnn/conv5-mask/stack"; -"7351 maskrcnn/conv5-mask/stack/2" -> "7353 maskrcnn/conv5-mask/stack"; -"7352 maskrcnn/conv5-mask/stack/3" -> "7353 maskrcnn/conv5-mask/stack"; -"7353 maskrcnn/conv5-mask/stack" -> "7357 maskrcnn/conv5-mask/strided_slice_1"; -"7353 maskrcnn/conv5-mask/stack" -> "7358 maskrcnn/conv5-mask/conv2d_transpose"; -"7354 maskrcnn/conv5-mask/strided_slice_1/stack" -> "7357 maskrcnn/conv5-mask/strided_slice_1"; -"7355 maskrcnn/conv5-mask/strided_slice_1/stack_1" -> "7357 maskrcnn/conv5-mask/strided_slice_1"; -"7356 maskrcnn/conv5-mask/strided_slice_1/stack_2" -> "7357 maskrcnn/conv5-mask/strided_slice_1"; -"7358 maskrcnn/conv5-mask/conv2d_transpose" -> "7361 maskrcnn/conv5-mask/BiasAdd"; -"7359 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp/resource" -> "7360 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp"; -"7360 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp" -> "7361 maskrcnn/conv5-mask/BiasAdd"; -"7361 maskrcnn/conv5-mask/BiasAdd" -> "7362 maskrcnn/conv5-mask/Relu"; -"7362 maskrcnn/conv5-mask/Relu" -> "7372 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7363 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "7364 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; -"7364 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "7365 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs"; -"7365 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs" -> "7367 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add"; -"7366 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add/y" -> "7367 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add"; -"7367 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add" -> "7370 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add_1"; -"7368 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "7369 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/ReadVariableOp"; -"7368 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "7371 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"7369 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/ReadVariableOp" -> "7370 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add_1"; -"7370 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add_1" -> "7372 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7371 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "7372 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; -"7372 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "7387 maskrcnn/mask_fcn_logits/Conv2D"; -"7373 maskrcnn/tf.cast_32/Cast" -> "7375 maskrcnn/tf.__operators__.add_67/AddV2"; -"7374 maskrcnn/tf.__operators__.add_67/y" -> "7375 maskrcnn/tf.__operators__.add_67/AddV2"; -"7375 maskrcnn/tf.__operators__.add_67/AddV2" -> "20390 maskrcnn/tf.stack_23/stack_1"; -"7375 maskrcnn/tf.__operators__.add_67/AddV2" -> "20396 Identity_3"; -"7376 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp/resource" -> "7377 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp"; -"7377 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp" -> "7378 maskrcnn/mask_fcn_logits/SymmQuant/Abs"; -"7378 maskrcnn/mask_fcn_logits/SymmQuant/Abs" -> "7380 maskrcnn/mask_fcn_logits/SymmQuant/add"; -"7379 maskrcnn/mask_fcn_logits/SymmQuant/add/y" -> "7380 maskrcnn/mask_fcn_logits/SymmQuant/add"; -"7380 maskrcnn/mask_fcn_logits/SymmQuant/add" -> "7383 maskrcnn/mask_fcn_logits/SymmQuant/mul"; -"7380 maskrcnn/mask_fcn_logits/SymmQuant/add" -> "7386 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7381 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp/resource" -> "7382 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp"; -"7382 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp" -> "7383 maskrcnn/mask_fcn_logits/SymmQuant/mul"; -"7383 maskrcnn/mask_fcn_logits/SymmQuant/mul" -> "7386 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7384 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "7385 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; -"7385 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "7386 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; -"7386 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "7387 maskrcnn/mask_fcn_logits/Conv2D"; -"7387 maskrcnn/mask_fcn_logits/Conv2D" -> "7390 maskrcnn/mask_fcn_logits/BiasAdd"; -"7388 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp/resource" -> "7389 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp"; -"7389 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp" -> "7390 maskrcnn/mask_fcn_logits/BiasAdd"; -"7390 maskrcnn/mask_fcn_logits/BiasAdd" -> "7392 maskrcnn/tf.reshape_63/Reshape"; -"7391 maskrcnn/tf.reshape_63/Reshape/shape" -> "7392 maskrcnn/tf.reshape_63/Reshape"; -"7392 maskrcnn/tf.reshape_63/Reshape" -> "7394 maskrcnn/tf.compat.v1.transpose/transpose"; -"7393 maskrcnn/tf.compat.v1.transpose/transpose/perm" -> "7394 maskrcnn/tf.compat.v1.transpose/transpose"; -"7394 maskrcnn/tf.compat.v1.transpose/transpose" -> "20391 maskrcnn/tf.compat.v1.gather_nd_1/GatherNd"; -"20195 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maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2847 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2845 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/ReadVariableOp" -> "2846 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/add_1"; +"2846 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/add_1" -> "2848 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2847 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2848 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2848 maskrcnn/tf.math.multiply_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2997 maskrcnn/tf.math.subtract_25/Sub"; +"2849 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2850 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2850 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2851 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/Abs"; +"2851 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/Abs" -> "2853 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/add"; +"2852 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/add/y" -> "2853 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/add"; +"2853 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/add" -> "2856 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/add_1"; +"2854 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2855 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/ReadVariableOp"; +"2854 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2857 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2855 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/ReadVariableOp" -> "2856 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/add_1"; +"2856 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/add_1" -> "2858 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2857 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2858 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2858 maskrcnn/tf.__operators__.add_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2998 maskrcnn/tf.math.subtract_24/Sub"; +"2859 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2860 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2860 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2861 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/Abs"; +"2861 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/Abs" -> "2863 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/add"; +"2862 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/add/y" -> "2863 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/add"; +"2863 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/add" -> "2866 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/add_1"; +"2864 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2865 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/ReadVariableOp"; +"2864 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2867 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2865 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/ReadVariableOp" -> "2866 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/add_1"; +"2866 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/add_1" -> "2868 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2867 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2868 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2868 maskrcnn/tf.math.multiply_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2998 maskrcnn/tf.math.subtract_24/Sub"; +"2869 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2870 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2870 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2871 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/Abs"; +"2871 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/Abs" -> "2873 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/add"; +"2872 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/add/y" -> "2873 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/add"; +"2873 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/add" -> "2876 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/add_1"; +"2874 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2875 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/ReadVariableOp"; +"2874 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2877 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2875 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/ReadVariableOp" -> "2876 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/add_1"; +"2876 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/add_1" -> "2878 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2877 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2878 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2878 maskrcnn/tf.__operators__.add_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2999 maskrcnn/tf.math.subtract_17/Sub"; +"2879 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2880 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2880 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2881 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/Abs"; +"2881 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/Abs" -> "2883 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/add"; +"2882 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/add/y" -> "2883 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/add"; +"2883 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/add" -> "2886 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/add_1"; +"2884 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2885 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/ReadVariableOp"; +"2884 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2887 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2885 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/ReadVariableOp" -> "2886 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/add_1"; +"2886 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/add_1" -> "2888 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2887 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2888 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2888 maskrcnn/tf.math.multiply_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "2999 maskrcnn/tf.math.subtract_17/Sub"; +"2889 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2890 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2890 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2891 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/Abs"; +"2891 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/Abs" -> "2893 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/add"; +"2892 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/add/y" -> "2893 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/add"; +"2893 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/add" -> "2896 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/add_1"; +"2894 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2895 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/ReadVariableOp"; +"2894 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2897 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2895 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/ReadVariableOp" -> "2896 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/add_1"; +"2896 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/add_1" -> "2898 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2897 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2898 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2898 maskrcnn/tf.__operators__.add_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3000 maskrcnn/tf.math.subtract_16/Sub"; +"2899 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2900 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2900 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2901 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/Abs"; +"2901 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/Abs" -> "2903 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/add"; +"2902 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/add/y" -> "2903 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/add"; +"2903 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/add" -> "2906 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/add_1"; +"2904 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2905 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/ReadVariableOp"; +"2904 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2907 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2905 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/ReadVariableOp" -> "2906 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/add_1"; +"2906 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/add_1" -> "2908 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2907 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2908 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2908 maskrcnn/tf.math.multiply_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3000 maskrcnn/tf.math.subtract_16/Sub"; +"2909 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2910 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2910 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2911 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/Abs"; +"2911 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/Abs" -> "2913 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/add"; +"2912 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/add/y" -> "2913 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/add"; +"2913 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/add" -> "2916 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/add_1"; +"2914 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2915 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/ReadVariableOp"; +"2914 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2917 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2915 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/ReadVariableOp" -> "2916 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/add_1"; +"2916 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/add_1" -> "2918 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2917 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2918 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2918 maskrcnn/tf.__operators__.add_24/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3001 maskrcnn/tf.math.subtract_9/Sub"; +"2919 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2920 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2920 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2921 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/Abs"; +"2921 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/Abs" -> "2923 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/add"; +"2922 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/add/y" -> "2923 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/add"; +"2923 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/add" -> "2926 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/add_1"; +"2924 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2925 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/ReadVariableOp"; +"2924 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2927 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2925 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/ReadVariableOp" -> "2926 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/add_1"; +"2926 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/add_1" -> "2928 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2927 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2928 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2928 maskrcnn/tf.math.multiply_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3001 maskrcnn/tf.math.subtract_9/Sub"; +"2929 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "2930 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"2930 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "2931 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/Abs"; +"2931 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/Abs" -> "2933 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/add"; +"2932 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/add/y" -> "2933 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/add"; +"2933 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/add" -> "2936 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/add_1"; +"2934 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2935 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/ReadVariableOp"; +"2934 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "2937 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"2935 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/ReadVariableOp" -> "2936 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/add_1"; +"2936 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/add_1" -> "2938 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"2937 maskrcnn/tf.__operators__.add_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "2938 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maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3148 maskrcnn/tf.math.subtract_21/Sub/y" -> "3149 maskrcnn/tf.math.subtract_21/Sub"; +"3149 maskrcnn/tf.math.subtract_21/Sub" -> "3319 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3150 maskrcnn/tf.math.subtract_20/Sub/y" -> "3151 maskrcnn/tf.math.subtract_20/Sub"; +"3151 maskrcnn/tf.math.subtract_20/Sub" -> "3309 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3152 maskrcnn/tf.math.subtract_19/Sub/y" -> "3153 maskrcnn/tf.math.subtract_19/Sub"; +"3153 maskrcnn/tf.math.subtract_19/Sub" -> "3359 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3154 maskrcnn/tf.math.subtract_18/Sub/y" -> "3155 maskrcnn/tf.math.subtract_18/Sub"; +"3155 maskrcnn/tf.math.subtract_18/Sub" -> "3349 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3156 maskrcnn/tf.math.subtract_15/Sub/y" -> "3157 maskrcnn/tf.math.subtract_15/Sub"; +"3157 maskrcnn/tf.math.subtract_15/Sub" -> "3399 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3158 maskrcnn/tf.math.subtract_14/Sub/y" -> "3159 maskrcnn/tf.math.subtract_14/Sub"; +"3159 maskrcnn/tf.math.subtract_14/Sub" -> "3389 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3160 maskrcnn/tf.math.subtract_13/Sub/y" -> "3161 maskrcnn/tf.math.subtract_13/Sub"; +"3161 maskrcnn/tf.math.subtract_13/Sub" -> "3379 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3162 maskrcnn/tf.math.subtract_12/Sub/y" -> "3163 maskrcnn/tf.math.subtract_12/Sub"; +"3163 maskrcnn/tf.math.subtract_12/Sub" -> "3369 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3164 maskrcnn/tf.math.subtract_11/Sub/y" -> "3165 maskrcnn/tf.math.subtract_11/Sub"; +"3165 maskrcnn/tf.math.subtract_11/Sub" -> "3419 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3166 maskrcnn/tf.math.subtract_10/Sub/y" -> "3167 maskrcnn/tf.math.subtract_10/Sub"; +"3167 maskrcnn/tf.math.subtract_10/Sub" -> "3409 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3168 maskrcnn/tf.math.subtract_7/Sub/y" -> "3169 maskrcnn/tf.math.subtract_7/Sub"; +"3169 maskrcnn/tf.math.subtract_7/Sub" -> "3459 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3170 maskrcnn/tf.math.subtract_6/Sub/y" -> "3171 maskrcnn/tf.math.subtract_6/Sub"; +"3171 maskrcnn/tf.math.subtract_6/Sub" -> "3449 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3172 maskrcnn/tf.math.subtract_5/Sub/y" -> "3173 maskrcnn/tf.math.subtract_5/Sub"; +"3173 maskrcnn/tf.math.subtract_5/Sub" -> "3439 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3174 maskrcnn/tf.math.subtract_4/Sub/y" -> "3175 maskrcnn/tf.math.subtract_4/Sub"; +"3175 maskrcnn/tf.math.subtract_4/Sub" -> "3429 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3176 maskrcnn/tf.math.subtract_3/Sub/y" -> "3177 maskrcnn/tf.math.subtract_3/Sub"; +"3177 maskrcnn/tf.math.subtract_3/Sub" -> "3479 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3178 maskrcnn/tf.math.subtract_2/Sub/y" -> "3179 maskrcnn/tf.math.subtract_2/Sub"; +"3179 maskrcnn/tf.math.subtract_2/Sub" -> "3469 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3180 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3181 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3181 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3182 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/Abs"; +"3182 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/Abs" -> "3184 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/add"; +"3183 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/add/y" -> "3184 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/add"; +"3184 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/add" -> "3187 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/add_1"; +"3185 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3186 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/ReadVariableOp"; +"3185 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3188 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3186 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/ReadVariableOp" -> "3187 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/add_1"; +"3187 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/add_1" -> "3189 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3188 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3189 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3189 maskrcnn/tf.math.subtract_36/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3541 maskrcnn/tf.stack_4/stack"; +"3190 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3191 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3191 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3192 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/Abs"; +"3192 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/Abs" -> "3194 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/add"; +"3193 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/add/y" -> "3194 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/add"; +"3194 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/add" -> "3197 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/add_1"; +"3195 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3196 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/ReadVariableOp"; +"3195 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3198 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3196 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/ReadVariableOp" -> "3197 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/add_1"; +"3197 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/add_1" -> "3199 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3198 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3199 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3199 maskrcnn/tf.math.subtract_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3541 maskrcnn/tf.stack_4/stack"; +"3200 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3201 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3201 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3202 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/Abs"; +"3202 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/Abs" -> "3204 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/add"; +"3203 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/add/y" -> "3204 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/add"; +"3204 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/add" -> "3207 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/add_1"; +"3205 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3206 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/ReadVariableOp"; +"3205 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3208 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3206 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/ReadVariableOp" -> "3207 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/add_1"; +"3207 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/add_1" -> "3209 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3208 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3209 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3209 maskrcnn/tf.math.subtract_38/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3541 maskrcnn/tf.stack_4/stack"; +"3210 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3211 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3211 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3212 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/Abs"; +"3212 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/Abs" -> "3214 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/add"; +"3213 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/add/y" -> "3214 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/add"; +"3214 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/add" -> "3217 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/add_1"; +"3215 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3216 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/ReadVariableOp"; +"3215 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3218 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3216 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/ReadVariableOp" -> "3217 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/add_1"; +"3217 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/add_1" -> "3219 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3218 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3219 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3219 maskrcnn/tf.math.subtract_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3541 maskrcnn/tf.stack_4/stack"; +"3220 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3221 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3221 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3222 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/Abs"; +"3222 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/Abs" -> "3224 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/add"; +"3223 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/add/y" -> "3224 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/add"; +"3224 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/add" -> "3227 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/add_1"; +"3225 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3226 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/ReadVariableOp"; +"3225 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3228 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3226 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/ReadVariableOp" -> "3227 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/add_1"; +"3227 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/add_1" -> "3229 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3228 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3229 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3229 maskrcnn/tf.math.subtract_34/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3540 maskrcnn/tf.concat_4/concat"; +"3230 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3231 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3231 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3232 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/Abs"; +"3232 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/Abs" -> "3234 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/add"; +"3233 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/add/y" -> "3234 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/add"; +"3234 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/add" -> "3237 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/add_1"; +"3235 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3236 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/ReadVariableOp"; +"3235 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3238 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3236 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/ReadVariableOp" -> "3237 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/add_1"; +"3237 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/add_1" -> "3239 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3238 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3239 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3239 maskrcnn/tf.math.subtract_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3540 maskrcnn/tf.concat_4/concat"; +"3240 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3241 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3241 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3242 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/Abs"; +"3242 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/Abs" -> "3244 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/add"; +"3243 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/add/y" -> "3244 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/add"; +"3244 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/add" -> "3247 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/add_1"; +"3245 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3246 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/ReadVariableOp"; +"3245 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3248 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3246 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/ReadVariableOp" -> "3247 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/add_1"; +"3247 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/add_1" -> "3249 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3248 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3249 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3249 maskrcnn/tf.math.subtract_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3544 maskrcnn/tf.stack_3/stack"; +"3250 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3251 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3251 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3252 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/Abs"; +"3252 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/Abs" -> "3254 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/add"; +"3253 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/add/y" -> "3254 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/add"; +"3254 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/add" -> "3257 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/add_1"; +"3255 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3256 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/ReadVariableOp"; +"3255 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3258 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3256 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/ReadVariableOp" -> "3257 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/add_1"; +"3257 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/add_1" -> "3259 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3258 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3259 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3259 maskrcnn/tf.math.subtract_29/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3544 maskrcnn/tf.stack_3/stack"; +"3260 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3261 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3261 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3262 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/Abs"; +"3262 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/Abs" -> "3264 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/add"; +"3263 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/add/y" -> "3264 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/add"; +"3264 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/add" -> "3267 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/add_1"; +"3265 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3266 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/ReadVariableOp"; +"3265 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3268 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3266 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/ReadVariableOp" -> "3267 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/add_1"; +"3267 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/add_1" -> "3269 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3268 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3269 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3269 maskrcnn/tf.math.subtract_30/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3544 maskrcnn/tf.stack_3/stack"; +"3270 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3271 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3271 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3272 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/Abs"; +"3272 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/Abs" -> "3274 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/add"; +"3273 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/add/y" -> "3274 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/add"; +"3274 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/add" -> "3277 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/add_1"; +"3275 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3276 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/ReadVariableOp"; +"3275 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3278 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3276 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/ReadVariableOp" -> "3277 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/add_1"; +"3277 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/add_1" -> "3279 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3278 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3279 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3279 maskrcnn/tf.math.subtract_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3544 maskrcnn/tf.stack_3/stack"; +"3280 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3281 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3281 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3282 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/Abs"; +"3282 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/Abs" -> "3284 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/add"; +"3283 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/add/y" -> "3284 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/add"; +"3284 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/add" -> "3287 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/add_1"; +"3285 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3286 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/ReadVariableOp"; +"3285 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3288 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3286 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/ReadVariableOp" -> "3287 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/add_1"; +"3287 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/add_1" -> "3289 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3288 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3289 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3289 maskrcnn/tf.math.subtract_26/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3543 maskrcnn/tf.concat_3/concat"; +"3290 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3291 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3291 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3292 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/Abs"; +"3292 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/Abs" -> "3294 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/add"; +"3293 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/add/y" -> "3294 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/add"; +"3294 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/add" -> "3297 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/add_1"; +"3295 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3296 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/ReadVariableOp"; +"3295 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3298 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3296 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/ReadVariableOp" -> "3297 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/add_1"; +"3297 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/add_1" -> "3299 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3298 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3299 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3299 maskrcnn/tf.math.subtract_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3543 maskrcnn/tf.concat_3/concat"; +"3300 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3301 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3301 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3302 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/Abs"; +"3302 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/Abs" -> "3304 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/add"; +"3303 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/add/y" -> "3304 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/add"; +"3304 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/add" -> "3307 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/add_1"; +"3305 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3306 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/ReadVariableOp"; +"3305 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3308 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3306 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/ReadVariableOp" -> "3307 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/add_1"; +"3307 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/add_1" -> "3309 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3308 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3309 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3309 maskrcnn/tf.math.subtract_20/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3547 maskrcnn/tf.stack_2/stack"; +"3310 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3311 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3311 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3312 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/Abs"; +"3312 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/Abs" -> "3314 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/add"; +"3313 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/add/y" -> "3314 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/add"; +"3314 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/add" -> "3317 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/add_1"; +"3315 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3316 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/ReadVariableOp"; +"3315 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3318 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3316 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/ReadVariableOp" -> "3317 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/add_1"; +"3317 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/add_1" -> "3319 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3318 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3319 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3319 maskrcnn/tf.math.subtract_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3547 maskrcnn/tf.stack_2/stack"; +"3320 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3321 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3321 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3322 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/Abs"; +"3322 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/Abs" -> "3324 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/add"; +"3323 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/add/y" -> "3324 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/add"; +"3324 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/add" -> "3327 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/add_1"; +"3325 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3326 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/ReadVariableOp"; +"3325 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3328 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3326 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/ReadVariableOp" -> "3327 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/add_1"; +"3327 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/add_1" -> "3329 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3328 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3329 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3329 maskrcnn/tf.math.subtract_22/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3547 maskrcnn/tf.stack_2/stack"; +"3330 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3331 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3331 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3332 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/Abs"; +"3332 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/Abs" -> "3334 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/add"; +"3333 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/add/y" -> "3334 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/add"; +"3334 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/add" -> "3337 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/add_1"; +"3335 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3336 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/ReadVariableOp"; +"3335 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3338 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3336 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/ReadVariableOp" -> "3337 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/add_1"; +"3337 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/add_1" -> "3339 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3338 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3339 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3339 maskrcnn/tf.math.subtract_23/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3547 maskrcnn/tf.stack_2/stack"; +"3340 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3341 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3341 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3342 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/Abs"; +"3342 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/Abs" -> "3344 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/add"; +"3343 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/add/y" -> "3344 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/add"; +"3344 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/add" -> "3347 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/add_1"; +"3345 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3346 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/ReadVariableOp"; +"3345 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3348 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3346 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/ReadVariableOp" -> "3347 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/add_1"; +"3347 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/add_1" -> "3349 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3348 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3349 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3349 maskrcnn/tf.math.subtract_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3546 maskrcnn/tf.concat_2/concat"; +"3350 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3351 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3351 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3352 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/Abs"; +"3352 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/Abs" -> "3354 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/add"; +"3353 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/add/y" -> "3354 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/add"; +"3354 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/add" -> "3357 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/add_1"; +"3355 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3356 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/ReadVariableOp"; +"3355 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3358 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3356 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/ReadVariableOp" -> "3357 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/add_1"; +"3357 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/add_1" -> "3359 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3358 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3359 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3359 maskrcnn/tf.math.subtract_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3546 maskrcnn/tf.concat_2/concat"; +"3360 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3361 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3361 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3362 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/Abs"; +"3362 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/Abs" -> "3364 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/add"; +"3363 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/add/y" -> "3364 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/add"; +"3364 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/add" -> "3367 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/add_1"; +"3365 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3366 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/ReadVariableOp"; +"3365 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3368 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3366 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/ReadVariableOp" -> "3367 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/add_1"; +"3367 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/add_1" -> "3369 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3368 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3369 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3369 maskrcnn/tf.math.subtract_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3550 maskrcnn/tf.stack_1/stack"; +"3370 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3371 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3371 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3372 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/Abs"; +"3372 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/Abs" -> "3374 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/add"; +"3373 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/add/y" -> "3374 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/add"; +"3374 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/add" -> "3377 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/add_1"; +"3375 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3376 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/ReadVariableOp"; +"3375 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3378 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3376 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/ReadVariableOp" -> "3377 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/add_1"; +"3377 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/add_1" -> "3379 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3378 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3379 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3379 maskrcnn/tf.math.subtract_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3550 maskrcnn/tf.stack_1/stack"; +"3380 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3381 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3381 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3382 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/Abs"; +"3382 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/Abs" -> "3384 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/add"; +"3383 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/add/y" -> "3384 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/add"; +"3384 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/add" -> "3387 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/add_1"; +"3385 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3386 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/ReadVariableOp"; +"3385 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3388 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3386 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/ReadVariableOp" -> "3387 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/add_1"; +"3387 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/add_1" -> "3389 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3388 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3389 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3389 maskrcnn/tf.math.subtract_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3550 maskrcnn/tf.stack_1/stack"; +"3390 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3391 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3391 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3392 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/Abs"; +"3392 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/Abs" -> "3394 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/add"; +"3393 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/add/y" -> "3394 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/add"; +"3394 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/add" -> "3397 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/add_1"; +"3395 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3396 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/ReadVariableOp"; +"3395 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3398 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3396 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/ReadVariableOp" -> "3397 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/add_1"; +"3397 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/add_1" -> "3399 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3398 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3399 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3399 maskrcnn/tf.math.subtract_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3550 maskrcnn/tf.stack_1/stack"; +"3400 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3401 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3401 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3402 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/Abs"; +"3402 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/Abs" -> "3404 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/add"; +"3403 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/add/y" -> "3404 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/add"; +"3404 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/add" -> "3407 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/add_1"; +"3405 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3406 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/ReadVariableOp"; +"3405 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3408 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3406 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/ReadVariableOp" -> "3407 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/add_1"; +"3407 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/add_1" -> "3409 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3408 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3409 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3409 maskrcnn/tf.math.subtract_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3549 maskrcnn/tf.concat_1/concat"; +"3410 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3411 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3411 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3412 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/Abs"; +"3412 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/Abs" -> "3414 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/add"; +"3413 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/add/y" -> "3414 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/add"; +"3414 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/add" -> "3417 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/add_1"; +"3415 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3416 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/ReadVariableOp"; +"3415 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3418 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3416 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/ReadVariableOp" -> "3417 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/add_1"; +"3417 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/add_1" -> "3419 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3418 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3419 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3419 maskrcnn/tf.math.subtract_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3549 maskrcnn/tf.concat_1/concat"; +"3420 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3421 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3421 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3422 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/Abs"; +"3422 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/Abs" -> "3424 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/add"; +"3423 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/add/y" -> "3424 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/add"; +"3424 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/add" -> "3427 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/add_1"; +"3425 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3426 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/ReadVariableOp"; +"3425 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3428 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3426 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/ReadVariableOp" -> "3427 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/add_1"; +"3427 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/add_1" -> "3429 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3428 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3429 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3429 maskrcnn/tf.math.subtract_4/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3553 maskrcnn/tf.stack/stack"; +"3430 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3431 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3431 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3432 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/Abs"; +"3432 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/Abs" -> "3434 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/add"; +"3433 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/add/y" -> "3434 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/add"; +"3434 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/add" -> "3437 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/add_1"; +"3435 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3436 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/ReadVariableOp"; +"3435 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3438 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3436 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/ReadVariableOp" -> "3437 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/add_1"; +"3437 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/add_1" -> "3439 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3438 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3439 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3439 maskrcnn/tf.math.subtract_5/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3553 maskrcnn/tf.stack/stack"; +"3440 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3441 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3441 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3442 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/Abs"; +"3442 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/Abs" -> "3444 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/add"; +"3443 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/add/y" -> "3444 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/add"; +"3444 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/add" -> "3447 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/add_1"; +"3445 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3446 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/ReadVariableOp"; +"3445 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3448 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3446 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/ReadVariableOp" -> "3447 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/add_1"; +"3447 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/add_1" -> "3449 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3448 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3449 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3449 maskrcnn/tf.math.subtract_6/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3553 maskrcnn/tf.stack/stack"; +"3450 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3451 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3451 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3452 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/Abs"; +"3452 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/Abs" -> "3454 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/add"; +"3453 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/add/y" -> "3454 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/add"; +"3454 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/add" -> "3457 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/add_1"; +"3455 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3456 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/ReadVariableOp"; +"3455 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3458 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3456 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/ReadVariableOp" -> "3457 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/add_1"; +"3457 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/add_1" -> "3459 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3458 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3459 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3459 maskrcnn/tf.math.subtract_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3553 maskrcnn/tf.stack/stack"; +"3460 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3461 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3461 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3462 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/Abs"; +"3462 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/Abs" -> "3464 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/add"; +"3463 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/add/y" -> "3464 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/add"; +"3464 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/add" -> "3467 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/add_1"; +"3465 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3466 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/ReadVariableOp"; +"3465 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3468 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3466 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/ReadVariableOp" -> "3467 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/add_1"; +"3467 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/add_1" -> "3469 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3468 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3469 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3469 maskrcnn/tf.math.subtract_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3552 maskrcnn/tf.concat/concat"; +"3470 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "3471 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"3471 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "3472 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/Abs"; +"3472 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/Abs" -> "3474 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/add"; +"3473 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/add/y" -> "3474 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/add"; +"3474 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/add" -> "3477 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/add_1"; +"3475 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3476 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/ReadVariableOp"; +"3475 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "3478 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3476 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/ReadVariableOp" -> "3477 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/add_1"; +"3477 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/add_1" -> "3479 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3478 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3479 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"3479 maskrcnn/tf.math.subtract_3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "3552 maskrcnn/tf.concat/concat"; +"3480 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maskrcnn/rpn-class/SymmQuant/add" -> "3490 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"3485 maskrcnn/rpn-class/SymmQuant/mul/ReadVariableOp/resource" -> "3486 maskrcnn/rpn-class/SymmQuant/mul/ReadVariableOp"; +"3485 maskrcnn/rpn-class/SymmQuant/mul/ReadVariableOp/resource" -> "3499 maskrcnn/rpn-class/SymmQuant_1/mul/ReadVariableOp"; +"3485 maskrcnn/rpn-class/SymmQuant/mul/ReadVariableOp/resource" -> "3510 maskrcnn/rpn-class/SymmQuant_2/mul/ReadVariableOp"; +"3485 maskrcnn/rpn-class/SymmQuant/mul/ReadVariableOp/resource" -> "3521 maskrcnn/rpn-class/SymmQuant_3/mul/ReadVariableOp"; +"3485 maskrcnn/rpn-class/SymmQuant/mul/ReadVariableOp/resource" -> "3532 maskrcnn/rpn-class/SymmQuant_4/mul/ReadVariableOp"; +"3486 maskrcnn/rpn-class/SymmQuant/mul/ReadVariableOp" -> "3487 maskrcnn/rpn-class/SymmQuant/mul"; +"3487 maskrcnn/rpn-class/SymmQuant/mul" -> "3490 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"3488 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "3489 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"3488 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "3501 maskrcnn/rpn-class/SymmQuant_1/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"3488 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "3512 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"3488 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "3523 maskrcnn/rpn-class/SymmQuant_3/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"3488 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "3534 maskrcnn/rpn-class/SymmQuant_4/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"3489 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "3490 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"3490 maskrcnn/rpn-class/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "3491 maskrcnn/rpn-class/Conv2D"; +"3491 maskrcnn/rpn-class/Conv2D" -> "3494 maskrcnn/rpn-class/BiasAdd"; +"3492 maskrcnn/rpn-class/BiasAdd/ReadVariableOp/resource" -> "3493 maskrcnn/rpn-class/BiasAdd/ReadVariableOp"; +"3492 maskrcnn/rpn-class/BiasAdd/ReadVariableOp/resource" -> "3504 maskrcnn/rpn-class/BiasAdd_1/ReadVariableOp"; +"3492 maskrcnn/rpn-class/BiasAdd/ReadVariableOp/resource" -> "3515 maskrcnn/rpn-class/BiasAdd_2/ReadVariableOp"; +"3492 maskrcnn/rpn-class/BiasAdd/ReadVariableOp/resource" -> "3526 maskrcnn/rpn-class/BiasAdd_3/ReadVariableOp"; +"3492 maskrcnn/rpn-class/BiasAdd/ReadVariableOp/resource" -> "3537 maskrcnn/rpn-class/BiasAdd_4/ReadVariableOp"; +"3493 maskrcnn/rpn-class/BiasAdd/ReadVariableOp" -> "3494 maskrcnn/rpn-class/BiasAdd"; +"3494 maskrcnn/rpn-class/BiasAdd" -> "3555 maskrcnn/tf.reshape_8/Reshape"; +"3494 maskrcnn/rpn-class/BiasAdd" -> "19809 Identity_16"; +"3495 maskrcnn/rpn-class/SymmQuant_1/Abs/ReadVariableOp" -> "3496 maskrcnn/rpn-class/SymmQuant_1/Abs"; +"3496 maskrcnn/rpn-class/SymmQuant_1/Abs" -> "3498 maskrcnn/rpn-class/SymmQuant_1/add"; +"3497 maskrcnn/rpn-class/SymmQuant_1/add/y" -> "3498 maskrcnn/rpn-class/SymmQuant_1/add"; +"3498 maskrcnn/rpn-class/SymmQuant_1/add" -> "3500 maskrcnn/rpn-class/SymmQuant_1/mul"; +"3498 maskrcnn/rpn-class/SymmQuant_1/add" -> "3502 maskrcnn/rpn-class/SymmQuant_1/FakeQuantWithMinMaxVarsPerChannel"; +"3499 maskrcnn/rpn-class/SymmQuant_1/mul/ReadVariableOp" -> "3500 maskrcnn/rpn-class/SymmQuant_1/mul"; +"3500 maskrcnn/rpn-class/SymmQuant_1/mul" -> "3502 maskrcnn/rpn-class/SymmQuant_1/FakeQuantWithMinMaxVarsPerChannel"; +"3501 maskrcnn/rpn-class/SymmQuant_1/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "3502 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maskrcnn/rpn-class/SymmQuant_2/mul/ReadVariableOp" -> "3511 maskrcnn/rpn-class/SymmQuant_2/mul"; +"3511 maskrcnn/rpn-class/SymmQuant_2/mul" -> "3513 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVarsPerChannel"; +"3512 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "3513 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVarsPerChannel"; +"3513 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVarsPerChannel" -> "3514 maskrcnn/rpn-class/Conv2D_2"; +"3514 maskrcnn/rpn-class/Conv2D_2" -> "3516 maskrcnn/rpn-class/BiasAdd_2"; +"3515 maskrcnn/rpn-class/BiasAdd_2/ReadVariableOp" -> "3516 maskrcnn/rpn-class/BiasAdd_2"; +"3516 maskrcnn/rpn-class/BiasAdd_2" -> "3561 maskrcnn/tf.reshape_4/Reshape"; +"3516 maskrcnn/rpn-class/BiasAdd_2" -> "19807 Identity_14"; +"3517 maskrcnn/rpn-class/SymmQuant_3/Abs/ReadVariableOp" -> "3518 maskrcnn/rpn-class/SymmQuant_3/Abs"; +"3518 maskrcnn/rpn-class/SymmQuant_3/Abs" -> "3520 maskrcnn/rpn-class/SymmQuant_3/add"; +"3519 maskrcnn/rpn-class/SymmQuant_3/add/y" -> "3520 maskrcnn/rpn-class/SymmQuant_3/add"; +"3520 maskrcnn/rpn-class/SymmQuant_3/add" -> "3522 maskrcnn/rpn-class/SymmQuant_3/mul"; +"3520 maskrcnn/rpn-class/SymmQuant_3/add" -> "3524 maskrcnn/rpn-class/SymmQuant_3/FakeQuantWithMinMaxVarsPerChannel"; +"3521 maskrcnn/rpn-class/SymmQuant_3/mul/ReadVariableOp" -> "3522 maskrcnn/rpn-class/SymmQuant_3/mul"; +"3522 maskrcnn/rpn-class/SymmQuant_3/mul" -> "3524 maskrcnn/rpn-class/SymmQuant_3/FakeQuantWithMinMaxVarsPerChannel"; +"3523 maskrcnn/rpn-class/SymmQuant_3/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "3524 maskrcnn/rpn-class/SymmQuant_3/FakeQuantWithMinMaxVarsPerChannel"; +"3524 maskrcnn/rpn-class/SymmQuant_3/FakeQuantWithMinMaxVarsPerChannel" -> "3525 maskrcnn/rpn-class/Conv2D_3"; +"3525 maskrcnn/rpn-class/Conv2D_3" -> "3527 maskrcnn/rpn-class/BiasAdd_3"; +"3526 maskrcnn/rpn-class/BiasAdd_3/ReadVariableOp" -> "3527 maskrcnn/rpn-class/BiasAdd_3"; +"3527 maskrcnn/rpn-class/BiasAdd_3" -> 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maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/iou_threshold" -> "3608 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3605 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/score_threshold" -> "3608 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3606 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/Const" -> "3608 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3607 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/CombinedNonMaxSuppression/max_output_size_per_class" -> "3608 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3608 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3630 maskrcnn/tf.concat_6/concat"; +"3608 maskrcnn/tf.image.combined_non_max_suppression/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3661 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; +"3609 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/iou_threshold" -> "3613 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3610 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/score_threshold" -> "3613 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3611 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/Const" -> "3613 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3612 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/CombinedNonMaxSuppression/max_output_size_per_class" -> "3613 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3613 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3630 maskrcnn/tf.concat_6/concat"; +"3613 maskrcnn/tf.image.combined_non_max_suppression_1/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3671 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars"; +"3614 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/iou_threshold" -> "3618 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3615 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/score_threshold" -> "3618 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3616 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/Const" -> "3618 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3617 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/CombinedNonMaxSuppression/max_output_size_per_class" -> "3618 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3618 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3630 maskrcnn/tf.concat_6/concat"; +"3618 maskrcnn/tf.image.combined_non_max_suppression_2/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3681 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/FakeQuantWithMinMaxVars"; +"3619 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/iou_threshold" -> "3623 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3620 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/score_threshold" -> "3623 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3621 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/Const" -> "3623 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3622 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/CombinedNonMaxSuppression/max_output_size_per_class" -> "3623 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3623 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3630 maskrcnn/tf.concat_6/concat"; +"3623 maskrcnn/tf.image.combined_non_max_suppression_3/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3691 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/FakeQuantWithMinMaxVars"; +"3624 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/iou_threshold" -> "3628 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3625 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/score_threshold" -> "3628 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3626 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/Const" -> "3628 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3627 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/CombinedNonMaxSuppression/max_output_size_per_class" -> "3628 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/CombinedNonMaxSuppression"; +"3628 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3630 maskrcnn/tf.concat_6/concat"; +"3628 maskrcnn/tf.image.combined_non_max_suppression_4/combined_non_max_suppression/CombinedNonMaxSuppression" -> "3701 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/FakeQuantWithMinMaxVars"; +"3629 maskrcnn/tf.concat_6/concat/axis" -> "3630 maskrcnn/tf.concat_6/concat"; +"3630 maskrcnn/tf.concat_6/concat" -> "3632 maskrcnn/tf.math.top_k/TopKV2"; +"3631 maskrcnn/tf.math.top_k/TopKV2/k" -> "3632 maskrcnn/tf.math.top_k/TopKV2"; +"3632 maskrcnn/tf.math.top_k/TopKV2" -> "3704 maskrcnn/tf.stack_5/stack"; +"3633 maskrcnn/tf.compat.v1.shape/Shape" -> "3637 maskrcnn/tf.__operators__.getitem_21/strided_slice"; +"3633 maskrcnn/tf.compat.v1.shape/Shape" -> "3641 maskrcnn/tf.__operators__.getitem_22/strided_slice"; +"3634 maskrcnn/tf.__operators__.getitem_21/strided_slice/stack" -> "3637 maskrcnn/tf.__operators__.getitem_21/strided_slice"; +"3635 maskrcnn/tf.__operators__.getitem_21/strided_slice/stack_1" -> "3637 maskrcnn/tf.__operators__.getitem_21/strided_slice"; +"3636 maskrcnn/tf.__operators__.getitem_21/strided_slice/stack_2" -> "3637 maskrcnn/tf.__operators__.getitem_21/strided_slice"; +"3637 maskrcnn/tf.__operators__.getitem_21/strided_slice" -> "3644 maskrcnn/tf.range/range"; +"3638 maskrcnn/tf.__operators__.getitem_22/strided_slice/stack" -> "3641 maskrcnn/tf.__operators__.getitem_22/strided_slice"; +"3639 maskrcnn/tf.__operators__.getitem_22/strided_slice/stack_1" -> "3641 maskrcnn/tf.__operators__.getitem_22/strided_slice"; +"3640 maskrcnn/tf.__operators__.getitem_22/strided_slice/stack_2" -> "3641 maskrcnn/tf.__operators__.getitem_22/strided_slice"; +"3641 maskrcnn/tf.__operators__.getitem_22/strided_slice" -> "3648 maskrcnn/tf.ones/ones/packed"; +"3642 maskrcnn/tf.range/range/start" -> "3644 maskrcnn/tf.range/range"; +"3643 maskrcnn/tf.range/range/delta" -> "3644 maskrcnn/tf.range/range"; +"3644 maskrcnn/tf.range/range" -> "3646 maskrcnn/tf.expand_dims_11/ExpandDims"; +"3645 maskrcnn/tf.expand_dims_11/ExpandDims/dim" -> "3646 maskrcnn/tf.expand_dims_11/ExpandDims"; +"3646 maskrcnn/tf.expand_dims_11/ExpandDims" -> "3651 maskrcnn/tf.math.multiply_30/Mul"; +"3648 maskrcnn/tf.ones/ones/packed" -> "3650 maskrcnn/tf.ones/ones"; +"3649 maskrcnn/tf.ones/ones/Const" -> "3650 maskrcnn/tf.ones/ones"; +"3650 maskrcnn/tf.ones/ones" -> "3651 maskrcnn/tf.math.multiply_30/Mul"; +"3651 maskrcnn/tf.math.multiply_30/Mul" -> "3704 maskrcnn/tf.stack_5/stack"; +"3652 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp/resource" -> "3653 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp"; +"3653 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/Abs/ReadVariableOp" -> "3654 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/Abs"; +"3654 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/Abs" -> "3656 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/add"; +"3655 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/add/y" -> "3656 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/add"; +"3656 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/add" -> "3659 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/add_1"; +"3657 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/ReadVariableOp/resource" -> "3658 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/ReadVariableOp"; +"3657 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/ReadVariableOp/resource" -> "3660 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3658 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/ReadVariableOp" -> "3659 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/add_1"; +"3659 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/add_1" -> "3661 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; +"3660 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3661 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; +"3661 maskrcnn/tf.concat_5/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars" -> "3703 maskrcnn/tf.concat_5/concat"; +"3662 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/Abs/ReadVariableOp/resource" -> "3663 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/Abs/ReadVariableOp"; +"3663 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/Abs/ReadVariableOp" -> "3664 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/Abs"; +"3664 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/Abs" -> "3666 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/add"; +"3665 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/add/y" -> "3666 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/add"; +"3666 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/add" -> "3669 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/add_1"; +"3667 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/ReadVariableOp/resource" -> "3668 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/ReadVariableOp"; +"3667 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/ReadVariableOp/resource" -> "3670 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3668 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/ReadVariableOp" -> "3669 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/add_1"; +"3669 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/add_1" -> "3671 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars"; +"3670 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3671 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars"; +"3671 maskrcnn/tf.concat_5/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars" -> "3703 maskrcnn/tf.concat_5/concat"; +"3672 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/Abs/ReadVariableOp/resource" -> "3673 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/Abs/ReadVariableOp"; +"3673 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/Abs/ReadVariableOp" -> "3674 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/Abs"; +"3674 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/Abs" -> "3676 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/add"; +"3675 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/add/y" -> "3676 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/add"; +"3676 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/add" -> "3679 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/add_1"; +"3677 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/ReadVariableOp/resource" -> "3678 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/ReadVariableOp"; +"3677 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/ReadVariableOp/resource" -> "3680 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3678 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/ReadVariableOp" -> "3679 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/add_1"; +"3679 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/add_1" -> "3681 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/FakeQuantWithMinMaxVars"; +"3680 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3681 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/FakeQuantWithMinMaxVars"; +"3681 maskrcnn/tf.concat_5/fake_quantize_I2/AsymmQuant/FakeQuantWithMinMaxVars" -> "3703 maskrcnn/tf.concat_5/concat"; +"3682 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/Abs/ReadVariableOp/resource" -> "3683 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/Abs/ReadVariableOp"; +"3683 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/Abs/ReadVariableOp" -> "3684 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/Abs"; +"3684 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/Abs" -> "3686 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/add"; +"3685 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/add/y" -> "3686 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/add"; +"3686 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/add" -> "3689 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/add_1"; +"3687 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/ReadVariableOp/resource" -> "3688 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/ReadVariableOp"; +"3687 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/ReadVariableOp/resource" -> "3690 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3688 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/ReadVariableOp" -> "3689 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/add_1"; +"3689 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/add_1" -> "3691 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/FakeQuantWithMinMaxVars"; +"3690 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3691 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/FakeQuantWithMinMaxVars"; +"3691 maskrcnn/tf.concat_5/fake_quantize_I3/AsymmQuant/FakeQuantWithMinMaxVars" -> "3703 maskrcnn/tf.concat_5/concat"; +"3692 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/Abs/ReadVariableOp/resource" -> "3693 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/Abs/ReadVariableOp"; +"3693 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/Abs/ReadVariableOp" -> "3694 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/Abs"; +"3694 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/Abs" -> "3696 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/add"; +"3695 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/add/y" -> "3696 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/add"; +"3696 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/add" -> "3699 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/add_1"; +"3697 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/ReadVariableOp/resource" -> "3698 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/ReadVariableOp"; +"3697 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/ReadVariableOp/resource" -> "3700 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"3698 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/ReadVariableOp" -> "3699 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/add_1"; +"3699 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/add_1" -> "3701 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/FakeQuantWithMinMaxVars"; +"3700 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3701 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/FakeQuantWithMinMaxVars"; +"3701 maskrcnn/tf.concat_5/fake_quantize_I4/AsymmQuant/FakeQuantWithMinMaxVars" -> "3703 maskrcnn/tf.concat_5/concat"; +"3702 maskrcnn/tf.concat_5/concat/axis" -> "3703 maskrcnn/tf.concat_5/concat"; +"3703 maskrcnn/tf.concat_5/concat" -> "3705 maskrcnn/tf.compat.v1.gather_nd/GatherNd"; +"3704 maskrcnn/tf.stack_5/stack" -> "3705 maskrcnn/tf.compat.v1.gather_nd/GatherNd"; +"3705 maskrcnn/tf.compat.v1.gather_nd/GatherNd" -> "3709 maskrcnn/tf.__operators__.getitem_23/strided_slice"; +"3705 maskrcnn/tf.compat.v1.gather_nd/GatherNd" -> "3713 maskrcnn/tf.__operators__.getitem_24/strided_slice"; +"3705 maskrcnn/tf.compat.v1.gather_nd/GatherNd" -> "3717 maskrcnn/tf.__operators__.getitem_25/strided_slice"; +"3705 maskrcnn/tf.compat.v1.gather_nd/GatherNd" -> "3721 maskrcnn/tf.__operators__.getitem_26/strided_slice"; +"3705 maskrcnn/tf.compat.v1.gather_nd/GatherNd" -> "3805 maskrcnn/tf.math.truediv_1/truediv"; +"3705 maskrcnn/tf.compat.v1.gather_nd/GatherNd" -> "4785 maskrcnn/tf.expand_dims_23/ExpandDims"; +"3706 maskrcnn/tf.__operators__.getitem_23/strided_slice/stack" -> "3709 maskrcnn/tf.__operators__.getitem_23/strided_slice"; +"3707 maskrcnn/tf.__operators__.getitem_23/strided_slice/stack_1" -> "3709 maskrcnn/tf.__operators__.getitem_23/strided_slice"; +"3708 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maskrcnn/tf.math.truediv_19/fake_quantize/AsymmQuant/add_1"; +"4154 maskrcnn/tf.math.truediv_19/fake_quantize/AsymmQuant/add_1" -> "4156 maskrcnn/tf.math.truediv_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4155 maskrcnn/tf.math.truediv_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4156 maskrcnn/tf.math.truediv_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4156 maskrcnn/tf.math.truediv_19/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4265 maskrcnn/tf.__operators__.add_53/AddV2"; +"4157 maskrcnn/tf.__operators__.getitem_50/strided_slice/stack" -> "4160 maskrcnn/tf.__operators__.getitem_50/strided_slice"; +"4158 maskrcnn/tf.__operators__.getitem_50/strided_slice/stack_1" -> "4160 maskrcnn/tf.__operators__.getitem_50/strided_slice"; +"4159 maskrcnn/tf.__operators__.getitem_50/strided_slice/stack_2" -> "4160 maskrcnn/tf.__operators__.getitem_50/strided_slice"; +"4160 maskrcnn/tf.__operators__.getitem_50/strided_slice" -> "4264 maskrcnn/tf.__operators__.add_51/AddV2"; +"4161 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4162 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4162 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4163 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/Abs"; +"4163 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/Abs" -> "4165 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/add"; +"4164 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/add/y" -> "4165 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/add"; +"4165 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/add" -> "4168 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/add_1"; +"4166 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4167 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/ReadVariableOp"; +"4166 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4169 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4167 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/ReadVariableOp" -> "4168 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/add_1"; +"4168 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/add_1" -> "4170 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4169 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4170 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4170 maskrcnn/tf.math.truediv_17/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4264 maskrcnn/tf.__operators__.add_51/AddV2"; +"4171 maskrcnn/tf.__operators__.getitem_46/strided_slice/stack" -> "4174 maskrcnn/tf.__operators__.getitem_46/strided_slice"; +"4172 maskrcnn/tf.__operators__.getitem_46/strided_slice/stack_1" -> "4174 maskrcnn/tf.__operators__.getitem_46/strided_slice"; +"4173 maskrcnn/tf.__operators__.getitem_46/strided_slice/stack_2" -> "4174 maskrcnn/tf.__operators__.getitem_46/strided_slice"; +"4174 maskrcnn/tf.__operators__.getitem_46/strided_slice" -> "4263 maskrcnn/tf.__operators__.add_49/AddV2"; +"4175 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4176 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4176 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4177 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs"; +"4177 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/Abs" -> "4179 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add"; +"4178 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add/y" -> "4179 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add"; +"4179 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add" -> "4182 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add_1"; +"4180 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4181 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/ReadVariableOp"; +"4180 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4183 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4181 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/ReadVariableOp" -> "4182 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add_1"; +"4182 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/add_1" -> "4184 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4183 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4184 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4184 maskrcnn/tf.math.truediv_15/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4263 maskrcnn/tf.__operators__.add_49/AddV2"; +"4185 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack" -> "4188 maskrcnn/tf.__operators__.getitem_42/strided_slice"; +"4186 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack_1" -> "4188 maskrcnn/tf.__operators__.getitem_42/strided_slice"; +"4187 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack_2" -> "4188 maskrcnn/tf.__operators__.getitem_42/strided_slice"; +"4188 maskrcnn/tf.__operators__.getitem_42/strided_slice" -> "4262 maskrcnn/tf.__operators__.add_47/AddV2"; +"4189 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4190 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4190 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4191 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/Abs"; +"4191 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/Abs" -> "4193 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/add"; +"4192 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/add/y" -> "4193 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/add"; +"4193 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/add" -> "4196 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/add_1"; +"4194 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4195 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/ReadVariableOp"; +"4194 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4197 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4195 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/ReadVariableOp" -> "4196 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/add_1"; +"4196 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/add_1" -> "4198 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4197 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4198 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4198 maskrcnn/tf.math.truediv_13/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4262 maskrcnn/tf.__operators__.add_47/AddV2"; +"4199 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack" -> "4202 maskrcnn/tf.__operators__.getitem_38/strided_slice"; +"4200 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack_1" -> "4202 maskrcnn/tf.__operators__.getitem_38/strided_slice"; +"4201 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack_2" -> "4202 maskrcnn/tf.__operators__.getitem_38/strided_slice"; +"4202 maskrcnn/tf.__operators__.getitem_38/strided_slice" -> "4261 maskrcnn/tf.__operators__.add_45/AddV2"; +"4203 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4204 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4204 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4205 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/Abs"; +"4205 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/Abs" -> "4207 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add"; +"4206 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add/y" -> "4207 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add"; +"4207 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add" -> "4210 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add_1"; +"4208 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4209 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/ReadVariableOp"; +"4208 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4211 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4209 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/ReadVariableOp" -> "4210 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add_1"; +"4210 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/add_1" -> "4212 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4211 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4212 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4212 maskrcnn/tf.math.truediv_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4261 maskrcnn/tf.__operators__.add_45/AddV2"; +"4213 maskrcnn/tf.__operators__.getitem_34/strided_slice/stack" -> "4216 maskrcnn/tf.__operators__.getitem_34/strided_slice"; +"4214 maskrcnn/tf.__operators__.getitem_34/strided_slice/stack_1" -> "4216 maskrcnn/tf.__operators__.getitem_34/strided_slice"; +"4215 maskrcnn/tf.__operators__.getitem_34/strided_slice/stack_2" -> "4216 maskrcnn/tf.__operators__.getitem_34/strided_slice"; +"4216 maskrcnn/tf.__operators__.getitem_34/strided_slice" -> "4260 maskrcnn/tf.__operators__.add_43/AddV2"; +"4217 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4218 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4218 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4219 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/Abs"; +"4219 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/Abs" -> "4221 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/add"; +"4220 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/add/y" -> "4221 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/add"; +"4221 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/add" -> "4224 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/add_1"; +"4222 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4223 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/ReadVariableOp"; +"4222 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4225 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4223 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/ReadVariableOp" -> "4224 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/add_1"; +"4224 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/add_1" -> "4226 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4225 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4226 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4226 maskrcnn/tf.math.truediv_9/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4260 maskrcnn/tf.__operators__.add_43/AddV2"; +"4227 maskrcnn/tf.__operators__.getitem_30/strided_slice/stack" -> "4230 maskrcnn/tf.__operators__.getitem_30/strided_slice"; +"4228 maskrcnn/tf.__operators__.getitem_30/strided_slice/stack_1" -> "4230 maskrcnn/tf.__operators__.getitem_30/strided_slice"; +"4229 maskrcnn/tf.__operators__.getitem_30/strided_slice/stack_2" -> "4230 maskrcnn/tf.__operators__.getitem_30/strided_slice"; +"4230 maskrcnn/tf.__operators__.getitem_30/strided_slice" -> "4259 maskrcnn/tf.__operators__.add_41/AddV2"; +"4231 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4232 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4232 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4233 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/Abs"; +"4233 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/Abs" -> "4235 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/add"; +"4234 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/add/y" -> "4235 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/add"; +"4235 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/add" -> "4238 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/add_1"; +"4236 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4237 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/ReadVariableOp"; +"4236 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4239 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4237 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/ReadVariableOp" -> "4238 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/add_1"; +"4238 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/add_1" -> "4240 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4239 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4240 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4240 maskrcnn/tf.math.truediv_7/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4259 maskrcnn/tf.__operators__.add_41/AddV2"; +"4241 maskrcnn/tf.math.truediv_18/truediv/y" -> "4242 maskrcnn/tf.math.truediv_18/truediv"; +"4242 maskrcnn/tf.math.truediv_18/truediv" -> "4280 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4243 maskrcnn/tf.math.truediv_16/truediv/y" -> "4244 maskrcnn/tf.math.truediv_16/truediv"; +"4244 maskrcnn/tf.math.truediv_16/truediv" -> "4294 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4245 maskrcnn/tf.math.truediv_14/truediv/y" -> "4246 maskrcnn/tf.math.truediv_14/truediv"; +"4246 maskrcnn/tf.math.truediv_14/truediv" -> "4308 maskrcnn/tf.math.truediv_14/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4247 maskrcnn/tf.math.truediv_12/truediv/y" -> "4248 maskrcnn/tf.math.truediv_12/truediv"; +"4248 maskrcnn/tf.math.truediv_12/truediv" -> "4322 maskrcnn/tf.math.truediv_12/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4249 maskrcnn/tf.math.truediv_10/truediv/y" -> "4250 maskrcnn/tf.math.truediv_10/truediv"; +"4250 maskrcnn/tf.math.truediv_10/truediv" -> "4336 maskrcnn/tf.math.truediv_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4251 maskrcnn/tf.math.truediv_8/truediv/y" -> "4252 maskrcnn/tf.math.truediv_8/truediv"; +"4252 maskrcnn/tf.math.truediv_8/truediv" -> "4350 maskrcnn/tf.math.truediv_8/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4253 maskrcnn/tf.math.truediv_6/truediv/y" -> "4254 maskrcnn/tf.math.truediv_6/truediv"; +"4254 maskrcnn/tf.math.truediv_6/truediv" -> "4364 maskrcnn/tf.math.truediv_6/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4255 maskrcnn/tf.expand_dims_15/ExpandDims/dim" -> "4256 maskrcnn/tf.expand_dims_15/ExpandDims"; +"4256 maskrcnn/tf.expand_dims_15/ExpandDims" -> "4366 maskrcnn/tf.concat_12/concat"; +"4257 maskrcnn/tf.expand_dims_16/ExpandDims/dim" -> "4258 maskrcnn/tf.expand_dims_16/ExpandDims"; +"4258 maskrcnn/tf.expand_dims_16/ExpandDims" -> "4366 maskrcnn/tf.concat_12/concat"; +"4259 maskrcnn/tf.__operators__.add_41/AddV2" -> "4266 maskrcnn/tf.stack_7/stack"; +"4260 maskrcnn/tf.__operators__.add_43/AddV2" -> "4266 maskrcnn/tf.stack_7/stack"; +"4261 maskrcnn/tf.__operators__.add_45/AddV2" -> "4266 maskrcnn/tf.stack_7/stack"; +"4262 maskrcnn/tf.__operators__.add_47/AddV2" -> "4266 maskrcnn/tf.stack_7/stack"; +"4263 maskrcnn/tf.__operators__.add_49/AddV2" -> "4266 maskrcnn/tf.stack_7/stack"; +"4264 maskrcnn/tf.__operators__.add_51/AddV2" -> "4266 maskrcnn/tf.stack_7/stack"; +"4265 maskrcnn/tf.__operators__.add_53/AddV2" -> "4266 maskrcnn/tf.stack_7/stack"; +"4266 maskrcnn/tf.stack_7/stack" -> "4374 maskrcnn/tf.math.floor/Floor"; +"4266 maskrcnn/tf.stack_7/stack" -> "4462 maskrcnn/tf.math.subtract_45/fake_quantize_I0/AsymmQuant/FakeQuantWithMinMaxVars"; +"4267 maskrcnn/tf.__operators__.getitem_52/strided_slice/stack" -> "4270 maskrcnn/tf.__operators__.getitem_52/strided_slice"; +"4268 maskrcnn/tf.__operators__.getitem_52/strided_slice/stack_1" -> "4270 maskrcnn/tf.__operators__.getitem_52/strided_slice"; +"4269 maskrcnn/tf.__operators__.getitem_52/strided_slice/stack_2" -> "4270 maskrcnn/tf.__operators__.getitem_52/strided_slice"; +"4270 maskrcnn/tf.__operators__.getitem_52/strided_slice" -> "4373 maskrcnn/tf.__operators__.add_52/AddV2"; +"4271 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4272 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4272 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4273 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs"; +"4273 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/Abs" -> "4275 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add"; +"4274 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add/y" -> "4275 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add"; +"4275 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add" -> "4278 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add_1"; +"4276 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4277 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/ReadVariableOp"; +"4276 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4279 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4277 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/ReadVariableOp" -> "4278 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add_1"; +"4278 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/add_1" -> "4280 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4279 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4280 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4280 maskrcnn/tf.math.truediv_18/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4373 maskrcnn/tf.__operators__.add_52/AddV2"; +"4281 maskrcnn/tf.__operators__.getitem_48/strided_slice/stack" -> "4284 maskrcnn/tf.__operators__.getitem_48/strided_slice"; +"4282 maskrcnn/tf.__operators__.getitem_48/strided_slice/stack_1" -> "4284 maskrcnn/tf.__operators__.getitem_48/strided_slice"; +"4283 maskrcnn/tf.__operators__.getitem_48/strided_slice/stack_2" -> "4284 maskrcnn/tf.__operators__.getitem_48/strided_slice"; +"4284 maskrcnn/tf.__operators__.getitem_48/strided_slice" -> "4372 maskrcnn/tf.__operators__.add_50/AddV2"; +"4285 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4286 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4286 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4287 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs"; +"4287 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/Abs" -> "4289 maskrcnn/tf.math.truediv_16/fake_quantize/AsymmQuant/add"; +"4288 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maskrcnn/tf.math.subtract_46/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4565 maskrcnn/tf.stack_11/stack"; +"4505 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4506 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4506 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4507 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/Abs"; +"4507 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/Abs" -> "4509 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/add"; +"4508 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/add/y" -> "4509 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/add"; +"4509 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/add" -> "4512 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/add_1"; +"4510 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4511 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/ReadVariableOp"; +"4510 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4513 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4511 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/ReadVariableOp" -> "4512 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/add_1"; +"4512 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/add_1" -> "4514 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4513 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4514 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4514 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4529 maskrcnn/tf.math.subtract_47/Sub"; +"4514 maskrcnn/tf.math.subtract_45/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4566 maskrcnn/tf.stack_10/stack"; +"4515 maskrcnn/tf.math.minimum_17/Minimum" -> "4530 maskrcnn/tf.stack_8/stack"; +"4521 maskrcnn/tf.compat.v1.gather_1/GatherV2/params" -> "4523 maskrcnn/tf.compat.v1.gather_1/GatherV2"; +"4522 maskrcnn/tf.compat.v1.gather_1/GatherV2/axis" -> "4523 maskrcnn/tf.compat.v1.gather_1/GatherV2"; +"4523 maskrcnn/tf.compat.v1.gather_1/GatherV2" -> "4533 maskrcnn/tf.expand_dims_21/ExpandDims"; +"4524 maskrcnn/tf.reshape_23/Reshape/shape" -> "4525 maskrcnn/tf.reshape_23/Reshape"; +"4525 maskrcnn/tf.reshape_23/Reshape" -> "4531 maskrcnn/tf.cast_26/Cast"; +"4527 maskrcnn/tf.math.subtract_48/Sub" -> "4543 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4529 maskrcnn/tf.math.subtract_47/Sub" -> "4553 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4530 maskrcnn/tf.stack_8/stack" -> "4555 maskrcnn/tf.reshape_22/Reshape"; +"4531 maskrcnn/tf.cast_26/Cast" -> "4556 maskrcnn/tf.math.multiply_46/Mul"; +"4532 maskrcnn/tf.expand_dims_21/ExpandDims/dim" -> "4533 maskrcnn/tf.expand_dims_21/ExpandDims"; +"4533 maskrcnn/tf.expand_dims_21/ExpandDims" -> "4556 maskrcnn/tf.math.multiply_46/Mul"; +"4534 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4535 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4535 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4536 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/Abs"; +"4536 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/Abs" -> "4538 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/add"; +"4537 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/add/y" -> "4538 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/add"; +"4538 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/add" -> "4541 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/add_1"; +"4539 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4540 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/ReadVariableOp"; +"4539 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4542 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4540 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/ReadVariableOp" -> "4541 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/add_1"; +"4541 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/add_1" -> "4543 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4542 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4543 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4543 maskrcnn/tf.math.subtract_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4565 maskrcnn/tf.stack_11/stack"; +"4544 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4545 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4545 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4546 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/Abs"; +"4546 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/Abs" -> "4548 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/add"; +"4547 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/add/y" -> "4548 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/add"; +"4548 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/add" -> "4551 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/add_1"; +"4549 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4550 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/ReadVariableOp"; +"4549 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4552 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4550 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/ReadVariableOp" -> "4551 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/add_1"; +"4551 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/add_1" -> "4553 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4552 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4553 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4553 maskrcnn/tf.math.subtract_47/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4566 maskrcnn/tf.stack_10/stack"; +"4554 maskrcnn/tf.reshape_22/Reshape/shape" -> "4555 maskrcnn/tf.reshape_22/Reshape"; +"4555 maskrcnn/tf.reshape_22/Reshape" -> "4567 maskrcnn/tf.cast_25/Cast"; +"4556 maskrcnn/tf.math.multiply_46/Mul" -> "4569 maskrcnn/tf.reshape_25/Reshape"; +"4562 maskrcnn/tf.compat.v1.gather/GatherV2/params" -> "4564 maskrcnn/tf.compat.v1.gather/GatherV2"; +"4563 maskrcnn/tf.compat.v1.gather/GatherV2/axis" -> "4564 maskrcnn/tf.compat.v1.gather/GatherV2"; +"4564 maskrcnn/tf.compat.v1.gather/GatherV2" -> "4571 maskrcnn/tf.reshape_24/Reshape"; +"4565 maskrcnn/tf.stack_11/stack" -> "4573 maskrcnn/tf.reshape_21/Reshape"; +"4566 maskrcnn/tf.stack_10/stack" -> "4575 maskrcnn/tf.reshape_20/Reshape"; +"4567 maskrcnn/tf.cast_25/Cast" -> "4577 maskrcnn/tf.reshape_26/Reshape"; +"4568 maskrcnn/tf.reshape_25/Reshape/shape" -> "4569 maskrcnn/tf.reshape_25/Reshape"; +"4569 maskrcnn/tf.reshape_25/Reshape" -> "4581 maskrcnn/tf.tile_1/Tile"; +"4570 maskrcnn/tf.reshape_24/Reshape/shape" -> "4571 maskrcnn/tf.reshape_24/Reshape"; +"4571 maskrcnn/tf.reshape_24/Reshape" -> "4579 maskrcnn/tf.tile/Tile"; +"4572 maskrcnn/tf.reshape_21/Reshape/shape" -> "4573 maskrcnn/tf.reshape_21/Reshape"; +"4573 maskrcnn/tf.reshape_21/Reshape" -> "4585 maskrcnn/tf.reshape_30/Reshape"; +"4574 maskrcnn/tf.reshape_20/Reshape/shape" -> "4575 maskrcnn/tf.reshape_20/Reshape"; +"4575 maskrcnn/tf.reshape_20/Reshape" -> "4583 maskrcnn/tf.reshape_29/Reshape"; +"4576 maskrcnn/tf.reshape_26/Reshape/shape" -> "4577 maskrcnn/tf.reshape_26/Reshape"; +"4577 maskrcnn/tf.reshape_26/Reshape" -> "4588 maskrcnn/tf.tile_2/Tile"; +"4578 maskrcnn/tf.tile/Tile/multiples" -> "4579 maskrcnn/tf.tile/Tile"; +"4579 maskrcnn/tf.tile/Tile" -> "4586 maskrcnn/tf.__operators__.add_56/AddV2"; +"4580 maskrcnn/tf.tile_1/Tile/multiples" -> "4581 maskrcnn/tf.tile_1/Tile"; +"4581 maskrcnn/tf.tile_1/Tile" -> "4586 maskrcnn/tf.__operators__.add_56/AddV2"; +"4582 maskrcnn/tf.reshape_29/Reshape/shape" -> "4583 maskrcnn/tf.reshape_29/Reshape"; +"4583 maskrcnn/tf.reshape_29/Reshape" -> "4589 maskrcnn/tf.math.multiply_47/Mul"; +"4584 maskrcnn/tf.reshape_30/Reshape/shape" -> "4585 maskrcnn/tf.reshape_30/Reshape"; +"4585 maskrcnn/tf.reshape_30/Reshape" -> "4589 maskrcnn/tf.math.multiply_47/Mul"; +"4586 maskrcnn/tf.__operators__.add_56/AddV2" -> "4590 maskrcnn/tf.__operators__.add_57/AddV2"; +"4587 maskrcnn/tf.tile_2/Tile/multiples" -> "4588 maskrcnn/tf.tile_2/Tile"; +"4588 maskrcnn/tf.tile_2/Tile" -> "4590 maskrcnn/tf.__operators__.add_57/AddV2"; +"4589 maskrcnn/tf.math.multiply_47/Mul" -> "4602 maskrcnn/tf.math.multiply_48/Mul"; +"4590 maskrcnn/tf.__operators__.add_57/AddV2" -> "4604 maskrcnn/tf.__operators__.add_58/AddV2"; +"4591 maskrcnn/tf.reshape_10/Reshape/shape" -> "4592 maskrcnn/tf.reshape_10/Reshape"; +"4592 maskrcnn/tf.reshape_10/Reshape" -> "4606 maskrcnn/tf.concat_10/concat"; +"4593 maskrcnn/tf.reshape_12/Reshape/shape" -> "4594 maskrcnn/tf.reshape_12/Reshape"; +"4594 maskrcnn/tf.reshape_12/Reshape" -> "4606 maskrcnn/tf.concat_10/concat"; +"4595 maskrcnn/tf.reshape_14/Reshape/shape" -> "4596 maskrcnn/tf.reshape_14/Reshape"; +"4596 maskrcnn/tf.reshape_14/Reshape" -> "4606 maskrcnn/tf.concat_10/concat"; +"4597 maskrcnn/tf.reshape_16/Reshape/shape" -> "4598 maskrcnn/tf.reshape_16/Reshape"; +"4598 maskrcnn/tf.reshape_16/Reshape" -> "4606 maskrcnn/tf.concat_10/concat"; +"4599 maskrcnn/tf.reshape_18/Reshape/shape" -> "4600 maskrcnn/tf.reshape_18/Reshape"; +"4600 maskrcnn/tf.reshape_18/Reshape" -> "4606 maskrcnn/tf.concat_10/concat"; +"4601 maskrcnn/tf.math.multiply_48/Mul/y" -> "4602 maskrcnn/tf.math.multiply_48/Mul"; +"4602 maskrcnn/tf.math.multiply_48/Mul" -> "4616 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4603 maskrcnn/tf.__operators__.add_58/y" -> "4604 maskrcnn/tf.__operators__.add_58/AddV2"; +"4604 maskrcnn/tf.__operators__.add_58/AddV2" -> "4620 maskrcnn/tf.reshape_27/Reshape"; +"4605 maskrcnn/tf.concat_10/concat/axis" -> "4606 maskrcnn/tf.concat_10/concat"; +"4606 maskrcnn/tf.concat_10/concat" -> "4618 maskrcnn/tf.reshape_19/Reshape"; +"4607 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4608 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4608 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4609 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/Abs"; +"4609 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/Abs" -> "4611 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/add"; +"4610 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/add/y" -> "4611 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/add"; +"4611 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/add" -> "4614 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/add_1"; +"4612 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4613 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/ReadVariableOp"; +"4612 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4615 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4613 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/ReadVariableOp" -> "4614 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/add_1"; +"4614 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/add_1" -> "4616 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4615 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4616 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4616 maskrcnn/tf.math.multiply_48/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4622 maskrcnn/tf.expand_dims_22/ExpandDims"; +"4617 maskrcnn/tf.reshape_19/Reshape/shape" -> "4618 maskrcnn/tf.reshape_19/Reshape"; +"4618 maskrcnn/tf.reshape_19/Reshape" -> "4624 maskrcnn/tf.compat.v1.gather_2/GatherV2"; +"4619 maskrcnn/tf.reshape_27/Reshape/shape" -> "4620 maskrcnn/tf.reshape_27/Reshape"; +"4620 maskrcnn/tf.reshape_27/Reshape" -> "4624 maskrcnn/tf.compat.v1.gather_2/GatherV2"; +"4621 maskrcnn/tf.expand_dims_22/ExpandDims/dim" -> "4622 maskrcnn/tf.expand_dims_22/ExpandDims"; +"4622 maskrcnn/tf.expand_dims_22/ExpandDims" -> "4627 maskrcnn/tf.math.multiply_49/Mul"; +"4623 maskrcnn/tf.compat.v1.gather_2/GatherV2/axis" -> "4624 maskrcnn/tf.compat.v1.gather_2/GatherV2"; +"4624 maskrcnn/tf.compat.v1.gather_2/GatherV2" -> "4626 maskrcnn/tf.reshape_28/Reshape"; +"4625 maskrcnn/tf.reshape_28/Reshape/shape" -> "4626 maskrcnn/tf.reshape_28/Reshape"; +"4626 maskrcnn/tf.reshape_28/Reshape" -> "4627 maskrcnn/tf.math.multiply_49/Mul"; +"4627 maskrcnn/tf.math.multiply_49/Mul" -> "4637 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4628 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4629 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4629 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4630 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/Abs"; +"4630 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/Abs" -> "4632 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/add"; +"4631 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/add/y" -> "4632 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/add"; +"4632 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/add" -> "4635 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/add_1"; +"4633 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4634 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/ReadVariableOp"; +"4633 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4636 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4634 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/ReadVariableOp" -> "4635 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/add_1"; +"4635 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/add_1" -> "4637 maskrcnn/tf.math.multiply_49/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4636 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maskrcnn/tf.tile_3/Tile/multiples/3" -> "4792 maskrcnn/tf.tile_3/Tile/multiples"; +"4792 maskrcnn/tf.tile_3/Tile/multiples" -> "4793 maskrcnn/tf.tile_3/Tile"; +"4793 maskrcnn/tf.tile_3/Tile" -> "4801 maskrcnn/tf.reshape_37/Reshape"; +"4795 maskrcnn/tf.stack_14/stack" -> "4801 maskrcnn/tf.reshape_37/Reshape"; +"4796 maskrcnn/tf.slice_1/Slice/begin" -> "4798 maskrcnn/tf.slice_1/Slice"; +"4797 maskrcnn/tf.slice_1/Slice/size" -> "4798 maskrcnn/tf.slice_1/Slice"; +"4798 maskrcnn/tf.slice_1/Slice" -> "4802 maskrcnn/tf.reshape_36/Reshape"; +"4800 maskrcnn/tf.stack_13/stack" -> "4802 maskrcnn/tf.reshape_36/Reshape"; +"4801 maskrcnn/tf.reshape_37/Reshape" -> "4806 maskrcnn/tf.__operators__.getitem_69/strided_slice"; +"4801 maskrcnn/tf.reshape_37/Reshape" -> "4810 maskrcnn/tf.__operators__.getitem_71/strided_slice"; +"4801 maskrcnn/tf.reshape_37/Reshape" -> "4814 maskrcnn/tf.__operators__.getitem_68/strided_slice"; +"4801 maskrcnn/tf.reshape_37/Reshape" -> "4818 maskrcnn/tf.__operators__.getitem_70/strided_slice"; +"4802 maskrcnn/tf.reshape_36/Reshape" -> "4822 maskrcnn/tf.__operators__.getitem_67/strided_slice"; +"4802 maskrcnn/tf.reshape_36/Reshape" -> "4827 maskrcnn/tf.__operators__.getitem_66/strided_slice"; +"4802 maskrcnn/tf.reshape_36/Reshape" -> "4852 maskrcnn/tf.__operators__.getitem_65/strided_slice"; +"4802 maskrcnn/tf.reshape_36/Reshape" -> "4868 maskrcnn/tf.__operators__.getitem_64/strided_slice"; +"4803 maskrcnn/tf.__operators__.getitem_69/strided_slice/stack" -> "4806 maskrcnn/tf.__operators__.getitem_69/strided_slice"; +"4804 maskrcnn/tf.__operators__.getitem_69/strided_slice/stack_1" -> "4806 maskrcnn/tf.__operators__.getitem_69/strided_slice"; +"4805 maskrcnn/tf.__operators__.getitem_69/strided_slice/stack_2" -> "4806 maskrcnn/tf.__operators__.getitem_69/strided_slice"; +"4806 maskrcnn/tf.__operators__.getitem_69/strided_slice" -> "4823 maskrcnn/tf.math.subtract_52/Sub"; +"4806 maskrcnn/tf.__operators__.getitem_69/strided_slice" -> "4940 maskrcnn/tf.__operators__.add_62/AddV2"; +"4807 maskrcnn/tf.__operators__.getitem_71/strided_slice/stack" -> "4810 maskrcnn/tf.__operators__.getitem_71/strided_slice"; +"4808 maskrcnn/tf.__operators__.getitem_71/strided_slice/stack_1" -> "4810 maskrcnn/tf.__operators__.getitem_71/strided_slice"; +"4809 maskrcnn/tf.__operators__.getitem_71/strided_slice/stack_2" -> "4810 maskrcnn/tf.__operators__.getitem_71/strided_slice"; +"4810 maskrcnn/tf.__operators__.getitem_71/strided_slice" -> "4823 maskrcnn/tf.math.subtract_52/Sub"; +"4811 maskrcnn/tf.__operators__.getitem_68/strided_slice/stack" -> "4814 maskrcnn/tf.__operators__.getitem_68/strided_slice"; +"4812 maskrcnn/tf.__operators__.getitem_68/strided_slice/stack_1" -> "4814 maskrcnn/tf.__operators__.getitem_68/strided_slice"; +"4813 maskrcnn/tf.__operators__.getitem_68/strided_slice/stack_2" -> "4814 maskrcnn/tf.__operators__.getitem_68/strided_slice"; +"4814 maskrcnn/tf.__operators__.getitem_68/strided_slice" -> "4828 maskrcnn/tf.math.subtract_51/Sub"; +"4814 maskrcnn/tf.__operators__.getitem_68/strided_slice" -> "4943 maskrcnn/tf.__operators__.add_61/AddV2"; +"4815 maskrcnn/tf.__operators__.getitem_70/strided_slice/stack" -> "4818 maskrcnn/tf.__operators__.getitem_70/strided_slice"; +"4816 maskrcnn/tf.__operators__.getitem_70/strided_slice/stack_1" -> "4818 maskrcnn/tf.__operators__.getitem_70/strided_slice"; +"4817 maskrcnn/tf.__operators__.getitem_70/strided_slice/stack_2" -> "4818 maskrcnn/tf.__operators__.getitem_70/strided_slice"; +"4818 maskrcnn/tf.__operators__.getitem_70/strided_slice" -> "4828 maskrcnn/tf.math.subtract_51/Sub"; +"4819 maskrcnn/tf.__operators__.getitem_67/strided_slice/stack" -> "4822 maskrcnn/tf.__operators__.getitem_67/strided_slice"; +"4820 maskrcnn/tf.__operators__.getitem_67/strided_slice/stack_1" -> "4822 maskrcnn/tf.__operators__.getitem_67/strided_slice"; +"4821 maskrcnn/tf.__operators__.getitem_67/strided_slice/stack_2" -> "4822 maskrcnn/tf.__operators__.getitem_67/strided_slice"; +"4822 maskrcnn/tf.__operators__.getitem_67/strided_slice" -> "4830 maskrcnn/tf.math.truediv_23/truediv"; +"4823 maskrcnn/tf.math.subtract_52/Sub" -> "4832 maskrcnn/tf.__operators__.add_60/AddV2"; +"4824 maskrcnn/tf.__operators__.getitem_66/strided_slice/stack" -> "4827 maskrcnn/tf.__operators__.getitem_66/strided_slice"; +"4825 maskrcnn/tf.__operators__.getitem_66/strided_slice/stack_1" -> "4827 maskrcnn/tf.__operators__.getitem_66/strided_slice"; +"4826 maskrcnn/tf.__operators__.getitem_66/strided_slice/stack_2" -> "4827 maskrcnn/tf.__operators__.getitem_66/strided_slice"; +"4827 maskrcnn/tf.__operators__.getitem_66/strided_slice" -> "4834 maskrcnn/tf.math.truediv_22/truediv"; +"4828 maskrcnn/tf.math.subtract_51/Sub" -> "4836 maskrcnn/tf.__operators__.add_59/AddV2"; +"4829 maskrcnn/tf.math.truediv_23/truediv/y" -> "4830 maskrcnn/tf.math.truediv_23/truediv"; +"4830 maskrcnn/tf.math.truediv_23/truediv" -> "4848 maskrcnn/tf.math.minimum_21/Minimum"; +"4831 maskrcnn/tf.__operators__.add_60/y" -> "4832 maskrcnn/tf.__operators__.add_60/AddV2"; +"4832 maskrcnn/tf.__operators__.add_60/AddV2" -> "4846 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4833 maskrcnn/tf.math.truediv_22/truediv/y" -> "4834 maskrcnn/tf.math.truediv_22/truediv"; +"4834 maskrcnn/tf.math.truediv_22/truediv" -> "4864 maskrcnn/tf.math.minimum_20/Minimum"; +"4835 maskrcnn/tf.__operators__.add_59/y" -> "4836 maskrcnn/tf.__operators__.add_59/AddV2"; +"4836 maskrcnn/tf.__operators__.add_59/AddV2" -> "4862 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4837 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4838 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4838 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4839 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/Abs"; +"4839 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/Abs" -> "4841 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/add"; +"4840 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/add/y" -> "4841 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/add"; +"4841 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/add" -> "4844 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/add_1"; +"4842 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4843 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/ReadVariableOp"; +"4842 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4845 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4843 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/ReadVariableOp" -> "4844 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/add_1"; +"4844 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/add_1" -> "4846 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4845 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4846 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4846 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4871 maskrcnn/tf.math.multiply_52/Mul"; +"4846 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4939 maskrcnn/tf.math.multiply_56/Mul"; +"4846 maskrcnn/tf.__operators__.add_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4941 maskrcnn/tf.math.multiply_54/Mul"; +"4847 maskrcnn/tf.math.minimum_21/Minimum/y" -> "4848 maskrcnn/tf.math.minimum_21/Minimum"; +"4848 maskrcnn/tf.math.minimum_21/Minimum" -> "4869 maskrcnn/tf.math.exp_11/Exp"; +"4849 maskrcnn/tf.__operators__.getitem_65/strided_slice/stack" -> "4852 maskrcnn/tf.__operators__.getitem_65/strided_slice"; +"4850 maskrcnn/tf.__operators__.getitem_65/strided_slice/stack_1" -> "4852 maskrcnn/tf.__operators__.getitem_65/strided_slice"; +"4851 maskrcnn/tf.__operators__.getitem_65/strided_slice/stack_2" -> "4852 maskrcnn/tf.__operators__.getitem_65/strided_slice"; +"4852 maskrcnn/tf.__operators__.getitem_65/strided_slice" -> "4873 maskrcnn/tf.math.truediv_21/truediv"; +"4853 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4854 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4854 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4855 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/Abs"; +"4855 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/Abs" -> "4857 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/add"; +"4856 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/add/y" -> "4857 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/add"; +"4857 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/add" -> "4860 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/add_1"; +"4858 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4859 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/ReadVariableOp"; +"4858 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4861 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4859 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/ReadVariableOp" -> "4860 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/add_1"; +"4860 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/add_1" -> "4862 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4861 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4862 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4862 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4876 maskrcnn/tf.math.multiply_51/Mul"; +"4862 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4942 maskrcnn/tf.math.multiply_55/Mul"; +"4862 maskrcnn/tf.__operators__.add_59/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4944 maskrcnn/tf.math.multiply_53/Mul"; +"4863 maskrcnn/tf.math.minimum_20/Minimum/y" -> "4864 maskrcnn/tf.math.minimum_20/Minimum"; +"4864 maskrcnn/tf.math.minimum_20/Minimum" -> "4874 maskrcnn/tf.math.exp_10/Exp"; +"4865 maskrcnn/tf.__operators__.getitem_64/strided_slice/stack" -> "4868 maskrcnn/tf.__operators__.getitem_64/strided_slice"; +"4866 maskrcnn/tf.__operators__.getitem_64/strided_slice/stack_1" -> "4868 maskrcnn/tf.__operators__.getitem_64/strided_slice"; +"4867 maskrcnn/tf.__operators__.getitem_64/strided_slice/stack_2" -> "4868 maskrcnn/tf.__operators__.getitem_64/strided_slice"; +"4868 maskrcnn/tf.__operators__.getitem_64/strided_slice" -> "4878 maskrcnn/tf.math.truediv_20/truediv"; +"4869 maskrcnn/tf.math.exp_11/Exp" -> "4888 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4871 maskrcnn/tf.math.multiply_52/Mul" -> "4898 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4872 maskrcnn/tf.math.truediv_21/truediv/y" -> "4873 maskrcnn/tf.math.truediv_21/truediv"; +"4873 maskrcnn/tf.math.truediv_21/truediv" -> "4908 maskrcnn/tf.math.truediv_21/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4874 maskrcnn/tf.math.exp_10/Exp" -> "4918 maskrcnn/tf.math.exp_10/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4876 maskrcnn/tf.math.multiply_51/Mul" -> "4928 maskrcnn/tf.math.multiply_51/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4877 maskrcnn/tf.math.truediv_20/truediv/y" -> "4878 maskrcnn/tf.math.truediv_20/truediv"; +"4878 maskrcnn/tf.math.truediv_20/truediv" -> "4938 maskrcnn/tf.math.truediv_20/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4879 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4880 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4880 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4881 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/Abs"; +"4881 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/Abs" -> "4883 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/add"; +"4882 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/add/y" -> "4883 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/add"; +"4883 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/add" -> "4886 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/add_1"; +"4884 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4885 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/ReadVariableOp"; +"4884 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4887 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4885 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/ReadVariableOp" -> "4886 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/add_1"; +"4886 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/add_1" -> "4888 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4887 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4888 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4888 maskrcnn/tf.math.exp_11/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4939 maskrcnn/tf.math.multiply_56/Mul"; +"4889 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4890 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4890 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4891 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/Abs"; +"4891 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/Abs" -> "4893 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/add"; +"4892 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/add/y" -> "4893 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/add"; +"4893 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/add" -> "4896 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/add_1"; +"4894 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4895 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/ReadVariableOp"; +"4894 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "4897 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4895 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/ReadVariableOp" -> "4896 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/add_1"; +"4896 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/add_1" -> "4898 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4897 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4898 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"4898 maskrcnn/tf.math.multiply_52/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "4940 maskrcnn/tf.__operators__.add_62/AddV2"; +"4899 maskrcnn/tf.math.truediv_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "4900 maskrcnn/tf.math.truediv_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"4900 maskrcnn/tf.math.truediv_21/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "4901 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maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5235 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/Abs"; +"5235 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/Abs" -> "5237 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add"; +"5236 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add/y" -> "5237 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add"; +"5237 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add" -> "5240 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add_1"; +"5238 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5239 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/ReadVariableOp"; +"5238 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5241 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"5239 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/ReadVariableOp" -> "5240 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add_1"; +"5240 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/add_1" -> "5242 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5241 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5242 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5242 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5253 maskrcnn/tf.math.multiply_60/Mul"; +"5242 maskrcnn/tf.math.subtract_62/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5312 maskrcnn/tf.math.truediv_27/truediv"; +"5243 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5244 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"5244 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5245 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/Abs"; 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maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5251 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5252 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5252 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5253 maskrcnn/tf.math.multiply_60/Mul"; +"5252 maskrcnn/tf.math.subtract_63/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5313 maskrcnn/tf.math.truediv_28/truediv"; +"5253 maskrcnn/tf.math.multiply_60/Mul" -> "5263 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5254 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5255 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"5255 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5256 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs"; +"5256 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/Abs" -> "5258 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add"; +"5257 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add/y" -> "5258 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add"; +"5258 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add" -> "5261 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add_1"; +"5259 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5260 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/ReadVariableOp"; +"5259 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5262 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"5260 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/ReadVariableOp" -> "5261 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add_1"; +"5261 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/add_1" -> "5263 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5262 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5263 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5263 maskrcnn/tf.math.multiply_60/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5264 maskrcnn/tf.math.sqrt_1/Sqrt"; +"5264 maskrcnn/tf.math.sqrt_1/Sqrt" -> "5274 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5265 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5266 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"5266 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5267 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs"; +"5267 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/Abs" -> "5269 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add"; +"5268 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add/y" -> "5269 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add"; +"5269 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add" -> "5272 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add_1"; +"5270 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5271 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/ReadVariableOp"; +"5270 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5273 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"5271 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/ReadVariableOp" -> "5272 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add_1"; +"5272 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/add_1" -> "5274 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5273 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5274 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5274 maskrcnn/tf.math.sqrt_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5276 maskrcnn/tf.math.truediv_25/truediv"; +"5275 maskrcnn/tf.math.truediv_25/truediv/y" -> "5276 maskrcnn/tf.math.truediv_25/truediv"; +"5276 maskrcnn/tf.math.truediv_25/truediv" -> "5277 maskrcnn/tf.math.log_1/Log"; +"5277 maskrcnn/tf.math.log_1/Log" -> "5279 maskrcnn/tf.compat.v1.floor_div_1/FloorDiv"; +"5278 maskrcnn/tf.compat.v1.floor_div_1/FloorDiv/y" -> "5279 maskrcnn/tf.compat.v1.floor_div_1/FloorDiv"; +"5279 maskrcnn/tf.compat.v1.floor_div_1/FloorDiv" -> "5281 maskrcnn/tf.__operators__.add_68/AddV2"; +"5280 maskrcnn/tf.__operators__.add_68/y" -> "5281 maskrcnn/tf.__operators__.add_68/AddV2"; +"5281 maskrcnn/tf.__operators__.add_68/AddV2" -> "5282 maskrcnn/tf.cast_34/Cast"; +"5282 maskrcnn/tf.cast_34/Cast" -> "5284 maskrcnn/tf.math.maximum_9/Maximum"; +"5283 maskrcnn/tf.math.maximum_9/Maximum/y" -> "5284 maskrcnn/tf.math.maximum_9/Maximum"; +"5284 maskrcnn/tf.math.maximum_9/Maximum" -> "5286 maskrcnn/tf.math.minimum_23/Minimum"; +"5286 maskrcnn/tf.math.minimum_23/Minimum" -> "5287 maskrcnn/tf.cast_35/Cast"; +"5286 maskrcnn/tf.math.minimum_23/Minimum" -> "5316 maskrcnn/tf.math.subtract_64/Sub"; +"5287 maskrcnn/tf.cast_35/Cast" -> "5297 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5288 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5289 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"5289 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5290 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/Abs"; +"5290 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/Abs" -> "5292 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/add"; +"5291 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/add/y" -> "5292 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/add"; +"5292 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/add" -> "5295 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/add_1"; +"5293 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5294 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/ReadVariableOp"; +"5293 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5296 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"5294 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/ReadVariableOp" -> "5295 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/add_1"; +"5295 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/add_1" -> "5297 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5296 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5297 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5297 maskrcnn/tf.cast_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5299 maskrcnn/tf.math.pow_2/Pow"; +"5299 maskrcnn/tf.math.pow_2/Pow" -> "5309 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5300 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5301 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"5301 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5302 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/Abs"; +"5302 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/Abs" -> "5304 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/add"; +"5303 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/add/y" -> "5304 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/add"; +"5304 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/add" -> "5307 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/add_1"; +"5305 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5306 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/ReadVariableOp"; +"5305 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5308 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"5306 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/ReadVariableOp" -> "5307 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/add_1"; +"5307 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/add_1" -> "5309 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5308 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "5309 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5309 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5311 maskrcnn/tf.expand_dims_24/ExpandDims"; +"5309 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5312 maskrcnn/tf.math.truediv_27/truediv"; +"5309 maskrcnn/tf.math.pow_2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "5313 maskrcnn/tf.math.truediv_28/truediv"; +"5310 maskrcnn/tf.expand_dims_24/ExpandDims/dim" -> "5311 maskrcnn/tf.expand_dims_24/ExpandDims"; +"5311 maskrcnn/tf.expand_dims_24/ExpandDims" -> "5314 maskrcnn/tf.math.truediv_26/truediv"; +"5312 maskrcnn/tf.math.truediv_27/truediv" -> "5326 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5313 maskrcnn/tf.math.truediv_28/truediv" -> "5336 maskrcnn/tf.math.truediv_28/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5314 maskrcnn/tf.math.truediv_26/truediv" -> "5346 maskrcnn/tf.math.truediv_26/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"5315 maskrcnn/tf.math.subtract_64/Sub/y" -> "5316 maskrcnn/tf.math.subtract_64/Sub"; +"5316 maskrcnn/tf.math.subtract_64/Sub" -> "5347 maskrcnn/tf.cast_37/Cast"; +"5316 maskrcnn/tf.math.subtract_64/Sub" -> "6494 maskrcnn/tf.compat.v1.gather_4/GatherV2"; +"5316 maskrcnn/tf.math.subtract_64/Sub" -> "6535 maskrcnn/tf.compat.v1.gather_3/GatherV2"; +"5317 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "5318 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"5318 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "5319 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/Abs"; +"5319 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/Abs" -> "5321 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/add"; +"5320 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/add/y" -> "5321 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/add"; +"5321 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/add" -> "5324 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/add_1"; +"5322 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5323 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/ReadVariableOp"; +"5322 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "5325 maskrcnn/tf.math.truediv_27/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"5323 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maskrcnn/tf.__operators__.add_81/AddV2"; +"6235 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6236 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6236 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6237 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/Abs"; +"6237 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/Abs" -> "6239 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/add"; +"6238 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/add/y" -> "6239 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/add"; +"6239 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/add" -> "6242 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/add_1"; +"6240 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6241 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/ReadVariableOp"; +"6240 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6243 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6241 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/ReadVariableOp" -> "6242 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/add_1"; +"6242 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/add_1" -> "6244 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6243 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6244 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6244 maskrcnn/tf.math.truediv_43/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6337 maskrcnn/tf.__operators__.add_81/AddV2"; +"6245 maskrcnn/tf.__operators__.getitem_97/strided_slice/stack" -> "6248 maskrcnn/tf.__operators__.getitem_97/strided_slice"; +"6246 maskrcnn/tf.__operators__.getitem_97/strided_slice/stack_1" -> "6248 maskrcnn/tf.__operators__.getitem_97/strided_slice"; +"6247 maskrcnn/tf.__operators__.getitem_97/strided_slice/stack_2" -> "6248 maskrcnn/tf.__operators__.getitem_97/strided_slice"; +"6248 maskrcnn/tf.__operators__.getitem_97/strided_slice" -> "6336 maskrcnn/tf.__operators__.add_79/AddV2"; +"6249 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6250 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6250 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6251 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/Abs"; +"6251 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/Abs" -> "6253 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/add"; +"6252 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/add/y" -> "6253 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/add"; +"6253 maskrcnn/tf.math.truediv_41/fake_quantize/AsymmQuant/add" -> "6256 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maskrcnn/tf.__operators__.add_79/AddV2"; +"6259 maskrcnn/tf.__operators__.getitem_93/strided_slice/stack" -> "6262 maskrcnn/tf.__operators__.getitem_93/strided_slice"; +"6260 maskrcnn/tf.__operators__.getitem_93/strided_slice/stack_1" -> "6262 maskrcnn/tf.__operators__.getitem_93/strided_slice"; +"6261 maskrcnn/tf.__operators__.getitem_93/strided_slice/stack_2" -> "6262 maskrcnn/tf.__operators__.getitem_93/strided_slice"; +"6262 maskrcnn/tf.__operators__.getitem_93/strided_slice" -> "6335 maskrcnn/tf.__operators__.add_77/AddV2"; +"6263 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6264 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6264 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6265 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/Abs"; +"6265 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/Abs" -> "6267 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/add"; +"6266 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/add/y" -> "6267 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/add"; +"6267 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/add" -> "6270 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/add_1"; +"6268 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6269 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/ReadVariableOp"; +"6268 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6271 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6269 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/ReadVariableOp" -> "6270 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/add_1"; +"6270 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/add_1" -> "6272 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6271 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6272 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6272 maskrcnn/tf.math.truediv_39/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6335 maskrcnn/tf.__operators__.add_77/AddV2"; +"6273 maskrcnn/tf.__operators__.getitem_89/strided_slice/stack" -> "6276 maskrcnn/tf.__operators__.getitem_89/strided_slice"; +"6274 maskrcnn/tf.__operators__.getitem_89/strided_slice/stack_1" -> "6276 maskrcnn/tf.__operators__.getitem_89/strided_slice"; +"6275 maskrcnn/tf.__operators__.getitem_89/strided_slice/stack_2" -> "6276 maskrcnn/tf.__operators__.getitem_89/strided_slice"; +"6276 maskrcnn/tf.__operators__.getitem_89/strided_slice" -> "6334 maskrcnn/tf.__operators__.add_75/AddV2"; +"6277 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6278 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6278 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6279 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/Abs"; +"6279 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/Abs" -> "6281 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/add"; +"6280 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/add/y" -> "6281 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/add"; +"6281 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/add" -> "6284 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/add_1"; +"6282 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6283 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/ReadVariableOp"; +"6282 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6285 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6283 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/ReadVariableOp" -> "6284 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/add_1"; +"6284 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/add_1" -> "6286 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6285 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6286 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6286 maskrcnn/tf.math.truediv_37/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6334 maskrcnn/tf.__operators__.add_75/AddV2"; +"6287 maskrcnn/tf.__operators__.getitem_85/strided_slice/stack" -> "6290 maskrcnn/tf.__operators__.getitem_85/strided_slice"; +"6288 maskrcnn/tf.__operators__.getitem_85/strided_slice/stack_1" -> "6290 maskrcnn/tf.__operators__.getitem_85/strided_slice"; +"6289 maskrcnn/tf.__operators__.getitem_85/strided_slice/stack_2" -> "6290 maskrcnn/tf.__operators__.getitem_85/strided_slice"; +"6290 maskrcnn/tf.__operators__.getitem_85/strided_slice" -> "6333 maskrcnn/tf.__operators__.add_73/AddV2"; +"6291 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6292 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6292 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6293 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/Abs"; +"6293 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/Abs" -> "6295 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/add"; +"6294 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/add/y" -> "6295 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/add"; +"6295 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/add" -> "6298 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/add_1"; +"6296 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6297 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/ReadVariableOp"; +"6296 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6299 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6297 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/ReadVariableOp" -> "6298 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/add_1"; +"6298 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/add_1" -> "6300 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6299 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6300 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6300 maskrcnn/tf.math.truediv_35/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6333 maskrcnn/tf.__operators__.add_73/AddV2"; +"6301 maskrcnn/tf.__operators__.getitem_81/strided_slice/stack" -> "6304 maskrcnn/tf.__operators__.getitem_81/strided_slice"; +"6302 maskrcnn/tf.__operators__.getitem_81/strided_slice/stack_1" -> "6304 maskrcnn/tf.__operators__.getitem_81/strided_slice"; +"6303 maskrcnn/tf.__operators__.getitem_81/strided_slice/stack_2" -> "6304 maskrcnn/tf.__operators__.getitem_81/strided_slice"; +"6304 maskrcnn/tf.__operators__.getitem_81/strided_slice" -> "6332 maskrcnn/tf.__operators__.add_71/AddV2"; +"6305 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6306 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6306 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6307 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/Abs"; +"6307 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/Abs" -> "6309 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/add"; +"6308 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/add/y" -> "6309 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/add"; +"6309 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/add" -> "6312 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/add_1"; +"6310 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6311 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/ReadVariableOp"; +"6310 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6313 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6311 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/ReadVariableOp" -> "6312 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/add_1"; +"6312 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/add_1" -> "6314 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6313 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6314 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6314 maskrcnn/tf.math.truediv_33/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6332 maskrcnn/tf.__operators__.add_71/AddV2"; +"6315 maskrcnn/tf.__operators__.getitem_77/strided_slice/stack" -> "6318 maskrcnn/tf.__operators__.getitem_77/strided_slice"; +"6316 maskrcnn/tf.__operators__.getitem_77/strided_slice/stack_1" -> "6318 maskrcnn/tf.__operators__.getitem_77/strided_slice"; +"6317 maskrcnn/tf.__operators__.getitem_77/strided_slice/stack_2" -> "6318 maskrcnn/tf.__operators__.getitem_77/strided_slice"; +"6318 maskrcnn/tf.__operators__.getitem_77/strided_slice" -> "6331 maskrcnn/tf.__operators__.add_69/AddV2"; +"6319 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6320 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6320 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6321 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/Abs"; +"6321 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/Abs" -> "6323 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/add"; +"6322 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/add/y" -> "6323 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/add"; +"6323 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/add" -> "6326 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/add_1"; +"6324 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6325 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/ReadVariableOp"; +"6324 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6327 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6325 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/ReadVariableOp" -> "6326 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/add_1"; +"6326 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/add_1" -> "6328 maskrcnn/tf.math.truediv_31/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6327 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maskrcnn/tf.math.subtract_67/fake_quantize_I1/AsymmQuant/add_1" -> "6443 maskrcnn/tf.math.subtract_67/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars"; +"6442 maskrcnn/tf.math.subtract_67/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6443 maskrcnn/tf.math.subtract_67/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars"; +"6443 maskrcnn/tf.math.subtract_67/fake_quantize_I1/AsymmQuant/FakeQuantWithMinMaxVars" -> "6452 maskrcnn/tf.math.subtract_67/Sub"; +"6444 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack" -> "6447 maskrcnn/tf.__operators__.getitem_134/strided_slice"; +"6445 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack_1" -> "6447 maskrcnn/tf.__operators__.getitem_134/strided_slice"; +"6446 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack_2" -> "6447 maskrcnn/tf.__operators__.getitem_134/strided_slice"; +"6447 maskrcnn/tf.__operators__.getitem_134/strided_slice" -> "6464 maskrcnn/tf.expand_dims_30/ExpandDims"; +"6448 maskrcnn/tf.__operators__.add_97/y" -> "6449 maskrcnn/tf.__operators__.add_97/AddV2"; +"6449 maskrcnn/tf.__operators__.add_97/AddV2" -> "6462 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6450 maskrcnn/tf.math.minimum_27/Minimum" -> "6465 maskrcnn/tf.stack_20/stack"; +"6451 maskrcnn/tf.math.subtract_68/Sub" -> "6475 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6452 maskrcnn/tf.math.subtract_67/Sub" -> "6485 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6453 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6454 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6454 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6455 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/Abs"; +"6455 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/Abs" -> "6457 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/add"; +"6456 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/add/y" -> "6457 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/add"; +"6457 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/add" -> "6460 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/add_1"; +"6458 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6459 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/ReadVariableOp"; +"6458 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6461 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6459 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/ReadVariableOp" -> "6460 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/add_1"; +"6460 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/add_1" -> "6462 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6461 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6462 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6462 maskrcnn/tf.__operators__.add_97/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6486 maskrcnn/tf.math.minimum_25/Minimum"; +"6463 maskrcnn/tf.expand_dims_30/ExpandDims/dim" -> "6464 maskrcnn/tf.expand_dims_30/ExpandDims"; +"6464 maskrcnn/tf.expand_dims_30/ExpandDims" -> "6486 maskrcnn/tf.math.minimum_25/Minimum"; +"6465 maskrcnn/tf.stack_20/stack" -> "6496 maskrcnn/tf.reshape_52/Reshape"; +"6466 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6467 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6467 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6468 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/Abs"; +"6468 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/Abs" -> "6470 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/add"; +"6469 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/add/y" -> "6470 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/add"; +"6470 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/add" -> "6473 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/add_1"; +"6471 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6472 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/ReadVariableOp"; +"6471 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6474 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6472 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/ReadVariableOp" -> "6473 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/add_1"; +"6473 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/add_1" -> "6475 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6474 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6475 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6475 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6498 maskrcnn/tf.math.subtract_70/Sub"; +"6475 maskrcnn/tf.math.subtract_68/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6536 maskrcnn/tf.stack_22/stack"; +"6476 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6477 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6477 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6478 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/Abs"; +"6478 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/Abs" -> "6480 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/add"; +"6479 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/add/y" -> "6480 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/add"; +"6480 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/add" -> "6483 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/add_1"; +"6481 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6482 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/ReadVariableOp"; +"6481 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6484 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6482 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/ReadVariableOp" -> "6483 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/add_1"; +"6483 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/add_1" -> "6485 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6484 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6485 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6485 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6500 maskrcnn/tf.math.subtract_69/Sub"; +"6485 maskrcnn/tf.math.subtract_67/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6537 maskrcnn/tf.stack_21/stack"; +"6486 maskrcnn/tf.math.minimum_25/Minimum" -> "6501 maskrcnn/tf.stack_19/stack"; +"6492 maskrcnn/tf.compat.v1.gather_4/GatherV2/params" -> "6494 maskrcnn/tf.compat.v1.gather_4/GatherV2"; +"6493 maskrcnn/tf.compat.v1.gather_4/GatherV2/axis" -> "6494 maskrcnn/tf.compat.v1.gather_4/GatherV2"; +"6494 maskrcnn/tf.compat.v1.gather_4/GatherV2" -> "6504 maskrcnn/tf.expand_dims_33/ExpandDims"; +"6495 maskrcnn/tf.reshape_52/Reshape/shape" -> "6496 maskrcnn/tf.reshape_52/Reshape"; +"6496 maskrcnn/tf.reshape_52/Reshape" -> "6502 maskrcnn/tf.cast_40/Cast"; +"6498 maskrcnn/tf.math.subtract_70/Sub" -> "6514 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6500 maskrcnn/tf.math.subtract_69/Sub" -> "6524 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6501 maskrcnn/tf.stack_19/stack" -> "6526 maskrcnn/tf.reshape_51/Reshape"; +"6502 maskrcnn/tf.cast_40/Cast" -> "6527 maskrcnn/tf.math.multiply_89/Mul"; +"6503 maskrcnn/tf.expand_dims_33/ExpandDims/dim" -> "6504 maskrcnn/tf.expand_dims_33/ExpandDims"; +"6504 maskrcnn/tf.expand_dims_33/ExpandDims" -> "6527 maskrcnn/tf.math.multiply_89/Mul"; +"6505 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6506 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6506 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6507 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/Abs"; +"6507 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/Abs" -> "6509 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/add"; +"6508 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/add/y" -> "6509 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/add"; +"6509 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/add" -> "6512 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/add_1"; +"6510 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6511 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/ReadVariableOp"; +"6510 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6513 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6511 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/ReadVariableOp" -> "6512 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/add_1"; +"6512 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/add_1" -> "6514 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6513 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6514 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6514 maskrcnn/tf.math.subtract_70/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6536 maskrcnn/tf.stack_22/stack"; +"6515 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6516 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6516 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6517 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/Abs"; +"6517 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/Abs" -> "6519 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/add"; +"6518 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/add/y" -> "6519 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/add"; +"6519 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/add" -> "6522 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/add_1"; +"6520 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6521 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/ReadVariableOp"; +"6520 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6523 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6521 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/ReadVariableOp" -> "6522 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/add_1"; +"6522 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/add_1" -> "6524 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6523 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6524 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6524 maskrcnn/tf.math.subtract_69/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6537 maskrcnn/tf.stack_21/stack"; +"6525 maskrcnn/tf.reshape_51/Reshape/shape" -> "6526 maskrcnn/tf.reshape_51/Reshape"; +"6526 maskrcnn/tf.reshape_51/Reshape" -> "6538 maskrcnn/tf.cast_39/Cast"; +"6527 maskrcnn/tf.math.multiply_89/Mul" -> "6540 maskrcnn/tf.reshape_54/Reshape"; +"6533 maskrcnn/tf.compat.v1.gather_3/GatherV2/params" -> "6535 maskrcnn/tf.compat.v1.gather_3/GatherV2"; +"6534 maskrcnn/tf.compat.v1.gather_3/GatherV2/axis" -> "6535 maskrcnn/tf.compat.v1.gather_3/GatherV2"; +"6535 maskrcnn/tf.compat.v1.gather_3/GatherV2" -> "6542 maskrcnn/tf.reshape_53/Reshape"; +"6536 maskrcnn/tf.stack_22/stack" -> "6544 maskrcnn/tf.reshape_50/Reshape"; +"6537 maskrcnn/tf.stack_21/stack" -> "6546 maskrcnn/tf.reshape_49/Reshape"; +"6538 maskrcnn/tf.cast_39/Cast" -> "6548 maskrcnn/tf.reshape_55/Reshape"; +"6539 maskrcnn/tf.reshape_54/Reshape/shape" -> "6540 maskrcnn/tf.reshape_54/Reshape"; +"6540 maskrcnn/tf.reshape_54/Reshape" -> "6552 maskrcnn/tf.tile_5/Tile"; +"6541 maskrcnn/tf.reshape_53/Reshape/shape" -> "6542 maskrcnn/tf.reshape_53/Reshape"; +"6542 maskrcnn/tf.reshape_53/Reshape" -> "6550 maskrcnn/tf.tile_4/Tile"; +"6543 maskrcnn/tf.reshape_50/Reshape/shape" -> "6544 maskrcnn/tf.reshape_50/Reshape"; +"6544 maskrcnn/tf.reshape_50/Reshape" -> "6556 maskrcnn/tf.reshape_59/Reshape"; +"6545 maskrcnn/tf.reshape_49/Reshape/shape" -> "6546 maskrcnn/tf.reshape_49/Reshape"; +"6546 maskrcnn/tf.reshape_49/Reshape" -> "6554 maskrcnn/tf.reshape_58/Reshape"; +"6547 maskrcnn/tf.reshape_55/Reshape/shape" -> "6548 maskrcnn/tf.reshape_55/Reshape"; +"6548 maskrcnn/tf.reshape_55/Reshape" -> "6559 maskrcnn/tf.tile_6/Tile"; +"6549 maskrcnn/tf.tile_4/Tile/multiples" -> "6550 maskrcnn/tf.tile_4/Tile"; +"6550 maskrcnn/tf.tile_4/Tile" -> "6557 maskrcnn/tf.__operators__.add_99/AddV2"; +"6551 maskrcnn/tf.tile_5/Tile/multiples" -> "6552 maskrcnn/tf.tile_5/Tile"; +"6552 maskrcnn/tf.tile_5/Tile" -> "6557 maskrcnn/tf.__operators__.add_99/AddV2"; +"6553 maskrcnn/tf.reshape_58/Reshape/shape" -> "6554 maskrcnn/tf.reshape_58/Reshape"; +"6554 maskrcnn/tf.reshape_58/Reshape" -> "6560 maskrcnn/tf.math.multiply_90/Mul"; +"6555 maskrcnn/tf.reshape_59/Reshape/shape" -> "6556 maskrcnn/tf.reshape_59/Reshape"; +"6556 maskrcnn/tf.reshape_59/Reshape" -> "6560 maskrcnn/tf.math.multiply_90/Mul"; +"6557 maskrcnn/tf.__operators__.add_99/AddV2" -> "6561 maskrcnn/tf.__operators__.add_100/AddV2"; +"6558 maskrcnn/tf.tile_6/Tile/multiples" -> "6559 maskrcnn/tf.tile_6/Tile"; +"6559 maskrcnn/tf.tile_6/Tile" -> "6561 maskrcnn/tf.__operators__.add_100/AddV2"; +"6560 maskrcnn/tf.math.multiply_90/Mul" -> "6573 maskrcnn/tf.math.multiply_91/Mul"; +"6561 maskrcnn/tf.__operators__.add_100/AddV2" -> "6575 maskrcnn/tf.__operators__.add_101/AddV2"; +"6562 maskrcnn/tf.reshape_39/Reshape/shape" -> "6563 maskrcnn/tf.reshape_39/Reshape"; +"6563 maskrcnn/tf.reshape_39/Reshape" -> "6577 maskrcnn/tf.concat_17/concat"; +"6564 maskrcnn/tf.reshape_41/Reshape/shape" -> "6565 maskrcnn/tf.reshape_41/Reshape"; +"6565 maskrcnn/tf.reshape_41/Reshape" -> "6577 maskrcnn/tf.concat_17/concat"; +"6566 maskrcnn/tf.reshape_43/Reshape/shape" -> "6567 maskrcnn/tf.reshape_43/Reshape"; +"6567 maskrcnn/tf.reshape_43/Reshape" -> "6577 maskrcnn/tf.concat_17/concat"; +"6568 maskrcnn/tf.reshape_45/Reshape/shape" -> "6569 maskrcnn/tf.reshape_45/Reshape"; +"6569 maskrcnn/tf.reshape_45/Reshape" -> "6577 maskrcnn/tf.concat_17/concat"; +"6570 maskrcnn/tf.reshape_47/Reshape/shape" -> "6571 maskrcnn/tf.reshape_47/Reshape"; +"6571 maskrcnn/tf.reshape_47/Reshape" -> "6577 maskrcnn/tf.concat_17/concat"; +"6572 maskrcnn/tf.math.multiply_91/Mul/y" -> "6573 maskrcnn/tf.math.multiply_91/Mul"; +"6573 maskrcnn/tf.math.multiply_91/Mul" -> "6587 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6574 maskrcnn/tf.__operators__.add_101/y" -> "6575 maskrcnn/tf.__operators__.add_101/AddV2"; +"6575 maskrcnn/tf.__operators__.add_101/AddV2" -> "6591 maskrcnn/tf.reshape_56/Reshape"; +"6576 maskrcnn/tf.concat_17/concat/axis" -> "6577 maskrcnn/tf.concat_17/concat"; +"6577 maskrcnn/tf.concat_17/concat" -> "6589 maskrcnn/tf.reshape_48/Reshape"; +"6578 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6579 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6579 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6580 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/Abs"; +"6580 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/Abs" -> "6582 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/add"; +"6581 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/add/y" -> "6582 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/add"; +"6582 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/add" -> "6585 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/add_1"; +"6583 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6584 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/ReadVariableOp"; +"6583 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6586 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6584 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/ReadVariableOp" -> "6585 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/add_1"; +"6585 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/add_1" -> "6587 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6586 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6587 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6587 maskrcnn/tf.math.multiply_91/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6593 maskrcnn/tf.expand_dims_34/ExpandDims"; +"6588 maskrcnn/tf.reshape_48/Reshape/shape" -> "6589 maskrcnn/tf.reshape_48/Reshape"; +"6589 maskrcnn/tf.reshape_48/Reshape" -> "6595 maskrcnn/tf.compat.v1.gather_5/GatherV2"; +"6590 maskrcnn/tf.reshape_56/Reshape/shape" -> "6591 maskrcnn/tf.reshape_56/Reshape"; +"6591 maskrcnn/tf.reshape_56/Reshape" -> "6595 maskrcnn/tf.compat.v1.gather_5/GatherV2"; +"6592 maskrcnn/tf.expand_dims_34/ExpandDims/dim" -> "6593 maskrcnn/tf.expand_dims_34/ExpandDims"; +"6593 maskrcnn/tf.expand_dims_34/ExpandDims" -> "6598 maskrcnn/tf.math.multiply_92/Mul"; +"6594 maskrcnn/tf.compat.v1.gather_5/GatherV2/axis" -> "6595 maskrcnn/tf.compat.v1.gather_5/GatherV2"; +"6595 maskrcnn/tf.compat.v1.gather_5/GatherV2" -> "6597 maskrcnn/tf.reshape_57/Reshape"; +"6596 maskrcnn/tf.reshape_57/Reshape/shape" -> "6597 maskrcnn/tf.reshape_57/Reshape"; +"6597 maskrcnn/tf.reshape_57/Reshape" -> "6598 maskrcnn/tf.math.multiply_92/Mul"; +"6598 maskrcnn/tf.math.multiply_92/Mul" -> "6608 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6599 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6600 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6600 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6601 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/Abs"; +"6601 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/Abs" -> "6603 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/add"; +"6602 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/add/y" -> "6603 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/add"; +"6603 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/add" -> "6606 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/add_1"; +"6604 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6605 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/ReadVariableOp"; +"6604 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6607 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6605 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/ReadVariableOp" -> "6606 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/add_1"; +"6606 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/add_1" -> "6608 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6607 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6608 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6608 maskrcnn/tf.math.multiply_92/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6610 maskrcnn/tf.reshape_60/Reshape"; +"6609 maskrcnn/tf.reshape_60/Reshape/shape" -> "6610 maskrcnn/tf.reshape_60/Reshape"; +"6610 maskrcnn/tf.reshape_60/Reshape" -> "6611 maskrcnn/tf.nn.avg_pool_1/AvgPool"; +"6611 maskrcnn/tf.nn.avg_pool_1/AvgPool" -> "6621 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6612 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6613 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6613 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6614 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/Abs"; +"6614 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/Abs" -> "6616 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/add"; +"6615 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/add/y" -> "6616 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/add"; +"6616 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/add" -> "6619 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/add_1"; +"6617 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6618 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/ReadVariableOp"; +"6617 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6620 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6618 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/ReadVariableOp" -> "6619 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/add_1"; +"6619 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/add_1" -> "6621 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6620 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6621 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6621 maskrcnn/tf.nn.avg_pool_1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6623 maskrcnn/tf.reshape_61/Reshape"; +"6622 maskrcnn/tf.reshape_61/Reshape/shape" -> "6623 maskrcnn/tf.reshape_61/Reshape"; +"6623 maskrcnn/tf.reshape_61/Reshape" -> "6625 maskrcnn/tf.reshape_62/Reshape"; +"6624 maskrcnn/tf.reshape_62/Reshape/shape" -> "6625 maskrcnn/tf.reshape_62/Reshape"; +"6625 maskrcnn/tf.reshape_62/Reshape" -> "6637 maskrcnn/mask-conv-l0/Conv2D"; +"6626 maskrcnn/mask-conv-l0/SymmQuant/Abs/ReadVariableOp/resource" -> "6627 maskrcnn/mask-conv-l0/SymmQuant/Abs/ReadVariableOp"; +"6627 maskrcnn/mask-conv-l0/SymmQuant/Abs/ReadVariableOp" -> "6628 maskrcnn/mask-conv-l0/SymmQuant/Abs"; +"6628 maskrcnn/mask-conv-l0/SymmQuant/Abs" -> "6630 maskrcnn/mask-conv-l0/SymmQuant/add"; +"6629 maskrcnn/mask-conv-l0/SymmQuant/add/y" -> "6630 maskrcnn/mask-conv-l0/SymmQuant/add"; +"6630 maskrcnn/mask-conv-l0/SymmQuant/add" -> "6633 maskrcnn/mask-conv-l0/SymmQuant/mul"; +"6630 maskrcnn/mask-conv-l0/SymmQuant/add" -> "6636 maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6631 maskrcnn/mask-conv-l0/SymmQuant/mul/ReadVariableOp/resource" -> "6632 maskrcnn/mask-conv-l0/SymmQuant/mul/ReadVariableOp"; +"6632 maskrcnn/mask-conv-l0/SymmQuant/mul/ReadVariableOp" -> "6633 maskrcnn/mask-conv-l0/SymmQuant/mul"; +"6633 maskrcnn/mask-conv-l0/SymmQuant/mul" -> "6636 maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6634 maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "6635 maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"6635 maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "6636 maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6636 maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "6637 maskrcnn/mask-conv-l0/Conv2D"; +"6637 maskrcnn/mask-conv-l0/Conv2D" -> "6640 maskrcnn/mask-conv-l0/BiasAdd"; +"6638 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp/resource" -> "6639 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp"; +"6639 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp" -> "6640 maskrcnn/mask-conv-l0/BiasAdd"; +"6640 maskrcnn/mask-conv-l0/BiasAdd" -> "6641 maskrcnn/mask-conv-l0/Relu"; +"6641 maskrcnn/mask-conv-l0/Relu" -> "6651 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6642 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6643 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6643 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6644 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/Abs"; +"6644 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/Abs" -> "6646 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add"; +"6645 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add/y" -> "6646 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add"; +"6646 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add" -> "6649 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add_1"; +"6647 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6648 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/ReadVariableOp"; +"6647 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6650 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6648 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/ReadVariableOp" -> "6649 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add_1"; +"6649 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/add_1" -> "6651 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6650 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6651 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6651 maskrcnn/mask-conv-l0/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6663 maskrcnn/mask-conv-l1/Conv2D"; +"6652 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp/resource" -> "6653 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp"; +"6653 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp" -> "6654 maskrcnn/mask-conv-l1/SymmQuant/Abs"; +"6654 maskrcnn/mask-conv-l1/SymmQuant/Abs" -> "6656 maskrcnn/mask-conv-l1/SymmQuant/add"; +"6655 maskrcnn/mask-conv-l1/SymmQuant/add/y" -> "6656 maskrcnn/mask-conv-l1/SymmQuant/add"; +"6656 maskrcnn/mask-conv-l1/SymmQuant/add" -> "6659 maskrcnn/mask-conv-l1/SymmQuant/mul"; +"6656 maskrcnn/mask-conv-l1/SymmQuant/add" -> "6662 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6657 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp/resource" -> "6658 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp"; +"6658 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp" -> "6659 maskrcnn/mask-conv-l1/SymmQuant/mul"; +"6659 maskrcnn/mask-conv-l1/SymmQuant/mul" -> "6662 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6660 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "6661 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"6661 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "6662 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6662 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "6663 maskrcnn/mask-conv-l1/Conv2D"; +"6663 maskrcnn/mask-conv-l1/Conv2D" -> "6666 maskrcnn/mask-conv-l1/BiasAdd"; +"6664 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp/resource" -> "6665 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp"; +"6665 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp" -> "6666 maskrcnn/mask-conv-l1/BiasAdd"; +"6666 maskrcnn/mask-conv-l1/BiasAdd" -> "6667 maskrcnn/mask-conv-l1/Relu"; +"6667 maskrcnn/mask-conv-l1/Relu" -> "6677 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6668 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6669 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6669 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6670 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs"; +"6670 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/Abs" -> "6672 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add"; +"6671 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add/y" -> "6672 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add"; +"6672 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add" -> "6675 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add_1"; +"6673 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6674 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/ReadVariableOp"; +"6673 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6676 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6674 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/ReadVariableOp" -> "6675 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add_1"; +"6675 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/add_1" -> "6677 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6676 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6677 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6677 maskrcnn/mask-conv-l1/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6689 maskrcnn/mask-conv-l2/Conv2D"; +"6678 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp/resource" -> "6679 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp"; +"6679 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp" -> "6680 maskrcnn/mask-conv-l2/SymmQuant/Abs"; +"6680 maskrcnn/mask-conv-l2/SymmQuant/Abs" -> "6682 maskrcnn/mask-conv-l2/SymmQuant/add"; +"6681 maskrcnn/mask-conv-l2/SymmQuant/add/y" -> "6682 maskrcnn/mask-conv-l2/SymmQuant/add"; +"6682 maskrcnn/mask-conv-l2/SymmQuant/add" -> "6685 maskrcnn/mask-conv-l2/SymmQuant/mul"; +"6682 maskrcnn/mask-conv-l2/SymmQuant/add" -> "6688 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6683 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp/resource" -> "6684 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp"; +"6684 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp" -> "6685 maskrcnn/mask-conv-l2/SymmQuant/mul"; +"6685 maskrcnn/mask-conv-l2/SymmQuant/mul" -> "6688 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6686 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "6687 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"6687 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "6688 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6688 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "6689 maskrcnn/mask-conv-l2/Conv2D"; +"6689 maskrcnn/mask-conv-l2/Conv2D" -> "6692 maskrcnn/mask-conv-l2/BiasAdd"; +"6690 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp/resource" -> "6691 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp"; +"6691 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp" -> "6692 maskrcnn/mask-conv-l2/BiasAdd"; +"6692 maskrcnn/mask-conv-l2/BiasAdd" -> "6693 maskrcnn/mask-conv-l2/Relu"; +"6693 maskrcnn/mask-conv-l2/Relu" -> "6703 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6694 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6695 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6695 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6696 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs"; +"6696 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/Abs" -> "6698 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add"; +"6697 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add/y" -> "6698 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add"; +"6698 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add" -> "6701 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add_1"; +"6699 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6700 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/ReadVariableOp"; +"6699 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6702 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6700 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/ReadVariableOp" -> "6701 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add_1"; +"6701 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/add_1" -> "6703 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6702 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6703 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6703 maskrcnn/mask-conv-l2/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6715 maskrcnn/mask-conv-l3/Conv2D"; +"6704 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp/resource" -> "6705 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp"; +"6705 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp" -> "6706 maskrcnn/mask-conv-l3/SymmQuant/Abs"; +"6706 maskrcnn/mask-conv-l3/SymmQuant/Abs" -> "6708 maskrcnn/mask-conv-l3/SymmQuant/add"; +"6707 maskrcnn/mask-conv-l3/SymmQuant/add/y" -> "6708 maskrcnn/mask-conv-l3/SymmQuant/add"; +"6708 maskrcnn/mask-conv-l3/SymmQuant/add" -> "6711 maskrcnn/mask-conv-l3/SymmQuant/mul"; +"6708 maskrcnn/mask-conv-l3/SymmQuant/add" -> "6714 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6709 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp/resource" -> "6710 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp"; +"6710 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp" -> "6711 maskrcnn/mask-conv-l3/SymmQuant/mul"; +"6711 maskrcnn/mask-conv-l3/SymmQuant/mul" -> "6714 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6712 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "6713 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"6713 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "6714 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6714 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "6715 maskrcnn/mask-conv-l3/Conv2D"; +"6715 maskrcnn/mask-conv-l3/Conv2D" -> "6718 maskrcnn/mask-conv-l3/BiasAdd"; +"6716 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp/resource" -> "6717 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp"; +"6717 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp" -> "6718 maskrcnn/mask-conv-l3/BiasAdd"; +"6718 maskrcnn/mask-conv-l3/BiasAdd" -> "6719 maskrcnn/mask-conv-l3/Relu"; +"6719 maskrcnn/mask-conv-l3/Relu" -> "6729 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6720 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6721 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6721 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6722 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs"; +"6722 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/Abs" -> "6724 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add"; +"6723 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add/y" -> "6724 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add"; +"6724 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add" -> "6727 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add_1"; +"6725 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6726 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/ReadVariableOp"; +"6725 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6728 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6726 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/ReadVariableOp" -> "6727 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add_1"; +"6727 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/add_1" -> "6729 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6728 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6729 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6729 maskrcnn/mask-conv-l3/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6758 maskrcnn/conv5-mask/conv2d_transpose"; +"6730 maskrcnn/conv5-mask/transpose/ReadVariableOp/resource" -> "6731 maskrcnn/conv5-mask/transpose/ReadVariableOp"; +"6731 maskrcnn/conv5-mask/transpose/ReadVariableOp" -> "6733 maskrcnn/conv5-mask/transpose"; +"6732 maskrcnn/conv5-mask/transpose/perm" -> "6733 maskrcnn/conv5-mask/transpose"; +"6733 maskrcnn/conv5-mask/transpose" -> "6742 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6734 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp/resource" -> "6735 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp"; +"6735 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp" -> "6736 maskrcnn/conv5-mask/SymmQuant/Abs"; +"6736 maskrcnn/conv5-mask/SymmQuant/Abs" -> "6738 maskrcnn/conv5-mask/SymmQuant/add"; +"6737 maskrcnn/conv5-mask/SymmQuant/add/y" -> "6738 maskrcnn/conv5-mask/SymmQuant/add"; +"6738 maskrcnn/conv5-mask/SymmQuant/add" -> "6741 maskrcnn/conv5-mask/SymmQuant/mul"; +"6738 maskrcnn/conv5-mask/SymmQuant/add" -> "6742 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6739 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp/resource" -> "6740 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp"; +"6740 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp" -> "6741 maskrcnn/conv5-mask/SymmQuant/mul"; +"6741 maskrcnn/conv5-mask/SymmQuant/mul" -> "6742 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6742 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "6744 maskrcnn/conv5-mask/transpose_1"; +"6743 maskrcnn/conv5-mask/transpose_1/perm" -> "6744 maskrcnn/conv5-mask/transpose_1"; +"6744 maskrcnn/conv5-mask/transpose_1" -> "6758 maskrcnn/conv5-mask/conv2d_transpose"; +"6745 maskrcnn/conv5-mask/Shape" -> "6749 maskrcnn/conv5-mask/strided_slice"; +"6746 maskrcnn/conv5-mask/strided_slice/stack" -> "6749 maskrcnn/conv5-mask/strided_slice"; +"6747 maskrcnn/conv5-mask/strided_slice/stack_1" -> "6749 maskrcnn/conv5-mask/strided_slice"; +"6748 maskrcnn/conv5-mask/strided_slice/stack_2" -> "6749 maskrcnn/conv5-mask/strided_slice"; +"6749 maskrcnn/conv5-mask/strided_slice" -> "6753 maskrcnn/conv5-mask/stack"; +"6750 maskrcnn/conv5-mask/stack/1" -> "6753 maskrcnn/conv5-mask/stack"; +"6751 maskrcnn/conv5-mask/stack/2" -> "6753 maskrcnn/conv5-mask/stack"; +"6752 maskrcnn/conv5-mask/stack/3" -> "6753 maskrcnn/conv5-mask/stack"; +"6753 maskrcnn/conv5-mask/stack" -> "6757 maskrcnn/conv5-mask/strided_slice_1"; +"6753 maskrcnn/conv5-mask/stack" -> "6758 maskrcnn/conv5-mask/conv2d_transpose"; +"6754 maskrcnn/conv5-mask/strided_slice_1/stack" -> "6757 maskrcnn/conv5-mask/strided_slice_1"; +"6755 maskrcnn/conv5-mask/strided_slice_1/stack_1" -> "6757 maskrcnn/conv5-mask/strided_slice_1"; +"6756 maskrcnn/conv5-mask/strided_slice_1/stack_2" -> "6757 maskrcnn/conv5-mask/strided_slice_1"; +"6758 maskrcnn/conv5-mask/conv2d_transpose" -> "6761 maskrcnn/conv5-mask/BiasAdd"; +"6759 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp/resource" -> "6760 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp"; +"6760 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp" -> "6761 maskrcnn/conv5-mask/BiasAdd"; +"6761 maskrcnn/conv5-mask/BiasAdd" -> "6762 maskrcnn/conv5-mask/Relu"; +"6762 maskrcnn/conv5-mask/Relu" -> "6772 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6763 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs/ReadVariableOp/resource" -> "6764 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs/ReadVariableOp"; +"6764 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs/ReadVariableOp" -> "6765 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs"; +"6765 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/Abs" -> "6767 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add"; +"6766 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add/y" -> "6767 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add"; +"6767 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add" -> "6770 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add_1"; +"6768 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6769 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/ReadVariableOp"; +"6768 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/ReadVariableOp/resource" -> "6771 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6769 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/ReadVariableOp" -> "6770 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add_1"; +"6770 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/add_1" -> "6772 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6771 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6772 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars"; +"6772 maskrcnn/conv5-mask/fake_quantize/AsymmQuant/FakeQuantWithMinMaxVars" -> "6787 maskrcnn/mask_fcn_logits/Conv2D"; +"6773 maskrcnn/tf.cast_32/Cast" -> "6775 maskrcnn/tf.__operators__.add_67/AddV2"; +"6774 maskrcnn/tf.__operators__.add_67/y" -> "6775 maskrcnn/tf.__operators__.add_67/AddV2"; +"6775 maskrcnn/tf.__operators__.add_67/AddV2" -> "19790 maskrcnn/tf.stack_23/stack_1"; +"6775 maskrcnn/tf.__operators__.add_67/AddV2" -> "19796 Identity_3"; +"6776 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp/resource" -> "6777 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp"; +"6777 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp" -> "6778 maskrcnn/mask_fcn_logits/SymmQuant/Abs"; +"6778 maskrcnn/mask_fcn_logits/SymmQuant/Abs" -> "6780 maskrcnn/mask_fcn_logits/SymmQuant/add"; +"6779 maskrcnn/mask_fcn_logits/SymmQuant/add/y" -> "6780 maskrcnn/mask_fcn_logits/SymmQuant/add"; +"6780 maskrcnn/mask_fcn_logits/SymmQuant/add" -> "6783 maskrcnn/mask_fcn_logits/SymmQuant/mul"; +"6780 maskrcnn/mask_fcn_logits/SymmQuant/add" -> "6786 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6781 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp/resource" -> "6782 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp"; +"6782 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp" -> "6783 maskrcnn/mask_fcn_logits/SymmQuant/mul"; +"6783 maskrcnn/mask_fcn_logits/SymmQuant/mul" -> "6786 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6784 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp/resource" -> "6785 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp"; +"6785 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel/ReadVariableOp" -> "6786 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel"; +"6786 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVarsPerChannel" -> "6787 maskrcnn/mask_fcn_logits/Conv2D"; +"6787 maskrcnn/mask_fcn_logits/Conv2D" -> "6790 maskrcnn/mask_fcn_logits/BiasAdd"; +"6788 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp/resource" -> "6789 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp"; +"6789 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp" -> "6790 maskrcnn/mask_fcn_logits/BiasAdd"; +"6790 maskrcnn/mask_fcn_logits/BiasAdd" -> "6792 maskrcnn/tf.reshape_63/Reshape"; +"6791 maskrcnn/tf.reshape_63/Reshape/shape" -> "6792 maskrcnn/tf.reshape_63/Reshape"; +"6792 maskrcnn/tf.reshape_63/Reshape" -> "6794 maskrcnn/tf.compat.v1.transpose/transpose"; +"6793 maskrcnn/tf.compat.v1.transpose/transpose/perm" -> "6794 maskrcnn/tf.compat.v1.transpose/transpose"; +"6794 maskrcnn/tf.compat.v1.transpose/transpose" -> "19791 maskrcnn/tf.compat.v1.gather_nd_1/GatherNd"; +"19595 maskrcnn/tf.stack_23/stack/0" -> "19659 maskrcnn/tf.stack_23/stack"; +"19596 maskrcnn/tf.stack_23/stack/1" -> "19659 maskrcnn/tf.stack_23/stack"; +"19597 maskrcnn/tf.stack_23/stack/2" -> "19659 maskrcnn/tf.stack_23/stack"; +"19598 maskrcnn/tf.stack_23/stack/3" -> "19659 maskrcnn/tf.stack_23/stack"; +"19599 maskrcnn/tf.stack_23/stack/4" -> "19659 maskrcnn/tf.stack_23/stack"; +"19600 maskrcnn/tf.stack_23/stack/5" -> "19659 maskrcnn/tf.stack_23/stack"; +"19601 maskrcnn/tf.stack_23/stack/6" -> "19659 maskrcnn/tf.stack_23/stack"; +"19602 maskrcnn/tf.stack_23/stack/7" -> "19659 maskrcnn/tf.stack_23/stack"; +"19603 maskrcnn/tf.stack_23/stack/8" -> "19659 maskrcnn/tf.stack_23/stack"; +"19604 maskrcnn/tf.stack_23/stack/9" -> "19659 maskrcnn/tf.stack_23/stack"; +"19605 maskrcnn/tf.stack_23/stack/10" -> "19659 maskrcnn/tf.stack_23/stack"; +"19606 maskrcnn/tf.stack_23/stack/11" -> "19659 maskrcnn/tf.stack_23/stack"; +"19607 maskrcnn/tf.stack_23/stack/12" -> "19659 maskrcnn/tf.stack_23/stack"; +"19608 maskrcnn/tf.stack_23/stack/13" -> "19659 maskrcnn/tf.stack_23/stack"; +"19609 maskrcnn/tf.stack_23/stack/14" -> "19659 maskrcnn/tf.stack_23/stack"; +"19610 maskrcnn/tf.stack_23/stack/15" -> "19659 maskrcnn/tf.stack_23/stack"; +"19611 maskrcnn/tf.stack_23/stack/16" -> "19659 maskrcnn/tf.stack_23/stack"; +"19612 maskrcnn/tf.stack_23/stack/17" -> "19659 maskrcnn/tf.stack_23/stack"; +"19613 maskrcnn/tf.stack_23/stack/18" -> "19659 maskrcnn/tf.stack_23/stack"; +"19614 maskrcnn/tf.stack_23/stack/19" -> "19659 maskrcnn/tf.stack_23/stack"; +"19615 maskrcnn/tf.stack_23/stack/20" -> "19659 maskrcnn/tf.stack_23/stack"; +"19616 maskrcnn/tf.stack_23/stack/21" -> "19659 maskrcnn/tf.stack_23/stack"; +"19617 maskrcnn/tf.stack_23/stack/22" -> "19659 maskrcnn/tf.stack_23/stack"; +"19618 maskrcnn/tf.stack_23/stack/23" -> "19659 maskrcnn/tf.stack_23/stack"; +"19619 maskrcnn/tf.stack_23/stack/24" -> "19659 maskrcnn/tf.stack_23/stack"; +"19620 maskrcnn/tf.stack_23/stack/25" -> "19659 maskrcnn/tf.stack_23/stack"; +"19621 maskrcnn/tf.stack_23/stack/26" -> "19659 maskrcnn/tf.stack_23/stack"; +"19622 maskrcnn/tf.stack_23/stack/27" -> "19659 maskrcnn/tf.stack_23/stack"; +"19623 maskrcnn/tf.stack_23/stack/28" -> "19659 maskrcnn/tf.stack_23/stack"; +"19624 maskrcnn/tf.stack_23/stack/29" -> "19659 maskrcnn/tf.stack_23/stack"; +"19625 maskrcnn/tf.stack_23/stack/30" -> "19659 maskrcnn/tf.stack_23/stack"; +"19626 maskrcnn/tf.stack_23/stack/31" -> "19659 maskrcnn/tf.stack_23/stack"; +"19627 maskrcnn/tf.stack_23/stack/32" -> "19659 maskrcnn/tf.stack_23/stack"; +"19628 maskrcnn/tf.stack_23/stack/33" -> "19659 maskrcnn/tf.stack_23/stack"; +"19629 maskrcnn/tf.stack_23/stack/34" -> "19659 maskrcnn/tf.stack_23/stack"; +"19630 maskrcnn/tf.stack_23/stack/35" -> "19659 maskrcnn/tf.stack_23/stack"; +"19631 maskrcnn/tf.stack_23/stack/36" -> "19659 maskrcnn/tf.stack_23/stack"; +"19632 maskrcnn/tf.stack_23/stack/37" -> "19659 maskrcnn/tf.stack_23/stack"; +"19633 maskrcnn/tf.stack_23/stack/38" -> "19659 maskrcnn/tf.stack_23/stack"; +"19634 maskrcnn/tf.stack_23/stack/39" -> "19659 maskrcnn/tf.stack_23/stack"; +"19635 maskrcnn/tf.stack_23/stack/40" -> "19659 maskrcnn/tf.stack_23/stack"; +"19636 maskrcnn/tf.stack_23/stack/41" -> "19659 maskrcnn/tf.stack_23/stack"; +"19637 maskrcnn/tf.stack_23/stack/42" -> "19659 maskrcnn/tf.stack_23/stack"; +"19638 maskrcnn/tf.stack_23/stack/43" -> "19659 maskrcnn/tf.stack_23/stack"; +"19639 maskrcnn/tf.stack_23/stack/44" -> "19659 maskrcnn/tf.stack_23/stack"; +"19640 maskrcnn/tf.stack_23/stack/45" -> "19659 maskrcnn/tf.stack_23/stack"; +"19641 maskrcnn/tf.stack_23/stack/46" -> "19659 maskrcnn/tf.stack_23/stack"; +"19642 maskrcnn/tf.stack_23/stack/47" -> "19659 maskrcnn/tf.stack_23/stack"; +"19643 maskrcnn/tf.stack_23/stack/48" -> "19659 maskrcnn/tf.stack_23/stack"; +"19644 maskrcnn/tf.stack_23/stack/49" -> "19659 maskrcnn/tf.stack_23/stack"; +"19645 maskrcnn/tf.stack_23/stack/50" -> "19659 maskrcnn/tf.stack_23/stack"; +"19646 maskrcnn/tf.stack_23/stack/51" -> "19659 maskrcnn/tf.stack_23/stack"; +"19647 maskrcnn/tf.stack_23/stack/52" -> "19659 maskrcnn/tf.stack_23/stack"; +"19648 maskrcnn/tf.stack_23/stack/53" -> "19659 maskrcnn/tf.stack_23/stack"; +"19649 maskrcnn/tf.stack_23/stack/54" -> "19659 maskrcnn/tf.stack_23/stack"; +"19650 maskrcnn/tf.stack_23/stack/55" -> "19659 maskrcnn/tf.stack_23/stack"; +"19651 maskrcnn/tf.stack_23/stack/56" -> "19659 maskrcnn/tf.stack_23/stack"; +"19652 maskrcnn/tf.stack_23/stack/57" -> "19659 maskrcnn/tf.stack_23/stack"; +"19653 maskrcnn/tf.stack_23/stack/58" -> "19659 maskrcnn/tf.stack_23/stack"; +"19654 maskrcnn/tf.stack_23/stack/59" -> "19659 maskrcnn/tf.stack_23/stack"; +"19655 maskrcnn/tf.stack_23/stack/60" -> "19659 maskrcnn/tf.stack_23/stack"; +"19656 maskrcnn/tf.stack_23/stack/61" -> "19659 maskrcnn/tf.stack_23/stack"; +"19657 maskrcnn/tf.stack_23/stack/62" -> "19659 maskrcnn/tf.stack_23/stack"; +"19658 maskrcnn/tf.stack_23/stack/63" -> "19659 maskrcnn/tf.stack_23/stack"; +"19660 maskrcnn/tf.stack_23/stack_1/values_0/0" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19661 maskrcnn/tf.stack_23/stack_1/values_0/1" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19662 maskrcnn/tf.stack_23/stack_1/values_0/2" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19663 maskrcnn/tf.stack_23/stack_1/values_0/3" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19664 maskrcnn/tf.stack_23/stack_1/values_0/4" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19665 maskrcnn/tf.stack_23/stack_1/values_0/5" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19666 maskrcnn/tf.stack_23/stack_1/values_0/6" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19667 maskrcnn/tf.stack_23/stack_1/values_0/7" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19668 maskrcnn/tf.stack_23/stack_1/values_0/8" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19669 maskrcnn/tf.stack_23/stack_1/values_0/9" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19670 maskrcnn/tf.stack_23/stack_1/values_0/10" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19671 maskrcnn/tf.stack_23/stack_1/values_0/11" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19672 maskrcnn/tf.stack_23/stack_1/values_0/12" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19673 maskrcnn/tf.stack_23/stack_1/values_0/13" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19674 maskrcnn/tf.stack_23/stack_1/values_0/14" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19675 maskrcnn/tf.stack_23/stack_1/values_0/15" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19676 maskrcnn/tf.stack_23/stack_1/values_0/16" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19677 maskrcnn/tf.stack_23/stack_1/values_0/17" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19678 maskrcnn/tf.stack_23/stack_1/values_0/18" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19679 maskrcnn/tf.stack_23/stack_1/values_0/19" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19680 maskrcnn/tf.stack_23/stack_1/values_0/20" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19681 maskrcnn/tf.stack_23/stack_1/values_0/21" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19682 maskrcnn/tf.stack_23/stack_1/values_0/22" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19683 maskrcnn/tf.stack_23/stack_1/values_0/23" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19684 maskrcnn/tf.stack_23/stack_1/values_0/24" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19685 maskrcnn/tf.stack_23/stack_1/values_0/25" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19686 maskrcnn/tf.stack_23/stack_1/values_0/26" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19687 maskrcnn/tf.stack_23/stack_1/values_0/27" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19688 maskrcnn/tf.stack_23/stack_1/values_0/28" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19689 maskrcnn/tf.stack_23/stack_1/values_0/29" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19690 maskrcnn/tf.stack_23/stack_1/values_0/30" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19691 maskrcnn/tf.stack_23/stack_1/values_0/31" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19692 maskrcnn/tf.stack_23/stack_1/values_0/32" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19693 maskrcnn/tf.stack_23/stack_1/values_0/33" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19694 maskrcnn/tf.stack_23/stack_1/values_0/34" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19695 maskrcnn/tf.stack_23/stack_1/values_0/35" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19696 maskrcnn/tf.stack_23/stack_1/values_0/36" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19697 maskrcnn/tf.stack_23/stack_1/values_0/37" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19698 maskrcnn/tf.stack_23/stack_1/values_0/38" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19699 maskrcnn/tf.stack_23/stack_1/values_0/39" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19700 maskrcnn/tf.stack_23/stack_1/values_0/40" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19701 maskrcnn/tf.stack_23/stack_1/values_0/41" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19702 maskrcnn/tf.stack_23/stack_1/values_0/42" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19703 maskrcnn/tf.stack_23/stack_1/values_0/43" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19704 maskrcnn/tf.stack_23/stack_1/values_0/44" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19705 maskrcnn/tf.stack_23/stack_1/values_0/45" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19706 maskrcnn/tf.stack_23/stack_1/values_0/46" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19707 maskrcnn/tf.stack_23/stack_1/values_0/47" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19708 maskrcnn/tf.stack_23/stack_1/values_0/48" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19709 maskrcnn/tf.stack_23/stack_1/values_0/49" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19710 maskrcnn/tf.stack_23/stack_1/values_0/50" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19711 maskrcnn/tf.stack_23/stack_1/values_0/51" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19712 maskrcnn/tf.stack_23/stack_1/values_0/52" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19713 maskrcnn/tf.stack_23/stack_1/values_0/53" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19714 maskrcnn/tf.stack_23/stack_1/values_0/54" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19715 maskrcnn/tf.stack_23/stack_1/values_0/55" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19716 maskrcnn/tf.stack_23/stack_1/values_0/56" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19717 maskrcnn/tf.stack_23/stack_1/values_0/57" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19718 maskrcnn/tf.stack_23/stack_1/values_0/58" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19719 maskrcnn/tf.stack_23/stack_1/values_0/59" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19720 maskrcnn/tf.stack_23/stack_1/values_0/60" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19721 maskrcnn/tf.stack_23/stack_1/values_0/61" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19722 maskrcnn/tf.stack_23/stack_1/values_0/62" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19723 maskrcnn/tf.stack_23/stack_1/values_0/63" -> "19724 maskrcnn/tf.stack_23/stack_1/values_0"; +"19724 maskrcnn/tf.stack_23/stack_1/values_0" -> "19790 maskrcnn/tf.stack_23/stack_1"; +"19725 maskrcnn/tf.stack_23/stack_1/values_1/0" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19726 maskrcnn/tf.stack_23/stack_1/values_1/1" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19727 maskrcnn/tf.stack_23/stack_1/values_1/2" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19728 maskrcnn/tf.stack_23/stack_1/values_1/3" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19729 maskrcnn/tf.stack_23/stack_1/values_1/4" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19730 maskrcnn/tf.stack_23/stack_1/values_1/5" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19731 maskrcnn/tf.stack_23/stack_1/values_1/6" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19732 maskrcnn/tf.stack_23/stack_1/values_1/7" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19733 maskrcnn/tf.stack_23/stack_1/values_1/8" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19734 maskrcnn/tf.stack_23/stack_1/values_1/9" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19735 maskrcnn/tf.stack_23/stack_1/values_1/10" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19736 maskrcnn/tf.stack_23/stack_1/values_1/11" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19737 maskrcnn/tf.stack_23/stack_1/values_1/12" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19738 maskrcnn/tf.stack_23/stack_1/values_1/13" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19739 maskrcnn/tf.stack_23/stack_1/values_1/14" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19740 maskrcnn/tf.stack_23/stack_1/values_1/15" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19741 maskrcnn/tf.stack_23/stack_1/values_1/16" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19742 maskrcnn/tf.stack_23/stack_1/values_1/17" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19743 maskrcnn/tf.stack_23/stack_1/values_1/18" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19744 maskrcnn/tf.stack_23/stack_1/values_1/19" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19745 maskrcnn/tf.stack_23/stack_1/values_1/20" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19746 maskrcnn/tf.stack_23/stack_1/values_1/21" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19747 maskrcnn/tf.stack_23/stack_1/values_1/22" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19748 maskrcnn/tf.stack_23/stack_1/values_1/23" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19749 maskrcnn/tf.stack_23/stack_1/values_1/24" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19750 maskrcnn/tf.stack_23/stack_1/values_1/25" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19751 maskrcnn/tf.stack_23/stack_1/values_1/26" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19752 maskrcnn/tf.stack_23/stack_1/values_1/27" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19753 maskrcnn/tf.stack_23/stack_1/values_1/28" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19754 maskrcnn/tf.stack_23/stack_1/values_1/29" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19755 maskrcnn/tf.stack_23/stack_1/values_1/30" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19756 maskrcnn/tf.stack_23/stack_1/values_1/31" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19757 maskrcnn/tf.stack_23/stack_1/values_1/32" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19758 maskrcnn/tf.stack_23/stack_1/values_1/33" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19759 maskrcnn/tf.stack_23/stack_1/values_1/34" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19760 maskrcnn/tf.stack_23/stack_1/values_1/35" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19761 maskrcnn/tf.stack_23/stack_1/values_1/36" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19762 maskrcnn/tf.stack_23/stack_1/values_1/37" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19763 maskrcnn/tf.stack_23/stack_1/values_1/38" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19764 maskrcnn/tf.stack_23/stack_1/values_1/39" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19765 maskrcnn/tf.stack_23/stack_1/values_1/40" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19766 maskrcnn/tf.stack_23/stack_1/values_1/41" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19767 maskrcnn/tf.stack_23/stack_1/values_1/42" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19768 maskrcnn/tf.stack_23/stack_1/values_1/43" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19769 maskrcnn/tf.stack_23/stack_1/values_1/44" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19770 maskrcnn/tf.stack_23/stack_1/values_1/45" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19771 maskrcnn/tf.stack_23/stack_1/values_1/46" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19772 maskrcnn/tf.stack_23/stack_1/values_1/47" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19773 maskrcnn/tf.stack_23/stack_1/values_1/48" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19774 maskrcnn/tf.stack_23/stack_1/values_1/49" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19775 maskrcnn/tf.stack_23/stack_1/values_1/50" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19776 maskrcnn/tf.stack_23/stack_1/values_1/51" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19777 maskrcnn/tf.stack_23/stack_1/values_1/52" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19778 maskrcnn/tf.stack_23/stack_1/values_1/53" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19779 maskrcnn/tf.stack_23/stack_1/values_1/54" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19780 maskrcnn/tf.stack_23/stack_1/values_1/55" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19781 maskrcnn/tf.stack_23/stack_1/values_1/56" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19782 maskrcnn/tf.stack_23/stack_1/values_1/57" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19783 maskrcnn/tf.stack_23/stack_1/values_1/58" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19784 maskrcnn/tf.stack_23/stack_1/values_1/59" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19785 maskrcnn/tf.stack_23/stack_1/values_1/60" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19786 maskrcnn/tf.stack_23/stack_1/values_1/61" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19787 maskrcnn/tf.stack_23/stack_1/values_1/62" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19788 maskrcnn/tf.stack_23/stack_1/values_1/63" -> "19789 maskrcnn/tf.stack_23/stack_1/values_1"; +"19789 maskrcnn/tf.stack_23/stack_1/values_1" -> "19790 maskrcnn/tf.stack_23/stack_1"; +"19790 maskrcnn/tf.stack_23/stack_1" -> "19791 maskrcnn/tf.compat.v1.gather_nd_1/GatherNd"; +"19791 maskrcnn/tf.compat.v1.gather_nd_1/GatherNd" -> "19792 maskrcnn/tf.math.sigmoid_5/Sigmoid"; +"19792 maskrcnn/tf.math.sigmoid_5/Sigmoid" -> "19797 Identity_4"; } diff --git a/tests/tensorflow/data/reference_graphs/2.11/quantized/w_sym_t_a_sym_t/mask_rcnn.dot b/tests/tensorflow/data/reference_graphs/2.11/quantized/w_sym_t_a_sym_t/mask_rcnn.dot index 379c7668e31..89b8250dcb8 100644 --- a/tests/tensorflow/data/reference_graphs/2.11/quantized/w_sym_t_a_sym_t/mask_rcnn.dot +++ b/tests/tensorflow/data/reference_graphs/2.11/quantized/w_sym_t_a_sym_t/mask_rcnn.dot @@ -2462,4642 +2462,4102 @@ strict digraph { "2460 maskrcnn/tf.math.exp_2/Exp" [id=2460, op=Exp]; "2461 maskrcnn/tf.math.exp_1/Exp" [id=2461, op=Exp]; "2462 maskrcnn/tf.math.exp/Exp" [id=2462, op=Exp]; -"2463 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" [id=2463, op=Placeholder]; -"2464 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/Abs/ReadVariableOp" [id=2464, op=ReadVariableOp]; -"2465 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/Abs" [id=2465, op=Abs]; -"2466 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/add/y" [id=2466, op=Const]; -"2467 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/add" [id=2467, op=AddV2]; -"2468 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" [id=2468, op=Placeholder]; -"2469 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul/ReadVariableOp" [id=2469, op=ReadVariableOp]; -"2470 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul" [id=2470, op=Mul]; -"2471 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" [id=2471, op=FakeQuantWithMinMaxVars]; -"2472 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack" [id=2472, op=Const]; -"2473 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack_1" [id=2473, op=Const]; -"2474 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack_2" [id=2474, op=Const]; -"2475 maskrcnn/tf.__operators__.getitem_18/strided_slice" [id=2475, op=StridedSlice]; -"2476 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" [id=2476, op=Placeholder]; -"2477 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/Abs/ReadVariableOp" [id=2477, op=ReadVariableOp]; -"2478 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/Abs" [id=2478, op=Abs]; -"2479 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/add/y" [id=2479, op=Const]; -"2480 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/add" [id=2480, op=AddV2]; -"2481 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" [id=2481, op=Placeholder]; -"2482 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul/ReadVariableOp" [id=2482, op=ReadVariableOp]; -"2483 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul" [id=2483, op=Mul]; -"2484 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" [id=2484, op=FakeQuantWithMinMaxVars]; -"2485 maskrcnn/tf.__operators__.getitem_17/strided_slice/stack" [id=2485, op=Const]; -"2486 maskrcnn/tf.__operators__.getitem_17/strided_slice/stack_1" [id=2486, op=Const]; -"2487 maskrcnn/tf.__operators__.getitem_17/strided_slice/stack_2" [id=2487, op=Const]; -"2488 maskrcnn/tf.__operators__.getitem_17/strided_slice" [id=2488, op=StridedSlice]; -"2489 maskrcnn/tf.math.exp_7/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" [id=2489, op=Placeholder]; -"2490 maskrcnn/tf.math.exp_7/fake_quantize/SymmQuant/Abs/ReadVariableOp" [id=2490, op=ReadVariableOp]; -"2491 maskrcnn/tf.math.exp_7/fake_quantize/SymmQuant/Abs" [id=2491, op=Abs]; -"2492 maskrcnn/tf.math.exp_7/fake_quantize/SymmQuant/add/y" [id=2492, op=Const]; -"2493 maskrcnn/tf.math.exp_7/fake_quantize/SymmQuant/add" [id=2493, op=AddV2]; -"2494 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maskrcnn/tf.compat.v1.gather_nd_1/GatherNd" [id=19437, op=GatherNd]; +"19438 maskrcnn/tf.math.sigmoid_5/Sigmoid" [id=19438, op=Sigmoid]; +"19439 Identity" [id=19439, op=Identity]; +"19440 Identity_1" [id=19440, op=Identity]; +"19441 Identity_2" [id=19441, op=Identity]; +"19442 Identity_3" [id=19442, op=Identity]; +"19443 Identity_4" [id=19443, op=Identity]; +"19444 Identity_5" [id=19444, op=Identity]; +"19445 Identity_6" [id=19445, op=Identity]; +"19446 Identity_7" [id=19446, op=Identity]; +"19447 Identity_8" [id=19447, op=Identity]; +"19448 Identity_9" [id=19448, op=Identity]; +"19449 Identity_10" [id=19449, op=Identity]; +"19450 Identity_11" [id=19450, op=Identity]; +"19451 Identity_12" [id=19451, op=Identity]; +"19452 Identity_13" [id=19452, op=Identity]; +"19453 Identity_14" [id=19453, op=Identity]; +"19454 Identity_15" [id=19454, op=Identity]; +"19455 Identity_16" [id=19455, op=Identity]; "0 args_0" -> "10 maskrcnn/image/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"1 args_0_1" -> "2861 maskrcnn/image_info/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"1 args_0_1" -> "2641 maskrcnn/image_info/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; "2 maskrcnn/image/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3 maskrcnn/image/fake_quantize/SymmQuant/Abs/ReadVariableOp"; "3 maskrcnn/image/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4 maskrcnn/image/fake_quantize/SymmQuant/Abs"; "4 maskrcnn/image/fake_quantize/SymmQuant/Abs" -> "6 maskrcnn/image/fake_quantize/SymmQuant/add"; @@ -9397,8 +8857,8 @@ strict digraph { "2131 maskrcnn/p6-bn/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2132 maskrcnn/p6-bn/fake_quantize/SymmQuant/mul"; "2132 maskrcnn/p6-bn/fake_quantize/SymmQuant/mul" -> "2133 maskrcnn/p6-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; "2133 maskrcnn/p6-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2181 maskrcnn/rpn/Conv2D"; -"2133 maskrcnn/p6-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4771 maskrcnn/tf.reshape_18/Reshape"; -"2133 maskrcnn/p6-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6769 maskrcnn/tf.reshape_47/Reshape"; +"2133 maskrcnn/p6-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4375 maskrcnn/tf.reshape_18/Reshape"; +"2133 maskrcnn/p6-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6229 maskrcnn/tf.reshape_47/Reshape"; "2134 maskrcnn/p5-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2135 maskrcnn/p5-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp"; "2135 maskrcnn/p5-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2136 maskrcnn/p5-bn/fake_quantize/SymmQuant/Abs"; "2136 maskrcnn/p5-bn/fake_quantize/SymmQuant/Abs" -> "2138 maskrcnn/p5-bn/fake_quantize/SymmQuant/add"; @@ -9409,8 +8869,8 @@ strict digraph { "2140 maskrcnn/p5-bn/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2141 maskrcnn/p5-bn/fake_quantize/SymmQuant/mul"; "2141 maskrcnn/p5-bn/fake_quantize/SymmQuant/mul" -> "2142 maskrcnn/p5-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; "2142 maskrcnn/p5-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2194 maskrcnn/rpn/Conv2D_1"; -"2142 maskrcnn/p5-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4769 maskrcnn/tf.reshape_16/Reshape"; -"2142 maskrcnn/p5-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6767 maskrcnn/tf.reshape_45/Reshape"; +"2142 maskrcnn/p5-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4373 maskrcnn/tf.reshape_16/Reshape"; +"2142 maskrcnn/p5-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6227 maskrcnn/tf.reshape_45/Reshape"; "2143 maskrcnn/p4-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2144 maskrcnn/p4-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp"; "2144 maskrcnn/p4-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2145 maskrcnn/p4-bn/fake_quantize/SymmQuant/Abs"; "2145 maskrcnn/p4-bn/fake_quantize/SymmQuant/Abs" -> "2147 maskrcnn/p4-bn/fake_quantize/SymmQuant/add"; @@ -9421,8 +8881,8 @@ strict digraph { "2149 maskrcnn/p4-bn/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2150 maskrcnn/p4-bn/fake_quantize/SymmQuant/mul"; "2150 maskrcnn/p4-bn/fake_quantize/SymmQuant/mul" -> "2151 maskrcnn/p4-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; "2151 maskrcnn/p4-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2206 maskrcnn/rpn/Conv2D_2"; -"2151 maskrcnn/p4-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4767 maskrcnn/tf.reshape_14/Reshape"; -"2151 maskrcnn/p4-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6765 maskrcnn/tf.reshape_43/Reshape"; +"2151 maskrcnn/p4-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4371 maskrcnn/tf.reshape_14/Reshape"; +"2151 maskrcnn/p4-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6225 maskrcnn/tf.reshape_43/Reshape"; "2152 maskrcnn/p3-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2153 maskrcnn/p3-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp"; "2153 maskrcnn/p3-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2154 maskrcnn/p3-bn/fake_quantize/SymmQuant/Abs"; "2154 maskrcnn/p3-bn/fake_quantize/SymmQuant/Abs" -> "2156 maskrcnn/p3-bn/fake_quantize/SymmQuant/add"; @@ -9433,8 +8893,8 @@ strict digraph { "2158 maskrcnn/p3-bn/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2159 maskrcnn/p3-bn/fake_quantize/SymmQuant/mul"; "2159 maskrcnn/p3-bn/fake_quantize/SymmQuant/mul" -> "2160 maskrcnn/p3-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; "2160 maskrcnn/p3-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2218 maskrcnn/rpn/Conv2D_3"; -"2160 maskrcnn/p3-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4765 maskrcnn/tf.reshape_12/Reshape"; -"2160 maskrcnn/p3-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6763 maskrcnn/tf.reshape_41/Reshape"; +"2160 maskrcnn/p3-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4369 maskrcnn/tf.reshape_12/Reshape"; +"2160 maskrcnn/p3-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6223 maskrcnn/tf.reshape_41/Reshape"; "2161 maskrcnn/p2-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2162 maskrcnn/p2-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp"; "2162 maskrcnn/p2-bn/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2163 maskrcnn/p2-bn/fake_quantize/SymmQuant/Abs"; "2163 maskrcnn/p2-bn/fake_quantize/SymmQuant/Abs" -> "2165 maskrcnn/p2-bn/fake_quantize/SymmQuant/add"; @@ -9445,8 +8905,8 @@ strict digraph { "2167 maskrcnn/p2-bn/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2168 maskrcnn/p2-bn/fake_quantize/SymmQuant/mul"; "2168 maskrcnn/p2-bn/fake_quantize/SymmQuant/mul" -> "2169 maskrcnn/p2-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; "2169 maskrcnn/p2-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2230 maskrcnn/rpn/Conv2D_4"; -"2169 maskrcnn/p2-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4763 maskrcnn/tf.reshape_10/Reshape"; -"2169 maskrcnn/p2-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6761 maskrcnn/tf.reshape_39/Reshape"; +"2169 maskrcnn/p2-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4367 maskrcnn/tf.reshape_10/Reshape"; +"2169 maskrcnn/p2-bn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6221 maskrcnn/tf.reshape_39/Reshape"; "2170 maskrcnn/rpn/SymmQuant/Abs/ReadVariableOp/resource" -> "2171 maskrcnn/rpn/SymmQuant/Abs/ReadVariableOp"; "2170 maskrcnn/rpn/SymmQuant/Abs/ReadVariableOp/resource" -> "2186 maskrcnn/rpn/SymmQuant_1/Abs/ReadVariableOp"; "2170 maskrcnn/rpn/SymmQuant/Abs/ReadVariableOp/resource" -> "2198 maskrcnn/rpn/SymmQuant_2/Abs/ReadVariableOp"; @@ -9542,7 +9002,7 @@ strict digraph { "2240 maskrcnn/rpn/fake_quantize_4/SymmQuant/mul/ReadVariableOp" -> "2241 maskrcnn/rpn/fake_quantize_4/SymmQuant/mul"; "2241 maskrcnn/rpn/fake_quantize_4/SymmQuant/mul" -> "2242 maskrcnn/rpn/fake_quantize_4/SymmQuant/FakeQuantWithMinMaxVars"; "2242 maskrcnn/rpn/fake_quantize_4/SymmQuant/FakeQuantWithMinMaxVars" -> "2290 maskrcnn/rpn-box/Conv2D"; -"2242 maskrcnn/rpn/fake_quantize_4/SymmQuant/FakeQuantWithMinMaxVars" -> "3609 maskrcnn/rpn-class/Conv2D"; +"2242 maskrcnn/rpn/fake_quantize_4/SymmQuant/FakeQuantWithMinMaxVars" -> "3324 maskrcnn/rpn-class/Conv2D"; "2243 maskrcnn/rpn/fake_quantize_3/SymmQuant/Abs/ReadVariableOp/resource" -> "2244 maskrcnn/rpn/fake_quantize_3/SymmQuant/Abs/ReadVariableOp"; "2244 maskrcnn/rpn/fake_quantize_3/SymmQuant/Abs/ReadVariableOp" -> "2245 maskrcnn/rpn/fake_quantize_3/SymmQuant/Abs"; "2245 maskrcnn/rpn/fake_quantize_3/SymmQuant/Abs" -> "2247 maskrcnn/rpn/fake_quantize_3/SymmQuant/add"; @@ -9553,7 +9013,7 @@ strict digraph { "2249 maskrcnn/rpn/fake_quantize_3/SymmQuant/mul/ReadVariableOp" -> "2250 maskrcnn/rpn/fake_quantize_3/SymmQuant/mul"; "2250 maskrcnn/rpn/fake_quantize_3/SymmQuant/mul" -> "2251 maskrcnn/rpn/fake_quantize_3/SymmQuant/FakeQuantWithMinMaxVars"; "2251 maskrcnn/rpn/fake_quantize_3/SymmQuant/FakeQuantWithMinMaxVars" -> "2302 maskrcnn/rpn-box/Conv2D_1"; -"2251 maskrcnn/rpn/fake_quantize_3/SymmQuant/FakeQuantWithMinMaxVars" -> "3621 maskrcnn/rpn-class/Conv2D_1"; +"2251 maskrcnn/rpn/fake_quantize_3/SymmQuant/FakeQuantWithMinMaxVars" -> "3336 maskrcnn/rpn-class/Conv2D_1"; "2252 maskrcnn/rpn/fake_quantize_2/SymmQuant/Abs/ReadVariableOp/resource" -> "2253 maskrcnn/rpn/fake_quantize_2/SymmQuant/Abs/ReadVariableOp"; "2253 maskrcnn/rpn/fake_quantize_2/SymmQuant/Abs/ReadVariableOp" -> "2254 maskrcnn/rpn/fake_quantize_2/SymmQuant/Abs"; "2254 maskrcnn/rpn/fake_quantize_2/SymmQuant/Abs" -> "2256 maskrcnn/rpn/fake_quantize_2/SymmQuant/add"; @@ -9564,7 +9024,7 @@ strict digraph { "2258 maskrcnn/rpn/fake_quantize_2/SymmQuant/mul/ReadVariableOp" -> "2259 maskrcnn/rpn/fake_quantize_2/SymmQuant/mul"; "2259 maskrcnn/rpn/fake_quantize_2/SymmQuant/mul" -> "2260 maskrcnn/rpn/fake_quantize_2/SymmQuant/FakeQuantWithMinMaxVars"; "2260 maskrcnn/rpn/fake_quantize_2/SymmQuant/FakeQuantWithMinMaxVars" -> "2313 maskrcnn/rpn-box/Conv2D_2"; -"2260 maskrcnn/rpn/fake_quantize_2/SymmQuant/FakeQuantWithMinMaxVars" -> "3632 maskrcnn/rpn-class/Conv2D_2"; +"2260 maskrcnn/rpn/fake_quantize_2/SymmQuant/FakeQuantWithMinMaxVars" -> "3347 maskrcnn/rpn-class/Conv2D_2"; "2261 maskrcnn/rpn/fake_quantize_1/SymmQuant/Abs/ReadVariableOp/resource" -> "2262 maskrcnn/rpn/fake_quantize_1/SymmQuant/Abs/ReadVariableOp"; "2262 maskrcnn/rpn/fake_quantize_1/SymmQuant/Abs/ReadVariableOp" -> "2263 maskrcnn/rpn/fake_quantize_1/SymmQuant/Abs"; "2263 maskrcnn/rpn/fake_quantize_1/SymmQuant/Abs" -> "2265 maskrcnn/rpn/fake_quantize_1/SymmQuant/add"; @@ -9575,7 +9035,7 @@ strict digraph { "2267 maskrcnn/rpn/fake_quantize_1/SymmQuant/mul/ReadVariableOp" -> "2268 maskrcnn/rpn/fake_quantize_1/SymmQuant/mul"; "2268 maskrcnn/rpn/fake_quantize_1/SymmQuant/mul" -> "2269 maskrcnn/rpn/fake_quantize_1/SymmQuant/FakeQuantWithMinMaxVars"; "2269 maskrcnn/rpn/fake_quantize_1/SymmQuant/FakeQuantWithMinMaxVars" -> "2324 maskrcnn/rpn-box/Conv2D_3"; -"2269 maskrcnn/rpn/fake_quantize_1/SymmQuant/FakeQuantWithMinMaxVars" -> "3643 maskrcnn/rpn-class/Conv2D_3"; +"2269 maskrcnn/rpn/fake_quantize_1/SymmQuant/FakeQuantWithMinMaxVars" -> "3358 maskrcnn/rpn-class/Conv2D_3"; "2270 maskrcnn/rpn/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2271 maskrcnn/rpn/fake_quantize/SymmQuant/Abs/ReadVariableOp"; "2271 maskrcnn/rpn/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2272 maskrcnn/rpn/fake_quantize/SymmQuant/Abs"; "2272 maskrcnn/rpn/fake_quantize/SymmQuant/Abs" -> "2274 maskrcnn/rpn/fake_quantize/SymmQuant/add"; @@ -9586,7 +9046,7 @@ strict digraph { "2276 maskrcnn/rpn/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2277 maskrcnn/rpn/fake_quantize/SymmQuant/mul"; "2277 maskrcnn/rpn/fake_quantize/SymmQuant/mul" -> "2278 maskrcnn/rpn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; "2278 maskrcnn/rpn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2335 maskrcnn/rpn-box/Conv2D_4"; -"2278 maskrcnn/rpn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3654 maskrcnn/rpn-class/Conv2D_4"; +"2278 maskrcnn/rpn/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3369 maskrcnn/rpn-class/Conv2D_4"; "2279 maskrcnn/rpn-box/SymmQuant/Abs/ReadVariableOp/resource" -> "2280 maskrcnn/rpn-box/SymmQuant/Abs/ReadVariableOp"; "2279 maskrcnn/rpn-box/SymmQuant/Abs/ReadVariableOp/resource" -> "2294 maskrcnn/rpn-box/SymmQuant_1/Abs/ReadVariableOp"; "2279 maskrcnn/rpn-box/SymmQuant/Abs/ReadVariableOp/resource" -> "2305 maskrcnn/rpn-box/SymmQuant_2/Abs/ReadVariableOp"; @@ -9619,7 +9079,7 @@ strict digraph { "2291 maskrcnn/rpn-box/BiasAdd/ReadVariableOp/resource" -> "2336 maskrcnn/rpn-box/BiasAdd_4/ReadVariableOp"; "2292 maskrcnn/rpn-box/BiasAdd/ReadVariableOp" -> "2293 maskrcnn/rpn-box/BiasAdd"; "2293 maskrcnn/rpn-box/BiasAdd" -> "2346 maskrcnn/tf.reshape_9/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"2293 maskrcnn/rpn-box/BiasAdd" -> "19990 Identity_11"; +"2293 maskrcnn/rpn-box/BiasAdd" -> "19450 Identity_11"; "2294 maskrcnn/rpn-box/SymmQuant_1/Abs/ReadVariableOp" -> "2295 maskrcnn/rpn-box/SymmQuant_1/Abs"; "2295 maskrcnn/rpn-box/SymmQuant_1/Abs" -> "2297 maskrcnn/rpn-box/SymmQuant_1/add"; "2296 maskrcnn/rpn-box/SymmQuant_1/add/y" -> "2297 maskrcnn/rpn-box/SymmQuant_1/add"; @@ -9632,7 +9092,7 @@ strict digraph { "2302 maskrcnn/rpn-box/Conv2D_1" -> "2304 maskrcnn/rpn-box/BiasAdd_1"; "2303 maskrcnn/rpn-box/BiasAdd_1/ReadVariableOp" -> "2304 maskrcnn/rpn-box/BiasAdd_1"; "2304 maskrcnn/rpn-box/BiasAdd_1" -> "2355 maskrcnn/tf.reshape_7/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"2304 maskrcnn/rpn-box/BiasAdd_1" -> "19989 Identity_10"; +"2304 maskrcnn/rpn-box/BiasAdd_1" -> "19449 Identity_10"; "2305 maskrcnn/rpn-box/SymmQuant_2/Abs/ReadVariableOp" -> "2306 maskrcnn/rpn-box/SymmQuant_2/Abs"; "2306 maskrcnn/rpn-box/SymmQuant_2/Abs" -> "2308 maskrcnn/rpn-box/SymmQuant_2/add"; "2307 maskrcnn/rpn-box/SymmQuant_2/add/y" -> "2308 maskrcnn/rpn-box/SymmQuant_2/add"; @@ -9645,7 +9105,7 @@ strict digraph { "2313 maskrcnn/rpn-box/Conv2D_2" -> "2315 maskrcnn/rpn-box/BiasAdd_2"; "2314 maskrcnn/rpn-box/BiasAdd_2/ReadVariableOp" -> "2315 maskrcnn/rpn-box/BiasAdd_2"; "2315 maskrcnn/rpn-box/BiasAdd_2" -> "2364 maskrcnn/tf.reshape_5/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"2315 maskrcnn/rpn-box/BiasAdd_2" -> "19988 Identity_9"; +"2315 maskrcnn/rpn-box/BiasAdd_2" -> "19448 Identity_9"; "2316 maskrcnn/rpn-box/SymmQuant_3/Abs/ReadVariableOp" -> "2317 maskrcnn/rpn-box/SymmQuant_3/Abs"; "2317 maskrcnn/rpn-box/SymmQuant_3/Abs" -> "2319 maskrcnn/rpn-box/SymmQuant_3/add"; "2318 maskrcnn/rpn-box/SymmQuant_3/add/y" -> "2319 maskrcnn/rpn-box/SymmQuant_3/add"; @@ -9658,7 +9118,7 @@ strict digraph { "2324 maskrcnn/rpn-box/Conv2D_3" -> "2326 maskrcnn/rpn-box/BiasAdd_3"; "2325 maskrcnn/rpn-box/BiasAdd_3/ReadVariableOp" -> "2326 maskrcnn/rpn-box/BiasAdd_3"; "2326 maskrcnn/rpn-box/BiasAdd_3" -> "2373 maskrcnn/tf.reshape_3/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"2326 maskrcnn/rpn-box/BiasAdd_3" -> "19987 Identity_8"; +"2326 maskrcnn/rpn-box/BiasAdd_3" -> "19447 Identity_8"; "2327 maskrcnn/rpn-box/SymmQuant_4/Abs/ReadVariableOp" -> "2328 maskrcnn/rpn-box/SymmQuant_4/Abs"; "2328 maskrcnn/rpn-box/SymmQuant_4/Abs" -> "2330 maskrcnn/rpn-box/SymmQuant_4/add"; "2329 maskrcnn/rpn-box/SymmQuant_4/add/y" -> "2330 maskrcnn/rpn-box/SymmQuant_4/add"; @@ -9671,7 +9131,7 @@ strict digraph { "2335 maskrcnn/rpn-box/Conv2D_4" -> "2337 maskrcnn/rpn-box/BiasAdd_4"; "2336 maskrcnn/rpn-box/BiasAdd_4/ReadVariableOp" -> "2337 maskrcnn/rpn-box/BiasAdd_4"; "2337 maskrcnn/rpn-box/BiasAdd_4" -> "2382 maskrcnn/tf.reshape_1/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"2337 maskrcnn/rpn-box/BiasAdd_4" -> "19986 Identity_7"; +"2337 maskrcnn/rpn-box/BiasAdd_4" -> "19446 Identity_7"; "2338 maskrcnn/tf.reshape_9/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp/resource" -> "2339 maskrcnn/tf.reshape_9/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp"; "2339 maskrcnn/tf.reshape_9/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "2340 maskrcnn/tf.reshape_9/fake_quantize_I0/SymmQuant/Abs"; "2340 maskrcnn/tf.reshape_9/fake_quantize_I0/SymmQuant/Abs" -> "2342 maskrcnn/tf.reshape_9/fake_quantize_I0/SymmQuant/add"; @@ -9725,28 +9185,28 @@ strict digraph { "2383 maskrcnn/tf.reshape_9/Reshape/shape" -> "2384 maskrcnn/tf.reshape_9/Reshape"; "2384 maskrcnn/tf.reshape_9/Reshape" -> "2396 maskrcnn/tf.__operators__.getitem_20/strided_slice"; "2384 maskrcnn/tf.reshape_9/Reshape" -> "2400 maskrcnn/tf.__operators__.getitem_19/strided_slice"; -"2384 maskrcnn/tf.reshape_9/Reshape" -> "2475 maskrcnn/tf.__operators__.getitem_18/strided_slice"; -"2384 maskrcnn/tf.reshape_9/Reshape" -> "2488 maskrcnn/tf.__operators__.getitem_17/strided_slice"; +"2384 maskrcnn/tf.reshape_9/Reshape" -> "2468 maskrcnn/tf.__operators__.getitem_18/strided_slice"; +"2384 maskrcnn/tf.reshape_9/Reshape" -> "2474 maskrcnn/tf.__operators__.getitem_17/strided_slice"; "2385 maskrcnn/tf.reshape_7/Reshape/shape" -> "2386 maskrcnn/tf.reshape_7/Reshape"; "2386 maskrcnn/tf.reshape_7/Reshape" -> "2404 maskrcnn/tf.__operators__.getitem_16/strided_slice"; "2386 maskrcnn/tf.reshape_7/Reshape" -> "2408 maskrcnn/tf.__operators__.getitem_15/strided_slice"; -"2386 maskrcnn/tf.reshape_7/Reshape" -> "2501 maskrcnn/tf.__operators__.getitem_14/strided_slice"; -"2386 maskrcnn/tf.reshape_7/Reshape" -> "2514 maskrcnn/tf.__operators__.getitem_13/strided_slice"; +"2386 maskrcnn/tf.reshape_7/Reshape" -> "2480 maskrcnn/tf.__operators__.getitem_14/strided_slice"; +"2386 maskrcnn/tf.reshape_7/Reshape" -> "2486 maskrcnn/tf.__operators__.getitem_13/strided_slice"; "2387 maskrcnn/tf.reshape_5/Reshape/shape" -> "2388 maskrcnn/tf.reshape_5/Reshape"; "2388 maskrcnn/tf.reshape_5/Reshape" -> "2412 maskrcnn/tf.__operators__.getitem_12/strided_slice"; "2388 maskrcnn/tf.reshape_5/Reshape" -> "2416 maskrcnn/tf.__operators__.getitem_11/strided_slice"; -"2388 maskrcnn/tf.reshape_5/Reshape" -> "2527 maskrcnn/tf.__operators__.getitem_10/strided_slice"; -"2388 maskrcnn/tf.reshape_5/Reshape" -> "2540 maskrcnn/tf.__operators__.getitem_9/strided_slice"; +"2388 maskrcnn/tf.reshape_5/Reshape" -> "2492 maskrcnn/tf.__operators__.getitem_10/strided_slice"; +"2388 maskrcnn/tf.reshape_5/Reshape" -> "2498 maskrcnn/tf.__operators__.getitem_9/strided_slice"; "2389 maskrcnn/tf.reshape_3/Reshape/shape" -> "2390 maskrcnn/tf.reshape_3/Reshape"; "2390 maskrcnn/tf.reshape_3/Reshape" -> "2420 maskrcnn/tf.__operators__.getitem_8/strided_slice"; "2390 maskrcnn/tf.reshape_3/Reshape" -> "2424 maskrcnn/tf.__operators__.getitem_7/strided_slice"; -"2390 maskrcnn/tf.reshape_3/Reshape" -> "2553 maskrcnn/tf.__operators__.getitem_6/strided_slice"; -"2390 maskrcnn/tf.reshape_3/Reshape" -> "2566 maskrcnn/tf.__operators__.getitem_5/strided_slice"; +"2390 maskrcnn/tf.reshape_3/Reshape" -> "2504 maskrcnn/tf.__operators__.getitem_6/strided_slice"; +"2390 maskrcnn/tf.reshape_3/Reshape" -> "2510 maskrcnn/tf.__operators__.getitem_5/strided_slice"; "2391 maskrcnn/tf.reshape_1/Reshape/shape" -> "2392 maskrcnn/tf.reshape_1/Reshape"; "2392 maskrcnn/tf.reshape_1/Reshape" -> "2428 maskrcnn/tf.__operators__.getitem_4/strided_slice"; "2392 maskrcnn/tf.reshape_1/Reshape" -> "2432 maskrcnn/tf.__operators__.getitem_3/strided_slice"; -"2392 maskrcnn/tf.reshape_1/Reshape" -> "2579 maskrcnn/tf.__operators__.getitem_2/strided_slice"; -"2392 maskrcnn/tf.reshape_1/Reshape" -> "2592 maskrcnn/tf.__operators__.getitem_1/strided_slice"; +"2392 maskrcnn/tf.reshape_1/Reshape" -> "2516 maskrcnn/tf.__operators__.getitem_2/strided_slice"; +"2392 maskrcnn/tf.reshape_1/Reshape" -> "2522 maskrcnn/tf.__operators__.getitem_1/strided_slice"; "2393 maskrcnn/tf.__operators__.getitem_20/strided_slice/stack" -> "2396 maskrcnn/tf.__operators__.getitem_20/strided_slice"; "2394 maskrcnn/tf.__operators__.getitem_20/strided_slice/stack_1" -> "2396 maskrcnn/tf.__operators__.getitem_20/strided_slice"; "2395 maskrcnn/tf.__operators__.getitem_20/strided_slice/stack_2" -> "2396 maskrcnn/tf.__operators__.getitem_20/strided_slice"; @@ -9807,5195 +9267,4595 @@ strict digraph { "2450 maskrcnn/tf.math.minimum_1/Minimum" -> "2461 maskrcnn/tf.math.exp_1/Exp"; "2451 maskrcnn/tf.math.minimum/Minimum/y" -> "2452 maskrcnn/tf.math.minimum/Minimum"; "2452 maskrcnn/tf.math.minimum/Minimum" -> "2462 maskrcnn/tf.math.exp/Exp"; -"2453 maskrcnn/tf.math.exp_9/Exp" -> "2471 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2454 maskrcnn/tf.math.exp_8/Exp" -> "2484 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2455 maskrcnn/tf.math.exp_7/Exp" -> "2497 maskrcnn/tf.math.exp_7/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2456 maskrcnn/tf.math.exp_6/Exp" -> "2510 maskrcnn/tf.math.exp_6/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2457 maskrcnn/tf.math.exp_5/Exp" -> "2523 maskrcnn/tf.math.exp_5/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2458 maskrcnn/tf.math.exp_4/Exp" -> "2536 maskrcnn/tf.math.exp_4/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2459 maskrcnn/tf.math.exp_3/Exp" -> "2549 maskrcnn/tf.math.exp_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2460 maskrcnn/tf.math.exp_2/Exp" -> "2562 maskrcnn/tf.math.exp_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2461 maskrcnn/tf.math.exp_1/Exp" -> "2575 maskrcnn/tf.math.exp_1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2462 maskrcnn/tf.math.exp/Exp" -> "2588 maskrcnn/tf.math.exp/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2463 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2464 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2464 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2465 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/Abs"; -"2465 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/Abs" -> "2467 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/add"; -"2466 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/add/y" -> "2467 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/add"; -"2467 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/add" -> "2470 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul"; -"2467 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/add" -> "2471 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2468 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2469 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2469 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2470 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul"; -"2470 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/mul" -> "2471 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2471 maskrcnn/tf.math.exp_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2594 maskrcnn/tf.math.multiply_27/Mul"; -"2472 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack" -> "2475 maskrcnn/tf.__operators__.getitem_18/strided_slice"; -"2473 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack_1" -> "2475 maskrcnn/tf.__operators__.getitem_18/strided_slice"; -"2474 maskrcnn/tf.__operators__.getitem_18/strided_slice/stack_2" -> "2475 maskrcnn/tf.__operators__.getitem_18/strided_slice"; -"2475 maskrcnn/tf.__operators__.getitem_18/strided_slice" -> "2596 maskrcnn/tf.math.multiply_25/Mul"; -"2476 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2477 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2477 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2478 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/Abs"; -"2478 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/Abs" -> "2480 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/add"; -"2479 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/add/y" -> "2480 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/add"; -"2480 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/add" -> "2483 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul"; -"2480 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/add" -> "2484 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2481 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2482 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2482 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2483 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul"; -"2483 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/mul" -> "2484 maskrcnn/tf.math.exp_8/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2484 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-> "2668 maskrcnn/tf.math.multiply_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2665 maskrcnn/tf.math.multiply_24/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2666 maskrcnn/tf.math.multiply_24/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2666 maskrcnn/tf.math.multiply_24/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2667 maskrcnn/tf.math.multiply_24/fake_quantize/SymmQuant/mul"; -"2667 maskrcnn/tf.math.multiply_24/fake_quantize/SymmQuant/mul" -> "2668 maskrcnn/tf.math.multiply_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2668 maskrcnn/tf.math.multiply_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2820 maskrcnn/tf.__operators__.add_35/AddV2"; -"2669 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2670 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2670 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2671 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/Abs"; -"2671 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/Abs" -> "2673 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/add"; -"2672 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/add/y" -> "2673 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/add"; -"2673 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/add" -> "2676 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/mul"; -"2673 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/add" -> "2677 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2674 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2675 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2675 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2676 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/mul"; -"2676 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/mul" -> "2677 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2677 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2822 maskrcnn/tf.math.multiply_23/Mul"; -"2677 maskrcnn/tf.math.multiply_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3150 maskrcnn/tf.__operators__.add_34/AddV2"; -"2678 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2679 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2679 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2680 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/Abs"; -"2680 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/Abs" -> "2682 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/add"; -"2681 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/add/y" -> "2682 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/add"; -"2682 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/add" -> "2685 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/mul"; -"2682 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/add" -> "2686 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2683 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2684 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2684 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2685 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/mul"; -"2685 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/mul" -> "2686 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2686 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2826 maskrcnn/tf.math.multiply_22/Mul"; -"2686 maskrcnn/tf.math.multiply_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3151 maskrcnn/tf.__operators__.add_33/AddV2"; -"2687 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2688 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2688 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2689 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/Abs"; -"2689 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/Abs" -> "2691 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/add"; -"2690 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/add/y" -> "2691 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/add"; -"2691 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/add" -> "2694 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/mul"; -"2691 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/add" -> "2695 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2692 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2693 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2693 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2694 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/mul"; -"2694 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/mul" -> "2695 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2695 maskrcnn/tf.math.multiply_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2824 maskrcnn/tf.__operators__.add_32/AddV2"; -"2696 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2697 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2697 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2698 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/Abs"; -"2698 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/Abs" -> "2700 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/add"; -"2699 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/add/y" -> "2700 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/add"; -"2700 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/add" -> "2703 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/mul"; -"2700 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/add" -> "2704 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2701 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2702 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2702 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2703 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/mul"; -"2703 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/mul" -> "2704 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2704 maskrcnn/tf.math.multiply_18/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2828 maskrcnn/tf.__operators__.add_31/AddV2"; -"2705 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2706 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2706 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2707 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/Abs"; -"2707 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/Abs" -> "2709 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/add"; -"2708 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/add/y" -> "2709 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/add"; -"2709 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/add" -> "2712 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/mul"; -"2709 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/add" -> "2713 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2710 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2711 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2711 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2712 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/mul"; -"2712 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/mul" -> "2713 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2713 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2830 maskrcnn/tf.math.multiply_17/Mul"; -"2713 maskrcnn/tf.math.multiply_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3152 maskrcnn/tf.__operators__.add_30/AddV2"; -"2714 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2715 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2715 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2716 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/Abs"; -"2716 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/Abs" -> "2718 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/add"; -"2717 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/add/y" -> "2718 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/add"; -"2718 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/add" -> "2721 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/mul"; -"2718 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/add" -> "2722 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2719 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2720 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2720 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2721 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/mul"; -"2721 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/mul" -> "2722 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2722 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2834 maskrcnn/tf.math.multiply_16/Mul"; -"2722 maskrcnn/tf.math.multiply_14/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3153 maskrcnn/tf.__operators__.add_29/AddV2"; -"2723 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2724 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2724 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2725 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/Abs"; -"2725 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/Abs" -> "2727 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/add"; -"2726 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/add/y" -> "2727 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/add"; -"2727 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/add" -> "2730 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/mul"; -"2727 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/add" -> "2731 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2728 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2729 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2729 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2730 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/mul"; -"2730 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/mul" -> "2731 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2731 maskrcnn/tf.math.multiply_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2832 maskrcnn/tf.__operators__.add_28/AddV2"; -"2732 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2733 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2733 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2734 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/Abs"; -"2734 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/Abs" -> "2736 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/add"; -"2735 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/add/y" -> "2736 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/add"; -"2736 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/add" -> "2739 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/mul"; -"2736 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/add" -> "2740 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2737 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2738 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2738 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2739 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/mul"; -"2739 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/mul" -> "2740 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2740 maskrcnn/tf.math.multiply_12/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2836 maskrcnn/tf.__operators__.add_27/AddV2"; -"2741 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2742 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2742 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2743 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/Abs"; -"2743 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/Abs" -> "2745 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/add"; -"2744 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/add/y" -> "2745 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/add"; -"2745 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/add" -> "2748 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/mul"; -"2745 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/add" -> "2749 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2746 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2747 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2747 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2748 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/mul"; -"2748 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/mul" -> "2749 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2749 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2838 maskrcnn/tf.math.multiply_11/Mul"; -"2749 maskrcnn/tf.math.multiply_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3154 maskrcnn/tf.__operators__.add_26/AddV2"; -"2750 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2751 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2751 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2752 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/Abs"; -"2752 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/Abs" -> "2754 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/add"; -"2753 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/add/y" -> "2754 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/add"; -"2754 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/add" -> "2757 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/mul"; -"2754 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/add" -> "2758 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2755 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2756 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2756 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2757 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/mul"; -"2757 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/mul" -> "2758 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2758 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2842 maskrcnn/tf.math.multiply_10/Mul"; -"2758 maskrcnn/tf.math.multiply_8/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3155 maskrcnn/tf.__operators__.add_25/AddV2"; -"2759 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2760 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2760 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2761 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/Abs"; -"2761 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/Abs" -> "2763 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/add"; -"2762 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/add/y" -> "2763 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/add"; -"2763 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/add" -> "2766 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/mul"; -"2763 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/add" -> "2767 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2764 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2765 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2765 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2766 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/mul"; -"2766 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/mul" -> "2767 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2767 maskrcnn/tf.math.multiply_7/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2840 maskrcnn/tf.__operators__.add_24/AddV2"; -"2768 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2769 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2769 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2770 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/Abs"; -"2770 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/Abs" -> "2772 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/add"; -"2771 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/add/y" -> "2772 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/add"; -"2772 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/add" -> "2775 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/mul"; -"2772 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/add" -> "2776 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2773 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2774 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2774 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2775 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/mul"; -"2775 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/mul" -> "2776 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2776 maskrcnn/tf.math.multiply_6/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2844 maskrcnn/tf.__operators__.add_23/AddV2"; -"2777 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2778 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2778 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2779 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/Abs"; -"2779 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/Abs" -> "2781 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/add"; -"2780 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/add/y" -> "2781 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/add"; -"2781 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/add" -> "2784 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/mul"; -"2781 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/add" -> "2785 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2782 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2783 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2783 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2784 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/mul"; -"2784 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/mul" -> "2785 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2785 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2846 maskrcnn/tf.math.multiply_5/Mul"; -"2785 maskrcnn/tf.math.multiply_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3156 maskrcnn/tf.__operators__.add_22/AddV2"; -"2786 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2787 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2787 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2788 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/Abs"; -"2788 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/Abs" -> "2790 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/add"; -"2789 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/add/y" -> "2790 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/add"; -"2790 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/add" -> "2793 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/mul"; -"2790 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/add" -> "2794 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2791 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2792 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2792 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2793 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/mul"; -"2793 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/mul" -> "2794 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2794 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2850 maskrcnn/tf.math.multiply_4/Mul"; -"2794 maskrcnn/tf.math.multiply_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3157 maskrcnn/tf.__operators__.add_21/AddV2"; -"2795 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2796 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2796 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2797 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/Abs"; -"2797 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/Abs" -> "2799 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/add"; -"2798 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/add/y" -> "2799 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/add"; -"2799 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/add" -> "2802 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/mul"; -"2799 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/add" -> "2803 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2800 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2801 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2801 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2802 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/mul"; -"2802 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/mul" -> "2803 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2803 maskrcnn/tf.math.multiply_1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2848 maskrcnn/tf.__operators__.add_20/AddV2"; -"2804 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2805 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2805 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2806 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/Abs"; -"2806 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/Abs" -> "2808 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/add"; -"2807 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/add/y" -> "2808 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/add"; -"2808 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/add" -> "2811 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/mul"; -"2808 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/add" -> "2812 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2809 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2810 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2810 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2811 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/mul"; -"2811 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/mul" -> "2812 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2812 maskrcnn/tf.math.multiply/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2852 maskrcnn/tf.__operators__.add_19/AddV2"; -"2814 maskrcnn/tf.math.multiply_29/Mul" -> "2879 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2815 maskrcnn/tf.__operators__.add_36/y" -> "2816 maskrcnn/tf.__operators__.add_36/AddV2"; -"2816 maskrcnn/tf.__operators__.add_36/AddV2" -> "2870 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2818 maskrcnn/tf.math.multiply_28/Mul" -> "2897 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2819 maskrcnn/tf.__operators__.add_35/y" -> "2820 maskrcnn/tf.__operators__.add_35/AddV2"; -"2820 maskrcnn/tf.__operators__.add_35/AddV2" -> "2888 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2822 maskrcnn/tf.math.multiply_23/Mul" -> "2915 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2823 maskrcnn/tf.__operators__.add_32/y" -> "2824 maskrcnn/tf.__operators__.add_32/AddV2"; -"2824 maskrcnn/tf.__operators__.add_32/AddV2" -> "2906 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2826 maskrcnn/tf.math.multiply_22/Mul" -> "2933 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2827 maskrcnn/tf.__operators__.add_31/y" -> "2828 maskrcnn/tf.__operators__.add_31/AddV2"; -"2828 maskrcnn/tf.__operators__.add_31/AddV2" -> "2924 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2830 maskrcnn/tf.math.multiply_17/Mul" -> "2951 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2831 maskrcnn/tf.__operators__.add_28/y" -> "2832 maskrcnn/tf.__operators__.add_28/AddV2"; -"2832 maskrcnn/tf.__operators__.add_28/AddV2" -> "2942 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2834 maskrcnn/tf.math.multiply_16/Mul" -> "2969 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2835 maskrcnn/tf.__operators__.add_27/y" -> "2836 maskrcnn/tf.__operators__.add_27/AddV2"; -"2836 maskrcnn/tf.__operators__.add_27/AddV2" -> "2960 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2838 maskrcnn/tf.math.multiply_11/Mul" -> "2987 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2839 maskrcnn/tf.__operators__.add_24/y" -> "2840 maskrcnn/tf.__operators__.add_24/AddV2"; -"2840 maskrcnn/tf.__operators__.add_24/AddV2" -> "2978 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2842 maskrcnn/tf.math.multiply_10/Mul" -> "3005 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2843 maskrcnn/tf.__operators__.add_23/y" -> "2844 maskrcnn/tf.__operators__.add_23/AddV2"; -"2844 maskrcnn/tf.__operators__.add_23/AddV2" -> "2996 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2846 maskrcnn/tf.math.multiply_5/Mul" -> "3023 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2847 maskrcnn/tf.__operators__.add_20/y" -> "2848 maskrcnn/tf.__operators__.add_20/AddV2"; -"2848 maskrcnn/tf.__operators__.add_20/AddV2" -> "3014 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2850 maskrcnn/tf.math.multiply_4/Mul" -> "3041 maskrcnn/tf.math.multiply_4/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2851 maskrcnn/tf.__operators__.add_19/y" -> "2852 maskrcnn/tf.__operators__.add_19/AddV2"; -"2852 maskrcnn/tf.__operators__.add_19/AddV2" -> "3032 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2853 maskrcnn/image_info/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2854 maskrcnn/image_info/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2854 maskrcnn/image_info/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2855 maskrcnn/image_info/fake_quantize/SymmQuant/Abs"; -"2855 maskrcnn/image_info/fake_quantize/SymmQuant/Abs" -> "2857 maskrcnn/image_info/fake_quantize/SymmQuant/add"; -"2856 maskrcnn/image_info/fake_quantize/SymmQuant/add/y" -> "2857 maskrcnn/image_info/fake_quantize/SymmQuant/add"; -"2857 maskrcnn/image_info/fake_quantize/SymmQuant/add" -> "2860 maskrcnn/image_info/fake_quantize/SymmQuant/mul"; -"2857 maskrcnn/image_info/fake_quantize/SymmQuant/add" -> "2861 maskrcnn/image_info/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2858 maskrcnn/image_info/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2859 maskrcnn/image_info/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2859 maskrcnn/image_info/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2860 maskrcnn/image_info/fake_quantize/SymmQuant/mul"; -"2860 maskrcnn/image_info/fake_quantize/SymmQuant/mul" -> "2861 maskrcnn/image_info/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2861 maskrcnn/image_info/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3053 maskrcnn/tf.__operators__.getitem/strided_slice"; -"2861 maskrcnn/image_info/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5239 maskrcnn/tf.__operators__.getitem_60/strided_slice"; -"2862 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2863 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2863 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2864 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/Abs"; -"2864 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/Abs" -> "2866 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/add"; -"2865 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/add/y" -> "2866 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/add"; -"2866 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/add" -> "2869 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/mul"; -"2866 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/add" -> "2870 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2867 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2868 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2868 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2869 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/mul"; -"2869 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/mul" -> "2870 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2870 maskrcnn/tf.__operators__.add_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3042 maskrcnn/tf.math.subtract_33/Sub"; -"2871 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2872 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2872 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2873 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/Abs"; -"2873 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/Abs" -> "2875 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/add"; -"2874 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/add/y" -> "2875 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/add"; -"2875 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/add" -> "2878 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/mul"; -"2875 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/add" -> "2879 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2876 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2877 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2877 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2878 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/mul"; -"2878 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/mul" -> "2879 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2879 maskrcnn/tf.math.multiply_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3042 maskrcnn/tf.math.subtract_33/Sub"; -"2880 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2881 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2881 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2882 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/Abs"; -"2882 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/Abs" -> "2884 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/add"; -"2883 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/add/y" -> "2884 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/add"; -"2884 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/add" -> "2887 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/mul"; -"2884 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/add" -> "2888 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2885 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2886 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2886 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2887 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/mul"; -"2887 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/mul" -> "2888 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2888 maskrcnn/tf.__operators__.add_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3043 maskrcnn/tf.math.subtract_32/Sub"; -"2889 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2890 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2890 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2891 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/Abs"; -"2891 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/Abs" -> "2893 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/add"; -"2892 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/add/y" -> "2893 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/add"; -"2893 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/add" -> "2896 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/mul"; -"2893 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/add" -> "2897 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2894 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2895 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2895 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2896 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/mul"; -"2896 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/mul" -> "2897 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2897 maskrcnn/tf.math.multiply_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3043 maskrcnn/tf.math.subtract_32/Sub"; -"2898 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2899 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2899 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2900 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/Abs"; -"2900 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/Abs" -> "2902 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/add"; -"2901 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/add/y" -> "2902 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/add"; -"2902 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/add" -> "2905 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/mul"; -"2902 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/add" -> "2906 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2903 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2904 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2904 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2905 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/mul"; -"2905 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/mul" -> "2906 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2906 maskrcnn/tf.__operators__.add_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3044 maskrcnn/tf.math.subtract_25/Sub"; -"2907 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2908 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2908 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2909 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/Abs"; -"2909 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/Abs" -> "2911 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/add"; -"2910 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/add/y" -> "2911 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/add"; -"2911 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/add" -> "2914 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/mul"; -"2911 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/add" -> "2915 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2912 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2913 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2913 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2914 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/mul"; -"2914 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/mul" -> "2915 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2915 maskrcnn/tf.math.multiply_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3044 maskrcnn/tf.math.subtract_25/Sub"; -"2916 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2917 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2917 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2918 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/Abs"; -"2918 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/Abs" -> "2920 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add"; -"2919 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add/y" -> "2920 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add"; -"2920 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add" -> "2923 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul"; -"2920 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add" -> "2924 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2921 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2922 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2922 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2923 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul"; -"2923 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul" -> "2924 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2924 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3045 maskrcnn/tf.math.subtract_24/Sub"; -"2925 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2926 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2926 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2927 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs"; -"2927 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs" -> "2929 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add"; -"2928 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add/y" -> "2929 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add"; -"2929 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add" -> "2932 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul"; -"2929 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add" -> "2933 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2930 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2931 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2931 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2932 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul"; -"2932 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul" -> "2933 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2933 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3045 maskrcnn/tf.math.subtract_24/Sub"; -"2934 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2935 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2935 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2936 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs"; -"2936 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs" -> "2938 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add"; -"2937 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add/y" -> "2938 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add"; -"2938 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add" -> "2941 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul"; -"2938 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add" -> "2942 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2939 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2940 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2940 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2941 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul"; -"2941 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul" -> "2942 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2942 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3046 maskrcnn/tf.math.subtract_17/Sub"; -"2943 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2944 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2944 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2945 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs"; -"2945 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs" -> "2947 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add"; -"2946 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add/y" -> "2947 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add"; -"2947 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add" -> "2950 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul"; -"2947 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add" -> "2951 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2948 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2949 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2949 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2950 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul"; -"2950 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul" -> "2951 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2951 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3046 maskrcnn/tf.math.subtract_17/Sub"; -"2952 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2953 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2953 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2954 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs"; -"2954 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs" -> "2956 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add"; -"2955 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add/y" -> "2956 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add"; -"2956 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add" -> "2959 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul"; -"2956 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add" -> "2960 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2957 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2958 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2958 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2959 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul"; -"2959 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul" -> "2960 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2960 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3047 maskrcnn/tf.math.subtract_16/Sub"; -"2961 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2962 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2962 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2963 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs"; -"2963 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs" -> "2965 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add"; -"2964 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add/y" -> "2965 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add"; -"2965 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add" -> "2968 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul"; -"2965 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add" -> "2969 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2966 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2967 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2967 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2968 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul"; -"2968 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul" -> "2969 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2969 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3047 maskrcnn/tf.math.subtract_16/Sub"; -"2970 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2971 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2971 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2972 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs"; -"2972 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs" -> "2974 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add"; -"2973 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add/y" -> "2974 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add"; -"2974 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add" -> "2977 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul"; -"2974 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add" -> "2978 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2975 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2976 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2976 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2977 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul"; -"2977 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul" -> "2978 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2978 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3048 maskrcnn/tf.math.subtract_9/Sub"; -"2979 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2980 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"2980 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2981 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs"; -"2981 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs" -> "2983 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add"; -"2982 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add/y" -> "2983 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add"; -"2983 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add" -> "2986 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul"; -"2983 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add" -> "2987 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"2984 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2985 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"2985 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2986 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul"; -"2986 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul" -> "2987 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maskrcnn/rpn-class/SymmQuant_2/mul/ReadVariableOp" -> "3629 maskrcnn/rpn-class/SymmQuant_2/mul"; -"3629 maskrcnn/rpn-class/SymmQuant_2/mul" -> "3631 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVars"; -"3630 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVars/ReadVariableOp" -> "3631 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVars"; -"3631 maskrcnn/rpn-class/SymmQuant_2/FakeQuantWithMinMaxVars" -> "3632 maskrcnn/rpn-class/Conv2D_2"; -"3632 maskrcnn/rpn-class/Conv2D_2" -> "3634 maskrcnn/rpn-class/BiasAdd_2"; -"3633 maskrcnn/rpn-class/BiasAdd_2/ReadVariableOp" -> "3634 maskrcnn/rpn-class/BiasAdd_2"; -"3634 maskrcnn/rpn-class/BiasAdd_2" -> "3685 maskrcnn/tf.reshape_4/Reshape"; -"3634 maskrcnn/rpn-class/BiasAdd_2" -> "19993 Identity_14"; -"3635 maskrcnn/rpn-class/SymmQuant_3/Abs/ReadVariableOp" -> "3636 maskrcnn/rpn-class/SymmQuant_3/Abs"; -"3636 maskrcnn/rpn-class/SymmQuant_3/Abs" -> "3638 maskrcnn/rpn-class/SymmQuant_3/add"; -"3637 maskrcnn/rpn-class/SymmQuant_3/add/y" -> 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"3890 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/add"; -"3890 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/add" -> "3893 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul"; -"3890 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/add" -> "3894 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3891 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3892 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"3892 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3893 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul"; -"3893 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul" -> "3894 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3894 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3895 maskrcnn/tf.math.sqrt/Sqrt"; -"3895 maskrcnn/tf.math.sqrt/Sqrt" -> "3904 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3896 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3897 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"3897 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3898 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs"; -"3898 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs" -> "3900 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add"; -"3899 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add/y" -> "3900 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add"; -"3900 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add" -> "3903 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul"; -"3900 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add" -> "3904 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3901 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3902 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"3902 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3903 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul"; -"3903 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul" -> "3904 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3904 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3906 maskrcnn/tf.math.truediv/truediv"; -"3905 maskrcnn/tf.math.truediv/truediv/y" -> "3906 maskrcnn/tf.math.truediv/truediv"; -"3906 maskrcnn/tf.math.truediv/truediv" -> "3907 maskrcnn/tf.math.log/Log"; -"3907 maskrcnn/tf.math.log/Log" -> "3916 maskrcnn/tf.math.log/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3908 maskrcnn/tf.math.log/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3909 maskrcnn/tf.math.log/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"3909 maskrcnn/tf.math.log/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3910 maskrcnn/tf.math.log/fake_quantize/SymmQuant/Abs"; -"3910 maskrcnn/tf.math.log/fake_quantize/SymmQuant/Abs" -> "3912 maskrcnn/tf.math.log/fake_quantize/SymmQuant/add"; -"3911 maskrcnn/tf.math.log/fake_quantize/SymmQuant/add/y" -> "3912 maskrcnn/tf.math.log/fake_quantize/SymmQuant/add"; -"3912 maskrcnn/tf.math.log/fake_quantize/SymmQuant/add" -> "3915 maskrcnn/tf.math.log/fake_quantize/SymmQuant/mul"; -"3912 maskrcnn/tf.math.log/fake_quantize/SymmQuant/add" -> "3916 maskrcnn/tf.math.log/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3913 maskrcnn/tf.math.log/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3914 maskrcnn/tf.math.log/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"3914 maskrcnn/tf.math.log/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3915 maskrcnn/tf.math.log/fake_quantize/SymmQuant/mul"; -"3915 maskrcnn/tf.math.log/fake_quantize/SymmQuant/mul" -> "3916 maskrcnn/tf.math.log/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3916 maskrcnn/tf.math.log/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3918 maskrcnn/tf.compat.v1.floor_div/FloorDiv"; -"3917 maskrcnn/tf.compat.v1.floor_div/FloorDiv/y" -> "3918 maskrcnn/tf.compat.v1.floor_div/FloorDiv"; -"3918 maskrcnn/tf.compat.v1.floor_div/FloorDiv" -> "3927 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3919 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3920 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"3920 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3921 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/Abs"; -"3921 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/Abs" -> "3923 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/add"; -"3922 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/add/y" -> "3923 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/add"; -"3923 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/add" -> "3926 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/mul"; -"3923 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/add" -> "3927 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3924 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3925 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"3925 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3926 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/mul"; -"3926 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/mul" -> "3927 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3927 maskrcnn/tf.compat.v1.floor_div/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3929 maskrcnn/tf.__operators__.add_39/AddV2"; -"3928 maskrcnn/tf.__operators__.add_39/y" -> "3929 maskrcnn/tf.__operators__.add_39/AddV2"; -"3929 maskrcnn/tf.__operators__.add_39/AddV2" -> "3930 maskrcnn/tf.cast_20/Cast"; -"3930 maskrcnn/tf.cast_20/Cast" -> "3932 maskrcnn/tf.math.maximum_5/Maximum"; -"3931 maskrcnn/tf.math.maximum_5/Maximum/y" -> "3932 maskrcnn/tf.math.maximum_5/Maximum"; -"3932 maskrcnn/tf.math.maximum_5/Maximum" -> "3934 maskrcnn/tf.math.minimum_15/Minimum"; -"3934 maskrcnn/tf.math.minimum_15/Minimum" -> "3935 maskrcnn/tf.cast_21/Cast"; -"3934 maskrcnn/tf.math.minimum_15/Minimum" -> "3962 maskrcnn/tf.math.subtract_42/Sub"; -"3935 maskrcnn/tf.cast_21/Cast" -> "3944 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3936 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3937 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"3937 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3938 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs"; -"3938 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs" -> "3940 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add"; -"3939 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add/y" -> "3940 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add"; -"3940 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add" -> "3943 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul"; -"3940 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add" -> "3944 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3941 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3942 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"3942 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3943 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul"; -"3943 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul" -> "3944 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3944 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3946 maskrcnn/tf.math.pow/Pow"; -"3946 maskrcnn/tf.math.pow/Pow" -> "3955 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3947 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3948 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"3948 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3949 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs"; -"3949 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs" -> "3951 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add"; -"3950 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add/y" -> "3951 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add"; -"3951 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add" -> "3954 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul"; -"3951 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add" -> "3955 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3952 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3953 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"3953 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3954 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul"; -"3954 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul" -> "3955 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3955 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3957 maskrcnn/tf.expand_dims_12/ExpandDims"; -"3955 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3958 maskrcnn/tf.math.truediv_2/truediv"; -"3955 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3959 maskrcnn/tf.math.truediv_3/truediv"; -"3956 maskrcnn/tf.expand_dims_12/ExpandDims/dim" -> "3957 maskrcnn/tf.expand_dims_12/ExpandDims"; -"3957 maskrcnn/tf.expand_dims_12/ExpandDims" -> "3960 maskrcnn/tf.math.truediv_1/truediv"; -"3958 maskrcnn/tf.math.truediv_2/truediv" -> "3971 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3959 maskrcnn/tf.math.truediv_3/truediv" -> "3980 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3960 maskrcnn/tf.math.truediv_1/truediv" -> "3989 maskrcnn/tf.math.truediv_1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3961 maskrcnn/tf.math.subtract_42/Sub/y" -> "3962 maskrcnn/tf.math.subtract_42/Sub"; -"3962 maskrcnn/tf.math.subtract_42/Sub" -> "3990 maskrcnn/tf.cast_23/Cast"; -"3962 maskrcnn/tf.math.subtract_42/Sub" -> "4691 maskrcnn/tf.compat.v1.gather_1/GatherV2"; -"3962 maskrcnn/tf.math.subtract_42/Sub" -> "4728 maskrcnn/tf.compat.v1.gather/GatherV2"; -"3963 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3964 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"3964 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3965 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs"; -"3965 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs" -> "3967 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add"; -"3966 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add/y" -> "3967 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add"; -"3967 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add" -> "3970 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul"; -"3967 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add" -> "3971 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3968 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3969 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"3969 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3970 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul"; -"3970 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul" -> "3971 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3971 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3998 maskrcnn/tf.expand_dims_14/ExpandDims"; -"3972 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3973 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"3973 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3974 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/Abs"; -"3974 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/Abs" -> "3976 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/add"; -"3975 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/add/y" -> "3976 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/add"; -"3976 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/add" -> "3979 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/mul"; -"3976 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/add" -> "3980 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3977 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3978 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"3978 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3979 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/mul"; -"3979 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/mul" -> "3980 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"3980 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3996 maskrcnn/tf.expand_dims_13/ExpandDims"; -"3981 maskrcnn/tf.math.truediv_1/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3982 maskrcnn/tf.math.truediv_1/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"3982 maskrcnn/tf.math.truediv_1/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3983 maskrcnn/tf.math.truediv_1/fake_quantize/SymmQuant/Abs"; -"3983 maskrcnn/tf.math.truediv_1/fake_quantize/SymmQuant/Abs" -> "3985 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maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "4586 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/Abs"; -"4586 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/Abs" -> "4588 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/add"; -"4587 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/add/y" -> "4588 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/add"; -"4588 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/add" -> "4591 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/mul"; -"4588 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/add" -> "4592 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"4589 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "4590 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; -"4590 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "4591 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/mul"; -"4591 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/mul" -> "4592 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"4592 maskrcnn/tf.math.subtract_46/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "4640 maskrcnn/tf.math.subtract_46/Sub"; -"4593 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp/resource" -> "4594 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp"; -"4594 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp" -> "4595 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/Abs"; -"4595 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/Abs" -> "4597 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/add"; -"4596 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/add/y" -> "4597 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/add"; -"4597 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/add" -> "4600 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul"; -"4597 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/add" -> "4601 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; -"4598 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul/ReadVariableOp/resource" -> "4599 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul/ReadVariableOp"; -"4599 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul/ReadVariableOp" -> "4600 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul"; -"4600 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul" -> "4601 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; -"4601 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars" -> "4640 maskrcnn/tf.math.subtract_46/Sub"; -"4602 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp/resource" -> "4603 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp"; -"4603 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "4604 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs"; -"4604 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs" -> "4606 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add"; -"4605 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add/y" -> "4606 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add"; -"4606 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add" -> "4609 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul"; -"4606 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add" -> "4610 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"4607 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "4608 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; -"4608 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "4609 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul"; -"4609 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul" -> "4610 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"4610 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "4641 maskrcnn/tf.math.subtract_45/Sub"; -"4611 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp/resource" -> "4612 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp"; -"4612 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp" -> "4613 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs"; -"4613 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs" -> "4615 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add"; -"4614 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add/y" -> "4615 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add"; -"4615 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add" -> "4618 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul"; -"4615 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add" -> "4619 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; -"4616 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul/ReadVariableOp/resource" -> "4617 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul/ReadVariableOp"; -"4617 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul/ReadVariableOp" -> "4618 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul"; -"4618 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul" -> "4619 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; -"4619 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars" -> "4641 maskrcnn/tf.math.subtract_45/Sub"; -"4620 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp/resource" -> "4621 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp"; -"4621 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "4622 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/Abs"; -"4622 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/Abs" -> "4624 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/add"; -"4623 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/add/y" -> "4624 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/add"; -"4624 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/add" -> "4627 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/mul"; -"4624 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/add" -> "4628 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"4625 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "4626 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; -"4626 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "4627 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/mul"; -"4627 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/mul" -> "4628 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"4628 maskrcnn/tf.__operators__.add_54/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "4647 maskrcnn/tf.__operators__.add_54/AddV2"; -"4629 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4630 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"4630 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4631 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/Abs"; -"4631 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/Abs" -> "4633 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/add"; -"4632 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/add/y" -> "4633 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/add"; -"4633 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/add" -> "4636 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/mul"; -"4633 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/add" -> "4637 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4634 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4635 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"4635 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4636 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/mul"; -"4636 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/mul" -> "4637 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4637 maskrcnn/tf.__operators__.add_55/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4648 maskrcnn/tf.math.minimum_19/Minimum"; -"4638 maskrcnn/tf.expand_dims_20/ExpandDims/dim" -> "4639 maskrcnn/tf.expand_dims_20/ExpandDims"; -"4639 maskrcnn/tf.expand_dims_20/ExpandDims" -> "4648 maskrcnn/tf.math.minimum_19/Minimum"; -"4640 maskrcnn/tf.math.subtract_46/Sub" -> "4657 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4641 maskrcnn/tf.math.subtract_45/Sub" -> "4666 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4642 maskrcnn/tf.__operators__.getitem_57/strided_slice/stack" -> "4645 maskrcnn/tf.__operators__.getitem_57/strided_slice"; -"4643 maskrcnn/tf.__operators__.getitem_57/strided_slice/stack_1" -> "4645 maskrcnn/tf.__operators__.getitem_57/strided_slice"; -"4644 maskrcnn/tf.__operators__.getitem_57/strided_slice/stack_2" -> "4645 maskrcnn/tf.__operators__.getitem_57/strided_slice"; -"4645 maskrcnn/tf.__operators__.getitem_57/strided_slice" -> "4677 maskrcnn/tf.expand_dims_18/ExpandDims"; -"4646 maskrcnn/tf.__operators__.add_54/y" -> "4647 maskrcnn/tf.__operators__.add_54/AddV2"; -"4647 maskrcnn/tf.__operators__.add_54/AddV2" -> "4675 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4648 maskrcnn/tf.math.minimum_19/Minimum" -> "4678 maskrcnn/tf.stack_9/stack"; -"4649 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4650 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"4650 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4651 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs"; -"4651 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs" -> "4653 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add"; -"4652 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add/y" -> "4653 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add"; -"4653 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add" -> "4656 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul"; -"4653 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add" -> "4657 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4654 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4655 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"4655 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4656 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul"; -"4656 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul" -> "4657 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4657 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4680 maskrcnn/tf.math.subtract_48/Sub"; -"4657 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4716 maskrcnn/tf.stack_11/stack"; -"4658 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4659 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"4659 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4660 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs"; -"4660 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs" -> "4662 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add"; -"4661 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add/y" -> "4662 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add"; -"4662 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add" -> "4665 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul"; -"4662 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add" -> "4666 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4663 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4664 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"4664 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4665 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul"; -"4665 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul" -> "4666 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4666 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4682 maskrcnn/tf.math.subtract_47/Sub"; -"4666 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4717 maskrcnn/tf.stack_10/stack"; -"4667 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4668 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"4668 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4669 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs"; -"4669 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs" -> "4671 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add"; -"4670 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add/y" -> "4671 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add"; -"4671 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add" -> "4674 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul"; -"4671 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add" -> "4675 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4672 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4673 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"4673 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4674 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul"; -"4674 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul" -> "4675 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4675 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4683 maskrcnn/tf.math.minimum_17/Minimum"; -"4676 maskrcnn/tf.expand_dims_18/ExpandDims/dim" -> "4677 maskrcnn/tf.expand_dims_18/ExpandDims"; -"4677 maskrcnn/tf.expand_dims_18/ExpandDims" -> "4683 maskrcnn/tf.math.minimum_17/Minimum"; -"4678 maskrcnn/tf.stack_9/stack" -> "4693 maskrcnn/tf.reshape_23/Reshape"; -"4680 maskrcnn/tf.math.subtract_48/Sub" -> "4702 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maskrcnn/tf.__operators__.getitem_67/strided_slice/stack" -> "4987 maskrcnn/tf.__operators__.getitem_67/strided_slice"; -"4985 maskrcnn/tf.__operators__.getitem_67/strided_slice/stack_1" -> "4987 maskrcnn/tf.__operators__.getitem_67/strided_slice"; -"4986 maskrcnn/tf.__operators__.getitem_67/strided_slice/stack_2" -> "4987 maskrcnn/tf.__operators__.getitem_67/strided_slice"; -"4987 maskrcnn/tf.__operators__.getitem_67/strided_slice" -> "4995 maskrcnn/tf.math.truediv_23/truediv"; -"4988 maskrcnn/tf.math.subtract_52/Sub" -> "5004 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4989 maskrcnn/tf.__operators__.getitem_66/strided_slice/stack" -> "4992 maskrcnn/tf.__operators__.getitem_66/strided_slice"; -"4990 maskrcnn/tf.__operators__.getitem_66/strided_slice/stack_1" -> "4992 maskrcnn/tf.__operators__.getitem_66/strided_slice"; -"4991 maskrcnn/tf.__operators__.getitem_66/strided_slice/stack_2" -> "4992 maskrcnn/tf.__operators__.getitem_66/strided_slice"; -"4992 maskrcnn/tf.__operators__.getitem_66/strided_slice" -> "5006 maskrcnn/tf.math.truediv_22/truediv"; -"4993 maskrcnn/tf.math.subtract_51/Sub" -> "5015 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4994 maskrcnn/tf.math.truediv_23/truediv/y" -> "4995 maskrcnn/tf.math.truediv_23/truediv"; -"4995 maskrcnn/tf.math.truediv_23/truediv" -> "5024 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"4996 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4997 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"4997 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4998 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/Abs"; -"4998 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/Abs" -> "5000 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/add"; -"4999 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/add/y" -> "5000 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/add"; -"5000 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/add" -> "5003 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/mul"; -"5000 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/add" -> "5004 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5001 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5002 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5002 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5003 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/mul"; -"5003 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/mul" -> "5004 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5004 maskrcnn/tf.math.subtract_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5026 maskrcnn/tf.__operators__.add_60/AddV2"; -"5005 maskrcnn/tf.math.truediv_22/truediv/y" -> "5006 maskrcnn/tf.math.truediv_22/truediv"; -"5006 maskrcnn/tf.math.truediv_22/truediv" -> "5035 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5007 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5008 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5008 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5009 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/Abs"; -"5009 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/Abs" -> "5011 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/add"; -"5010 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/add/y" -> "5011 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/add"; -"5011 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/add" -> "5014 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/mul"; -"5011 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/add" -> "5015 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5012 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5013 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5013 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5014 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/mul"; -"5014 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/mul" -> "5015 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5015 maskrcnn/tf.math.subtract_51/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5037 maskrcnn/tf.__operators__.add_59/AddV2"; -"5016 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5017 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5017 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5018 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/Abs"; -"5018 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/Abs" -> "5020 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/add"; -"5019 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/add/y" -> "5020 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/add"; -"5020 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/add" -> "5023 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/mul"; -"5020 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/add" -> "5024 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5021 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5022 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5022 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5023 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/mul"; -"5023 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/mul" -> "5024 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5024 maskrcnn/tf.math.truediv_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5048 maskrcnn/tf.math.minimum_21/Minimum"; -"5025 maskrcnn/tf.__operators__.add_60/y" -> "5026 maskrcnn/tf.__operators__.add_60/AddV2"; -"5026 maskrcnn/tf.__operators__.add_60/AddV2" -> "5046 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5027 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5028 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5028 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5029 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/Abs"; -"5029 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/Abs" -> "5031 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/add"; -"5030 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/add/y" -> "5031 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/add"; -"5031 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/add" -> "5034 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/mul"; -"5031 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/add" -> "5035 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5032 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5033 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5033 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5034 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/mul"; -"5034 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/mul" -> "5035 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5035 maskrcnn/tf.math.truediv_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5063 maskrcnn/tf.math.minimum_20/Minimum"; -"5036 maskrcnn/tf.__operators__.add_59/y" -> "5037 maskrcnn/tf.__operators__.add_59/AddV2"; -"5037 maskrcnn/tf.__operators__.add_59/AddV2" -> "5061 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5038 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5039 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5039 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5040 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/Abs"; -"5040 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/Abs" -> "5042 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/add"; -"5041 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/add/y" -> "5042 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/add"; -"5042 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/add" -> "5045 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/mul"; -"5042 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/add" -> "5046 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5043 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5044 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5044 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5045 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/mul"; -"5045 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/mul" -> "5046 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5046 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5070 maskrcnn/tf.math.multiply_52/Mul"; -"5046 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5132 maskrcnn/tf.math.multiply_56/Mul"; -"5046 maskrcnn/tf.__operators__.add_60/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5134 maskrcnn/tf.math.multiply_54/Mul"; -"5047 maskrcnn/tf.math.minimum_21/Minimum/y" -> "5048 maskrcnn/tf.math.minimum_21/Minimum"; -"5048 maskrcnn/tf.math.minimum_21/Minimum" -> "5068 maskrcnn/tf.math.exp_11/Exp"; -"5049 maskrcnn/tf.__operators__.getitem_65/strided_slice/stack" -> "5052 maskrcnn/tf.__operators__.getitem_65/strided_slice"; -"5050 maskrcnn/tf.__operators__.getitem_65/strided_slice/stack_1" -> "5052 maskrcnn/tf.__operators__.getitem_65/strided_slice"; -"5051 maskrcnn/tf.__operators__.getitem_65/strided_slice/stack_2" -> "5052 maskrcnn/tf.__operators__.getitem_65/strided_slice"; -"5052 maskrcnn/tf.__operators__.getitem_65/strided_slice" -> "5072 maskrcnn/tf.math.truediv_21/truediv"; -"5053 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5054 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5054 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5055 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/Abs"; -"5055 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/Abs" -> "5057 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/add"; -"5056 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/add/y" -> "5057 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/add"; -"5057 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/add" -> "5060 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/mul"; -"5057 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/add" -> "5061 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5058 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5059 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5059 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5060 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/mul"; -"5060 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/mul" -> "5061 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5061 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5075 maskrcnn/tf.math.multiply_51/Mul"; -"5061 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5135 maskrcnn/tf.math.multiply_55/Mul"; -"5061 maskrcnn/tf.__operators__.add_59/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5137 maskrcnn/tf.math.multiply_53/Mul"; -"5062 maskrcnn/tf.math.minimum_20/Minimum/y" -> "5063 maskrcnn/tf.math.minimum_20/Minimum"; -"5063 maskrcnn/tf.math.minimum_20/Minimum" -> "5073 maskrcnn/tf.math.exp_10/Exp"; -"5064 maskrcnn/tf.__operators__.getitem_64/strided_slice/stack" -> "5067 maskrcnn/tf.__operators__.getitem_64/strided_slice"; -"5065 maskrcnn/tf.__operators__.getitem_64/strided_slice/stack_1" -> "5067 maskrcnn/tf.__operators__.getitem_64/strided_slice"; -"5066 maskrcnn/tf.__operators__.getitem_64/strided_slice/stack_2" -> "5067 maskrcnn/tf.__operators__.getitem_64/strided_slice"; -"5067 maskrcnn/tf.__operators__.getitem_64/strided_slice" -> "5077 maskrcnn/tf.math.truediv_20/truediv"; -"5068 maskrcnn/tf.math.exp_11/Exp" -> "5086 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5070 maskrcnn/tf.math.multiply_52/Mul" -> "5095 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5071 maskrcnn/tf.math.truediv_21/truediv/y" -> "5072 maskrcnn/tf.math.truediv_21/truediv"; -"5072 maskrcnn/tf.math.truediv_21/truediv" -> "5104 maskrcnn/tf.math.truediv_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5073 maskrcnn/tf.math.exp_10/Exp" -> "5113 maskrcnn/tf.math.exp_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5075 maskrcnn/tf.math.multiply_51/Mul" -> "5122 maskrcnn/tf.math.multiply_51/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5076 maskrcnn/tf.math.truediv_20/truediv/y" -> "5077 maskrcnn/tf.math.truediv_20/truediv"; -"5077 maskrcnn/tf.math.truediv_20/truediv" -> "5131 maskrcnn/tf.math.truediv_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5078 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5079 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5079 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5080 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/Abs"; -"5080 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/Abs" -> "5082 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/add"; -"5081 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/add/y" -> "5082 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/add"; -"5082 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/add" -> "5085 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/mul"; -"5082 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/add" -> "5086 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5083 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5084 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5084 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5085 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/mul"; -"5085 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/mul" -> "5086 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5086 maskrcnn/tf.math.exp_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5132 maskrcnn/tf.math.multiply_56/Mul"; -"5087 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5088 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5088 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5089 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/Abs"; -"5089 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/Abs" -> "5091 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/add"; -"5090 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/add/y" -> "5091 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/add"; -"5091 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/add" -> "5094 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/mul"; -"5091 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/add" -> "5095 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5092 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5093 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5093 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5094 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/mul"; -"5094 maskrcnn/tf.math.multiply_52/fake_quantize/SymmQuant/mul" -> 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maskrcnn/tf.math.truediv_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5903 maskrcnn/tf.math.truediv_29/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5904 maskrcnn/tf.math.truediv_29/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5904 maskrcnn/tf.math.truediv_29/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5905 maskrcnn/tf.math.truediv_29/fake_quantize/SymmQuant/mul"; -"5905 maskrcnn/tf.math.truediv_29/fake_quantize/SymmQuant/mul" -> "5906 maskrcnn/tf.math.truediv_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5906 maskrcnn/tf.math.truediv_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6064 maskrcnn/tf.math.subtract_65/Sub"; -"5907 maskrcnn/tf.math.truediv_58/truediv/y" -> "5908 maskrcnn/tf.math.truediv_58/truediv"; -"5908 maskrcnn/tf.math.truediv_58/truediv" -> "6077 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5909 maskrcnn/tf.math.truediv_56/truediv/y" -> "5910 maskrcnn/tf.math.truediv_56/truediv"; -"5910 maskrcnn/tf.math.truediv_56/truediv" -> "6090 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5911 maskrcnn/tf.math.truediv_54/truediv/y" -> "5912 maskrcnn/tf.math.truediv_54/truediv"; -"5912 maskrcnn/tf.math.truediv_54/truediv" -> "6103 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5913 maskrcnn/tf.math.truediv_52/truediv/y" -> "5914 maskrcnn/tf.math.truediv_52/truediv"; -"5914 maskrcnn/tf.math.truediv_52/truediv" -> "6116 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5915 maskrcnn/tf.math.truediv_50/truediv/y" -> "5916 maskrcnn/tf.math.truediv_50/truediv"; -"5916 maskrcnn/tf.math.truediv_50/truediv" -> "6129 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5917 maskrcnn/tf.math.truediv_48/truediv/y" -> "5918 maskrcnn/tf.math.truediv_48/truediv"; -"5918 maskrcnn/tf.math.truediv_48/truediv" -> "6142 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5919 maskrcnn/tf.math.truediv_46/truediv/y" -> "5920 maskrcnn/tf.math.truediv_46/truediv"; -"5920 maskrcnn/tf.math.truediv_46/truediv" -> "6155 maskrcnn/tf.math.truediv_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5921 maskrcnn/tf.math.truediv_44/truediv/y" -> "5922 maskrcnn/tf.math.truediv_44/truediv"; -"5922 maskrcnn/tf.math.truediv_44/truediv" -> "6168 maskrcnn/tf.math.truediv_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5923 maskrcnn/tf.math.truediv_42/truediv/y" -> "5924 maskrcnn/tf.math.truediv_42/truediv"; -"5924 maskrcnn/tf.math.truediv_42/truediv" -> "6181 maskrcnn/tf.math.truediv_42/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5925 maskrcnn/tf.math.truediv_40/truediv/y" -> "5926 maskrcnn/tf.math.truediv_40/truediv"; -"5926 maskrcnn/tf.math.truediv_40/truediv" -> "6194 maskrcnn/tf.math.truediv_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5927 maskrcnn/tf.math.truediv_38/truediv/y" -> "5928 maskrcnn/tf.math.truediv_38/truediv"; -"5928 maskrcnn/tf.math.truediv_38/truediv" -> "6207 maskrcnn/tf.math.truediv_38/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5929 maskrcnn/tf.math.truediv_36/truediv/y" -> "5930 maskrcnn/tf.math.truediv_36/truediv"; -"5930 maskrcnn/tf.math.truediv_36/truediv" -> "6220 maskrcnn/tf.math.truediv_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5931 maskrcnn/tf.math.truediv_34/truediv/y" -> "5932 maskrcnn/tf.math.truediv_34/truediv"; -"5932 maskrcnn/tf.math.truediv_34/truediv" -> "6233 maskrcnn/tf.math.truediv_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5933 maskrcnn/tf.math.truediv_32/truediv/y" -> "5934 maskrcnn/tf.math.truediv_32/truediv"; -"5934 maskrcnn/tf.math.truediv_32/truediv" -> "6246 maskrcnn/tf.math.truediv_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5935 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5936 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5936 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5937 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs"; -"5937 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs" -> "5939 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add"; -"5938 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add/y" -> "5939 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add"; -"5939 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add" -> "5942 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul"; -"5939 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add" -> "5943 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5940 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5941 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5941 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5942 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul"; -"5942 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul" -> "5943 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5943 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6248 maskrcnn/tf.math.truediv_57/truediv"; -"5944 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5945 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5945 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5946 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs"; -"5946 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs" -> "5948 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add"; -"5947 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add/y" -> "5948 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add"; -"5948 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add" -> "5951 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul"; -"5948 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add" -> "5952 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5949 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5950 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5950 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5951 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul"; -"5951 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul" -> "5952 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5952 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6250 maskrcnn/tf.math.truediv_55/truediv"; -"5953 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5954 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5954 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5955 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs"; -"5955 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs" -> "5957 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add"; -"5956 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add/y" -> "5957 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add"; -"5957 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add" -> "5960 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul"; -"5957 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add" -> "5961 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5958 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5959 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5959 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5960 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul"; -"5960 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul" -> "5961 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5961 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6252 maskrcnn/tf.math.truediv_53/truediv"; -"5962 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5963 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5963 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5964 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs"; -"5964 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs" -> "5966 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add"; -"5965 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add/y" -> "5966 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add"; -"5966 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add" -> "5969 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul"; -"5966 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add" -> "5970 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5967 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5968 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5968 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5969 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul"; -"5969 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul" -> "5970 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5970 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6254 maskrcnn/tf.math.truediv_51/truediv"; -"5971 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5972 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5972 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5973 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs"; -"5973 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs" -> "5975 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add"; -"5974 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add/y" -> "5975 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add"; -"5975 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add" -> "5978 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul"; -"5975 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add" -> "5979 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5976 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5977 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5977 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5978 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul"; -"5978 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul" -> "5979 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5979 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6256 maskrcnn/tf.math.truediv_49/truediv"; -"5980 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5981 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5981 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5982 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs"; -"5982 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs" -> "5984 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add"; -"5983 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add/y" -> "5984 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add"; -"5984 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add" -> "5987 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul"; -"5984 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add" -> "5988 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5985 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5986 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5986 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5987 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul"; -"5987 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul" -> "5988 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5988 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6258 maskrcnn/tf.math.truediv_47/truediv"; -"5989 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5990 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5990 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5991 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs"; -"5991 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs" -> "5993 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add"; -"5992 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add/y" -> "5993 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add"; -"5993 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add" -> "5996 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul"; -"5993 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add" -> "5997 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5994 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5995 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"5995 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5996 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul"; -"5996 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul" -> "5997 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"5997 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6260 maskrcnn/tf.math.truediv_45/truediv"; -"5998 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5999 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"5999 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6000 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs"; -"6000 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs" -> "6002 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add"; -"6001 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add/y" -> "6002 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add"; -"6002 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add" -> "6005 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul"; -"6002 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add" -> "6006 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6003 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6004 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6004 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6005 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul"; -"6005 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul" -> "6006 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6006 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6262 maskrcnn/tf.math.truediv_43/truediv"; -"6007 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6008 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6008 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6009 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs"; -"6009 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs" -> "6011 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add"; -"6010 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add/y" -> "6011 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add"; -"6011 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add" -> "6014 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul"; -"6011 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add" -> "6015 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6012 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6013 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6013 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6014 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul"; -"6014 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul" -> "6015 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6015 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6264 maskrcnn/tf.math.truediv_41/truediv"; -"6016 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6017 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6017 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6018 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs"; -"6018 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs" -> "6020 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add"; -"6019 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add/y" -> "6020 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add"; -"6020 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add" -> "6023 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul"; -"6020 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add" -> "6024 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6021 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6022 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6022 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6023 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul"; -"6023 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul" -> "6024 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6024 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6266 maskrcnn/tf.math.truediv_39/truediv"; -"6025 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6026 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6026 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6027 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs"; -"6027 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs" -> "6029 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add"; -"6028 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add/y" -> "6029 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add"; -"6029 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add" -> "6032 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul"; -"6029 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add" -> "6033 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6030 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6031 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6031 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6032 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul"; -"6032 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul" -> "6033 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6033 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6268 maskrcnn/tf.math.truediv_37/truediv"; -"6034 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6035 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6035 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6036 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs"; -"6036 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs" -> "6038 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add"; -"6037 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add/y" -> "6038 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add"; -"6038 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add" -> "6041 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul"; -"6038 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add" -> "6042 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6039 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6040 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6040 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6041 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul"; -"6041 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul" -> "6042 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6042 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6270 maskrcnn/tf.math.truediv_35/truediv"; -"6043 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6044 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6044 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6045 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs"; -"6045 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs" -> "6047 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add"; -"6046 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add/y" -> "6047 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add"; -"6047 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add" -> "6050 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul"; -"6047 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add" -> "6051 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6048 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6049 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6049 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6050 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul"; -"6050 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul" -> "6051 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6051 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6272 maskrcnn/tf.math.truediv_33/truediv"; -"6052 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6053 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6053 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6054 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs"; -"6054 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs" -> "6056 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add"; -"6055 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add/y" -> "6056 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add"; -"6056 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add" -> "6059 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul"; -"6056 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add" -> "6060 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6057 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6058 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6058 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6059 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul"; -"6059 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul" -> "6060 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6060 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6274 maskrcnn/tf.math.truediv_31/truediv"; -"6061 maskrcnn/tf.math.subtract_66/Sub/y" -> "6062 maskrcnn/tf.math.subtract_66/Sub"; -"6062 maskrcnn/tf.math.subtract_66/Sub" -> "6283 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6063 maskrcnn/tf.math.subtract_65/Sub/y" -> "6064 maskrcnn/tf.math.subtract_65/Sub"; -"6064 maskrcnn/tf.math.subtract_65/Sub" -> "6292 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6065 maskrcnn/tf.__operators__.getitem_131/strided_slice/stack" -> "6068 maskrcnn/tf.__operators__.getitem_131/strided_slice"; -"6066 maskrcnn/tf.__operators__.getitem_131/strided_slice/stack_1" -> "6068 maskrcnn/tf.__operators__.getitem_131/strided_slice"; -"6067 maskrcnn/tf.__operators__.getitem_131/strided_slice/stack_2" -> "6068 maskrcnn/tf.__operators__.getitem_131/strided_slice"; -"6068 maskrcnn/tf.__operators__.getitem_131/strided_slice" -> "6306 maskrcnn/tf.__operators__.add_96/AddV2"; -"6069 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6070 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6070 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6071 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs"; -"6071 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs" -> "6073 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add"; -"6072 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add/y" -> "6073 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add"; -"6073 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add" -> "6076 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul"; -"6073 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add" -> "6077 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6074 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6075 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6075 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6076 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul"; -"6076 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul" -> "6077 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6077 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6306 maskrcnn/tf.__operators__.add_96/AddV2"; -"6078 maskrcnn/tf.__operators__.getitem_127/strided_slice/stack" -> "6081 maskrcnn/tf.__operators__.getitem_127/strided_slice"; -"6079 maskrcnn/tf.__operators__.getitem_127/strided_slice/stack_1" -> "6081 maskrcnn/tf.__operators__.getitem_127/strided_slice"; -"6080 maskrcnn/tf.__operators__.getitem_127/strided_slice/stack_2" -> "6081 maskrcnn/tf.__operators__.getitem_127/strided_slice"; -"6081 maskrcnn/tf.__operators__.getitem_127/strided_slice" -> "6305 maskrcnn/tf.__operators__.add_94/AddV2"; -"6082 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6083 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6083 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6084 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs"; -"6084 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs" -> "6086 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add"; -"6085 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add/y" -> "6086 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add"; -"6086 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add" -> "6089 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul"; -"6086 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add" -> "6090 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6087 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6088 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6088 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6089 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul"; -"6089 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul" -> "6090 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6090 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6305 maskrcnn/tf.__operators__.add_94/AddV2"; -"6091 maskrcnn/tf.__operators__.getitem_123/strided_slice/stack" -> "6094 maskrcnn/tf.__operators__.getitem_123/strided_slice"; -"6092 maskrcnn/tf.__operators__.getitem_123/strided_slice/stack_1" -> "6094 maskrcnn/tf.__operators__.getitem_123/strided_slice"; -"6093 maskrcnn/tf.__operators__.getitem_123/strided_slice/stack_2" -> "6094 maskrcnn/tf.__operators__.getitem_123/strided_slice"; -"6094 maskrcnn/tf.__operators__.getitem_123/strided_slice" -> "6304 maskrcnn/tf.__operators__.add_92/AddV2"; -"6095 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6096 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6096 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6097 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs"; -"6097 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs" -> "6099 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add"; -"6098 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add/y" -> "6099 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add"; -"6099 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add" -> "6102 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul"; -"6099 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add" -> "6103 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6100 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6101 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6101 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6102 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul"; -"6102 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul" -> "6103 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6103 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6304 maskrcnn/tf.__operators__.add_92/AddV2"; -"6104 maskrcnn/tf.__operators__.getitem_119/strided_slice/stack" -> "6107 maskrcnn/tf.__operators__.getitem_119/strided_slice"; -"6105 maskrcnn/tf.__operators__.getitem_119/strided_slice/stack_1" -> "6107 maskrcnn/tf.__operators__.getitem_119/strided_slice"; -"6106 maskrcnn/tf.__operators__.getitem_119/strided_slice/stack_2" -> "6107 maskrcnn/tf.__operators__.getitem_119/strided_slice"; -"6107 maskrcnn/tf.__operators__.getitem_119/strided_slice" -> "6303 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maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6114 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6115 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul"; -"6115 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul" -> "6116 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6116 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6303 maskrcnn/tf.__operators__.add_90/AddV2"; -"6117 maskrcnn/tf.__operators__.getitem_115/strided_slice/stack" -> "6120 maskrcnn/tf.__operators__.getitem_115/strided_slice"; -"6118 maskrcnn/tf.__operators__.getitem_115/strided_slice/stack_1" -> "6120 maskrcnn/tf.__operators__.getitem_115/strided_slice"; -"6119 maskrcnn/tf.__operators__.getitem_115/strided_slice/stack_2" -> "6120 maskrcnn/tf.__operators__.getitem_115/strided_slice"; -"6120 maskrcnn/tf.__operators__.getitem_115/strided_slice" -> "6302 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maskrcnn/tf.math.truediv_32/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6244 maskrcnn/tf.math.truediv_32/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6245 maskrcnn/tf.math.truediv_32/fake_quantize/SymmQuant/mul"; -"6245 maskrcnn/tf.math.truediv_32/fake_quantize/SymmQuant/mul" -> "6246 maskrcnn/tf.math.truediv_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6246 maskrcnn/tf.math.truediv_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6293 maskrcnn/tf.__operators__.add_70/AddV2"; -"6247 maskrcnn/tf.math.truediv_57/truediv/y" -> "6248 maskrcnn/tf.math.truediv_57/truediv"; -"6248 maskrcnn/tf.math.truediv_57/truediv" -> "6320 maskrcnn/tf.math.truediv_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6249 maskrcnn/tf.math.truediv_55/truediv/y" -> "6250 maskrcnn/tf.math.truediv_55/truediv"; -"6250 maskrcnn/tf.math.truediv_55/truediv" -> "6333 maskrcnn/tf.math.truediv_55/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6251 maskrcnn/tf.math.truediv_53/truediv/y" -> 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maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/Abs" -> "6279 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add"; -"6278 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add/y" -> "6279 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add"; -"6279 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add" -> "6282 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul"; -"6279 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add" -> "6283 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6280 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6281 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6281 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6282 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul"; -"6282 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul" -> "6283 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6283 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6493 maskrcnn/tf.expand_dims_28/ExpandDims"; -"6284 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6285 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6285 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6286 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs"; -"6286 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs" -> "6288 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add"; -"6287 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add/y" -> "6288 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add"; -"6288 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add" -> "6291 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/mul"; -"6288 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add" 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maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add/y" -> "6586 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add"; -"6586 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add" -> "6589 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul"; -"6586 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add" -> "6590 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"6587 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "6588 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; -"6588 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "6589 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul"; -"6589 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul" -> "6590 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"6590 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "6638 maskrcnn/tf.math.subtract_68/Sub"; -"6591 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp/resource" -> "6592 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp"; -"6592 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp" -> "6593 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs"; -"6593 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs" -> "6595 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add"; -"6594 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add/y" -> "6595 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add"; -"6595 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add" -> "6598 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul"; -"6595 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add" -> "6599 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; -"6596 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul/ReadVariableOp/resource" -> "6597 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul/ReadVariableOp"; -"6597 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul/ReadVariableOp" -> "6598 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul"; -"6598 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul" -> "6599 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; -"6599 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars" -> "6638 maskrcnn/tf.math.subtract_68/Sub"; -"6600 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp/resource" -> "6601 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp"; -"6601 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "6602 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs"; -"6602 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs" -> "6604 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add"; -"6603 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add/y" -> "6604 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add"; -"6604 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add" -> "6607 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul"; -"6604 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add" -> "6608 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"6605 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "6606 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; -"6606 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "6607 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul"; -"6607 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul" -> "6608 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"6608 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "6639 maskrcnn/tf.math.subtract_67/Sub"; -"6609 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp/resource" -> "6610 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp"; -"6610 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp" -> "6611 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs"; -"6611 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs" -> "6613 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add"; -"6612 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add/y" -> "6613 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add"; -"6613 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add" -> "6616 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul"; -"6613 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add" -> "6617 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; -"6614 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul/ReadVariableOp/resource" -> "6615 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul/ReadVariableOp"; -"6615 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul/ReadVariableOp" -> "6616 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul"; -"6616 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul" -> "6617 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; -"6617 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars" -> "6639 maskrcnn/tf.math.subtract_67/Sub"; -"6618 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp/resource" -> "6619 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp"; -"6619 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "6620 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/Abs"; -"6620 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/Abs" -> "6622 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/add"; -"6621 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/add/y" -> "6622 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/add"; -"6622 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/add" -> "6625 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/mul"; -"6622 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/add" -> "6626 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"6623 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "6624 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; -"6624 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "6625 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/mul"; -"6625 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/mul" -> "6626 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; -"6626 maskrcnn/tf.__operators__.add_97/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "6645 maskrcnn/tf.__operators__.add_97/AddV2"; -"6627 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6628 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6628 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6629 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs"; -"6629 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs" -> "6631 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add"; -"6630 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add/y" -> "6631 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add"; -"6631 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add" -> "6634 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul"; -"6631 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add" -> "6635 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6632 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6633 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6633 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6634 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul"; -"6634 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul" -> "6635 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6635 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6646 maskrcnn/tf.math.minimum_27/Minimum"; -"6636 maskrcnn/tf.expand_dims_32/ExpandDims/dim" -> "6637 maskrcnn/tf.expand_dims_32/ExpandDims"; -"6637 maskrcnn/tf.expand_dims_32/ExpandDims" -> "6646 maskrcnn/tf.math.minimum_27/Minimum"; -"6638 maskrcnn/tf.math.subtract_68/Sub" -> "6655 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6639 maskrcnn/tf.math.subtract_67/Sub" -> "6664 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6640 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack" -> "6643 maskrcnn/tf.__operators__.getitem_134/strided_slice"; -"6641 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack_1" -> "6643 maskrcnn/tf.__operators__.getitem_134/strided_slice"; -"6642 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack_2" -> "6643 maskrcnn/tf.__operators__.getitem_134/strided_slice"; -"6643 maskrcnn/tf.__operators__.getitem_134/strided_slice" -> "6675 maskrcnn/tf.expand_dims_30/ExpandDims"; -"6644 maskrcnn/tf.__operators__.add_97/y" -> "6645 maskrcnn/tf.__operators__.add_97/AddV2"; -"6645 maskrcnn/tf.__operators__.add_97/AddV2" -> "6673 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6646 maskrcnn/tf.math.minimum_27/Minimum" -> "6676 maskrcnn/tf.stack_20/stack"; -"6647 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6648 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6648 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6649 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/Abs"; -"6649 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/Abs" -> "6651 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/add"; -"6650 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/add/y" -> "6651 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/add"; -"6651 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/add" -> "6654 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/mul"; -"6651 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/add" -> "6655 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6652 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6653 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6653 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6654 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/mul"; -"6654 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/mul" -> "6655 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6655 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6678 maskrcnn/tf.math.subtract_70/Sub"; -"6655 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6714 maskrcnn/tf.stack_22/stack"; -"6656 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6657 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6657 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6658 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/Abs"; -"6658 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/Abs" -> "6660 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/add"; -"6659 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/add/y" -> "6660 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/add"; -"6660 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/add" -> "6663 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/mul"; -"6660 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/add" -> "6664 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6661 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6662 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6662 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6663 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/mul"; -"6663 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/mul" -> "6664 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6664 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6680 maskrcnn/tf.math.subtract_69/Sub"; -"6664 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6715 maskrcnn/tf.stack_21/stack"; -"6665 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6666 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6666 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6667 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs"; -"6667 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs" -> "6669 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add"; -"6668 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add/y" -> "6669 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add"; -"6669 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add" -> "6672 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul"; -"6669 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add" -> "6673 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6670 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6671 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6671 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6672 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul"; -"6672 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul" -> "6673 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6673 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6681 maskrcnn/tf.math.minimum_25/Minimum"; -"6674 maskrcnn/tf.expand_dims_30/ExpandDims/dim" -> "6675 maskrcnn/tf.expand_dims_30/ExpandDims"; -"6675 maskrcnn/tf.expand_dims_30/ExpandDims" -> "6681 maskrcnn/tf.math.minimum_25/Minimum"; -"6676 maskrcnn/tf.stack_20/stack" -> "6691 maskrcnn/tf.reshape_52/Reshape"; -"6678 maskrcnn/tf.math.subtract_70/Sub" -> "6700 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6680 maskrcnn/tf.math.subtract_69/Sub" -> "6709 maskrcnn/tf.math.subtract_69/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6681 maskrcnn/tf.math.minimum_25/Minimum" -> "6710 maskrcnn/tf.stack_19/stack"; -"6687 maskrcnn/tf.compat.v1.gather_4/GatherV2/params" -> "6689 maskrcnn/tf.compat.v1.gather_4/GatherV2"; -"6688 maskrcnn/tf.compat.v1.gather_4/GatherV2/axis" -> "6689 maskrcnn/tf.compat.v1.gather_4/GatherV2"; -"6689 maskrcnn/tf.compat.v1.gather_4/GatherV2" -> "6713 maskrcnn/tf.expand_dims_33/ExpandDims"; -"6690 maskrcnn/tf.reshape_52/Reshape/shape" -> "6691 maskrcnn/tf.reshape_52/Reshape"; -"6691 maskrcnn/tf.reshape_52/Reshape" -> "6711 maskrcnn/tf.cast_40/Cast"; -"6692 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6693 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6693 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6694 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/Abs"; -"6694 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/Abs" -> "6696 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/add"; -"6695 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/add/y" -> "6696 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/add"; -"6696 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/add" -> "6699 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/mul"; -"6696 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/add" -> "6700 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6697 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6698 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6698 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6699 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/mul"; -"6699 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/mul" -> "6700 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6700 maskrcnn/tf.math.subtract_70/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6714 maskrcnn/tf.stack_22/stack"; -"6701 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maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVars"; -"6831 maskrcnn/mask-conv-l0/SymmQuant/FakeQuantWithMinMaxVars" -> "6832 maskrcnn/mask-conv-l0/Conv2D"; -"6832 maskrcnn/mask-conv-l0/Conv2D" -> "6835 maskrcnn/mask-conv-l0/BiasAdd"; -"6833 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp/resource" -> "6834 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp"; -"6834 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp" -> "6835 maskrcnn/mask-conv-l0/BiasAdd"; -"6835 maskrcnn/mask-conv-l0/BiasAdd" -> "6836 maskrcnn/mask-conv-l0/Relu"; -"6836 maskrcnn/mask-conv-l0/Relu" -> "6845 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6837 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6838 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6838 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6839 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs"; -"6839 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs" -> 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maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp/resource" -> "6847 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp"; -"6847 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp" -> "6848 maskrcnn/mask-conv-l1/SymmQuant/Abs"; -"6848 maskrcnn/mask-conv-l1/SymmQuant/Abs" -> "6850 maskrcnn/mask-conv-l1/SymmQuant/add"; -"6849 maskrcnn/mask-conv-l1/SymmQuant/add/y" -> "6850 maskrcnn/mask-conv-l1/SymmQuant/add"; -"6850 maskrcnn/mask-conv-l1/SymmQuant/add" -> "6853 maskrcnn/mask-conv-l1/SymmQuant/mul"; -"6850 maskrcnn/mask-conv-l1/SymmQuant/add" -> "6856 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars"; -"6851 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp/resource" -> "6852 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp"; -"6852 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp" -> "6853 maskrcnn/mask-conv-l1/SymmQuant/mul"; -"6853 maskrcnn/mask-conv-l1/SymmQuant/mul" -> "6856 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars"; -"6854 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6855 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"6855 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6856 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars"; -"6856 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars" -> "6857 maskrcnn/mask-conv-l1/Conv2D"; -"6857 maskrcnn/mask-conv-l1/Conv2D" -> "6860 maskrcnn/mask-conv-l1/BiasAdd"; -"6858 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp/resource" -> "6859 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp"; -"6859 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp" -> "6860 maskrcnn/mask-conv-l1/BiasAdd"; -"6860 maskrcnn/mask-conv-l1/BiasAdd" -> "6861 maskrcnn/mask-conv-l1/Relu"; -"6861 maskrcnn/mask-conv-l1/Relu" -> "6870 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6862 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6863 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6863 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6864 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs"; -"6864 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs" -> "6866 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add"; -"6865 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add/y" -> "6866 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add"; -"6866 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add" -> "6869 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul"; -"6866 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add" -> "6870 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6867 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6868 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6868 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6869 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul"; -"6869 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul" -> "6870 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6870 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6882 maskrcnn/mask-conv-l2/Conv2D"; -"6871 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp/resource" -> "6872 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp"; -"6872 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp" -> "6873 maskrcnn/mask-conv-l2/SymmQuant/Abs"; -"6873 maskrcnn/mask-conv-l2/SymmQuant/Abs" -> "6875 maskrcnn/mask-conv-l2/SymmQuant/add"; -"6874 maskrcnn/mask-conv-l2/SymmQuant/add/y" -> "6875 maskrcnn/mask-conv-l2/SymmQuant/add"; -"6875 maskrcnn/mask-conv-l2/SymmQuant/add" -> "6878 maskrcnn/mask-conv-l2/SymmQuant/mul"; -"6875 maskrcnn/mask-conv-l2/SymmQuant/add" -> "6881 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars"; -"6876 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp/resource" -> "6877 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp"; -"6877 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp" -> "6878 maskrcnn/mask-conv-l2/SymmQuant/mul"; -"6878 maskrcnn/mask-conv-l2/SymmQuant/mul" -> "6881 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars"; -"6879 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6880 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"6880 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6881 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars"; -"6881 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars" -> "6882 maskrcnn/mask-conv-l2/Conv2D"; -"6882 maskrcnn/mask-conv-l2/Conv2D" -> "6885 maskrcnn/mask-conv-l2/BiasAdd"; -"6883 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp/resource" -> "6884 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp"; -"6884 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp" -> "6885 maskrcnn/mask-conv-l2/BiasAdd"; -"6885 maskrcnn/mask-conv-l2/BiasAdd" -> "6886 maskrcnn/mask-conv-l2/Relu"; -"6886 maskrcnn/mask-conv-l2/Relu" -> "6895 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6887 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6888 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6888 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6889 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs"; -"6889 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs" -> "6891 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add"; -"6890 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add/y" -> "6891 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add"; -"6891 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add" -> "6894 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul"; -"6891 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add" -> "6895 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6892 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6893 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6893 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6894 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul"; -"6894 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul" -> "6895 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6895 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6907 maskrcnn/mask-conv-l3/Conv2D"; -"6896 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp/resource" -> "6897 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp"; -"6897 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp" -> "6898 maskrcnn/mask-conv-l3/SymmQuant/Abs"; -"6898 maskrcnn/mask-conv-l3/SymmQuant/Abs" -> "6900 maskrcnn/mask-conv-l3/SymmQuant/add"; -"6899 maskrcnn/mask-conv-l3/SymmQuant/add/y" -> "6900 maskrcnn/mask-conv-l3/SymmQuant/add"; -"6900 maskrcnn/mask-conv-l3/SymmQuant/add" -> "6903 maskrcnn/mask-conv-l3/SymmQuant/mul"; -"6900 maskrcnn/mask-conv-l3/SymmQuant/add" -> "6906 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars"; -"6901 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp/resource" -> "6902 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp"; -"6902 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp" -> "6903 maskrcnn/mask-conv-l3/SymmQuant/mul"; -"6903 maskrcnn/mask-conv-l3/SymmQuant/mul" -> "6906 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars"; -"6904 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6905 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"6905 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6906 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars"; -"6906 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars" -> "6907 maskrcnn/mask-conv-l3/Conv2D"; -"6907 maskrcnn/mask-conv-l3/Conv2D" -> "6910 maskrcnn/mask-conv-l3/BiasAdd"; -"6908 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp/resource" -> "6909 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp"; -"6909 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp" -> "6910 maskrcnn/mask-conv-l3/BiasAdd"; -"6910 maskrcnn/mask-conv-l3/BiasAdd" -> "6911 maskrcnn/mask-conv-l3/Relu"; -"6911 maskrcnn/mask-conv-l3/Relu" -> "6920 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6912 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6913 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6913 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6914 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs"; -"6914 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs" -> "6916 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add"; -"6915 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add/y" -> "6916 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add"; -"6916 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add" -> "6919 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul"; -"6916 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add" -> "6920 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6917 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6918 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6918 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6919 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul"; -"6919 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul" -> "6920 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6920 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6945 maskrcnn/conv5-mask/conv2d_transpose"; -"6921 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp/resource" -> "6922 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp"; -"6922 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp" -> "6923 maskrcnn/conv5-mask/SymmQuant/Abs"; -"6923 maskrcnn/conv5-mask/SymmQuant/Abs" -> "6925 maskrcnn/conv5-mask/SymmQuant/add"; -"6924 maskrcnn/conv5-mask/SymmQuant/add/y" -> "6925 maskrcnn/conv5-mask/SymmQuant/add"; -"6925 maskrcnn/conv5-mask/SymmQuant/add" -> "6928 maskrcnn/conv5-mask/SymmQuant/mul"; -"6925 maskrcnn/conv5-mask/SymmQuant/add" -> "6931 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars"; -"6926 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp/resource" -> "6927 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp"; -"6927 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp" -> "6928 maskrcnn/conv5-mask/SymmQuant/mul"; -"6928 maskrcnn/conv5-mask/SymmQuant/mul" -> "6931 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars"; -"6929 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6930 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"6930 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6931 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars"; -"6931 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars" -> "6945 maskrcnn/conv5-mask/conv2d_transpose"; -"6932 maskrcnn/conv5-mask/Shape" -> "6936 maskrcnn/conv5-mask/strided_slice"; -"6933 maskrcnn/conv5-mask/strided_slice/stack" -> "6936 maskrcnn/conv5-mask/strided_slice"; -"6934 maskrcnn/conv5-mask/strided_slice/stack_1" -> "6936 maskrcnn/conv5-mask/strided_slice"; -"6935 maskrcnn/conv5-mask/strided_slice/stack_2" -> "6936 maskrcnn/conv5-mask/strided_slice"; -"6936 maskrcnn/conv5-mask/strided_slice" -> "6940 maskrcnn/conv5-mask/stack"; -"6937 maskrcnn/conv5-mask/stack/1" -> "6940 maskrcnn/conv5-mask/stack"; -"6938 maskrcnn/conv5-mask/stack/2" -> "6940 maskrcnn/conv5-mask/stack"; -"6939 maskrcnn/conv5-mask/stack/3" -> "6940 maskrcnn/conv5-mask/stack"; -"6940 maskrcnn/conv5-mask/stack" -> "6944 maskrcnn/conv5-mask/strided_slice_1"; -"6940 maskrcnn/conv5-mask/stack" -> "6945 maskrcnn/conv5-mask/conv2d_transpose"; -"6941 maskrcnn/conv5-mask/strided_slice_1/stack" -> "6944 maskrcnn/conv5-mask/strided_slice_1"; -"6942 maskrcnn/conv5-mask/strided_slice_1/stack_1" -> "6944 maskrcnn/conv5-mask/strided_slice_1"; -"6943 maskrcnn/conv5-mask/strided_slice_1/stack_2" -> "6944 maskrcnn/conv5-mask/strided_slice_1"; -"6945 maskrcnn/conv5-mask/conv2d_transpose" -> "6948 maskrcnn/conv5-mask/BiasAdd"; -"6946 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp/resource" -> "6947 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp"; -"6947 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp" -> "6948 maskrcnn/conv5-mask/BiasAdd"; -"6948 maskrcnn/conv5-mask/BiasAdd" -> "6949 maskrcnn/conv5-mask/Relu"; -"6949 maskrcnn/conv5-mask/Relu" -> "6958 maskrcnn/conv5-mask/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6950 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6951 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs/ReadVariableOp"; -"6951 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6952 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs"; -"6952 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs" -> "6954 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add"; -"6953 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add/y" -> "6954 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add"; -"6954 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add" -> "6957 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul"; -"6954 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add" -> "6958 maskrcnn/conv5-mask/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6955 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6956 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul/ReadVariableOp"; -"6956 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6957 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul"; -"6957 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul" -> "6958 maskrcnn/conv5-mask/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; -"6958 maskrcnn/conv5-mask/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6973 maskrcnn/mask_fcn_logits/Conv2D"; -"6959 maskrcnn/tf.cast_32/Cast" -> "6961 maskrcnn/tf.__operators__.add_67/AddV2"; -"6960 maskrcnn/tf.__operators__.add_67/y" -> "6961 maskrcnn/tf.__operators__.add_67/AddV2"; -"6961 maskrcnn/tf.__operators__.add_67/AddV2" -> "19976 maskrcnn/tf.stack_23/stack_1"; -"6961 maskrcnn/tf.__operators__.add_67/AddV2" -> "19982 Identity_3"; -"6962 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp/resource" -> "6963 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp"; -"6963 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp" -> "6964 maskrcnn/mask_fcn_logits/SymmQuant/Abs"; -"6964 maskrcnn/mask_fcn_logits/SymmQuant/Abs" -> "6966 maskrcnn/mask_fcn_logits/SymmQuant/add"; -"6965 maskrcnn/mask_fcn_logits/SymmQuant/add/y" -> "6966 maskrcnn/mask_fcn_logits/SymmQuant/add"; -"6966 maskrcnn/mask_fcn_logits/SymmQuant/add" -> "6969 maskrcnn/mask_fcn_logits/SymmQuant/mul"; -"6966 maskrcnn/mask_fcn_logits/SymmQuant/add" -> "6972 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVars"; -"6967 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp/resource" -> "6968 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp"; -"6968 maskrcnn/mask_fcn_logits/SymmQuant/mul/ReadVariableOp" -> "6969 maskrcnn/mask_fcn_logits/SymmQuant/mul"; -"6969 maskrcnn/mask_fcn_logits/SymmQuant/mul" -> "6972 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVars"; -"6970 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6971 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; -"6971 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6972 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVars"; -"6972 maskrcnn/mask_fcn_logits/SymmQuant/FakeQuantWithMinMaxVars" -> "6973 maskrcnn/mask_fcn_logits/Conv2D"; -"6973 maskrcnn/mask_fcn_logits/Conv2D" -> "6976 maskrcnn/mask_fcn_logits/BiasAdd"; -"6974 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp/resource" -> "6975 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp"; -"6975 maskrcnn/mask_fcn_logits/BiasAdd/ReadVariableOp" -> "6976 maskrcnn/mask_fcn_logits/BiasAdd"; -"6976 maskrcnn/mask_fcn_logits/BiasAdd" -> "6978 maskrcnn/tf.reshape_63/Reshape"; -"6977 maskrcnn/tf.reshape_63/Reshape/shape" -> "6978 maskrcnn/tf.reshape_63/Reshape"; -"6978 maskrcnn/tf.reshape_63/Reshape" -> "6980 maskrcnn/tf.compat.v1.transpose/transpose"; -"6979 maskrcnn/tf.compat.v1.transpose/transpose/perm" -> "6980 maskrcnn/tf.compat.v1.transpose/transpose"; -"6980 maskrcnn/tf.compat.v1.transpose/transpose" -> "19977 maskrcnn/tf.compat.v1.gather_nd_1/GatherNd"; -"19781 maskrcnn/tf.stack_23/stack/0" -> "19845 maskrcnn/tf.stack_23/stack"; -"19782 maskrcnn/tf.stack_23/stack/1" -> "19845 maskrcnn/tf.stack_23/stack"; -"19783 maskrcnn/tf.stack_23/stack/2" -> "19845 maskrcnn/tf.stack_23/stack"; -"19784 maskrcnn/tf.stack_23/stack/3" -> "19845 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maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/Abs" -> "2740 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add"; +"2739 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add/y" -> "2740 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add"; +"2740 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add" -> "2743 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul"; +"2740 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/add" -> "2744 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2741 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2742 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2742 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2743 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul"; +"2743 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/mul" -> "2744 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2744 maskrcnn/tf.__operators__.add_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2871 maskrcnn/tf.math.subtract_24/Sub"; +"2745 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2746 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2746 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2747 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs"; +"2747 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/Abs" -> "2749 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add"; +"2748 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add/y" -> "2749 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add"; +"2749 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add" -> "2752 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul"; +"2749 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/add" -> "2753 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2750 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2751 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2751 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2752 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul"; +"2752 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/mul" -> "2753 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2753 maskrcnn/tf.math.multiply_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2871 maskrcnn/tf.math.subtract_24/Sub"; +"2754 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2755 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2755 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2756 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs"; +"2756 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/Abs" -> "2758 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add"; +"2757 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add/y" -> "2758 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add"; +"2758 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add" -> "2761 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul"; +"2758 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/add" -> "2762 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2759 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2760 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2760 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2761 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul"; +"2761 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/mul" -> "2762 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2762 maskrcnn/tf.__operators__.add_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2872 maskrcnn/tf.math.subtract_17/Sub"; +"2763 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2764 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2764 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2765 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs"; +"2765 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/Abs" -> "2767 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add"; +"2766 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add/y" -> "2767 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add"; +"2767 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add" -> "2770 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul"; +"2767 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/add" -> "2771 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2768 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2769 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2769 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2770 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul"; +"2770 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/mul" -> "2771 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2771 maskrcnn/tf.math.multiply_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2872 maskrcnn/tf.math.subtract_17/Sub"; +"2772 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2773 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2773 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2774 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs"; +"2774 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/Abs" -> "2776 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add"; +"2775 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add/y" -> "2776 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add"; +"2776 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add" -> "2779 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul"; +"2776 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/add" -> "2780 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2777 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2778 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2778 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2779 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul"; +"2779 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/mul" -> "2780 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2780 maskrcnn/tf.__operators__.add_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2873 maskrcnn/tf.math.subtract_16/Sub"; +"2781 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2782 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2782 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2783 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs"; +"2783 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/Abs" -> "2785 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add"; +"2784 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add/y" -> "2785 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add"; +"2785 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add" -> "2788 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul"; +"2785 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/add" -> "2789 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2786 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2787 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2787 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2788 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul"; +"2788 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/mul" -> "2789 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2789 maskrcnn/tf.math.multiply_16/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2873 maskrcnn/tf.math.subtract_16/Sub"; +"2790 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2791 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2791 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2792 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs"; +"2792 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/Abs" -> "2794 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add"; +"2793 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add/y" -> "2794 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add"; +"2794 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add" -> "2797 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul"; +"2794 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/add" -> "2798 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2795 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2796 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2796 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2797 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul"; +"2797 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/mul" -> "2798 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2798 maskrcnn/tf.__operators__.add_24/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2874 maskrcnn/tf.math.subtract_9/Sub"; +"2799 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2800 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2800 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2801 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs"; +"2801 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/Abs" -> "2803 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add"; +"2802 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add/y" -> "2803 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add"; +"2803 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add" -> "2806 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul"; +"2803 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/add" -> "2807 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2804 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2805 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2805 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2806 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul"; +"2806 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/mul" -> "2807 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2807 maskrcnn/tf.math.multiply_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2874 maskrcnn/tf.math.subtract_9/Sub"; +"2808 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2809 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2809 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2810 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/Abs"; +"2810 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/Abs" -> "2812 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/add"; +"2811 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/add/y" -> "2812 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/add"; +"2812 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/add" -> "2815 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/mul"; +"2812 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/add" -> "2816 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2813 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2814 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2814 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2815 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/mul"; +"2815 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/mul" -> "2816 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2816 maskrcnn/tf.__operators__.add_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2875 maskrcnn/tf.math.subtract_8/Sub"; +"2817 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2818 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2818 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2819 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/Abs"; +"2819 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/Abs" -> "2821 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/add"; +"2820 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/add/y" -> "2821 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/add"; +"2821 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/add" -> "2824 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/mul"; +"2821 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/add" -> "2825 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2822 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2823 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2823 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2824 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/mul"; +"2824 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/mul" -> "2825 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2825 maskrcnn/tf.math.multiply_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2875 maskrcnn/tf.math.subtract_8/Sub"; +"2826 maskrcnn/tf.__operators__.getitem/strided_slice/stack" -> "2829 maskrcnn/tf.__operators__.getitem/strided_slice"; +"2827 maskrcnn/tf.__operators__.getitem/strided_slice/stack_1" -> "2829 maskrcnn/tf.__operators__.getitem/strided_slice"; +"2828 maskrcnn/tf.__operators__.getitem/strided_slice/stack_2" -> "2829 maskrcnn/tf.__operators__.getitem/strided_slice"; +"2829 maskrcnn/tf.__operators__.getitem/strided_slice" -> "2867 maskrcnn/tf.expand_dims/ExpandDims"; +"2830 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2831 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2831 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2832 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/Abs"; +"2832 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/Abs" -> "2834 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/add"; +"2833 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/add/y" -> "2834 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/add"; +"2834 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/add" -> "2837 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/mul"; +"2834 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/add" -> "2838 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2835 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2836 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2836 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2837 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/mul"; +"2837 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/mul" -> "2838 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2838 maskrcnn/tf.__operators__.add_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2876 maskrcnn/tf.math.subtract_1/Sub"; +"2839 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2840 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2840 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2841 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/Abs"; +"2841 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/Abs" -> "2843 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/add"; +"2842 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/add/y" -> "2843 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/add"; +"2843 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/add" -> "2846 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/mul"; +"2843 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/add" -> "2847 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2844 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2845 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"2845 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "2846 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/mul"; +"2846 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/mul" -> "2847 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2847 maskrcnn/tf.math.multiply_5/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "2876 maskrcnn/tf.math.subtract_1/Sub"; +"2848 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "2849 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"2849 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "2850 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/Abs"; +"2850 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/Abs" -> "2852 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/add"; +"2851 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/add/y" -> "2852 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/add"; +"2852 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/add" -> "2855 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/mul"; +"2852 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/add" -> "2856 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"2853 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "2854 maskrcnn/tf.__operators__.add_19/fake_quantize/SymmQuant/mul/ReadVariableOp"; 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maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/add/y" -> "3083 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/add"; +"3083 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/add" -> "3086 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/mul"; +"3083 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/add" -> "3087 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3084 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3085 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3085 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3086 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/mul"; +"3086 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/mul" -> "3087 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3087 maskrcnn/tf.math.subtract_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3373 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"3094 maskrcnn/tf.math.subtract_35/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3094 maskrcnn/tf.math.subtract_35/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3095 maskrcnn/tf.math.subtract_35/fake_quantize/SymmQuant/mul"; +"3095 maskrcnn/tf.math.subtract_35/fake_quantize/SymmQuant/mul" -> "3096 maskrcnn/tf.math.subtract_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3096 maskrcnn/tf.math.subtract_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3373 maskrcnn/tf.concat_4/concat"; +"3097 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3098 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3098 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3099 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/Abs"; +"3099 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/Abs" -> "3101 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/add"; +"3100 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/add/y" -> "3101 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/add"; +"3101 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/add" -> "3104 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/mul"; +"3101 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/add" -> "3105 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3102 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3103 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3103 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3104 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/mul"; +"3104 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/mul" -> "3105 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3105 maskrcnn/tf.math.subtract_28/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3377 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maskrcnn/tf.math.subtract_29/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3112 maskrcnn/tf.math.subtract_29/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3113 maskrcnn/tf.math.subtract_29/fake_quantize/SymmQuant/mul"; +"3113 maskrcnn/tf.math.subtract_29/fake_quantize/SymmQuant/mul" -> "3114 maskrcnn/tf.math.subtract_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3114 maskrcnn/tf.math.subtract_29/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3377 maskrcnn/tf.stack_3/stack"; +"3115 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3116 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3116 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3117 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/Abs"; +"3117 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/Abs" -> "3119 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/add"; +"3118 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/add/y" -> "3119 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/add"; +"3119 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/add" -> "3122 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/mul"; +"3119 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/add" -> "3123 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3120 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3121 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3121 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3122 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/mul"; +"3122 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/mul" -> "3123 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3123 maskrcnn/tf.math.subtract_30/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3377 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maskrcnn/tf.math.subtract_31/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3130 maskrcnn/tf.math.subtract_31/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3131 maskrcnn/tf.math.subtract_31/fake_quantize/SymmQuant/mul"; +"3131 maskrcnn/tf.math.subtract_31/fake_quantize/SymmQuant/mul" -> "3132 maskrcnn/tf.math.subtract_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3132 maskrcnn/tf.math.subtract_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3377 maskrcnn/tf.stack_3/stack"; +"3133 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3134 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3134 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3135 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/Abs"; +"3135 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/Abs" -> "3137 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/add"; +"3136 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/add/y" -> "3137 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/add"; +"3137 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/add" -> "3140 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/mul"; +"3137 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/add" -> "3141 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3138 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3139 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3139 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3140 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/mul"; +"3140 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/mul" -> "3141 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3141 maskrcnn/tf.math.subtract_26/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3376 maskrcnn/tf.concat_3/concat"; +"3142 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3143 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3143 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3144 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/Abs"; +"3144 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/Abs" -> "3146 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/add"; +"3145 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/add/y" -> "3146 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/add"; +"3146 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/add" -> "3149 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/mul"; +"3146 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/add" -> "3150 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3147 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3148 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3148 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3149 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/mul"; +"3149 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/mul" -> "3150 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3150 maskrcnn/tf.math.subtract_27/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3376 maskrcnn/tf.concat_3/concat"; +"3151 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3152 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3152 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3153 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/Abs"; +"3153 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/Abs" -> "3155 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/add"; +"3154 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/add/y" -> "3155 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/add"; +"3155 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/add" -> "3158 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/mul"; +"3155 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/add" -> "3159 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3156 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3157 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3157 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3158 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/mul"; +"3158 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/mul" -> "3159 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3159 maskrcnn/tf.math.subtract_20/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3380 maskrcnn/tf.stack_2/stack"; +"3160 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3161 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3161 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3162 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/Abs"; +"3162 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/Abs" -> "3164 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/add"; +"3163 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/add/y" -> "3164 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/add"; +"3164 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/add" -> "3167 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/mul"; +"3164 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/add" -> "3168 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3165 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3166 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3166 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3167 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/mul"; +"3167 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/mul" -> "3168 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3168 maskrcnn/tf.math.subtract_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3380 maskrcnn/tf.stack_2/stack"; +"3169 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3170 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3170 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3171 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/Abs"; +"3171 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/Abs" -> "3173 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/add"; +"3172 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/add/y" -> "3173 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/add"; +"3173 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/add" -> "3176 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/mul"; +"3173 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/add" -> "3177 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3174 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3175 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3175 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3176 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/mul"; +"3176 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/mul" -> "3177 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3177 maskrcnn/tf.math.subtract_22/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3380 maskrcnn/tf.stack_2/stack"; +"3178 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3179 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3179 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3180 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/Abs"; +"3180 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/Abs" -> "3182 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/add"; +"3181 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/add/y" -> "3182 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/add"; +"3182 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/add" -> "3185 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/mul"; +"3182 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/add" -> "3186 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3183 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3184 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3184 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3185 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/mul"; +"3185 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/mul" -> "3186 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3186 maskrcnn/tf.math.subtract_23/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3380 maskrcnn/tf.stack_2/stack"; +"3187 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3188 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3188 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3189 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/Abs"; +"3189 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/Abs" -> "3191 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/add"; +"3190 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/add/y" -> "3191 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/add"; +"3191 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/add" -> "3194 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/mul"; +"3191 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/add" -> "3195 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3192 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3193 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3193 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3194 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/mul"; +"3194 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/mul" -> "3195 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3195 maskrcnn/tf.math.subtract_18/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3379 maskrcnn/tf.concat_2/concat"; +"3196 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3197 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3197 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3198 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/Abs"; +"3198 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/Abs" -> "3200 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/add"; +"3199 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/add/y" -> "3200 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/add"; +"3200 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/add" -> "3203 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/mul"; +"3200 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/add" -> "3204 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3201 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3202 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3202 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3203 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/mul"; +"3203 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/mul" -> "3204 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3204 maskrcnn/tf.math.subtract_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3379 maskrcnn/tf.concat_2/concat"; +"3205 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3206 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3206 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3207 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/Abs"; +"3207 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/Abs" -> "3209 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/add"; +"3208 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/add/y" -> "3209 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/add"; +"3209 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/add" -> "3212 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/mul"; +"3209 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/add" -> "3213 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3210 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3211 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3211 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3212 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/mul"; +"3212 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/mul" -> "3213 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3213 maskrcnn/tf.math.subtract_12/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3383 maskrcnn/tf.stack_1/stack"; +"3214 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3215 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3215 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3216 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/Abs"; +"3216 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/Abs" -> "3218 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/add"; +"3217 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/add/y" -> "3218 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/add"; +"3218 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/add" -> "3221 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/mul"; +"3218 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/add" -> "3222 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3219 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3220 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3220 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3221 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/mul"; +"3221 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/mul" -> "3222 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3222 maskrcnn/tf.math.subtract_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3383 maskrcnn/tf.stack_1/stack"; +"3223 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3224 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3224 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3225 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/Abs"; +"3225 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/Abs" -> "3227 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/add"; +"3226 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/add/y" -> "3227 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/add"; +"3227 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/add" -> "3230 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/mul"; +"3227 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/add" -> "3231 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3228 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3229 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3229 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3230 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/mul"; +"3230 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/mul" -> "3231 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3231 maskrcnn/tf.math.subtract_14/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3383 maskrcnn/tf.stack_1/stack"; +"3232 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3233 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3233 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3234 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/Abs"; +"3234 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/Abs" -> "3236 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/add"; +"3235 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/add/y" -> "3236 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/add"; +"3236 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/add" -> "3239 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/mul"; +"3236 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/add" -> "3240 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3237 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3238 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3238 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3239 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/mul"; +"3239 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/mul" -> "3240 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3240 maskrcnn/tf.math.subtract_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3383 maskrcnn/tf.stack_1/stack"; +"3241 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3242 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3242 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3243 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/Abs"; +"3243 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/Abs" -> "3245 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/add"; +"3244 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/add/y" -> "3245 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/add"; +"3245 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/add" -> "3248 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/mul"; +"3245 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/add" -> "3249 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3246 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3247 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3247 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3248 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/mul"; +"3248 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/mul" -> "3249 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3249 maskrcnn/tf.math.subtract_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3382 maskrcnn/tf.concat_1/concat"; +"3250 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3251 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3251 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3252 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/Abs"; +"3252 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/Abs" -> "3254 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/add"; +"3253 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/add/y" -> "3254 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/add"; +"3254 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/add" -> "3257 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/mul"; +"3254 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/add" -> "3258 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3255 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3256 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3256 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3257 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/mul"; +"3257 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/mul" -> "3258 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3258 maskrcnn/tf.math.subtract_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3382 maskrcnn/tf.concat_1/concat"; +"3259 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3260 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3260 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3261 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/Abs"; +"3261 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/Abs" -> "3263 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/add"; +"3262 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/add/y" -> "3263 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/add"; +"3263 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/add" -> "3266 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/mul"; +"3263 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/add" -> "3267 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3264 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3265 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3265 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3266 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/mul"; +"3266 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/mul" -> "3267 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3267 maskrcnn/tf.math.subtract_4/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3386 maskrcnn/tf.stack/stack"; +"3268 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3269 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3269 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3270 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/Abs"; +"3270 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/Abs" -> "3272 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/add"; +"3271 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/add/y" -> "3272 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/add"; +"3272 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/add" -> "3275 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/mul"; +"3272 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/add" -> "3276 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3273 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3274 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3274 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3275 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/mul"; +"3275 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/mul" -> "3276 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3276 maskrcnn/tf.math.subtract_5/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3386 maskrcnn/tf.stack/stack"; +"3277 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3278 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3278 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3279 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/Abs"; +"3279 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/Abs" -> "3281 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/add"; +"3280 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/add/y" -> "3281 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/add"; +"3281 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/add" -> "3284 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/mul"; +"3281 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/add" -> "3285 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3282 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3283 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3283 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3284 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/mul"; +"3284 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/mul" -> "3285 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3285 maskrcnn/tf.math.subtract_6/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3386 maskrcnn/tf.stack/stack"; +"3286 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maskrcnn/tf.__operators__.getitem_23/strided_slice/stack_1" -> "3537 maskrcnn/tf.__operators__.getitem_23/strided_slice"; +"3536 maskrcnn/tf.__operators__.getitem_23/strided_slice/stack_2" -> "3537 maskrcnn/tf.__operators__.getitem_23/strided_slice"; +"3537 maskrcnn/tf.__operators__.getitem_23/strided_slice" -> "3550 maskrcnn/tf.math.subtract_40/Sub"; +"3538 maskrcnn/tf.__operators__.getitem_24/strided_slice/stack" -> "3541 maskrcnn/tf.__operators__.getitem_24/strided_slice"; +"3539 maskrcnn/tf.__operators__.getitem_24/strided_slice/stack_1" -> "3541 maskrcnn/tf.__operators__.getitem_24/strided_slice"; +"3540 maskrcnn/tf.__operators__.getitem_24/strided_slice/stack_2" -> "3541 maskrcnn/tf.__operators__.getitem_24/strided_slice"; +"3541 maskrcnn/tf.__operators__.getitem_24/strided_slice" -> "3550 maskrcnn/tf.math.subtract_40/Sub"; +"3542 maskrcnn/tf.__operators__.getitem_25/strided_slice/stack" -> "3545 maskrcnn/tf.__operators__.getitem_25/strided_slice"; +"3543 maskrcnn/tf.__operators__.getitem_25/strided_slice/stack_1" -> "3545 maskrcnn/tf.__operators__.getitem_25/strided_slice"; +"3544 maskrcnn/tf.__operators__.getitem_25/strided_slice/stack_2" -> "3545 maskrcnn/tf.__operators__.getitem_25/strided_slice"; +"3545 maskrcnn/tf.__operators__.getitem_25/strided_slice" -> "3551 maskrcnn/tf.math.subtract_41/Sub"; +"3546 maskrcnn/tf.__operators__.getitem_26/strided_slice/stack" -> "3549 maskrcnn/tf.__operators__.getitem_26/strided_slice"; +"3547 maskrcnn/tf.__operators__.getitem_26/strided_slice/stack_1" -> "3549 maskrcnn/tf.__operators__.getitem_26/strided_slice"; +"3548 maskrcnn/tf.__operators__.getitem_26/strided_slice/stack_2" -> "3549 maskrcnn/tf.__operators__.getitem_26/strided_slice"; +"3549 maskrcnn/tf.__operators__.getitem_26/strided_slice" -> "3551 maskrcnn/tf.math.subtract_41/Sub"; +"3550 maskrcnn/tf.math.subtract_40/Sub" -> "3560 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3551 maskrcnn/tf.math.subtract_41/Sub" -> "3569 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3552 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3553 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3553 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3554 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/Abs"; +"3554 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/Abs" -> "3556 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/add"; +"3555 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/add/y" -> "3556 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/add"; +"3556 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/add" -> "3559 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/mul"; +"3556 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/add" -> "3560 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3557 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3558 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3558 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3559 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/mul"; +"3559 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/mul" -> "3560 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3560 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3570 maskrcnn/tf.math.multiply_31/Mul"; +"3560 maskrcnn/tf.math.subtract_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3625 maskrcnn/tf.math.truediv_2/truediv"; +"3561 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3562 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3562 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3563 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/Abs"; +"3563 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/Abs" -> "3565 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/add"; +"3564 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/add/y" -> "3565 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/add"; +"3565 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/add" -> "3568 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/mul"; +"3565 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/add" -> "3569 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3566 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3567 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3567 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3568 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/mul"; +"3568 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/mul" -> "3569 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3569 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3570 maskrcnn/tf.math.multiply_31/Mul"; +"3569 maskrcnn/tf.math.subtract_41/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3626 maskrcnn/tf.math.truediv_3/truediv"; +"3570 maskrcnn/tf.math.multiply_31/Mul" -> "3579 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3571 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3572 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3572 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3573 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/Abs"; +"3573 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/Abs" -> "3575 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/add"; +"3574 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/add/y" -> "3575 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/add"; +"3575 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/add" -> "3578 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul"; +"3575 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/add" -> "3579 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3576 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3577 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3577 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3578 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul"; +"3578 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/mul" -> "3579 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3579 maskrcnn/tf.math.multiply_31/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3580 maskrcnn/tf.math.sqrt/Sqrt"; +"3580 maskrcnn/tf.math.sqrt/Sqrt" -> "3589 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3581 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3582 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3582 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3583 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs"; +"3583 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/Abs" -> "3585 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add"; +"3584 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add/y" -> "3585 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add"; +"3585 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add" -> "3588 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul"; +"3585 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/add" -> "3589 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3586 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3587 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3587 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3588 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul"; +"3588 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/mul" -> "3589 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3589 maskrcnn/tf.math.sqrt/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3591 maskrcnn/tf.math.truediv/truediv"; +"3590 maskrcnn/tf.math.truediv/truediv/y" -> "3591 maskrcnn/tf.math.truediv/truediv"; +"3591 maskrcnn/tf.math.truediv/truediv" -> "3592 maskrcnn/tf.math.log/Log"; +"3592 maskrcnn/tf.math.log/Log" -> "3594 maskrcnn/tf.compat.v1.floor_div/FloorDiv"; +"3593 maskrcnn/tf.compat.v1.floor_div/FloorDiv/y" -> "3594 maskrcnn/tf.compat.v1.floor_div/FloorDiv"; +"3594 maskrcnn/tf.compat.v1.floor_div/FloorDiv" -> "3596 maskrcnn/tf.__operators__.add_39/AddV2"; +"3595 maskrcnn/tf.__operators__.add_39/y" -> "3596 maskrcnn/tf.__operators__.add_39/AddV2"; +"3596 maskrcnn/tf.__operators__.add_39/AddV2" -> "3597 maskrcnn/tf.cast_20/Cast"; +"3597 maskrcnn/tf.cast_20/Cast" -> "3599 maskrcnn/tf.math.maximum_5/Maximum"; +"3598 maskrcnn/tf.math.maximum_5/Maximum/y" -> "3599 maskrcnn/tf.math.maximum_5/Maximum"; +"3599 maskrcnn/tf.math.maximum_5/Maximum" -> "3601 maskrcnn/tf.math.minimum_15/Minimum"; +"3601 maskrcnn/tf.math.minimum_15/Minimum" -> "3602 maskrcnn/tf.cast_21/Cast"; +"3601 maskrcnn/tf.math.minimum_15/Minimum" -> "3629 maskrcnn/tf.math.subtract_42/Sub"; +"3602 maskrcnn/tf.cast_21/Cast" -> "3611 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3603 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3604 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3604 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3605 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs"; +"3605 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/Abs" -> "3607 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add"; +"3606 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add/y" -> "3607 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add"; +"3607 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add" -> "3610 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul"; +"3607 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/add" -> "3611 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3608 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3609 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3609 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3610 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul"; +"3610 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/mul" -> "3611 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3611 maskrcnn/tf.cast_21/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3613 maskrcnn/tf.math.pow/Pow"; +"3613 maskrcnn/tf.math.pow/Pow" -> "3622 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3614 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3615 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3615 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3616 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs"; +"3616 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/Abs" -> "3618 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add"; +"3617 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add/y" -> "3618 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add"; +"3618 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add" -> "3621 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul"; +"3618 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/add" -> "3622 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3619 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3620 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3620 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3621 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul"; +"3621 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/mul" -> "3622 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3622 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3624 maskrcnn/tf.expand_dims_12/ExpandDims"; +"3622 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3625 maskrcnn/tf.math.truediv_2/truediv"; +"3622 maskrcnn/tf.math.pow/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3626 maskrcnn/tf.math.truediv_3/truediv"; +"3623 maskrcnn/tf.expand_dims_12/ExpandDims/dim" -> "3624 maskrcnn/tf.expand_dims_12/ExpandDims"; +"3624 maskrcnn/tf.expand_dims_12/ExpandDims" -> "3627 maskrcnn/tf.math.truediv_1/truediv"; +"3625 maskrcnn/tf.math.truediv_2/truediv" -> "3638 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3626 maskrcnn/tf.math.truediv_3/truediv" -> "3647 maskrcnn/tf.math.truediv_3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3627 maskrcnn/tf.math.truediv_1/truediv" -> "3656 maskrcnn/tf.math.truediv_1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3628 maskrcnn/tf.math.subtract_42/Sub/y" -> "3629 maskrcnn/tf.math.subtract_42/Sub"; +"3629 maskrcnn/tf.math.subtract_42/Sub" -> "3657 maskrcnn/tf.cast_23/Cast"; +"3629 maskrcnn/tf.math.subtract_42/Sub" -> "4300 maskrcnn/tf.compat.v1.gather_1/GatherV2"; +"3629 maskrcnn/tf.math.subtract_42/Sub" -> "4339 maskrcnn/tf.compat.v1.gather/GatherV2"; +"3630 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3631 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3631 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3632 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs"; +"3632 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/Abs" -> "3634 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add"; +"3633 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add/y" -> "3634 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add"; +"3634 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add" -> "3637 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul"; +"3634 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/add" -> "3638 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3635 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3636 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3636 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3637 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul"; +"3637 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/mul" -> "3638 maskrcnn/tf.math.truediv_2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3638 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maskrcnn/tf.math.truediv_5/truediv/x/0" -> "3762 maskrcnn/tf.math.truediv_5/truediv/x"; +"3762 maskrcnn/tf.math.truediv_5/truediv/x" -> "3763 maskrcnn/tf.math.truediv_5/truediv"; +"3763 maskrcnn/tf.math.truediv_5/truediv" -> "3846 maskrcnn/tf.math.subtract_44/Sub"; +"3765 maskrcnn/tf.math.truediv_4/truediv/x/0" -> "3766 maskrcnn/tf.math.truediv_4/truediv/x"; +"3766 maskrcnn/tf.math.truediv_4/truediv/x" -> "3767 maskrcnn/tf.math.truediv_4/truediv"; +"3767 maskrcnn/tf.math.truediv_4/truediv" -> "3848 maskrcnn/tf.math.subtract_43/Sub"; +"3768 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3769 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3769 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3770 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/Abs"; +"3770 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/Abs" -> "3772 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/add"; +"3771 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/add/y" -> "3772 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/add"; +"3772 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/add" -> "3775 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/mul"; +"3772 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/add" -> "3776 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3773 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3774 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3774 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3775 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/mul"; +"3775 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/mul" -> "3776 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3776 maskrcnn/tf.math.multiply_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3850 maskrcnn/tf.math.truediv_19/truediv"; +"3777 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3778 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3778 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3779 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/Abs"; +"3779 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/Abs" -> "3781 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/add"; +"3780 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/add/y" -> "3781 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/add"; +"3781 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/add" -> "3784 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/mul"; +"3781 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/add" -> "3785 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3782 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3783 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3783 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3784 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/mul"; +"3784 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/mul" -> "3785 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3785 maskrcnn/tf.math.multiply_43/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3852 maskrcnn/tf.math.truediv_17/truediv"; +"3786 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3787 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3787 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3788 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/Abs"; +"3788 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/Abs" -> "3790 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/add"; +"3789 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/add/y" -> "3790 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/add"; +"3790 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/add" -> "3793 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/mul"; +"3790 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/add" -> "3794 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3791 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3792 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3792 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3793 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/mul"; +"3793 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/mul" -> "3794 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3794 maskrcnn/tf.math.multiply_41/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3854 maskrcnn/tf.math.truediv_15/truediv"; +"3795 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3796 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3796 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3797 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/Abs"; +"3797 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/Abs" -> "3799 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/add"; +"3798 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/add/y" -> "3799 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/add"; +"3799 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/add" -> "3802 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/mul"; +"3799 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/add" -> "3803 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3800 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3801 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3801 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3802 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/mul"; +"3802 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/mul" -> "3803 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3803 maskrcnn/tf.math.multiply_39/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3856 maskrcnn/tf.math.truediv_13/truediv"; +"3804 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3805 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3805 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3806 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/Abs"; +"3806 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/Abs" -> "3808 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/add"; +"3807 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/add/y" -> "3808 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/add"; +"3808 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/add" -> "3811 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/mul"; +"3808 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/add" -> "3812 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3809 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3810 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3810 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3811 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/mul"; +"3811 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/mul" -> "3812 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3812 maskrcnn/tf.math.multiply_37/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3858 maskrcnn/tf.math.truediv_11/truediv"; +"3813 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3814 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3814 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3815 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/Abs"; +"3815 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/Abs" -> "3817 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/add"; +"3816 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/add/y" -> "3817 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/add"; +"3817 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/add" -> "3820 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/mul"; +"3817 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/add" -> "3821 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3818 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3819 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3819 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3820 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/mul"; +"3820 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/mul" -> "3821 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3821 maskrcnn/tf.math.multiply_35/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3860 maskrcnn/tf.math.truediv_9/truediv"; +"3822 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3823 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3823 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3824 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/Abs"; +"3824 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/Abs" -> "3826 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/add"; +"3825 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/add/y" -> "3826 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/add"; +"3826 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/add" -> "3829 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/mul"; +"3826 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/add" -> "3830 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3827 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3828 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3828 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3829 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/mul"; +"3829 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/mul" -> "3830 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3830 maskrcnn/tf.math.multiply_33/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "3862 maskrcnn/tf.math.truediv_7/truediv"; +"3832 maskrcnn/tf.math.multiply_44/Mul" -> "3871 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3834 maskrcnn/tf.math.multiply_42/Mul" -> "3880 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3836 maskrcnn/tf.math.multiply_40/Mul" -> "3889 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3838 maskrcnn/tf.math.multiply_38/Mul" -> "3898 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3840 maskrcnn/tf.math.multiply_36/Mul" -> "3907 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3842 maskrcnn/tf.math.multiply_34/Mul" -> "3916 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3844 maskrcnn/tf.math.multiply_32/Mul" -> "3925 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3845 maskrcnn/tf.math.subtract_44/Sub/y" -> "3846 maskrcnn/tf.math.subtract_44/Sub"; +"3846 maskrcnn/tf.math.subtract_44/Sub" -> "3934 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3847 maskrcnn/tf.math.subtract_43/Sub/y" -> "3848 maskrcnn/tf.math.subtract_43/Sub"; +"3848 maskrcnn/tf.math.subtract_43/Sub" -> "3943 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3849 maskrcnn/tf.math.truediv_19/truediv/y" -> "3850 maskrcnn/tf.math.truediv_19/truediv"; +"3850 maskrcnn/tf.math.truediv_19/truediv" -> "3956 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3851 maskrcnn/tf.math.truediv_17/truediv/y" -> "3852 maskrcnn/tf.math.truediv_17/truediv"; +"3852 maskrcnn/tf.math.truediv_17/truediv" -> "3969 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3853 maskrcnn/tf.math.truediv_15/truediv/y" -> "3854 maskrcnn/tf.math.truediv_15/truediv"; +"3854 maskrcnn/tf.math.truediv_15/truediv" -> "3982 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3855 maskrcnn/tf.math.truediv_13/truediv/y" -> "3856 maskrcnn/tf.math.truediv_13/truediv"; +"3856 maskrcnn/tf.math.truediv_13/truediv" -> "3995 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3857 maskrcnn/tf.math.truediv_11/truediv/y" -> "3858 maskrcnn/tf.math.truediv_11/truediv"; +"3858 maskrcnn/tf.math.truediv_11/truediv" -> "4008 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3859 maskrcnn/tf.math.truediv_9/truediv/y" -> "3860 maskrcnn/tf.math.truediv_9/truediv"; +"3860 maskrcnn/tf.math.truediv_9/truediv" -> "4021 maskrcnn/tf.math.truediv_9/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3861 maskrcnn/tf.math.truediv_7/truediv/y" -> "3862 maskrcnn/tf.math.truediv_7/truediv"; +"3862 maskrcnn/tf.math.truediv_7/truediv" -> "4034 maskrcnn/tf.math.truediv_7/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3863 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3864 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3864 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3865 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/Abs"; +"3865 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/Abs" -> "3867 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/add"; +"3866 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/add/y" -> "3867 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/add"; +"3867 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/add" -> "3870 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/mul"; +"3867 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/add" -> "3871 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3868 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3869 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3869 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3870 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/mul"; +"3870 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/mul" -> "3871 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3871 maskrcnn/tf.math.multiply_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4036 maskrcnn/tf.math.truediv_18/truediv"; +"3872 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3873 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3873 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3874 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/Abs"; +"3874 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/Abs" -> "3876 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/add"; +"3875 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/add/y" -> "3876 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/add"; +"3876 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/add" -> "3879 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/mul"; +"3876 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/add" -> "3880 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3877 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3878 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3878 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3879 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/mul"; +"3879 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/mul" -> "3880 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3880 maskrcnn/tf.math.multiply_42/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4038 maskrcnn/tf.math.truediv_16/truediv"; +"3881 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3882 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3882 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3883 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/Abs"; +"3883 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/Abs" -> "3885 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/add"; +"3884 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/add/y" -> "3885 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/add"; +"3885 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/add" -> "3888 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/mul"; +"3885 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/add" -> "3889 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3886 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3887 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3887 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3888 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/mul"; +"3888 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/mul" -> "3889 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3889 maskrcnn/tf.math.multiply_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4040 maskrcnn/tf.math.truediv_14/truediv"; +"3890 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3891 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3891 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3892 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/Abs"; +"3892 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/Abs" -> "3894 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/add"; +"3893 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/add/y" -> "3894 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/add"; +"3894 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/add" -> "3897 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/mul"; +"3894 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/add" -> "3898 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3895 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3896 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3896 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3897 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/mul"; +"3897 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/mul" -> "3898 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3898 maskrcnn/tf.math.multiply_38/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4042 maskrcnn/tf.math.truediv_12/truediv"; +"3899 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3900 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3900 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3901 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/Abs"; +"3901 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/Abs" -> "3903 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/add"; +"3902 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/add/y" -> "3903 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/add"; +"3903 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/add" -> "3906 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/mul"; +"3903 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/add" -> "3907 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3904 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3905 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3905 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3906 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/mul"; +"3906 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/mul" -> "3907 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3907 maskrcnn/tf.math.multiply_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4044 maskrcnn/tf.math.truediv_10/truediv"; +"3908 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3909 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3909 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3910 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/Abs"; +"3910 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/Abs" -> "3912 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/add"; +"3911 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/add/y" -> "3912 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/add"; +"3912 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/add" -> "3915 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/mul"; +"3912 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/add" -> "3916 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3913 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3914 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3914 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3915 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/mul"; +"3915 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/mul" -> "3916 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3916 maskrcnn/tf.math.multiply_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4046 maskrcnn/tf.math.truediv_8/truediv"; +"3917 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3918 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3918 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3919 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/Abs"; +"3919 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/Abs" -> "3921 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/add"; +"3920 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/add/y" -> "3921 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/add"; +"3921 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/add" -> "3924 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/mul"; +"3921 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/add" -> "3925 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3922 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3923 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3923 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3924 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/mul"; +"3924 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/mul" -> "3925 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3925 maskrcnn/tf.math.multiply_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4048 maskrcnn/tf.math.truediv_6/truediv"; +"3926 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3927 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3927 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3928 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/Abs"; +"3928 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/Abs" -> "3930 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/add"; +"3929 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/add/y" -> "3930 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/add"; +"3930 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/add" -> "3933 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/mul"; +"3930 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/add" -> "3934 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3931 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3932 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3932 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3933 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/mul"; +"3933 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/mul" -> "3934 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3934 maskrcnn/tf.math.subtract_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4052 maskrcnn/tf.expand_dims_16/ExpandDims"; +"3935 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3936 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3936 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3937 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/Abs"; +"3937 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/Abs" -> "3939 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/add"; +"3938 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/add/y" -> "3939 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/add"; +"3939 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/add" -> "3942 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/mul"; +"3939 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/add" -> "3943 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3940 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3941 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3941 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3942 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/mul"; +"3942 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/mul" -> "3943 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3943 maskrcnn/tf.math.subtract_43/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4050 maskrcnn/tf.expand_dims_15/ExpandDims"; +"3944 maskrcnn/tf.__operators__.getitem_54/strided_slice/stack" -> "3947 maskrcnn/tf.__operators__.getitem_54/strided_slice"; +"3945 maskrcnn/tf.__operators__.getitem_54/strided_slice/stack_1" -> "3947 maskrcnn/tf.__operators__.getitem_54/strided_slice"; +"3946 maskrcnn/tf.__operators__.getitem_54/strided_slice/stack_2" -> "3947 maskrcnn/tf.__operators__.getitem_54/strided_slice"; +"3947 maskrcnn/tf.__operators__.getitem_54/strided_slice" -> "4059 maskrcnn/tf.__operators__.add_53/AddV2"; +"3948 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3949 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3949 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3950 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/Abs"; +"3950 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/Abs" -> "3952 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/add"; +"3951 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/add/y" -> "3952 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/add"; +"3952 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/add" -> "3955 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/mul"; +"3952 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/add" -> "3956 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3953 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3954 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3954 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3955 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/mul"; +"3955 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/mul" -> "3956 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3956 maskrcnn/tf.math.truediv_19/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4059 maskrcnn/tf.__operators__.add_53/AddV2"; +"3957 maskrcnn/tf.__operators__.getitem_50/strided_slice/stack" -> "3960 maskrcnn/tf.__operators__.getitem_50/strided_slice"; +"3958 maskrcnn/tf.__operators__.getitem_50/strided_slice/stack_1" -> "3960 maskrcnn/tf.__operators__.getitem_50/strided_slice"; +"3959 maskrcnn/tf.__operators__.getitem_50/strided_slice/stack_2" -> "3960 maskrcnn/tf.__operators__.getitem_50/strided_slice"; +"3960 maskrcnn/tf.__operators__.getitem_50/strided_slice" -> "4058 maskrcnn/tf.__operators__.add_51/AddV2"; +"3961 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3962 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3962 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3963 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/Abs"; +"3963 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/Abs" -> "3965 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/add"; +"3964 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/add/y" -> "3965 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/add"; +"3965 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/add" -> "3968 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/mul"; +"3965 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/add" -> "3969 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3966 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3967 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3967 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3968 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/mul"; +"3968 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/mul" -> "3969 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3969 maskrcnn/tf.math.truediv_17/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4058 maskrcnn/tf.__operators__.add_51/AddV2"; +"3970 maskrcnn/tf.__operators__.getitem_46/strided_slice/stack" -> "3973 maskrcnn/tf.__operators__.getitem_46/strided_slice"; +"3971 maskrcnn/tf.__operators__.getitem_46/strided_slice/stack_1" -> "3973 maskrcnn/tf.__operators__.getitem_46/strided_slice"; +"3972 maskrcnn/tf.__operators__.getitem_46/strided_slice/stack_2" -> "3973 maskrcnn/tf.__operators__.getitem_46/strided_slice"; +"3973 maskrcnn/tf.__operators__.getitem_46/strided_slice" -> "4057 maskrcnn/tf.__operators__.add_49/AddV2"; +"3974 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3975 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3975 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3976 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/Abs"; +"3976 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/Abs" -> "3978 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/add"; +"3977 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/add/y" -> "3978 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/add"; +"3978 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/add" -> "3981 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/mul"; +"3978 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/add" -> "3982 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3979 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3980 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3980 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3981 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/mul"; +"3981 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/mul" -> "3982 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3982 maskrcnn/tf.math.truediv_15/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4057 maskrcnn/tf.__operators__.add_49/AddV2"; +"3983 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack" -> "3986 maskrcnn/tf.__operators__.getitem_42/strided_slice"; +"3984 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack_1" -> "3986 maskrcnn/tf.__operators__.getitem_42/strided_slice"; +"3985 maskrcnn/tf.__operators__.getitem_42/strided_slice/stack_2" -> "3986 maskrcnn/tf.__operators__.getitem_42/strided_slice"; +"3986 maskrcnn/tf.__operators__.getitem_42/strided_slice" -> "4056 maskrcnn/tf.__operators__.add_47/AddV2"; +"3987 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "3988 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"3988 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "3989 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/Abs"; +"3989 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/Abs" -> "3991 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/add"; +"3990 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/add/y" -> "3991 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/add"; +"3991 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/add" -> "3994 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/mul"; +"3991 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/add" -> "3995 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3992 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "3993 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"3993 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "3994 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/mul"; +"3994 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/mul" -> "3995 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"3995 maskrcnn/tf.math.truediv_13/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4056 maskrcnn/tf.__operators__.add_47/AddV2"; +"3996 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack" -> "3999 maskrcnn/tf.__operators__.getitem_38/strided_slice"; +"3997 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack_1" -> "3999 maskrcnn/tf.__operators__.getitem_38/strided_slice"; +"3998 maskrcnn/tf.__operators__.getitem_38/strided_slice/stack_2" -> "3999 maskrcnn/tf.__operators__.getitem_38/strided_slice"; +"3999 maskrcnn/tf.__operators__.getitem_38/strided_slice" -> "4055 maskrcnn/tf.__operators__.add_45/AddV2"; +"4000 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4001 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4001 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4002 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/Abs"; +"4002 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/Abs" -> "4004 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/add"; +"4003 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/add/y" -> "4004 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/add"; +"4004 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/add" -> "4007 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/mul"; +"4004 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/add" -> "4008 maskrcnn/tf.math.truediv_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4005 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maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/add" -> "4233 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul"; +"4230 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/add" -> "4234 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"4231 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul/ReadVariableOp/resource" -> "4232 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul/ReadVariableOp"; +"4232 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul/ReadVariableOp" -> "4233 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul"; +"4233 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/mul" -> "4234 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"4234 maskrcnn/tf.math.subtract_46/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars" -> "4260 maskrcnn/tf.math.subtract_46/Sub"; +"4235 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp/resource" -> "4236 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp"; +"4236 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "4237 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs"; +"4237 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/Abs" -> "4239 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add"; +"4238 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add/y" -> "4239 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add"; +"4239 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add" -> "4242 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul"; +"4239 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/add" -> "4243 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; +"4240 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "4241 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; +"4241 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "4242 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul"; +"4242 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/mul" -> "4243 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; +"4243 maskrcnn/tf.math.subtract_45/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "4261 maskrcnn/tf.math.subtract_45/Sub"; +"4244 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp/resource" -> "4245 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp"; +"4245 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp" -> "4246 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs"; +"4246 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/Abs" -> "4248 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add"; +"4247 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add/y" -> "4248 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add"; +"4248 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add" -> "4251 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul"; +"4248 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/add" -> "4252 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"4249 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul/ReadVariableOp/resource" -> "4250 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul/ReadVariableOp"; +"4250 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul/ReadVariableOp" -> "4251 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul"; +"4251 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/mul" -> "4252 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"4252 maskrcnn/tf.math.subtract_45/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars" -> "4261 maskrcnn/tf.math.subtract_45/Sub"; +"4253 maskrcnn/tf.__operators__.getitem_57/strided_slice/stack" -> "4256 maskrcnn/tf.__operators__.getitem_57/strided_slice"; +"4254 maskrcnn/tf.__operators__.getitem_57/strided_slice/stack_1" -> "4256 maskrcnn/tf.__operators__.getitem_57/strided_slice"; +"4255 maskrcnn/tf.__operators__.getitem_57/strided_slice/stack_2" -> "4256 maskrcnn/tf.__operators__.getitem_57/strided_slice"; +"4256 maskrcnn/tf.__operators__.getitem_57/strided_slice" -> "4272 maskrcnn/tf.expand_dims_18/ExpandDims"; +"4257 maskrcnn/tf.__operators__.add_54/y" -> "4258 maskrcnn/tf.__operators__.add_54/AddV2"; +"4258 maskrcnn/tf.__operators__.add_54/AddV2" -> "4270 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4259 maskrcnn/tf.math.minimum_19/Minimum" -> "4273 maskrcnn/tf.stack_9/stack"; +"4260 maskrcnn/tf.math.subtract_46/Sub" -> "4282 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4261 maskrcnn/tf.math.subtract_45/Sub" -> "4291 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4262 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4263 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4263 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4264 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs"; +"4264 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/Abs" -> "4266 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add"; +"4265 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add/y" -> "4266 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add"; +"4266 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add" -> "4269 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul"; +"4266 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/add" -> "4270 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4267 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4268 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4268 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4269 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul"; +"4269 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/mul" -> "4270 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4270 maskrcnn/tf.__operators__.add_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4292 maskrcnn/tf.math.minimum_17/Minimum"; +"4271 maskrcnn/tf.expand_dims_18/ExpandDims/dim" -> "4272 maskrcnn/tf.expand_dims_18/ExpandDims"; +"4272 maskrcnn/tf.expand_dims_18/ExpandDims" -> "4292 maskrcnn/tf.math.minimum_17/Minimum"; +"4273 maskrcnn/tf.stack_9/stack" -> "4302 maskrcnn/tf.reshape_23/Reshape"; +"4274 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4275 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4275 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4276 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs"; +"4276 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/Abs" -> "4278 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add"; +"4277 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add/y" -> "4278 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add"; +"4278 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add" -> "4281 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul"; +"4278 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/add" -> "4282 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4279 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4280 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4280 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4281 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul"; +"4281 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/mul" -> "4282 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4282 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4304 maskrcnn/tf.math.subtract_48/Sub"; +"4282 maskrcnn/tf.math.subtract_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4340 maskrcnn/tf.stack_11/stack"; +"4283 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4284 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4284 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4285 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs"; +"4285 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/Abs" -> "4287 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add"; +"4286 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add/y" -> "4287 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add"; +"4287 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add" -> "4290 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul"; +"4287 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/add" -> "4291 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4288 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4289 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4289 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4290 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul"; +"4290 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/mul" -> "4291 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4291 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4306 maskrcnn/tf.math.subtract_47/Sub"; +"4291 maskrcnn/tf.math.subtract_45/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4341 maskrcnn/tf.stack_10/stack"; +"4292 maskrcnn/tf.math.minimum_17/Minimum" -> "4307 maskrcnn/tf.stack_8/stack"; +"4298 maskrcnn/tf.compat.v1.gather_1/GatherV2/params" -> "4300 maskrcnn/tf.compat.v1.gather_1/GatherV2"; +"4299 maskrcnn/tf.compat.v1.gather_1/GatherV2/axis" -> "4300 maskrcnn/tf.compat.v1.gather_1/GatherV2"; +"4300 maskrcnn/tf.compat.v1.gather_1/GatherV2" -> "4310 maskrcnn/tf.expand_dims_21/ExpandDims"; +"4301 maskrcnn/tf.reshape_23/Reshape/shape" -> "4302 maskrcnn/tf.reshape_23/Reshape"; +"4302 maskrcnn/tf.reshape_23/Reshape" -> "4308 maskrcnn/tf.cast_26/Cast"; +"4304 maskrcnn/tf.math.subtract_48/Sub" -> "4319 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4306 maskrcnn/tf.math.subtract_47/Sub" -> "4328 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4307 maskrcnn/tf.stack_8/stack" -> "4330 maskrcnn/tf.reshape_22/Reshape"; +"4308 maskrcnn/tf.cast_26/Cast" -> "4331 maskrcnn/tf.math.multiply_46/Mul"; +"4309 maskrcnn/tf.expand_dims_21/ExpandDims/dim" -> "4310 maskrcnn/tf.expand_dims_21/ExpandDims"; +"4310 maskrcnn/tf.expand_dims_21/ExpandDims" -> "4331 maskrcnn/tf.math.multiply_46/Mul"; +"4311 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4312 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4312 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4313 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/Abs"; +"4313 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/Abs" -> "4315 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/add"; +"4314 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/add/y" -> "4315 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/add"; +"4315 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/add" -> "4318 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/mul"; +"4315 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/add" -> "4319 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4316 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4317 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4317 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4318 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/mul"; +"4318 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/mul" -> "4319 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4319 maskrcnn/tf.math.subtract_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4340 maskrcnn/tf.stack_11/stack"; +"4320 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4321 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4321 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4322 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/Abs"; +"4322 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/Abs" -> "4324 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/add"; +"4323 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/add/y" -> "4324 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/add"; +"4324 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/add" -> "4327 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/mul"; +"4324 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/add" -> "4328 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4325 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4326 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4326 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4327 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/mul"; +"4327 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/mul" -> "4328 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4328 maskrcnn/tf.math.subtract_47/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4341 maskrcnn/tf.stack_10/stack"; +"4329 maskrcnn/tf.reshape_22/Reshape/shape" -> "4330 maskrcnn/tf.reshape_22/Reshape"; +"4330 maskrcnn/tf.reshape_22/Reshape" -> "4342 maskrcnn/tf.cast_25/Cast"; +"4331 maskrcnn/tf.math.multiply_46/Mul" -> "4344 maskrcnn/tf.reshape_25/Reshape"; +"4337 maskrcnn/tf.compat.v1.gather/GatherV2/params" -> "4339 maskrcnn/tf.compat.v1.gather/GatherV2"; +"4338 maskrcnn/tf.compat.v1.gather/GatherV2/axis" -> "4339 maskrcnn/tf.compat.v1.gather/GatherV2"; +"4339 maskrcnn/tf.compat.v1.gather/GatherV2" -> "4346 maskrcnn/tf.reshape_24/Reshape"; +"4340 maskrcnn/tf.stack_11/stack" -> "4348 maskrcnn/tf.reshape_21/Reshape"; +"4341 maskrcnn/tf.stack_10/stack" -> "4350 maskrcnn/tf.reshape_20/Reshape"; +"4342 maskrcnn/tf.cast_25/Cast" -> "4352 maskrcnn/tf.reshape_26/Reshape"; +"4343 maskrcnn/tf.reshape_25/Reshape/shape" -> "4344 maskrcnn/tf.reshape_25/Reshape"; +"4344 maskrcnn/tf.reshape_25/Reshape" -> "4356 maskrcnn/tf.tile_1/Tile"; +"4345 maskrcnn/tf.reshape_24/Reshape/shape" -> "4346 maskrcnn/tf.reshape_24/Reshape"; +"4346 maskrcnn/tf.reshape_24/Reshape" -> "4354 maskrcnn/tf.tile/Tile"; +"4347 maskrcnn/tf.reshape_21/Reshape/shape" -> "4348 maskrcnn/tf.reshape_21/Reshape"; +"4348 maskrcnn/tf.reshape_21/Reshape" -> "4360 maskrcnn/tf.reshape_30/Reshape"; +"4349 maskrcnn/tf.reshape_20/Reshape/shape" -> "4350 maskrcnn/tf.reshape_20/Reshape"; +"4350 maskrcnn/tf.reshape_20/Reshape" -> "4358 maskrcnn/tf.reshape_29/Reshape"; +"4351 maskrcnn/tf.reshape_26/Reshape/shape" -> "4352 maskrcnn/tf.reshape_26/Reshape"; +"4352 maskrcnn/tf.reshape_26/Reshape" -> "4363 maskrcnn/tf.tile_2/Tile"; +"4353 maskrcnn/tf.tile/Tile/multiples" -> "4354 maskrcnn/tf.tile/Tile"; +"4354 maskrcnn/tf.tile/Tile" -> "4361 maskrcnn/tf.__operators__.add_56/AddV2"; +"4355 maskrcnn/tf.tile_1/Tile/multiples" -> "4356 maskrcnn/tf.tile_1/Tile"; +"4356 maskrcnn/tf.tile_1/Tile" -> "4361 maskrcnn/tf.__operators__.add_56/AddV2"; +"4357 maskrcnn/tf.reshape_29/Reshape/shape" -> "4358 maskrcnn/tf.reshape_29/Reshape"; +"4358 maskrcnn/tf.reshape_29/Reshape" -> "4364 maskrcnn/tf.math.multiply_47/Mul"; +"4359 maskrcnn/tf.reshape_30/Reshape/shape" -> "4360 maskrcnn/tf.reshape_30/Reshape"; +"4360 maskrcnn/tf.reshape_30/Reshape" -> "4364 maskrcnn/tf.math.multiply_47/Mul"; +"4361 maskrcnn/tf.__operators__.add_56/AddV2" -> "4365 maskrcnn/tf.__operators__.add_57/AddV2"; +"4362 maskrcnn/tf.tile_2/Tile/multiples" -> "4363 maskrcnn/tf.tile_2/Tile"; +"4363 maskrcnn/tf.tile_2/Tile" -> "4365 maskrcnn/tf.__operators__.add_57/AddV2"; +"4364 maskrcnn/tf.math.multiply_47/Mul" -> "4377 maskrcnn/tf.math.multiply_48/Mul"; +"4365 maskrcnn/tf.__operators__.add_57/AddV2" -> "4379 maskrcnn/tf.__operators__.add_58/AddV2"; +"4366 maskrcnn/tf.reshape_10/Reshape/shape" -> "4367 maskrcnn/tf.reshape_10/Reshape"; +"4367 maskrcnn/tf.reshape_10/Reshape" -> "4381 maskrcnn/tf.concat_10/concat"; +"4368 maskrcnn/tf.reshape_12/Reshape/shape" -> "4369 maskrcnn/tf.reshape_12/Reshape"; +"4369 maskrcnn/tf.reshape_12/Reshape" -> "4381 maskrcnn/tf.concat_10/concat"; +"4370 maskrcnn/tf.reshape_14/Reshape/shape" -> "4371 maskrcnn/tf.reshape_14/Reshape"; +"4371 maskrcnn/tf.reshape_14/Reshape" -> "4381 maskrcnn/tf.concat_10/concat"; +"4372 maskrcnn/tf.reshape_16/Reshape/shape" -> "4373 maskrcnn/tf.reshape_16/Reshape"; +"4373 maskrcnn/tf.reshape_16/Reshape" -> "4381 maskrcnn/tf.concat_10/concat"; +"4374 maskrcnn/tf.reshape_18/Reshape/shape" -> "4375 maskrcnn/tf.reshape_18/Reshape"; +"4375 maskrcnn/tf.reshape_18/Reshape" -> "4381 maskrcnn/tf.concat_10/concat"; +"4376 maskrcnn/tf.math.multiply_48/Mul/y" -> "4377 maskrcnn/tf.math.multiply_48/Mul"; +"4377 maskrcnn/tf.math.multiply_48/Mul" -> "4390 maskrcnn/tf.math.multiply_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4378 maskrcnn/tf.__operators__.add_58/y" -> "4379 maskrcnn/tf.__operators__.add_58/AddV2"; +"4379 maskrcnn/tf.__operators__.add_58/AddV2" -> "4394 maskrcnn/tf.reshape_27/Reshape"; +"4380 maskrcnn/tf.concat_10/concat/axis" -> "4381 maskrcnn/tf.concat_10/concat"; +"4381 maskrcnn/tf.concat_10/concat" -> "4392 maskrcnn/tf.reshape_19/Reshape"; +"4382 maskrcnn/tf.math.multiply_48/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4383 maskrcnn/tf.math.multiply_48/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4383 maskrcnn/tf.math.multiply_48/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4384 maskrcnn/tf.math.multiply_48/fake_quantize/SymmQuant/Abs"; 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maskrcnn/tf.math.multiply_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4390 maskrcnn/tf.math.multiply_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4396 maskrcnn/tf.expand_dims_22/ExpandDims"; +"4391 maskrcnn/tf.reshape_19/Reshape/shape" -> "4392 maskrcnn/tf.reshape_19/Reshape"; +"4392 maskrcnn/tf.reshape_19/Reshape" -> "4398 maskrcnn/tf.compat.v1.gather_2/GatherV2"; +"4393 maskrcnn/tf.reshape_27/Reshape/shape" -> "4394 maskrcnn/tf.reshape_27/Reshape"; +"4394 maskrcnn/tf.reshape_27/Reshape" -> "4398 maskrcnn/tf.compat.v1.gather_2/GatherV2"; +"4395 maskrcnn/tf.expand_dims_22/ExpandDims/dim" -> "4396 maskrcnn/tf.expand_dims_22/ExpandDims"; +"4396 maskrcnn/tf.expand_dims_22/ExpandDims" -> "4401 maskrcnn/tf.math.multiply_49/Mul"; +"4397 maskrcnn/tf.compat.v1.gather_2/GatherV2/axis" -> "4398 maskrcnn/tf.compat.v1.gather_2/GatherV2"; +"4398 maskrcnn/tf.compat.v1.gather_2/GatherV2" -> "4400 maskrcnn/tf.reshape_28/Reshape"; +"4399 maskrcnn/tf.reshape_28/Reshape/shape" -> "4400 maskrcnn/tf.reshape_28/Reshape"; +"4400 maskrcnn/tf.reshape_28/Reshape" -> "4401 maskrcnn/tf.math.multiply_49/Mul"; +"4401 maskrcnn/tf.math.multiply_49/Mul" -> "4410 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4402 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4403 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4403 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4404 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/Abs"; +"4404 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/Abs" -> "4406 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/add"; +"4405 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/add/y" -> "4406 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/add"; +"4406 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/add" -> "4409 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/mul"; +"4406 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/add" -> "4410 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4407 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4408 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4408 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4409 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/mul"; +"4409 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/mul" -> "4410 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4410 maskrcnn/tf.math.multiply_49/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4412 maskrcnn/tf.reshape_31/Reshape"; +"4411 maskrcnn/tf.reshape_31/Reshape/shape" -> "4412 maskrcnn/tf.reshape_31/Reshape"; +"4412 maskrcnn/tf.reshape_31/Reshape" -> "4413 maskrcnn/tf.nn.avg_pool/AvgPool"; +"4413 maskrcnn/tf.nn.avg_pool/AvgPool" -> "4422 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4414 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4415 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4415 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4416 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/Abs"; +"4416 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/Abs" -> "4418 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/add"; +"4417 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/add/y" -> "4418 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/add"; +"4418 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/add" -> "4421 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/mul"; +"4418 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/add" -> "4422 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4419 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4420 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4420 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4421 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/mul"; +"4421 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/mul" -> "4422 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4422 maskrcnn/tf.nn.avg_pool/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4424 maskrcnn/tf.reshape_32/Reshape"; +"4423 maskrcnn/tf.reshape_32/Reshape/shape" -> "4424 maskrcnn/tf.reshape_32/Reshape"; +"4424 maskrcnn/tf.reshape_32/Reshape" -> "4426 maskrcnn/tf.reshape_33/Reshape"; +"4425 maskrcnn/tf.reshape_33/Reshape/shape" -> "4426 maskrcnn/tf.reshape_33/Reshape"; +"4426 maskrcnn/tf.reshape_33/Reshape" -> "4428 maskrcnn/tf.reshape_34/Reshape"; +"4427 maskrcnn/tf.reshape_34/Reshape/shape" -> "4428 maskrcnn/tf.reshape_34/Reshape"; +"4428 maskrcnn/tf.reshape_34/Reshape" -> "4441 maskrcnn/fc0/Tensordot/Reshape"; +"4429 maskrcnn/fc0/SymmQuant/Abs/ReadVariableOp/resource" -> "4430 maskrcnn/fc0/SymmQuant/Abs/ReadVariableOp"; +"4430 maskrcnn/fc0/SymmQuant/Abs/ReadVariableOp" -> "4431 maskrcnn/fc0/SymmQuant/Abs"; +"4431 maskrcnn/fc0/SymmQuant/Abs" -> "4433 maskrcnn/fc0/SymmQuant/add"; +"4432 maskrcnn/fc0/SymmQuant/add/y" -> "4433 maskrcnn/fc0/SymmQuant/add"; +"4433 maskrcnn/fc0/SymmQuant/add" -> "4436 maskrcnn/fc0/SymmQuant/mul"; +"4433 maskrcnn/fc0/SymmQuant/add" -> "4439 maskrcnn/fc0/SymmQuant/FakeQuantWithMinMaxVars"; +"4434 maskrcnn/fc0/SymmQuant/mul/ReadVariableOp/resource" -> "4435 maskrcnn/fc0/SymmQuant/mul/ReadVariableOp"; +"4435 maskrcnn/fc0/SymmQuant/mul/ReadVariableOp" -> "4436 maskrcnn/fc0/SymmQuant/mul"; +"4436 maskrcnn/fc0/SymmQuant/mul" -> "4439 maskrcnn/fc0/SymmQuant/FakeQuantWithMinMaxVars"; +"4437 maskrcnn/fc0/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "4438 maskrcnn/fc0/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4438 maskrcnn/fc0/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4439 maskrcnn/fc0/SymmQuant/FakeQuantWithMinMaxVars"; +"4439 maskrcnn/fc0/SymmQuant/FakeQuantWithMinMaxVars" -> "4442 maskrcnn/fc0/Tensordot/MatMul"; +"4440 maskrcnn/fc0/Tensordot/Reshape/shape" -> "4441 maskrcnn/fc0/Tensordot/Reshape"; +"4441 maskrcnn/fc0/Tensordot/Reshape" -> "4442 maskrcnn/fc0/Tensordot/MatMul"; +"4442 maskrcnn/fc0/Tensordot/MatMul" -> "4444 maskrcnn/fc0/Tensordot"; +"4443 maskrcnn/fc0/Tensordot/shape" -> "4444 maskrcnn/fc0/Tensordot"; +"4444 maskrcnn/fc0/Tensordot" -> "4447 maskrcnn/fc0/BiasAdd"; +"4445 maskrcnn/fc0/BiasAdd/ReadVariableOp/resource" -> "4446 maskrcnn/fc0/BiasAdd/ReadVariableOp"; +"4446 maskrcnn/fc0/BiasAdd/ReadVariableOp" -> "4447 maskrcnn/fc0/BiasAdd"; +"4447 maskrcnn/fc0/BiasAdd" -> "4448 maskrcnn/fc0/Relu"; +"4448 maskrcnn/fc0/Relu" -> "4457 maskrcnn/fc0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4449 maskrcnn/fc0/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4450 maskrcnn/fc0/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4450 maskrcnn/fc0/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4451 maskrcnn/fc0/fake_quantize/SymmQuant/Abs"; +"4451 maskrcnn/fc0/fake_quantize/SymmQuant/Abs" -> "4453 maskrcnn/fc0/fake_quantize/SymmQuant/add"; +"4452 maskrcnn/fc0/fake_quantize/SymmQuant/add/y" -> "4453 maskrcnn/fc0/fake_quantize/SymmQuant/add"; +"4453 maskrcnn/fc0/fake_quantize/SymmQuant/add" -> "4456 maskrcnn/fc0/fake_quantize/SymmQuant/mul"; +"4453 maskrcnn/fc0/fake_quantize/SymmQuant/add" -> "4457 maskrcnn/fc0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4454 maskrcnn/fc0/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4455 maskrcnn/fc0/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4455 maskrcnn/fc0/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4456 maskrcnn/fc0/fake_quantize/SymmQuant/mul"; +"4456 maskrcnn/fc0/fake_quantize/SymmQuant/mul" -> "4457 maskrcnn/fc0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4457 maskrcnn/fc0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4470 maskrcnn/fc1/Tensordot/Reshape"; +"4458 maskrcnn/fc1/SymmQuant/Abs/ReadVariableOp/resource" -> "4459 maskrcnn/fc1/SymmQuant/Abs/ReadVariableOp"; +"4459 maskrcnn/fc1/SymmQuant/Abs/ReadVariableOp" -> "4460 maskrcnn/fc1/SymmQuant/Abs"; +"4460 maskrcnn/fc1/SymmQuant/Abs" -> "4462 maskrcnn/fc1/SymmQuant/add"; +"4461 maskrcnn/fc1/SymmQuant/add/y" -> "4462 maskrcnn/fc1/SymmQuant/add"; +"4462 maskrcnn/fc1/SymmQuant/add" -> "4465 maskrcnn/fc1/SymmQuant/mul"; +"4462 maskrcnn/fc1/SymmQuant/add" -> "4468 maskrcnn/fc1/SymmQuant/FakeQuantWithMinMaxVars"; +"4463 maskrcnn/fc1/SymmQuant/mul/ReadVariableOp/resource" -> "4464 maskrcnn/fc1/SymmQuant/mul/ReadVariableOp"; +"4464 maskrcnn/fc1/SymmQuant/mul/ReadVariableOp" -> "4465 maskrcnn/fc1/SymmQuant/mul"; +"4465 maskrcnn/fc1/SymmQuant/mul" -> "4468 maskrcnn/fc1/SymmQuant/FakeQuantWithMinMaxVars"; +"4466 maskrcnn/fc1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "4467 maskrcnn/fc1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4467 maskrcnn/fc1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4468 maskrcnn/fc1/SymmQuant/FakeQuantWithMinMaxVars"; +"4468 maskrcnn/fc1/SymmQuant/FakeQuantWithMinMaxVars" -> "4471 maskrcnn/fc1/Tensordot/MatMul"; +"4469 maskrcnn/fc1/Tensordot/Reshape/shape" -> "4470 maskrcnn/fc1/Tensordot/Reshape"; +"4470 maskrcnn/fc1/Tensordot/Reshape" -> "4471 maskrcnn/fc1/Tensordot/MatMul"; +"4471 maskrcnn/fc1/Tensordot/MatMul" -> "4473 maskrcnn/fc1/Tensordot"; +"4472 maskrcnn/fc1/Tensordot/shape" -> "4473 maskrcnn/fc1/Tensordot"; +"4473 maskrcnn/fc1/Tensordot" -> "4476 maskrcnn/fc1/BiasAdd"; +"4474 maskrcnn/fc1/BiasAdd/ReadVariableOp/resource" -> "4475 maskrcnn/fc1/BiasAdd/ReadVariableOp"; +"4475 maskrcnn/fc1/BiasAdd/ReadVariableOp" -> "4476 maskrcnn/fc1/BiasAdd"; +"4476 maskrcnn/fc1/BiasAdd" -> "4477 maskrcnn/fc1/Relu"; +"4477 maskrcnn/fc1/Relu" -> "4486 maskrcnn/fc1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4478 maskrcnn/fc1/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4479 maskrcnn/fc1/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4479 maskrcnn/fc1/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4480 maskrcnn/fc1/fake_quantize/SymmQuant/Abs"; +"4480 maskrcnn/fc1/fake_quantize/SymmQuant/Abs" -> "4482 maskrcnn/fc1/fake_quantize/SymmQuant/add"; +"4481 maskrcnn/fc1/fake_quantize/SymmQuant/add/y" -> "4482 maskrcnn/fc1/fake_quantize/SymmQuant/add"; +"4482 maskrcnn/fc1/fake_quantize/SymmQuant/add" -> "4485 maskrcnn/fc1/fake_quantize/SymmQuant/mul"; +"4482 maskrcnn/fc1/fake_quantize/SymmQuant/add" -> "4486 maskrcnn/fc1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4483 maskrcnn/fc1/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4484 maskrcnn/fc1/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4484 maskrcnn/fc1/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4485 maskrcnn/fc1/fake_quantize/SymmQuant/mul"; +"4485 maskrcnn/fc1/fake_quantize/SymmQuant/mul" -> "4486 maskrcnn/fc1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4486 maskrcnn/fc1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4499 maskrcnn/class-predict/Tensordot/Reshape"; +"4486 maskrcnn/fc1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4532 maskrcnn/box-predict/Tensordot/Reshape"; +"4487 maskrcnn/class-predict/SymmQuant/Abs/ReadVariableOp/resource" -> "4488 maskrcnn/class-predict/SymmQuant/Abs/ReadVariableOp"; +"4488 maskrcnn/class-predict/SymmQuant/Abs/ReadVariableOp" -> "4489 maskrcnn/class-predict/SymmQuant/Abs"; +"4489 maskrcnn/class-predict/SymmQuant/Abs" -> "4491 maskrcnn/class-predict/SymmQuant/add"; +"4490 maskrcnn/class-predict/SymmQuant/add/y" -> "4491 maskrcnn/class-predict/SymmQuant/add"; +"4491 maskrcnn/class-predict/SymmQuant/add" -> "4494 maskrcnn/class-predict/SymmQuant/mul"; +"4491 maskrcnn/class-predict/SymmQuant/add" -> "4497 maskrcnn/class-predict/SymmQuant/FakeQuantWithMinMaxVars"; +"4492 maskrcnn/class-predict/SymmQuant/mul/ReadVariableOp/resource" -> "4493 maskrcnn/class-predict/SymmQuant/mul/ReadVariableOp"; +"4493 maskrcnn/class-predict/SymmQuant/mul/ReadVariableOp" -> "4494 maskrcnn/class-predict/SymmQuant/mul"; +"4494 maskrcnn/class-predict/SymmQuant/mul" -> "4497 maskrcnn/class-predict/SymmQuant/FakeQuantWithMinMaxVars"; +"4495 maskrcnn/class-predict/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "4496 maskrcnn/class-predict/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"4496 maskrcnn/class-predict/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "4497 maskrcnn/class-predict/SymmQuant/FakeQuantWithMinMaxVars"; +"4497 maskrcnn/class-predict/SymmQuant/FakeQuantWithMinMaxVars" -> "4500 maskrcnn/class-predict/Tensordot/MatMul"; +"4498 maskrcnn/class-predict/Tensordot/Reshape/shape" -> "4499 maskrcnn/class-predict/Tensordot/Reshape"; +"4499 maskrcnn/class-predict/Tensordot/Reshape" -> "4500 maskrcnn/class-predict/Tensordot/MatMul"; +"4500 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maskrcnn/tf.__operators__.add_61/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4765 maskrcnn/tf.__operators__.add_63/AddV2"; +"4761 maskrcnn/tf.math.multiply_58/Mul" -> "4783 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4762 maskrcnn/tf.__operators__.add_64/AddV2" -> "4774 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4764 maskrcnn/tf.math.multiply_57/Mul" -> "4801 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4765 maskrcnn/tf.__operators__.add_63/AddV2" -> "4792 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4766 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4767 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4767 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4768 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/Abs"; +"4768 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/Abs" -> "4770 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/add"; +"4769 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/add/y" -> "4770 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/add"; +"4770 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/add" -> "4773 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/mul"; +"4770 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/add" -> "4774 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4771 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4772 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4772 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4773 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/mul"; +"4773 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/mul" -> "4774 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4774 maskrcnn/tf.__operators__.add_64/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4806 maskrcnn/tf.math.subtract_54/Sub"; +"4775 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4776 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4776 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4777 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/Abs"; +"4777 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/Abs" -> "4779 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/add"; +"4778 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/add/y" -> "4779 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/add"; +"4779 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/add" -> "4782 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/mul"; +"4779 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/add" -> "4783 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4780 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4781 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4781 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4782 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/mul"; +"4782 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/mul" -> "4783 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4783 maskrcnn/tf.math.multiply_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4806 maskrcnn/tf.math.subtract_54/Sub"; +"4784 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4785 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4785 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4786 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/Abs"; +"4786 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/Abs" -> "4788 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/add"; +"4787 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/add/y" -> "4788 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/add"; +"4788 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/add" -> "4791 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/mul"; +"4788 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/add" -> "4792 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4789 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4790 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4790 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4791 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/mul"; +"4791 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/mul" -> "4792 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4792 maskrcnn/tf.__operators__.add_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4807 maskrcnn/tf.math.subtract_53/Sub"; +"4793 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4794 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4794 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4795 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/Abs"; +"4795 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/Abs" -> "4797 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/add"; +"4796 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/add/y" -> "4797 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/add"; +"4797 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/add" -> "4800 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/mul"; +"4797 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/add" -> "4801 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4798 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4799 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4799 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4800 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/mul"; +"4800 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/mul" -> "4801 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4801 maskrcnn/tf.math.multiply_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4807 maskrcnn/tf.math.subtract_53/Sub"; +"4802 maskrcnn/tf.__operators__.getitem_60/strided_slice/stack" -> "4805 maskrcnn/tf.__operators__.getitem_60/strided_slice"; +"4803 maskrcnn/tf.__operators__.getitem_60/strided_slice/stack_1" -> "4805 maskrcnn/tf.__operators__.getitem_60/strided_slice"; +"4804 maskrcnn/tf.__operators__.getitem_60/strided_slice/stack_2" -> "4805 maskrcnn/tf.__operators__.getitem_60/strided_slice"; +"4805 maskrcnn/tf.__operators__.getitem_60/strided_slice" -> "4826 maskrcnn/tf.unstack_5/unstack"; +"4806 maskrcnn/tf.math.subtract_54/Sub" -> "4825 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4807 maskrcnn/tf.math.subtract_53/Sub" -> "4816 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4808 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4809 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4809 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4810 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/Abs"; +"4810 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/Abs" -> "4812 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/add"; +"4811 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/add/y" -> "4812 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/add"; +"4812 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/add" -> "4815 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/mul"; +"4812 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/add" -> "4816 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4813 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4814 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4814 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4815 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/mul"; +"4815 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/mul" -> "4816 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4816 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4828 maskrcnn/tf.__operators__.add_65/AddV2"; +"4816 maskrcnn/tf.math.subtract_53/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4896 maskrcnn/tf.concat_13/concat"; +"4817 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4818 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4818 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4819 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/Abs"; +"4819 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/Abs" -> "4821 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/add"; +"4820 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/add/y" -> "4821 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/add"; +"4821 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/add" -> "4824 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/mul"; +"4821 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/add" -> "4825 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4822 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4823 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4823 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4824 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/mul"; +"4824 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/mul" -> "4825 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4825 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4827 maskrcnn/tf.__operators__.add_66/AddV2"; +"4825 maskrcnn/tf.math.subtract_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4896 maskrcnn/tf.concat_13/concat"; +"4826 maskrcnn/tf.unstack_5/unstack" -> "4830 maskrcnn/tf.math.subtract_60/Sub"; +"4826 maskrcnn/tf.unstack_5/unstack" -> "4832 maskrcnn/tf.math.subtract_59/Sub"; +"4826 maskrcnn/tf.unstack_5/unstack" -> "4834 maskrcnn/tf.math.subtract_58/Sub"; +"4826 maskrcnn/tf.unstack_5/unstack" -> "4836 maskrcnn/tf.math.subtract_57/Sub"; +"4827 maskrcnn/tf.__operators__.add_66/AddV2" -> "4838 maskrcnn/tf.math.subtract_56/Sub"; +"4828 maskrcnn/tf.__operators__.add_65/AddV2" -> "4840 maskrcnn/tf.math.subtract_55/Sub"; +"4829 maskrcnn/tf.math.subtract_60/Sub/y" -> "4830 maskrcnn/tf.math.subtract_60/Sub"; +"4830 maskrcnn/tf.math.subtract_60/Sub" -> "4876 maskrcnn/tf.math.subtract_60/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4831 maskrcnn/tf.math.subtract_59/Sub/y" -> "4832 maskrcnn/tf.math.subtract_59/Sub"; +"4832 maskrcnn/tf.math.subtract_59/Sub" -> "4867 maskrcnn/tf.math.subtract_59/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4833 maskrcnn/tf.math.subtract_58/Sub/y" -> "4834 maskrcnn/tf.math.subtract_58/Sub"; +"4834 maskrcnn/tf.math.subtract_58/Sub" -> "4858 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4835 maskrcnn/tf.math.subtract_57/Sub/y" -> "4836 maskrcnn/tf.math.subtract_57/Sub"; +"4836 maskrcnn/tf.math.subtract_57/Sub" -> "4849 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4837 maskrcnn/tf.math.subtract_56/Sub/y" -> "4838 maskrcnn/tf.math.subtract_56/Sub"; +"4838 maskrcnn/tf.math.subtract_56/Sub" -> "4894 maskrcnn/tf.math.subtract_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4839 maskrcnn/tf.math.subtract_55/Sub/y" -> "4840 maskrcnn/tf.math.subtract_55/Sub"; +"4840 maskrcnn/tf.math.subtract_55/Sub" -> "4885 maskrcnn/tf.math.subtract_55/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4841 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4842 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4842 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4843 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/Abs"; +"4843 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/Abs" -> "4845 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/add"; +"4844 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/add/y" -> "4845 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/add"; +"4845 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/add" -> "4848 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/mul"; +"4845 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/add" -> "4849 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4846 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4847 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4847 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4848 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/mul"; +"4848 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/mul" -> "4849 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4849 maskrcnn/tf.math.subtract_57/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4897 maskrcnn/tf.stack_15/stack"; +"4850 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4851 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4851 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "4852 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/Abs"; +"4852 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/Abs" -> "4854 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/add"; +"4853 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/add/y" -> "4854 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/add"; +"4854 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/add" -> "4857 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/mul"; +"4854 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/add" -> "4858 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4855 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "4856 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"4856 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "4857 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/mul"; +"4857 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/mul" -> "4858 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"4858 maskrcnn/tf.math.subtract_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "4897 maskrcnn/tf.stack_15/stack"; +"4859 maskrcnn/tf.math.subtract_59/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "4860 maskrcnn/tf.math.subtract_59/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"4860 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maskrcnn/tf.__operators__.getitem_118/strided_slice/stack_1" -> "5209 maskrcnn/tf.__operators__.getitem_118/strided_slice"; +"5208 maskrcnn/tf.__operators__.getitem_118/strided_slice/stack_2" -> "5209 maskrcnn/tf.__operators__.getitem_118/strided_slice"; +"5209 maskrcnn/tf.__operators__.getitem_118/strided_slice" -> "5391 maskrcnn/tf.math.multiply_81/Mul"; +"5210 maskrcnn/tf.__operators__.getitem_114/strided_slice/stack" -> "5213 maskrcnn/tf.__operators__.getitem_114/strided_slice"; +"5211 maskrcnn/tf.__operators__.getitem_114/strided_slice/stack_1" -> "5213 maskrcnn/tf.__operators__.getitem_114/strided_slice"; +"5212 maskrcnn/tf.__operators__.getitem_114/strided_slice/stack_2" -> "5213 maskrcnn/tf.__operators__.getitem_114/strided_slice"; +"5213 maskrcnn/tf.__operators__.getitem_114/strided_slice" -> "5393 maskrcnn/tf.math.multiply_79/Mul"; +"5214 maskrcnn/tf.__operators__.getitem_110/strided_slice/stack" -> "5217 maskrcnn/tf.__operators__.getitem_110/strided_slice"; +"5215 maskrcnn/tf.__operators__.getitem_110/strided_slice/stack_1" -> "5217 maskrcnn/tf.__operators__.getitem_110/strided_slice"; +"5216 maskrcnn/tf.__operators__.getitem_110/strided_slice/stack_2" -> "5217 maskrcnn/tf.__operators__.getitem_110/strided_slice"; +"5217 maskrcnn/tf.__operators__.getitem_110/strided_slice" -> "5395 maskrcnn/tf.math.multiply_77/Mul"; +"5218 maskrcnn/tf.__operators__.getitem_106/strided_slice/stack" -> "5221 maskrcnn/tf.__operators__.getitem_106/strided_slice"; +"5219 maskrcnn/tf.__operators__.getitem_106/strided_slice/stack_1" -> "5221 maskrcnn/tf.__operators__.getitem_106/strided_slice"; +"5220 maskrcnn/tf.__operators__.getitem_106/strided_slice/stack_2" -> "5221 maskrcnn/tf.__operators__.getitem_106/strided_slice"; +"5221 maskrcnn/tf.__operators__.getitem_106/strided_slice" -> "5397 maskrcnn/tf.math.multiply_75/Mul"; +"5222 maskrcnn/tf.__operators__.getitem_102/strided_slice/stack" -> "5225 maskrcnn/tf.__operators__.getitem_102/strided_slice"; +"5223 maskrcnn/tf.__operators__.getitem_102/strided_slice/stack_1" -> "5225 maskrcnn/tf.__operators__.getitem_102/strided_slice"; +"5224 maskrcnn/tf.__operators__.getitem_102/strided_slice/stack_2" -> "5225 maskrcnn/tf.__operators__.getitem_102/strided_slice"; +"5225 maskrcnn/tf.__operators__.getitem_102/strided_slice" -> "5399 maskrcnn/tf.math.multiply_73/Mul"; +"5226 maskrcnn/tf.__operators__.getitem_98/strided_slice/stack" -> "5229 maskrcnn/tf.__operators__.getitem_98/strided_slice"; +"5227 maskrcnn/tf.__operators__.getitem_98/strided_slice/stack_1" -> "5229 maskrcnn/tf.__operators__.getitem_98/strided_slice"; +"5228 maskrcnn/tf.__operators__.getitem_98/strided_slice/stack_2" -> "5229 maskrcnn/tf.__operators__.getitem_98/strided_slice"; +"5229 maskrcnn/tf.__operators__.getitem_98/strided_slice" -> "5401 maskrcnn/tf.math.multiply_71/Mul"; +"5230 maskrcnn/tf.__operators__.getitem_94/strided_slice/stack" -> "5233 maskrcnn/tf.__operators__.getitem_94/strided_slice"; +"5231 maskrcnn/tf.__operators__.getitem_94/strided_slice/stack_1" -> "5233 maskrcnn/tf.__operators__.getitem_94/strided_slice"; +"5232 maskrcnn/tf.__operators__.getitem_94/strided_slice/stack_2" -> "5233 maskrcnn/tf.__operators__.getitem_94/strided_slice"; +"5233 maskrcnn/tf.__operators__.getitem_94/strided_slice" -> "5403 maskrcnn/tf.math.multiply_69/Mul"; +"5234 maskrcnn/tf.__operators__.getitem_90/strided_slice/stack" -> "5237 maskrcnn/tf.__operators__.getitem_90/strided_slice"; +"5235 maskrcnn/tf.__operators__.getitem_90/strided_slice/stack_1" -> "5237 maskrcnn/tf.__operators__.getitem_90/strided_slice"; +"5236 maskrcnn/tf.__operators__.getitem_90/strided_slice/stack_2" -> "5237 maskrcnn/tf.__operators__.getitem_90/strided_slice"; +"5237 maskrcnn/tf.__operators__.getitem_90/strided_slice" -> "5405 maskrcnn/tf.math.multiply_67/Mul"; +"5238 maskrcnn/tf.__operators__.getitem_86/strided_slice/stack" -> "5241 maskrcnn/tf.__operators__.getitem_86/strided_slice"; +"5239 maskrcnn/tf.__operators__.getitem_86/strided_slice/stack_1" -> "5241 maskrcnn/tf.__operators__.getitem_86/strided_slice"; +"5240 maskrcnn/tf.__operators__.getitem_86/strided_slice/stack_2" -> "5241 maskrcnn/tf.__operators__.getitem_86/strided_slice"; +"5241 maskrcnn/tf.__operators__.getitem_86/strided_slice" -> "5407 maskrcnn/tf.math.multiply_65/Mul"; +"5242 maskrcnn/tf.__operators__.getitem_82/strided_slice/stack" -> "5245 maskrcnn/tf.__operators__.getitem_82/strided_slice"; +"5243 maskrcnn/tf.__operators__.getitem_82/strided_slice/stack_1" -> "5245 maskrcnn/tf.__operators__.getitem_82/strided_slice"; +"5244 maskrcnn/tf.__operators__.getitem_82/strided_slice/stack_2" -> "5245 maskrcnn/tf.__operators__.getitem_82/strided_slice"; +"5245 maskrcnn/tf.__operators__.getitem_82/strided_slice" -> "5409 maskrcnn/tf.math.multiply_63/Mul"; +"5246 maskrcnn/tf.__operators__.getitem_78/strided_slice/stack" -> "5249 maskrcnn/tf.__operators__.getitem_78/strided_slice"; +"5247 maskrcnn/tf.__operators__.getitem_78/strided_slice/stack_1" -> "5249 maskrcnn/tf.__operators__.getitem_78/strided_slice"; +"5248 maskrcnn/tf.__operators__.getitem_78/strided_slice/stack_2" -> "5249 maskrcnn/tf.__operators__.getitem_78/strided_slice"; +"5249 maskrcnn/tf.__operators__.getitem_78/strided_slice" -> "5411 maskrcnn/tf.math.multiply_61/Mul"; +"5251 maskrcnn/tf.math.truediv_30/truediv/x/0" -> "5252 maskrcnn/tf.math.truediv_30/truediv/x"; +"5252 maskrcnn/tf.math.truediv_30/truediv/x" -> "5253 maskrcnn/tf.math.truediv_30/truediv"; +"5253 maskrcnn/tf.math.truediv_30/truediv" -> "5413 maskrcnn/tf.math.subtract_66/Sub"; +"5255 maskrcnn/tf.math.truediv_29/truediv/x/0" -> "5256 maskrcnn/tf.math.truediv_29/truediv/x"; +"5256 maskrcnn/tf.math.truediv_29/truediv/x" -> "5257 maskrcnn/tf.math.truediv_29/truediv"; +"5257 maskrcnn/tf.math.truediv_29/truediv" -> "5415 maskrcnn/tf.math.subtract_65/Sub"; +"5258 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5259 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5259 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5260 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/Abs"; +"5260 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/Abs" -> "5262 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/add"; +"5261 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/add/y" -> "5262 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/add"; +"5262 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/add" -> "5265 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/mul"; +"5262 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/add" -> "5266 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5263 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5264 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5264 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5265 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/mul"; +"5265 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/mul" -> "5266 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5266 maskrcnn/tf.math.multiply_88/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5417 maskrcnn/tf.math.truediv_58/truediv"; +"5267 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5268 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5268 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5269 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/Abs"; +"5269 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/Abs" -> "5271 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/add"; +"5270 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/add/y" -> "5271 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/add"; +"5271 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/add" -> "5274 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/mul"; +"5271 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/add" -> "5275 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5272 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5273 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5273 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5274 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/mul"; +"5274 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/mul" -> "5275 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5275 maskrcnn/tf.math.multiply_86/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5419 maskrcnn/tf.math.truediv_56/truediv"; +"5276 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5277 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5277 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5278 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/Abs"; +"5278 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/Abs" -> "5280 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/add"; +"5279 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/add/y" -> "5280 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/add"; +"5280 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/add" -> "5283 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/mul"; +"5280 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/add" -> "5284 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5281 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5282 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5282 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5283 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/mul"; +"5283 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/mul" -> "5284 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5284 maskrcnn/tf.math.multiply_84/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5421 maskrcnn/tf.math.truediv_54/truediv"; +"5285 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5286 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5286 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5287 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/Abs"; +"5287 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/Abs" -> "5289 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/add"; +"5288 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/add/y" -> "5289 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/add"; +"5289 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/add" -> "5292 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/mul"; +"5289 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/add" -> "5293 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5290 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5291 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5291 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5292 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/mul"; +"5292 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/mul" -> "5293 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5293 maskrcnn/tf.math.multiply_82/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5423 maskrcnn/tf.math.truediv_52/truediv"; +"5294 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5295 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5295 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5296 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/Abs"; +"5296 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/Abs" -> "5298 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/add"; +"5297 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/add/y" -> "5298 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/add"; +"5298 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/add" -> "5301 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/mul"; +"5298 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/add" -> "5302 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5299 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5300 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5300 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5301 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/mul"; +"5301 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/mul" -> "5302 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5302 maskrcnn/tf.math.multiply_80/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5425 maskrcnn/tf.math.truediv_50/truediv"; +"5303 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5304 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5304 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5305 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/Abs"; +"5305 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/Abs" -> "5307 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/add"; +"5306 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/add/y" -> "5307 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/add"; +"5307 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/add" -> "5310 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/mul"; +"5307 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/add" -> "5311 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5308 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5309 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5309 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5310 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/mul"; +"5310 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/mul" -> "5311 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5311 maskrcnn/tf.math.multiply_78/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5427 maskrcnn/tf.math.truediv_48/truediv"; +"5312 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5313 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5313 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5314 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/Abs"; +"5314 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/Abs" -> "5316 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/add"; +"5315 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/add/y" -> "5316 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/add"; +"5316 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/add" -> "5319 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/mul"; +"5316 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/add" -> "5320 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5317 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5318 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5318 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5319 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/mul"; +"5319 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/mul" -> "5320 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5320 maskrcnn/tf.math.multiply_76/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5429 maskrcnn/tf.math.truediv_46/truediv"; +"5321 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5322 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5322 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5323 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/Abs"; +"5323 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/Abs" -> "5325 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/add"; +"5324 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/add/y" -> "5325 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/add"; +"5325 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/add" -> "5328 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/mul"; +"5325 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/add" -> "5329 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5326 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5327 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5327 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5328 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/mul"; +"5328 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/mul" -> "5329 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5329 maskrcnn/tf.math.multiply_74/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5431 maskrcnn/tf.math.truediv_44/truediv"; +"5330 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5331 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5331 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5332 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/Abs"; +"5332 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/Abs" -> "5334 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/add"; +"5333 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/add/y" -> "5334 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/add"; +"5334 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/add" -> "5337 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/mul"; +"5334 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/add" -> "5338 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5335 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5336 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5336 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5337 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/mul"; +"5337 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/mul" -> "5338 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5338 maskrcnn/tf.math.multiply_72/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5433 maskrcnn/tf.math.truediv_42/truediv"; +"5339 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5340 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5340 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5341 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/Abs"; +"5341 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/Abs" -> "5343 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/add"; +"5342 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/add/y" -> "5343 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/add"; +"5343 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/add" -> "5346 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/mul"; +"5343 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/add" -> "5347 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5344 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5345 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5345 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5346 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/mul"; +"5346 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/mul" -> "5347 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5347 maskrcnn/tf.math.multiply_70/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5435 maskrcnn/tf.math.truediv_40/truediv"; +"5348 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5349 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5349 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5350 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/Abs"; +"5350 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/Abs" -> "5352 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/add"; +"5351 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/add/y" -> "5352 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/add"; +"5352 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/add" -> "5355 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/mul"; +"5352 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/add" -> "5356 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5353 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5354 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5354 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5355 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/mul"; +"5355 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/mul" -> "5356 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5356 maskrcnn/tf.math.multiply_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5437 maskrcnn/tf.math.truediv_38/truediv"; +"5357 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5358 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5358 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5359 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/Abs"; +"5359 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/Abs" -> "5361 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/add"; +"5360 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/add/y" -> "5361 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/add"; +"5361 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/add" -> "5364 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/mul"; +"5361 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/add" -> "5365 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5362 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5363 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5363 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5364 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/mul"; +"5364 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/mul" -> "5365 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5365 maskrcnn/tf.math.multiply_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5439 maskrcnn/tf.math.truediv_36/truediv"; +"5366 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5367 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5367 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5368 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/Abs"; +"5368 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/Abs" -> "5370 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/add"; +"5369 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/add/y" -> "5370 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/add"; +"5370 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/add" -> "5373 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/mul"; +"5370 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/add" -> "5374 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5371 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5372 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5372 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5373 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/mul"; +"5373 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/mul" -> "5374 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5374 maskrcnn/tf.math.multiply_64/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5441 maskrcnn/tf.math.truediv_34/truediv"; +"5375 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5376 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5376 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5377 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/Abs"; +"5377 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/Abs" -> "5379 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/add"; +"5378 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/add/y" -> "5379 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/add"; +"5379 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/add" -> "5382 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/mul"; +"5379 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/add" -> "5383 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5380 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5381 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5381 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5382 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/mul"; +"5382 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/mul" -> "5383 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5383 maskrcnn/tf.math.multiply_62/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5443 maskrcnn/tf.math.truediv_32/truediv"; +"5385 maskrcnn/tf.math.multiply_87/Mul" -> "5452 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5387 maskrcnn/tf.math.multiply_85/Mul" -> "5461 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5389 maskrcnn/tf.math.multiply_83/Mul" -> "5470 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5391 maskrcnn/tf.math.multiply_81/Mul" -> "5479 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5393 maskrcnn/tf.math.multiply_79/Mul" -> "5488 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5395 maskrcnn/tf.math.multiply_77/Mul" -> "5497 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5397 maskrcnn/tf.math.multiply_75/Mul" -> "5506 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5399 maskrcnn/tf.math.multiply_73/Mul" -> "5515 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5401 maskrcnn/tf.math.multiply_71/Mul" -> "5524 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5403 maskrcnn/tf.math.multiply_69/Mul" -> "5533 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5405 maskrcnn/tf.math.multiply_67/Mul" -> "5542 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5407 maskrcnn/tf.math.multiply_65/Mul" -> "5551 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5409 maskrcnn/tf.math.multiply_63/Mul" -> "5560 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5411 maskrcnn/tf.math.multiply_61/Mul" -> "5569 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5412 maskrcnn/tf.math.subtract_66/Sub/y" -> "5413 maskrcnn/tf.math.subtract_66/Sub"; +"5413 maskrcnn/tf.math.subtract_66/Sub" -> "5578 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5414 maskrcnn/tf.math.subtract_65/Sub/y" -> "5415 maskrcnn/tf.math.subtract_65/Sub"; +"5415 maskrcnn/tf.math.subtract_65/Sub" -> "5587 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5416 maskrcnn/tf.math.truediv_58/truediv/y" -> "5417 maskrcnn/tf.math.truediv_58/truediv"; +"5417 maskrcnn/tf.math.truediv_58/truediv" -> "5600 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5418 maskrcnn/tf.math.truediv_56/truediv/y" -> "5419 maskrcnn/tf.math.truediv_56/truediv"; +"5419 maskrcnn/tf.math.truediv_56/truediv" -> "5613 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5420 maskrcnn/tf.math.truediv_54/truediv/y" -> "5421 maskrcnn/tf.math.truediv_54/truediv"; +"5421 maskrcnn/tf.math.truediv_54/truediv" -> "5626 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5422 maskrcnn/tf.math.truediv_52/truediv/y" -> "5423 maskrcnn/tf.math.truediv_52/truediv"; +"5423 maskrcnn/tf.math.truediv_52/truediv" -> "5639 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5424 maskrcnn/tf.math.truediv_50/truediv/y" -> "5425 maskrcnn/tf.math.truediv_50/truediv"; +"5425 maskrcnn/tf.math.truediv_50/truediv" -> "5652 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5426 maskrcnn/tf.math.truediv_48/truediv/y" -> "5427 maskrcnn/tf.math.truediv_48/truediv"; +"5427 maskrcnn/tf.math.truediv_48/truediv" -> "5665 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5428 maskrcnn/tf.math.truediv_46/truediv/y" -> "5429 maskrcnn/tf.math.truediv_46/truediv"; +"5429 maskrcnn/tf.math.truediv_46/truediv" -> "5678 maskrcnn/tf.math.truediv_46/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5430 maskrcnn/tf.math.truediv_44/truediv/y" -> "5431 maskrcnn/tf.math.truediv_44/truediv"; +"5431 maskrcnn/tf.math.truediv_44/truediv" -> "5691 maskrcnn/tf.math.truediv_44/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5432 maskrcnn/tf.math.truediv_42/truediv/y" -> "5433 maskrcnn/tf.math.truediv_42/truediv"; +"5433 maskrcnn/tf.math.truediv_42/truediv" -> "5704 maskrcnn/tf.math.truediv_42/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5434 maskrcnn/tf.math.truediv_40/truediv/y" -> "5435 maskrcnn/tf.math.truediv_40/truediv"; +"5435 maskrcnn/tf.math.truediv_40/truediv" -> "5717 maskrcnn/tf.math.truediv_40/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5436 maskrcnn/tf.math.truediv_38/truediv/y" -> "5437 maskrcnn/tf.math.truediv_38/truediv"; +"5437 maskrcnn/tf.math.truediv_38/truediv" -> "5730 maskrcnn/tf.math.truediv_38/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5438 maskrcnn/tf.math.truediv_36/truediv/y" -> "5439 maskrcnn/tf.math.truediv_36/truediv"; +"5439 maskrcnn/tf.math.truediv_36/truediv" -> "5743 maskrcnn/tf.math.truediv_36/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5440 maskrcnn/tf.math.truediv_34/truediv/y" -> "5441 maskrcnn/tf.math.truediv_34/truediv"; +"5441 maskrcnn/tf.math.truediv_34/truediv" -> "5756 maskrcnn/tf.math.truediv_34/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5442 maskrcnn/tf.math.truediv_32/truediv/y" -> "5443 maskrcnn/tf.math.truediv_32/truediv"; +"5443 maskrcnn/tf.math.truediv_32/truediv" -> "5769 maskrcnn/tf.math.truediv_32/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5444 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5445 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5445 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5446 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs"; +"5446 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/Abs" -> "5448 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add"; +"5447 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add/y" -> "5448 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add"; +"5448 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add" -> "5451 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul"; +"5448 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/add" -> "5452 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5449 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5450 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5450 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5451 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul"; +"5451 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/mul" -> "5452 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5452 maskrcnn/tf.math.multiply_87/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5771 maskrcnn/tf.math.truediv_57/truediv"; +"5453 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5454 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5454 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5455 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs"; +"5455 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/Abs" -> "5457 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add"; +"5456 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add/y" -> "5457 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add"; +"5457 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add" -> "5460 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul"; +"5457 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/add" -> "5461 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5458 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5459 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5459 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5460 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul"; +"5460 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/mul" -> "5461 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5461 maskrcnn/tf.math.multiply_85/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5773 maskrcnn/tf.math.truediv_55/truediv"; +"5462 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5463 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5463 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5464 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs"; +"5464 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/Abs" -> "5466 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add"; +"5465 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add/y" -> "5466 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add"; +"5466 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add" -> "5469 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul"; +"5466 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/add" -> "5470 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5467 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5468 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5468 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5469 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul"; +"5469 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/mul" -> "5470 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5470 maskrcnn/tf.math.multiply_83/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5775 maskrcnn/tf.math.truediv_53/truediv"; +"5471 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5472 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5472 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5473 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs"; +"5473 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/Abs" -> "5475 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add"; +"5474 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add/y" -> "5475 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add"; +"5475 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add" -> "5478 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul"; +"5475 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/add" -> "5479 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5476 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5477 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5477 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5478 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul"; +"5478 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/mul" -> "5479 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5479 maskrcnn/tf.math.multiply_81/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5777 maskrcnn/tf.math.truediv_51/truediv"; +"5480 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5481 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5481 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5482 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs"; +"5482 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/Abs" -> "5484 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add"; +"5483 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add/y" -> "5484 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add"; +"5484 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add" -> "5487 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul"; +"5484 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/add" -> "5488 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5485 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5486 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5486 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5487 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul"; +"5487 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/mul" -> "5488 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5488 maskrcnn/tf.math.multiply_79/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5779 maskrcnn/tf.math.truediv_49/truediv"; +"5489 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5490 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5490 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5491 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs"; +"5491 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/Abs" -> "5493 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add"; +"5492 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add/y" -> "5493 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add"; +"5493 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add" -> "5496 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul"; +"5493 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/add" -> "5497 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5494 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5495 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5495 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5496 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul"; +"5496 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/mul" -> "5497 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5497 maskrcnn/tf.math.multiply_77/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5781 maskrcnn/tf.math.truediv_47/truediv"; +"5498 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5499 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5499 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5500 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs"; +"5500 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/Abs" -> "5502 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add"; +"5501 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add/y" -> "5502 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add"; +"5502 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add" -> "5505 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul"; +"5502 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/add" -> "5506 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5503 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5504 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5504 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5505 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul"; +"5505 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/mul" -> "5506 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5506 maskrcnn/tf.math.multiply_75/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5783 maskrcnn/tf.math.truediv_45/truediv"; +"5507 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5508 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5508 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5509 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs"; +"5509 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/Abs" -> "5511 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add"; +"5510 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add/y" -> "5511 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add"; +"5511 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add" -> "5514 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul"; +"5511 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/add" -> "5515 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5512 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5513 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5513 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5514 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul"; +"5514 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/mul" -> "5515 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5515 maskrcnn/tf.math.multiply_73/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5785 maskrcnn/tf.math.truediv_43/truediv"; +"5516 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5517 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5517 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5518 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs"; +"5518 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/Abs" -> "5520 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add"; +"5519 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add/y" -> "5520 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add"; +"5520 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add" -> "5523 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul"; +"5520 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/add" -> "5524 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5521 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5522 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5522 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5523 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul"; +"5523 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/mul" -> "5524 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5524 maskrcnn/tf.math.multiply_71/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5787 maskrcnn/tf.math.truediv_41/truediv"; +"5525 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5526 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5526 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5527 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs"; +"5527 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/Abs" -> "5529 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add"; +"5528 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add/y" -> "5529 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add"; +"5529 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add" -> "5532 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul"; +"5529 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/add" -> "5533 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5530 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5531 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5531 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5532 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul"; +"5532 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/mul" -> "5533 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5533 maskrcnn/tf.math.multiply_69/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5789 maskrcnn/tf.math.truediv_39/truediv"; +"5534 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5535 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5535 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5536 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs"; +"5536 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/Abs" -> "5538 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add"; +"5537 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add/y" -> "5538 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add"; +"5538 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add" -> "5541 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul"; +"5538 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/add" -> "5542 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5539 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5540 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5540 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5541 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul"; +"5541 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/mul" -> "5542 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5542 maskrcnn/tf.math.multiply_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5791 maskrcnn/tf.math.truediv_37/truediv"; +"5543 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5544 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5544 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5545 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs"; +"5545 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/Abs" -> "5547 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add"; +"5546 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add/y" -> "5547 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add"; +"5547 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add" -> "5550 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul"; +"5547 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/add" -> "5551 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5548 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5549 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5549 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5550 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul"; +"5550 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/mul" -> "5551 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5551 maskrcnn/tf.math.multiply_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5793 maskrcnn/tf.math.truediv_35/truediv"; +"5552 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5553 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5553 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5554 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs"; +"5554 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/Abs" -> "5556 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add"; +"5555 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add/y" -> "5556 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add"; +"5556 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add" -> "5559 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul"; +"5556 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/add" -> "5560 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5557 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5558 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5558 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5559 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul"; +"5559 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/mul" -> "5560 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5560 maskrcnn/tf.math.multiply_63/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5795 maskrcnn/tf.math.truediv_33/truediv"; +"5561 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5562 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5562 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5563 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs"; +"5563 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/Abs" -> "5565 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add"; +"5564 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add/y" -> "5565 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add"; +"5565 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add" -> "5568 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul"; +"5565 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/add" -> "5569 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5566 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5567 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5567 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5568 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul"; +"5568 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/mul" -> "5569 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5569 maskrcnn/tf.math.multiply_61/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5797 maskrcnn/tf.math.truediv_31/truediv"; +"5570 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5571 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5571 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5572 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/Abs"; +"5572 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/Abs" -> "5574 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add"; +"5573 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add/y" -> "5574 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add"; +"5574 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add" -> "5577 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul"; +"5574 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/add" -> "5578 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5575 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5576 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5576 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5577 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul"; +"5577 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/mul" -> "5578 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5578 maskrcnn/tf.math.subtract_66/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5801 maskrcnn/tf.expand_dims_28/ExpandDims"; +"5579 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5580 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5580 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5581 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs"; +"5581 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/Abs" -> "5583 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add"; +"5582 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add/y" -> "5583 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add"; +"5583 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add" -> "5586 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/mul"; +"5583 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/add" -> "5587 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5584 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5585 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5585 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5586 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/mul"; +"5586 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/mul" -> "5587 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5587 maskrcnn/tf.math.subtract_65/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5799 maskrcnn/tf.expand_dims_27/ExpandDims"; +"5588 maskrcnn/tf.__operators__.getitem_131/strided_slice/stack" -> "5591 maskrcnn/tf.__operators__.getitem_131/strided_slice"; +"5589 maskrcnn/tf.__operators__.getitem_131/strided_slice/stack_1" -> "5591 maskrcnn/tf.__operators__.getitem_131/strided_slice"; +"5590 maskrcnn/tf.__operators__.getitem_131/strided_slice/stack_2" -> "5591 maskrcnn/tf.__operators__.getitem_131/strided_slice"; +"5591 maskrcnn/tf.__operators__.getitem_131/strided_slice" -> "5815 maskrcnn/tf.__operators__.add_96/AddV2"; +"5592 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5593 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5593 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5594 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs"; +"5594 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/Abs" -> "5596 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add"; +"5595 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add/y" -> "5596 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add"; +"5596 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add" -> "5599 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul"; +"5596 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/add" -> "5600 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5597 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5598 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5598 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5599 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul"; +"5599 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/mul" -> "5600 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5600 maskrcnn/tf.math.truediv_58/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5815 maskrcnn/tf.__operators__.add_96/AddV2"; +"5601 maskrcnn/tf.__operators__.getitem_127/strided_slice/stack" -> "5604 maskrcnn/tf.__operators__.getitem_127/strided_slice"; +"5602 maskrcnn/tf.__operators__.getitem_127/strided_slice/stack_1" -> "5604 maskrcnn/tf.__operators__.getitem_127/strided_slice"; +"5603 maskrcnn/tf.__operators__.getitem_127/strided_slice/stack_2" -> "5604 maskrcnn/tf.__operators__.getitem_127/strided_slice"; +"5604 maskrcnn/tf.__operators__.getitem_127/strided_slice" -> "5814 maskrcnn/tf.__operators__.add_94/AddV2"; +"5605 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5606 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5606 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5607 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs"; +"5607 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/Abs" -> "5609 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add"; +"5608 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add/y" -> "5609 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add"; +"5609 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add" -> "5612 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul"; +"5609 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/add" -> "5613 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5610 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5611 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5611 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5612 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul"; +"5612 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/mul" -> "5613 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5613 maskrcnn/tf.math.truediv_56/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5814 maskrcnn/tf.__operators__.add_94/AddV2"; +"5614 maskrcnn/tf.__operators__.getitem_123/strided_slice/stack" -> "5617 maskrcnn/tf.__operators__.getitem_123/strided_slice"; +"5615 maskrcnn/tf.__operators__.getitem_123/strided_slice/stack_1" -> "5617 maskrcnn/tf.__operators__.getitem_123/strided_slice"; +"5616 maskrcnn/tf.__operators__.getitem_123/strided_slice/stack_2" -> "5617 maskrcnn/tf.__operators__.getitem_123/strided_slice"; +"5617 maskrcnn/tf.__operators__.getitem_123/strided_slice" -> "5813 maskrcnn/tf.__operators__.add_92/AddV2"; +"5618 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5619 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5619 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5620 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs"; +"5620 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/Abs" -> "5622 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add"; +"5621 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add/y" -> "5622 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add"; +"5622 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add" -> "5625 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul"; +"5622 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/add" -> "5626 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5623 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5624 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5624 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5625 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul"; +"5625 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/mul" -> "5626 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5626 maskrcnn/tf.math.truediv_54/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5813 maskrcnn/tf.__operators__.add_92/AddV2"; +"5627 maskrcnn/tf.__operators__.getitem_119/strided_slice/stack" -> "5630 maskrcnn/tf.__operators__.getitem_119/strided_slice"; +"5628 maskrcnn/tf.__operators__.getitem_119/strided_slice/stack_1" -> "5630 maskrcnn/tf.__operators__.getitem_119/strided_slice"; +"5629 maskrcnn/tf.__operators__.getitem_119/strided_slice/stack_2" -> "5630 maskrcnn/tf.__operators__.getitem_119/strided_slice"; +"5630 maskrcnn/tf.__operators__.getitem_119/strided_slice" -> "5812 maskrcnn/tf.__operators__.add_90/AddV2"; +"5631 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5632 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5632 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5633 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/Abs"; +"5633 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/Abs" -> "5635 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/add"; +"5634 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/add/y" -> "5635 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/add"; +"5635 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/add" -> "5638 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul"; +"5635 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/add" -> "5639 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5636 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5637 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5637 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5638 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul"; +"5638 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/mul" -> "5639 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5639 maskrcnn/tf.math.truediv_52/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5812 maskrcnn/tf.__operators__.add_90/AddV2"; +"5640 maskrcnn/tf.__operators__.getitem_115/strided_slice/stack" -> "5643 maskrcnn/tf.__operators__.getitem_115/strided_slice"; +"5641 maskrcnn/tf.__operators__.getitem_115/strided_slice/stack_1" -> "5643 maskrcnn/tf.__operators__.getitem_115/strided_slice"; +"5642 maskrcnn/tf.__operators__.getitem_115/strided_slice/stack_2" -> "5643 maskrcnn/tf.__operators__.getitem_115/strided_slice"; +"5643 maskrcnn/tf.__operators__.getitem_115/strided_slice" -> "5811 maskrcnn/tf.__operators__.add_88/AddV2"; +"5644 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5645 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5645 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5646 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/Abs"; +"5646 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/Abs" -> "5648 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/add"; +"5647 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/add/y" -> "5648 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/add"; +"5648 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/add" -> "5651 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/mul"; +"5648 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/add" -> "5652 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5649 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "5650 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"5650 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "5651 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/mul"; +"5651 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/mul" -> "5652 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5652 maskrcnn/tf.math.truediv_50/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "5811 maskrcnn/tf.__operators__.add_88/AddV2"; +"5653 maskrcnn/tf.__operators__.getitem_111/strided_slice/stack" -> "5656 maskrcnn/tf.__operators__.getitem_111/strided_slice"; +"5654 maskrcnn/tf.__operators__.getitem_111/strided_slice/stack_1" -> "5656 maskrcnn/tf.__operators__.getitem_111/strided_slice"; +"5655 maskrcnn/tf.__operators__.getitem_111/strided_slice/stack_2" -> "5656 maskrcnn/tf.__operators__.getitem_111/strided_slice"; +"5656 maskrcnn/tf.__operators__.getitem_111/strided_slice" -> "5810 maskrcnn/tf.__operators__.add_86/AddV2"; +"5657 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "5658 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"5658 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "5659 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/Abs"; +"5659 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/Abs" -> "5661 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/add"; +"5660 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/add/y" -> "5661 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/add"; +"5661 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/add" -> "5664 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/mul"; +"5661 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/add" -> "5665 maskrcnn/tf.math.truediv_48/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"5662 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maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/mul"; +"6028 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/add" -> "6032 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6029 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6030 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6030 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6031 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/mul"; +"6031 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/mul" -> "6032 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6032 maskrcnn/tf.math.maximum_11/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6035 maskrcnn/tf.math.minimum_26/Minimum"; +"6033 maskrcnn/tf.expand_dims_31/ExpandDims/dim" -> "6034 maskrcnn/tf.expand_dims_31/ExpandDims"; +"6034 maskrcnn/tf.expand_dims_31/ExpandDims" -> "6035 maskrcnn/tf.math.minimum_26/Minimum"; +"6035 maskrcnn/tf.math.minimum_26/Minimum" -> "6058 maskrcnn/tf.__operators__.add_98/AddV2"; +"6035 maskrcnn/tf.math.minimum_26/Minimum" -> "6106 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"6035 maskrcnn/tf.math.minimum_26/Minimum" -> "6127 maskrcnn/tf.stack_20/stack"; +"6036 maskrcnn/tf.__operators__.getitem_133/strided_slice/stack" -> "6039 maskrcnn/tf.__operators__.getitem_133/strided_slice"; +"6037 maskrcnn/tf.__operators__.getitem_133/strided_slice/stack_1" -> "6039 maskrcnn/tf.__operators__.getitem_133/strided_slice"; +"6038 maskrcnn/tf.__operators__.getitem_133/strided_slice/stack_2" -> "6039 maskrcnn/tf.__operators__.getitem_133/strided_slice"; +"6039 maskrcnn/tf.__operators__.getitem_133/strided_slice" -> "6052 maskrcnn/tf.expand_dims_29/ExpandDims"; +"6041 maskrcnn/tf.math.maximum_10/Maximum" -> "6050 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6042 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6043 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"6043 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6044 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/Abs"; +"6044 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/Abs" -> "6046 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/add"; +"6045 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/add/y" -> "6046 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/add"; +"6046 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/add" -> "6049 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/mul"; +"6046 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/add" -> "6050 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6047 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6048 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6048 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6049 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/mul"; +"6049 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/mul" -> "6050 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6050 maskrcnn/tf.math.maximum_10/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6059 maskrcnn/tf.math.minimum_24/Minimum"; +"6051 maskrcnn/tf.expand_dims_29/ExpandDims/dim" -> "6052 maskrcnn/tf.expand_dims_29/ExpandDims"; +"6052 maskrcnn/tf.expand_dims_29/ExpandDims" -> "6059 maskrcnn/tf.math.minimum_24/Minimum"; +"6053 maskrcnn/tf.__operators__.getitem_136/strided_slice/stack" -> "6056 maskrcnn/tf.__operators__.getitem_136/strided_slice"; +"6054 maskrcnn/tf.__operators__.getitem_136/strided_slice/stack_1" -> "6056 maskrcnn/tf.__operators__.getitem_136/strided_slice"; +"6055 maskrcnn/tf.__operators__.getitem_136/strided_slice/stack_2" -> "6056 maskrcnn/tf.__operators__.getitem_136/strided_slice"; +"6056 maskrcnn/tf.__operators__.getitem_136/strided_slice" -> "6070 maskrcnn/tf.expand_dims_32/ExpandDims"; +"6057 maskrcnn/tf.__operators__.add_98/y" -> "6058 maskrcnn/tf.__operators__.add_98/AddV2"; +"6058 maskrcnn/tf.__operators__.add_98/AddV2" -> "6068 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6059 maskrcnn/tf.math.minimum_24/Minimum" -> "6088 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"6059 maskrcnn/tf.math.minimum_24/Minimum" -> "6112 maskrcnn/tf.__operators__.add_97/AddV2"; +"6059 maskrcnn/tf.math.minimum_24/Minimum" -> "6161 maskrcnn/tf.stack_19/stack"; +"6060 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6061 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"6061 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6062 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs"; +"6062 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/Abs" -> "6064 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add"; +"6063 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add/y" -> "6064 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add"; +"6064 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add" -> "6067 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul"; +"6064 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/add" -> "6068 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6065 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6066 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6066 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6067 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul"; +"6067 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/mul" -> "6068 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6068 maskrcnn/tf.__operators__.add_98/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6113 maskrcnn/tf.math.minimum_27/Minimum"; +"6069 maskrcnn/tf.expand_dims_32/ExpandDims/dim" -> "6070 maskrcnn/tf.expand_dims_32/ExpandDims"; +"6070 maskrcnn/tf.expand_dims_32/ExpandDims" -> "6113 maskrcnn/tf.math.minimum_27/Minimum"; +"6071 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp/resource" -> "6072 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp"; +"6072 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "6073 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/Abs"; +"6073 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/Abs" -> "6075 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add"; +"6074 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add/y" -> "6075 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add"; +"6075 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add" -> "6078 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul"; +"6075 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/add" -> "6079 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; +"6076 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "6077 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; +"6077 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "6078 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul"; +"6078 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/mul" -> "6079 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; +"6079 maskrcnn/tf.math.subtract_68/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "6114 maskrcnn/tf.math.subtract_68/Sub"; +"6080 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp/resource" -> "6081 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp"; +"6081 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp" -> "6082 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs"; +"6082 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/Abs" -> "6084 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add"; +"6083 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add/y" -> "6084 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add"; +"6084 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add" -> "6087 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul"; +"6084 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/add" -> "6088 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"6085 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul/ReadVariableOp/resource" -> "6086 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul/ReadVariableOp"; +"6086 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul/ReadVariableOp" -> "6087 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul"; +"6087 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/mul" -> "6088 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"6088 maskrcnn/tf.math.subtract_68/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars" -> "6114 maskrcnn/tf.math.subtract_68/Sub"; +"6089 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp/resource" -> "6090 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp"; +"6090 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs/ReadVariableOp" -> "6091 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs"; +"6091 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/Abs" -> "6093 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add"; +"6092 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add/y" -> "6093 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add"; +"6093 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add" -> "6096 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul"; +"6093 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/add" -> "6097 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; +"6094 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul/ReadVariableOp/resource" -> "6095 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul/ReadVariableOp"; +"6095 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul/ReadVariableOp" -> "6096 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul"; +"6096 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/mul" -> "6097 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars"; +"6097 maskrcnn/tf.math.subtract_67/fake_quantize_I0/SymmQuant/FakeQuantWithMinMaxVars" -> "6115 maskrcnn/tf.math.subtract_67/Sub"; +"6098 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp/resource" -> "6099 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp"; +"6099 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs/ReadVariableOp" -> "6100 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs"; +"6100 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/Abs" -> "6102 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add"; +"6101 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add/y" -> "6102 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add"; +"6102 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add" -> "6105 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul"; +"6102 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/add" -> "6106 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"6103 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul/ReadVariableOp/resource" -> "6104 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul/ReadVariableOp"; +"6104 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul/ReadVariableOp" -> "6105 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul"; +"6105 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/mul" -> "6106 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars"; +"6106 maskrcnn/tf.math.subtract_67/fake_quantize_I1/SymmQuant/FakeQuantWithMinMaxVars" -> "6115 maskrcnn/tf.math.subtract_67/Sub"; +"6107 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack" -> "6110 maskrcnn/tf.__operators__.getitem_134/strided_slice"; +"6108 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack_1" -> "6110 maskrcnn/tf.__operators__.getitem_134/strided_slice"; +"6109 maskrcnn/tf.__operators__.getitem_134/strided_slice/stack_2" -> "6110 maskrcnn/tf.__operators__.getitem_134/strided_slice"; +"6110 maskrcnn/tf.__operators__.getitem_134/strided_slice" -> "6126 maskrcnn/tf.expand_dims_30/ExpandDims"; +"6111 maskrcnn/tf.__operators__.add_97/y" -> "6112 maskrcnn/tf.__operators__.add_97/AddV2"; +"6112 maskrcnn/tf.__operators__.add_97/AddV2" -> "6124 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6113 maskrcnn/tf.math.minimum_27/Minimum" -> "6127 maskrcnn/tf.stack_20/stack"; +"6114 maskrcnn/tf.math.subtract_68/Sub" -> "6136 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6115 maskrcnn/tf.math.subtract_67/Sub" -> "6145 maskrcnn/tf.math.subtract_67/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6116 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6117 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"6117 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6118 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs"; +"6118 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/Abs" -> "6120 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add"; +"6119 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add/y" -> "6120 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add"; +"6120 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add" -> "6123 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul"; +"6120 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/add" -> "6124 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6121 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6122 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6122 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6123 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul"; +"6123 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/mul" -> "6124 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6124 maskrcnn/tf.__operators__.add_97/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6146 maskrcnn/tf.math.minimum_25/Minimum"; +"6125 maskrcnn/tf.expand_dims_30/ExpandDims/dim" -> "6126 maskrcnn/tf.expand_dims_30/ExpandDims"; +"6126 maskrcnn/tf.expand_dims_30/ExpandDims" -> "6146 maskrcnn/tf.math.minimum_25/Minimum"; +"6127 maskrcnn/tf.stack_20/stack" -> "6156 maskrcnn/tf.reshape_52/Reshape"; +"6128 maskrcnn/tf.math.subtract_68/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6129 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maskrcnn/tf.nn.avg_pool_1/fake_quantize/SymmQuant/mul" -> "6276 maskrcnn/tf.nn.avg_pool_1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6276 maskrcnn/tf.nn.avg_pool_1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6278 maskrcnn/tf.reshape_61/Reshape"; +"6277 maskrcnn/tf.reshape_61/Reshape/shape" -> "6278 maskrcnn/tf.reshape_61/Reshape"; +"6278 maskrcnn/tf.reshape_61/Reshape" -> "6280 maskrcnn/tf.reshape_62/Reshape"; +"6279 maskrcnn/tf.reshape_62/Reshape/shape" -> "6280 maskrcnn/tf.reshape_62/Reshape"; +"6280 maskrcnn/tf.reshape_62/Reshape" -> "6292 maskrcnn/mask-conv-l0/Conv2D"; +"6281 maskrcnn/mask-conv-l0/SymmQuant/Abs/ReadVariableOp/resource" -> "6282 maskrcnn/mask-conv-l0/SymmQuant/Abs/ReadVariableOp"; +"6282 maskrcnn/mask-conv-l0/SymmQuant/Abs/ReadVariableOp" -> "6283 maskrcnn/mask-conv-l0/SymmQuant/Abs"; +"6283 maskrcnn/mask-conv-l0/SymmQuant/Abs" -> "6285 maskrcnn/mask-conv-l0/SymmQuant/add"; +"6284 maskrcnn/mask-conv-l0/SymmQuant/add/y" -> "6285 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maskrcnn/mask-conv-l0/Conv2D"; +"6292 maskrcnn/mask-conv-l0/Conv2D" -> "6295 maskrcnn/mask-conv-l0/BiasAdd"; +"6293 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp/resource" -> "6294 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp"; +"6294 maskrcnn/mask-conv-l0/BiasAdd/ReadVariableOp" -> "6295 maskrcnn/mask-conv-l0/BiasAdd"; +"6295 maskrcnn/mask-conv-l0/BiasAdd" -> "6296 maskrcnn/mask-conv-l0/Relu"; +"6296 maskrcnn/mask-conv-l0/Relu" -> "6305 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6297 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6298 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"6298 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6299 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs"; +"6299 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/Abs" -> "6301 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/add"; +"6300 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/add/y" -> "6301 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/add"; +"6301 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/add" -> "6304 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/mul"; +"6301 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/add" -> "6305 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6302 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6303 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6303 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6304 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/mul"; +"6304 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/mul" -> "6305 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6305 maskrcnn/mask-conv-l0/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6317 maskrcnn/mask-conv-l1/Conv2D"; +"6306 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp/resource" -> "6307 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp"; +"6307 maskrcnn/mask-conv-l1/SymmQuant/Abs/ReadVariableOp" -> "6308 maskrcnn/mask-conv-l1/SymmQuant/Abs"; +"6308 maskrcnn/mask-conv-l1/SymmQuant/Abs" -> "6310 maskrcnn/mask-conv-l1/SymmQuant/add"; +"6309 maskrcnn/mask-conv-l1/SymmQuant/add/y" -> "6310 maskrcnn/mask-conv-l1/SymmQuant/add"; +"6310 maskrcnn/mask-conv-l1/SymmQuant/add" -> "6313 maskrcnn/mask-conv-l1/SymmQuant/mul"; +"6310 maskrcnn/mask-conv-l1/SymmQuant/add" -> "6316 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars"; +"6311 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp/resource" -> "6312 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp"; +"6312 maskrcnn/mask-conv-l1/SymmQuant/mul/ReadVariableOp" -> "6313 maskrcnn/mask-conv-l1/SymmQuant/mul"; +"6313 maskrcnn/mask-conv-l1/SymmQuant/mul" -> "6316 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars"; +"6314 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6315 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6315 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6316 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars"; +"6316 maskrcnn/mask-conv-l1/SymmQuant/FakeQuantWithMinMaxVars" -> "6317 maskrcnn/mask-conv-l1/Conv2D"; +"6317 maskrcnn/mask-conv-l1/Conv2D" -> "6320 maskrcnn/mask-conv-l1/BiasAdd"; +"6318 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp/resource" -> "6319 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp"; +"6319 maskrcnn/mask-conv-l1/BiasAdd/ReadVariableOp" -> "6320 maskrcnn/mask-conv-l1/BiasAdd"; +"6320 maskrcnn/mask-conv-l1/BiasAdd" -> "6321 maskrcnn/mask-conv-l1/Relu"; +"6321 maskrcnn/mask-conv-l1/Relu" -> "6330 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6322 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6323 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"6323 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6324 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs"; +"6324 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/Abs" -> "6326 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add"; +"6325 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add/y" -> "6326 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add"; +"6326 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add" -> "6329 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul"; +"6326 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/add" -> "6330 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6327 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6328 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6328 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6329 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul"; +"6329 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/mul" -> "6330 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6330 maskrcnn/mask-conv-l1/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6342 maskrcnn/mask-conv-l2/Conv2D"; +"6331 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp/resource" -> "6332 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp"; +"6332 maskrcnn/mask-conv-l2/SymmQuant/Abs/ReadVariableOp" -> "6333 maskrcnn/mask-conv-l2/SymmQuant/Abs"; +"6333 maskrcnn/mask-conv-l2/SymmQuant/Abs" -> "6335 maskrcnn/mask-conv-l2/SymmQuant/add"; +"6334 maskrcnn/mask-conv-l2/SymmQuant/add/y" -> "6335 maskrcnn/mask-conv-l2/SymmQuant/add"; +"6335 maskrcnn/mask-conv-l2/SymmQuant/add" -> "6338 maskrcnn/mask-conv-l2/SymmQuant/mul"; +"6335 maskrcnn/mask-conv-l2/SymmQuant/add" -> "6341 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars"; +"6336 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp/resource" -> "6337 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp"; +"6337 maskrcnn/mask-conv-l2/SymmQuant/mul/ReadVariableOp" -> "6338 maskrcnn/mask-conv-l2/SymmQuant/mul"; +"6338 maskrcnn/mask-conv-l2/SymmQuant/mul" -> "6341 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars"; +"6339 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6340 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6340 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6341 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars"; +"6341 maskrcnn/mask-conv-l2/SymmQuant/FakeQuantWithMinMaxVars" -> "6342 maskrcnn/mask-conv-l2/Conv2D"; +"6342 maskrcnn/mask-conv-l2/Conv2D" -> "6345 maskrcnn/mask-conv-l2/BiasAdd"; +"6343 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp/resource" -> "6344 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp"; +"6344 maskrcnn/mask-conv-l2/BiasAdd/ReadVariableOp" -> "6345 maskrcnn/mask-conv-l2/BiasAdd"; +"6345 maskrcnn/mask-conv-l2/BiasAdd" -> "6346 maskrcnn/mask-conv-l2/Relu"; +"6346 maskrcnn/mask-conv-l2/Relu" -> "6355 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6347 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6348 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"6348 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6349 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs"; +"6349 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/Abs" -> "6351 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add"; +"6350 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add/y" -> "6351 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add"; +"6351 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add" -> "6354 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul"; +"6351 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/add" -> "6355 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6352 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6353 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6353 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6354 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul"; +"6354 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/mul" -> "6355 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6355 maskrcnn/mask-conv-l2/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6367 maskrcnn/mask-conv-l3/Conv2D"; +"6356 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp/resource" -> "6357 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp"; +"6357 maskrcnn/mask-conv-l3/SymmQuant/Abs/ReadVariableOp" -> "6358 maskrcnn/mask-conv-l3/SymmQuant/Abs"; +"6358 maskrcnn/mask-conv-l3/SymmQuant/Abs" -> "6360 maskrcnn/mask-conv-l3/SymmQuant/add"; +"6359 maskrcnn/mask-conv-l3/SymmQuant/add/y" -> "6360 maskrcnn/mask-conv-l3/SymmQuant/add"; +"6360 maskrcnn/mask-conv-l3/SymmQuant/add" -> "6363 maskrcnn/mask-conv-l3/SymmQuant/mul"; +"6360 maskrcnn/mask-conv-l3/SymmQuant/add" -> "6366 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars"; +"6361 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp/resource" -> "6362 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp"; +"6362 maskrcnn/mask-conv-l3/SymmQuant/mul/ReadVariableOp" -> "6363 maskrcnn/mask-conv-l3/SymmQuant/mul"; +"6363 maskrcnn/mask-conv-l3/SymmQuant/mul" -> "6366 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars"; +"6364 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6365 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6365 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6366 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars"; +"6366 maskrcnn/mask-conv-l3/SymmQuant/FakeQuantWithMinMaxVars" -> "6367 maskrcnn/mask-conv-l3/Conv2D"; +"6367 maskrcnn/mask-conv-l3/Conv2D" -> "6370 maskrcnn/mask-conv-l3/BiasAdd"; +"6368 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp/resource" -> "6369 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp"; +"6369 maskrcnn/mask-conv-l3/BiasAdd/ReadVariableOp" -> "6370 maskrcnn/mask-conv-l3/BiasAdd"; +"6370 maskrcnn/mask-conv-l3/BiasAdd" -> "6371 maskrcnn/mask-conv-l3/Relu"; +"6371 maskrcnn/mask-conv-l3/Relu" -> "6380 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6372 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6373 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"6373 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6374 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs"; +"6374 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/Abs" -> "6376 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add"; +"6375 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add/y" -> "6376 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add"; +"6376 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add" -> "6379 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul"; +"6376 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/add" -> "6380 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6377 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6378 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6378 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6379 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul"; +"6379 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/mul" -> "6380 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6380 maskrcnn/mask-conv-l3/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6405 maskrcnn/conv5-mask/conv2d_transpose"; +"6381 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp/resource" -> "6382 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp"; +"6382 maskrcnn/conv5-mask/SymmQuant/Abs/ReadVariableOp" -> "6383 maskrcnn/conv5-mask/SymmQuant/Abs"; +"6383 maskrcnn/conv5-mask/SymmQuant/Abs" -> "6385 maskrcnn/conv5-mask/SymmQuant/add"; +"6384 maskrcnn/conv5-mask/SymmQuant/add/y" -> "6385 maskrcnn/conv5-mask/SymmQuant/add"; +"6385 maskrcnn/conv5-mask/SymmQuant/add" -> "6388 maskrcnn/conv5-mask/SymmQuant/mul"; +"6385 maskrcnn/conv5-mask/SymmQuant/add" -> "6391 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars"; +"6386 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp/resource" -> "6387 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp"; +"6387 maskrcnn/conv5-mask/SymmQuant/mul/ReadVariableOp" -> "6388 maskrcnn/conv5-mask/SymmQuant/mul"; +"6388 maskrcnn/conv5-mask/SymmQuant/mul" -> "6391 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars"; +"6389 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp/resource" -> "6390 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp"; +"6390 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars/ReadVariableOp" -> "6391 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars"; +"6391 maskrcnn/conv5-mask/SymmQuant/FakeQuantWithMinMaxVars" -> "6405 maskrcnn/conv5-mask/conv2d_transpose"; +"6392 maskrcnn/conv5-mask/Shape" -> "6396 maskrcnn/conv5-mask/strided_slice"; +"6393 maskrcnn/conv5-mask/strided_slice/stack" -> "6396 maskrcnn/conv5-mask/strided_slice"; +"6394 maskrcnn/conv5-mask/strided_slice/stack_1" -> "6396 maskrcnn/conv5-mask/strided_slice"; +"6395 maskrcnn/conv5-mask/strided_slice/stack_2" -> "6396 maskrcnn/conv5-mask/strided_slice"; +"6396 maskrcnn/conv5-mask/strided_slice" -> "6400 maskrcnn/conv5-mask/stack"; +"6397 maskrcnn/conv5-mask/stack/1" -> "6400 maskrcnn/conv5-mask/stack"; +"6398 maskrcnn/conv5-mask/stack/2" -> "6400 maskrcnn/conv5-mask/stack"; +"6399 maskrcnn/conv5-mask/stack/3" -> "6400 maskrcnn/conv5-mask/stack"; +"6400 maskrcnn/conv5-mask/stack" -> "6404 maskrcnn/conv5-mask/strided_slice_1"; +"6400 maskrcnn/conv5-mask/stack" -> "6405 maskrcnn/conv5-mask/conv2d_transpose"; +"6401 maskrcnn/conv5-mask/strided_slice_1/stack" -> "6404 maskrcnn/conv5-mask/strided_slice_1"; +"6402 maskrcnn/conv5-mask/strided_slice_1/stack_1" -> "6404 maskrcnn/conv5-mask/strided_slice_1"; +"6403 maskrcnn/conv5-mask/strided_slice_1/stack_2" -> "6404 maskrcnn/conv5-mask/strided_slice_1"; +"6405 maskrcnn/conv5-mask/conv2d_transpose" -> "6408 maskrcnn/conv5-mask/BiasAdd"; +"6406 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp/resource" -> "6407 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp"; +"6407 maskrcnn/conv5-mask/BiasAdd/ReadVariableOp" -> "6408 maskrcnn/conv5-mask/BiasAdd"; +"6408 maskrcnn/conv5-mask/BiasAdd" -> "6409 maskrcnn/conv5-mask/Relu"; +"6409 maskrcnn/conv5-mask/Relu" -> "6418 maskrcnn/conv5-mask/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6410 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs/ReadVariableOp/resource" -> "6411 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs/ReadVariableOp"; +"6411 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs/ReadVariableOp" -> "6412 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs"; +"6412 maskrcnn/conv5-mask/fake_quantize/SymmQuant/Abs" -> "6414 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add"; +"6413 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add/y" -> "6414 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add"; +"6414 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add" -> "6417 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul"; +"6414 maskrcnn/conv5-mask/fake_quantize/SymmQuant/add" -> "6418 maskrcnn/conv5-mask/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6415 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul/ReadVariableOp/resource" -> "6416 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul/ReadVariableOp"; +"6416 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul/ReadVariableOp" -> "6417 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul"; +"6417 maskrcnn/conv5-mask/fake_quantize/SymmQuant/mul" -> "6418 maskrcnn/conv5-mask/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars"; +"6418 maskrcnn/conv5-mask/fake_quantize/SymmQuant/FakeQuantWithMinMaxVars" -> "6433 maskrcnn/mask_fcn_logits/Conv2D"; +"6419 maskrcnn/tf.cast_32/Cast" -> "6421 maskrcnn/tf.__operators__.add_67/AddV2"; +"6420 maskrcnn/tf.__operators__.add_67/y" -> "6421 maskrcnn/tf.__operators__.add_67/AddV2"; +"6421 maskrcnn/tf.__operators__.add_67/AddV2" -> "19436 maskrcnn/tf.stack_23/stack_1"; +"6421 maskrcnn/tf.__operators__.add_67/AddV2" -> "19442 Identity_3"; +"6422 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp/resource" -> "6423 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp"; +"6423 maskrcnn/mask_fcn_logits/SymmQuant/Abs/ReadVariableOp" -> "6424 maskrcnn/mask_fcn_logits/SymmQuant/Abs"; +"6424 maskrcnn/mask_fcn_logits/SymmQuant/Abs" -> "6426 maskrcnn/mask_fcn_logits/SymmQuant/add"; +"6425 maskrcnn/mask_fcn_logits/SymmQuant/add/y" -> "6426 maskrcnn/mask_fcn_logits/SymmQuant/add"; +"6426 maskrcnn/mask_fcn_logits/SymmQuant/add" -> "6429 maskrcnn/mask_fcn_logits/SymmQuant/mul"; +"6426 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maskrcnn/tf.stack_23/stack_1/values_1/27" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19399 maskrcnn/tf.stack_23/stack_1/values_1/28" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19400 maskrcnn/tf.stack_23/stack_1/values_1/29" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19401 maskrcnn/tf.stack_23/stack_1/values_1/30" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19402 maskrcnn/tf.stack_23/stack_1/values_1/31" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19403 maskrcnn/tf.stack_23/stack_1/values_1/32" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19404 maskrcnn/tf.stack_23/stack_1/values_1/33" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19405 maskrcnn/tf.stack_23/stack_1/values_1/34" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19406 maskrcnn/tf.stack_23/stack_1/values_1/35" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19407 maskrcnn/tf.stack_23/stack_1/values_1/36" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19408 maskrcnn/tf.stack_23/stack_1/values_1/37" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19409 maskrcnn/tf.stack_23/stack_1/values_1/38" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19410 maskrcnn/tf.stack_23/stack_1/values_1/39" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19411 maskrcnn/tf.stack_23/stack_1/values_1/40" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19412 maskrcnn/tf.stack_23/stack_1/values_1/41" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19413 maskrcnn/tf.stack_23/stack_1/values_1/42" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19414 maskrcnn/tf.stack_23/stack_1/values_1/43" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19415 maskrcnn/tf.stack_23/stack_1/values_1/44" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19416 maskrcnn/tf.stack_23/stack_1/values_1/45" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19417 maskrcnn/tf.stack_23/stack_1/values_1/46" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19418 maskrcnn/tf.stack_23/stack_1/values_1/47" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19419 maskrcnn/tf.stack_23/stack_1/values_1/48" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19420 maskrcnn/tf.stack_23/stack_1/values_1/49" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19421 maskrcnn/tf.stack_23/stack_1/values_1/50" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19422 maskrcnn/tf.stack_23/stack_1/values_1/51" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19423 maskrcnn/tf.stack_23/stack_1/values_1/52" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19424 maskrcnn/tf.stack_23/stack_1/values_1/53" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19425 maskrcnn/tf.stack_23/stack_1/values_1/54" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19426 maskrcnn/tf.stack_23/stack_1/values_1/55" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19427 maskrcnn/tf.stack_23/stack_1/values_1/56" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19428 maskrcnn/tf.stack_23/stack_1/values_1/57" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19429 maskrcnn/tf.stack_23/stack_1/values_1/58" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19430 maskrcnn/tf.stack_23/stack_1/values_1/59" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19431 maskrcnn/tf.stack_23/stack_1/values_1/60" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19432 maskrcnn/tf.stack_23/stack_1/values_1/61" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19433 maskrcnn/tf.stack_23/stack_1/values_1/62" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19434 maskrcnn/tf.stack_23/stack_1/values_1/63" -> "19435 maskrcnn/tf.stack_23/stack_1/values_1"; +"19435 maskrcnn/tf.stack_23/stack_1/values_1" -> "19436 maskrcnn/tf.stack_23/stack_1"; +"19436 maskrcnn/tf.stack_23/stack_1" -> "19437 maskrcnn/tf.compat.v1.gather_nd_1/GatherNd"; +"19437 maskrcnn/tf.compat.v1.gather_nd_1/GatherNd" -> "19438 maskrcnn/tf.math.sigmoid_5/Sigmoid"; +"19438 maskrcnn/tf.math.sigmoid_5/Sigmoid" -> "19443 Identity_4"; } diff --git a/tests/tensorflow/experimental/test_models/resnet.py b/tests/tensorflow/experimental/test_models/resnet.py index f3becac3eca..ea7fc252a06 100644 --- a/tests/tensorflow/experimental/test_models/resnet.py +++ b/tests/tensorflow/experimental/test_models/resnet.py @@ -57,7 +57,7 @@ def __init__( activation="relu", gating_activation="sigmoid", round_down_protect=True, - **kwargs + **kwargs, ): """Initializes a squeeze and excitation layer. @@ -288,7 +288,7 @@ def __init__( norm_momentum=0.99, norm_epsilon=0.001, bn_trainable=True, - **kwargs + **kwargs, ): """Initializes a residual block with BN after convolutions. @@ -487,7 +487,7 @@ def __init__( norm_momentum=0.99, norm_epsilon=0.001, bn_trainable=True, - **kwargs + **kwargs, ): """Initializes a standard bottleneck block with BN after convolutions. @@ -795,7 +795,7 @@ def __init__( kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None, bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None, bn_trainable: bool = True, - **kwargs + **kwargs, ): """Initializes a ResNet model. @@ -1063,7 +1063,7 @@ def __init__( norm_momentum: float = 0.99, norm_epsilon: float = 0.001, skip_logits_layer: bool = False, - **kwargs + **kwargs, ): """Classification initialization function. diff --git a/tests/tensorflow/helpers.py b/tests/tensorflow/helpers.py index f8e66e8119d..a7ab7e26803 100644 --- a/tests/tensorflow/helpers.py +++ b/tests/tensorflow/helpers.py @@ -20,7 +20,7 @@ from nncf import NNCFConfig from nncf.common.compression import BaseCompressionAlgorithmController from nncf.tensorflow.helpers.model_creation import create_compressed_model -from tests.shared.helpers import BaseTensorListComparator +from tests.cross_fw.shared.helpers import BaseTensorListComparator TensorType = Union[tf.Tensor, tf.Variable, np.ndarray, numbers.Number] diff --git a/tests/tensorflow/quantization/test_builder_state.py b/tests/tensorflow/quantization/test_builder_state.py index 7e7335c0628..754bbf41409 100644 --- a/tests/tensorflow/quantization/test_builder_state.py +++ b/tests/tensorflow/quantization/test_builder_state.py @@ -33,7 +33,7 @@ from nncf.tensorflow.quantization.algorithm import TFQuantizationSetup from nncf.tensorflow.quantization.quantizers import TFQuantizerSpec from nncf.tensorflow.utils.state import TFCompressionState -from tests.shared.serialization import check_serialization +from tests.cross_fw.shared.serialization import check_serialization from tests.tensorflow.helpers import create_compressed_model_and_algo_for_test from tests.tensorflow.helpers import get_basic_conv_test_model from tests.tensorflow.quantization.test_algorithm_quantization import check_default_qspecs diff --git a/tests/tensorflow/quantization/test_unified_scales.py b/tests/tensorflow/quantization/test_unified_scales.py index ba27fd246a0..591a67975d4 100644 --- a/tests/tensorflow/quantization/test_unified_scales.py +++ b/tests/tensorflow/quantization/test_unified_scales.py @@ -73,8 +73,8 @@ def get_unet_like_test_model(input_shapes): CAT_UNIFIED_SCALE_TEST_STRUCTS = [ - (get_single_concat_test_model, 3, 4), - (get_double_concat_test_model, 3, 4), + (get_single_concat_test_model, 1, 2), + (get_double_concat_test_model, 1, 2), (get_unet_like_test_model, 4, 6), ] @@ -100,7 +100,6 @@ def test_unified_scales_with_concat(target_device, model_creator, ref_aq_module_ model = model_creator([x_shape, y_shape]) compressed_model, _ = create_compressed_model_and_algo_for_test(model, nncf_config, force_no_init=True) - non_weight_quantizers = len(collect_fake_quantize_layers(compressed_model)) assert non_weight_quantizers == ref_aq_module_count diff --git a/tests/tensorflow/requirements.txt b/tests/tensorflow/requirements.txt index d9924cc6765..b63a60eeed2 100644 --- a/tests/tensorflow/requirements.txt +++ b/tests/tensorflow/requirements.txt @@ -1,7 +1,5 @@ -c ../../constraints.txt PyYAML -tensorflow_addons==0.20.0; python_version < '3.9' -tensorflow_addons==0.23.0; python_version >= '3.9' tensorflow-metadata==1.13.0 pytest pytest-cov diff --git a/tests/tensorflow/sota_checkpoints_eval.json b/tests/tensorflow/sota_checkpoints_eval.json index 4d5c11b11f1..ceb65f510ab 100644 --- a/tests/tensorflow/sota_checkpoints_eval.json +++ b/tests/tensorflow/sota_checkpoints_eval.json @@ -258,7 +258,7 @@ "model_description": "RetinaNet", "batch_per_gpu": 15, "target_tf": 33.43, - "target_ov": 33.33 + "target_ov": 33.35 }, "retinanet_coco_int8": { "config": "examples/tensorflow/object_detection/configs/quantization/retinanet_coco_int8.json", @@ -272,7 +272,7 @@ "diff_target_tf_min": -0.15, "diff_target_tf_max": 0.15, "target_tf": 33.19, - "target_ov": 33.23 + "target_ov": 33.25 }, "retinanet_coco_magnitude_sparsity": { "config": "examples/tensorflow/object_detection/configs/sparsity/retinanet_coco_magnitude_sparsity.json", @@ -284,7 +284,7 @@ "compression_description": "Magnitude sparsity (50%)", "batch_per_gpu": 15, "target_tf": 33.1, - "target_ov": 33.09 + "target_ov": 33.11 }, "retinanet_coco_pruning_geometric_median": { "config": "examples/tensorflow/object_detection/configs/pruning/retinanet_coco_pruning_geometric_median.json", @@ -297,7 +297,7 @@ "batch_per_gpu": 15, "diff_fp32_max": 0.4, "target_tf": 32.72, - "target_ov": 32.59 + "target_ov": 32.62 }, "retinanet_coco_pruning_geometric_median_int8": { "config": "examples/tensorflow/object_detection/configs/pruning_quantization/retinanet_coco_pruning_geometric_median_int8.json", @@ -309,7 +309,7 @@ "compression_description": "INT8 (per-tensor symmetric for weights, per-tensor asymmetric half-range for activations) + filter pruning 40%", "batch_per_gpu": 15, "target_tf": 32.61, - "target_ov": 32.47, + "target_ov": 32.49, "diff_target_tf_min": -0.15, "diff_target_tf_max": 0.15 }, @@ -320,7 +320,9 @@ "model_description": "YOLO v4", "batch_per_gpu": 15, "target_tf": 47.07, - "target_ov": 48.46 + "target_ov": 48.46, + "diff_target_ov_min": -0.02, + "diff_target_ov_max": 0.02 }, "yolo_v4_coco_int8": { "config": "examples/tensorflow/object_detection/configs/quantization/yolo_v4_coco_int8.json", @@ -349,7 +351,7 @@ "diff_target_tf_min": -0.15, "diff_target_tf_max": 0.15, "target_tf": 46.49, - "target_ov": 48.08 + "target_ov": 48.10 } } } diff --git a/tests/tensorflow/sparsity/rb/test_integration.py b/tests/tensorflow/sparsity/rb/test_integration.py index 0935b3d2ef3..728634bbf76 100644 --- a/tests/tensorflow/sparsity/rb/test_integration.py +++ b/tests/tensorflow/sparsity/rb/test_integration.py @@ -14,7 +14,6 @@ import pytest import tensorflow as tf -import tensorflow_addons as tfa from tensorflow.python.framework.config import disable_op_determinism from tensorflow.python.framework.config import enable_op_determinism @@ -191,8 +190,8 @@ def on_epoch_end(self, epoch, logs=None): metrics = [ tf.keras.metrics.CategoricalAccuracy(name="acc@1"), tf.keras.metrics.TopKCategoricalAccuracy(k=5, name="acc@5"), - tfa.metrics.MeanMetricWrapper(loss_obj, name="ce_loss"), - tfa.metrics.MeanMetricWrapper(compress_algo.loss, name="cr_loss"), + tf.keras.metrics.MeanMetricWrapper(loss_obj, name="ce_loss"), + tf.keras.metrics.MeanMetricWrapper(compress_algo.loss, name="cr_loss"), ] compress_model.add_loss(compress_algo.loss) diff --git a/tests/tensorflow/test_compressed_graph.py b/tests/tensorflow/test_compressed_graph.py index 831514ce4fe..e3fd28bdb66 100644 --- a/tests/tensorflow/test_compressed_graph.py +++ b/tests/tensorflow/test_compressed_graph.py @@ -19,7 +19,7 @@ from nncf import NNCFConfig from nncf.common.hardware.config import HWConfigType -from tests.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference from tests.tensorflow import test_models from tests.tensorflow.helpers import create_compressed_model_and_algo_for_test from tests.tensorflow.helpers import get_empty_config diff --git a/tests/tensorflow/test_models/maskrcnn.py b/tests/tensorflow/test_models/maskrcnn.py index 872a1cd024e..b442651abfd 100644 --- a/tests/tensorflow/test_models/maskrcnn.py +++ b/tests/tensorflow/test_models/maskrcnn.py @@ -12,7 +12,7 @@ from examples.common.sample_config import SampleConfig from examples.tensorflow.segmentation.models.model_selector import get_model_builder from examples.tensorflow.segmentation.models.model_selector import get_predefined_config -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT def MaskRCNN(input_shape=None): diff --git a/tests/tensorflow/test_models/retinanet.py b/tests/tensorflow/test_models/retinanet.py index 405c2f97c0f..fee2154fa07 100644 --- a/tests/tensorflow/test_models/retinanet.py +++ b/tests/tensorflow/test_models/retinanet.py @@ -12,7 +12,7 @@ from examples.common.sample_config import SampleConfig from examples.tensorflow.object_detection.models.model_selector import get_model_builder from examples.tensorflow.object_detection.models.model_selector import get_predefined_config -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT def RetinaNet(input_shape=None): diff --git a/tests/tensorflow/test_models/yolo_v4.py b/tests/tensorflow/test_models/yolo_v4.py index 1fc403615de..72d503101c0 100644 --- a/tests/tensorflow/test_models/yolo_v4.py +++ b/tests/tensorflow/test_models/yolo_v4.py @@ -12,7 +12,7 @@ from examples.common.sample_config import SampleConfig from examples.tensorflow.object_detection.models.model_selector import get_model_builder from examples.tensorflow.object_detection.models.model_selector import get_predefined_config -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT def YOLOv4(input_shape=None): diff --git a/tests/tensorflow/test_sanity_sample.py b/tests/tensorflow/test_sanity_sample.py index fea641326e4..f9285a5f5be 100644 --- a/tests/tensorflow/test_sanity_sample.py +++ b/tests/tensorflow/test_sanity_sample.py @@ -27,10 +27,10 @@ from examples.tensorflow.object_detection import main as od_main from examples.tensorflow.segmentation import evaluation as seg_eval from examples.tensorflow.segmentation import train as seg_train -from tests.shared.config_factory import ConfigFactory -from tests.shared.helpers import remove_line_breaks -from tests.shared.paths import TEST_ROOT -from tests.shared.paths import get_accuracy_aware_checkpoint_dir_path +from tests.cross_fw.shared.config_factory import ConfigFactory +from tests.cross_fw.shared.helpers import remove_line_breaks +from tests.cross_fw.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import get_accuracy_aware_checkpoint_dir_path from tests.tensorflow.helpers import get_cifar10_dataset_builders from tests.tensorflow.helpers import get_coco_dataset_builders from tests.tensorflow.test_models import SequentialModel diff --git a/tests/tensorflow/test_sota_checkpoints.py b/tests/tensorflow/test_sota_checkpoints.py index c9f07ee1cf4..c180466046a 100644 --- a/tests/tensorflow/test_sota_checkpoints.py +++ b/tests/tensorflow/test_sota_checkpoints.py @@ -25,13 +25,13 @@ import yaml from pytest import FixtureRequest -from tests.shared.command import Command -from tests.shared.metric_thresholds import DIFF_FP32_MAX_GLOBAL -from tests.shared.metric_thresholds import DIFF_FP32_MIN_GLOBAL -from tests.shared.openvino_version import get_openvino_version -from tests.shared.paths import DATASET_DEFINITIONS_PATH -from tests.shared.paths import PROJECT_ROOT -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.command import Command +from tests.cross_fw.shared.metric_thresholds import DIFF_FP32_MAX_GLOBAL +from tests.cross_fw.shared.metric_thresholds import DIFF_FP32_MIN_GLOBAL +from tests.cross_fw.shared.openvino_version import get_openvino_version +from tests.cross_fw.shared.paths import DATASET_DEFINITIONS_PATH +from tests.cross_fw.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT DIFF_TARGET_TF_MIN = -0.1 DIFF_TARGET_TF_MAX = 0.1 @@ -361,6 +361,19 @@ def get_weight_params( return {"pretrained": True} raise RuntimeError("Incorrect config") + @staticmethod + def get_env(): + """ + Returns a copy of the current environment with the `PYTHONPATH` variable updated + to include the project root directory + """ + env = os.environ.copy() + if "PYTHONPATH" in env: + env["PYTHONPATH"] += ":" + str(PROJECT_ROOT) + else: + env["PYTHONPATH"] = str(PROJECT_ROOT) + return env + @pytest.mark.eval @pytest.mark.parametrize("eval_test_struct", EVAL_TEST_STRUCT, ids=idfn) @pytest.mark.parametrize("dataset_type", ("tfds", "tfrecords")) @@ -405,7 +418,7 @@ def test_eval( **weights_param, ) - runner = Command(cmd, cwd=PROJECT_ROOT) + runner = Command(cmd, cwd=PROJECT_ROOT, env=self.get_env()) exit_code = runner.run(assert_returncode_zero=False) is_ok = exit_code == 0 and metrics_dump_file_path.exists() @@ -484,7 +497,7 @@ def test_openvino_eval( pytest.skip(status) # WA to avoid OS error - env = os.environ.copy() + env = self.get_env() env["HDF5_USE_FILE_LOCKING"] = "FALSE" sample_type = eval_test_struct.sample_type diff --git a/tests/tensorflow/test_telemetry.py b/tests/tensorflow/test_telemetry.py index 898e7690ea6..10327cc0c60 100644 --- a/tests/tensorflow/test_telemetry.py +++ b/tests/tensorflow/test_telemetry.py @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from tests.shared.helpers import telemetry_send_event_test_driver +from tests.cross_fw.shared.helpers import telemetry_send_event_test_driver from tests.tensorflow.helpers import create_compressed_model_and_algo_for_test from tests.tensorflow.helpers import get_empty_config from tests.tensorflow.helpers import get_mock_model diff --git a/tests/tensorflow/test_transformations.py b/tests/tensorflow/test_transformations.py index 7aded82ce1f..a5c6c208f88 100644 --- a/tests/tensorflow/test_transformations.py +++ b/tests/tensorflow/test_transformations.py @@ -38,7 +38,7 @@ from nncf.tensorflow.layers.operation import NNCFOperation from nncf.tensorflow.quantization.layers import FakeQuantize from nncf.tensorflow.quantization.quantizers import TFQuantizerSpec -from tests.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference from tests.tensorflow.helpers import remove_node_by_name from tests.tensorflow.test_compressed_graph import get_nx_graph_from_tf_graph from tests.tensorflow.test_compressed_graph import keras_model_to_tf_graph diff --git a/tests/tensorflow/test_weekly.py b/tests/tensorflow/test_weekly.py index d6b2bbd9c9f..a02af22b153 100644 --- a/tests/tensorflow/test_weekly.py +++ b/tests/tensorflow/test_weekly.py @@ -16,7 +16,7 @@ import pytest from pytest import approx -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT from tests.tensorflow.test_sanity_sample import get_sample_fn EXAMPLES_DIR = PROJECT_ROOT.joinpath("examples", "tensorflow") diff --git a/tests/tools/data/phi3_asym.csv b/tests/tools/data/phi3_asym.csv new file mode 100644 index 00000000000..5bd197b04f0 --- /dev/null +++ b/tests/tools/data/phi3_asym.csv @@ -0,0 +1,12 @@ +"model, int4_asym, gs64",mode,%int4,%int8,lora rank,plot name,"model size, Gb",compression rate,"wikitext, word perplexity","lambada-openai, acc","lambada-openai, perplexity","WWB, similarity",average relative error,"compression time, min" +Phi-3-mini-4k-instruct,fp32,0.0,0.0,,,14.235,1.0,9.48394027691655,0.654764215020377,5.09378699019839,1.0,0.0,0.0 +Phi-3-mini-4k-instruct,int8,0.0,1.0,,,3.562,3.9963503649635,9.499335040825285,0.6549582767320008,5.052950754896191,0.9527044384567825,0.01045364376495017,0.73 +Phi-3-mini-4k-instruct,int4 + scale estimation,0.4,0.6,,40% int4,2.953,4.820521503555706,9.71854910518393,0.650300795653018,5.30571206241349,0.9110616776678298,0.04083810560342551,7.66 +Phi-3-mini-4k-instruct,int4 + scale estimation,0.6,0.4,,60% int4,2.646,5.379818594104308,9.85636814588443,0.644673006015913,5.291942925143432,0.9213788685975252,0.04336565988152735,11.19 +Phi-3-mini-4k-instruct,int4 + scale estimation + lora correction,1.0,0.0,256.0,rank 256,2.382,5.976070528967254,9.988029919971328,0.6555404618668736,5.233390277183369,0.9246453907754686,0.03899610491099074,60.22 +Phi-3-mini-4k-instruct,int4 + scale estimation,0.8,0.2,,80% int4,2.324,6.125215146299483,10.02766689136818,0.6456433145740346,5.503355706799129,0.9220877753363715,0.05771907718987122,15.02 +Phi-3-mini-4k-instruct,int4 + scale estimation + lora correction,1.0,0.0,128.0,rank 128,2.194,6.488149498632635,10.06818844548755,0.6545701533087522,5.251984488969776,0.9090713858604431,0.04628722290049148,59.02 +Phi-3-mini-4k-instruct,int4 + scale estimation + gptq,1.0,0.0,,gptq,2.004,7.103293413173652,10.16727993333731,0.6444789443042888,5.444539130651724,0.9119987841005679,0.06147882553656911,137.77 +Phi-3-mini-4k-instruct,int4 + scale estimation + lora correction,1.0,0.0,8.0,rank 8,2.018,7.054013875123886,10.20127160713859,0.6497186105181447,5.441740470297863,0.9188693364461263,0.05851965015464443,43.83 +Phi-3-mini-4k-instruct,int4 + scale estimation,1.0,0.0,,100% int4,2.004,7.103293413173652,10.36438870990786,0.6413739569183,5.573979676833424,0.9068410683561254,0.07550933307474214,17.3 +Phi-3-mini-4k-instruct,int4,1.0,0.0,,data-free,2.004,7.103293413173652,10.6753930252974,0.622355909179119,6.088275680704702,0.8961785568131341,0.1188976835750515,2.71 diff --git a/tests/tools/data/phi3_asym.md b/tests/tools/data/phi3_asym.md new file mode 100644 index 00000000000..4bc128ab142 --- /dev/null +++ b/tests/tools/data/phi3_asym.md @@ -0,0 +1,13 @@ +| mode | %int4 | %int8 | lora
rank | average
relative
error | compression
rate | +|:------------------------------------------|:--------|:--------|:---------------|:-------------------------------|:----------------------| +| fp32 | 0% | 0% | | 0.0% | 1.0x | +| int8 | 0% | 100% | | 1.0% | 4.0x | +| int4 + scale estimation + lora correction | 100% | 0% | 256.0 | 3.9% | 6.0x | +| int4 + scale estimation | 40% | 60% | | 4.1% | 4.8x | +| int4 + scale estimation | 60% | 40% | | 4.3% | 5.4x | +| int4 + scale estimation + lora correction | 100% | 0% | 128.0 | 4.6% | 6.5x | +| int4 + scale estimation | 80% | 20% | | 5.7% | 6.1x | +| int4 + scale estimation + lora correction | 100% | 0% | 8.0 | 5.8% | 7.1x | +| int4 + scale estimation + gptq | 100% | 0% | | 6.1% | 7.1x | +| int4 + scale estimation | 100% | 0% | | 7.5% | 7.1x | +| int4 | 100% | 0% | | 11.9% | 7.1x | diff --git a/tests/tools/requirements.txt b/tests/tools/requirements.txt index b3c28b380b4..9e02f4cf7aa 100644 --- a/tests/tools/requirements.txt +++ b/tests/tools/requirements.txt @@ -1,4 +1,5 @@ matplotlib psutil pytest +pandas tabulate>=0.9.0 \ No newline at end of file diff --git a/tests/tools/test_compression_visualization.py b/tests/tools/test_compression_visualization.py new file mode 100644 index 00000000000..8e8be05f4f3 --- /dev/null +++ b/tests/tools/test_compression_visualization.py @@ -0,0 +1,25 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tests.cross_fw.shared.paths import TEST_ROOT +from tools.visualize_compression_results import visualize + + +def test_visualization_of_compression_results(tmp_path): + in_file = TEST_ROOT / "tools" / "data" / "phi3_asym.csv" + ref_md_file = TEST_ROOT / "tools" / "data" / "phi3_asym.md" + + visualize(in_file, tmp_path) + + md_file = tmp_path / (in_file.stem + ".md") + assert md_file.exists() + assert md_file.with_suffix(".png").exists() + assert ref_md_file.read_text()[:-1] == md_file.read_text() # ref file ends with a newline character by code style diff --git a/tests/tools/test_memory_monitor.py b/tests/tools/test_memory_monitor.py index a094625b169..4d702f7b28d 100644 --- a/tests/tools/test_memory_monitor.py +++ b/tests/tools/test_memory_monitor.py @@ -19,8 +19,8 @@ import numpy as np import pytest -from tests.shared.isolation_runner import ISOLATION_RUN_ENV_VAR -from tests.shared.isolation_runner import run_pytest_case_function_in_separate_process +from tests.cross_fw.shared.isolation_runner import ISOLATION_RUN_ENV_VAR +from tests.cross_fw.shared.isolation_runner import run_pytest_case_function_in_separate_process from tools.memory_monitor import MemoryMonitor from tools.memory_monitor import MemoryType from tools.memory_monitor import MemoryUnit @@ -148,8 +148,8 @@ def test_memory_values_isolated(): else: memory_values = list(map(lambda it: it - baseline_memory, memory_values)) assert max(memory_values) == pytest.approx(BYTES_TO_ALLOCATE_LARGE, rel=5e-2) - assert abs(memory_values[0]) < BYTES_TO_ALLOCATE_LARGE * 1e-2 - assert abs(memory_values[-1]) < BYTES_TO_ALLOCATE_LARGE * 1e-2 + assert abs(memory_values[0]) < BYTES_TO_ALLOCATE_LARGE * 5e-2 + assert abs(memory_values[-1]) < BYTES_TO_ALLOCATE_LARGE * 5e-2 def test_memory_values(): diff --git a/tests/torch/conftest.py b/tests/torch/conftest.py index c4a8910e826..fd3bc9937b5 100644 --- a/tests/torch/conftest.py +++ b/tests/torch/conftest.py @@ -23,10 +23,10 @@ except: # noqa: E722 torch = None from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode -from tests.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS -from tests.shared.case_collection import skip_marked_cases_if_options_not_specified -from tests.shared.install_fixtures import tmp_venv_with_nncf # noqa: F401 -from tests.shared.logging import nncf_caplog # noqa: F401 +from tests.cross_fw.shared.case_collection import COMMON_SCOPE_MARKS_VS_OPTIONS +from tests.cross_fw.shared.case_collection import skip_marked_cases_if_options_not_specified +from tests.cross_fw.shared.install_fixtures import tmp_venv_with_nncf # noqa: F401 +from tests.cross_fw.shared.logging import nncf_caplog # noqa: F401 pytest.register_assert_rewrite("tests.torch.helpers") @@ -54,6 +54,14 @@ def pytest_addoption(parser: Parser): "reference .dot files will be regenerated " "using the current state of the repository.", ) + parser.addoption( + "--regen-json", + action="store_true", + default=False, + help="If specified, the " + "reference .json files will be regenerated " + "using the current state of the repository.", + ) parser.addoption( "--torch-home", type=str, default=None, help="Path to cached test models, downloaded by torchvision" ) @@ -116,9 +124,9 @@ def pytest_addoption(parser: Parser): def pytest_configure(config: Config): - regen_dot = config.getoption("--regen-dot", False) - if regen_dot: - os.environ["NNCF_TEST_REGEN_DOT"] = "1" + for regen_option in ["dot", "json"]: + if config.getoption(f"--regen-{regen_option}", False): + os.environ[f"NNCF_TEST_REGEN_{regen_option.upper()}"] = "1" @pytest.fixture(scope="module") diff --git a/tests/torch/data/experimental/sparsify_activations/dummy_llama_int8_sym_weights_sparse_activations.dot b/tests/torch/data/experimental/sparsify_activations/dummy_llama_int8_sym_weights_sparse_activations.dot index c3e5cf0d0c9..7c4f62b7994 100644 --- a/tests/torch/data/experimental/sparsify_activations/dummy_llama_int8_sym_weights_sparse_activations.dot +++ b/tests/torch/data/experimental/sparsify_activations/dummy_llama_int8_sym_weights_sparse_activations.dot @@ -1,8 +1,8 @@ strict digraph { "0 /nncf_model_input_0" [id=0, type=nncf_model_input]; "1 model.embed_tokens.weight" [id=1, type=nncf_model_const]; -"2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric]; -"3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/type_0" [id=3, type=type]; +"2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric]; +"3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/type_0" [id=3, type=type]; "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0" [id=4, type=embedding]; "5 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/to_0" [id=5, type=to]; "6 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/pow_0" [id=6, type=pow]; @@ -14,16 +14,16 @@ strict digraph { "12 model.layers.0.input_layernorm.weight" [id=12, type=nncf_model_const]; "13 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___1" [id=13, type=__mul__]; "14 model.layers.0.self_attn.q_proj.weight" [id=14, type=nncf_model_const]; -"15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=15, type=decompress_symmetric]; -"16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0" [id=16, type=type]; +"15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=15, type=decompress_symmetric]; +"16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=16, type=type]; "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0" [id=17, type=linear]; "18 model.layers.0.self_attn.k_proj.weight" [id=18, type=nncf_model_const]; -"19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=19, type=decompress_symmetric]; -"20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0" [id=20, type=type]; +"19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=19, type=decompress_symmetric]; +"20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=20, type=type]; "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0" [id=21, type=linear]; "22 model.layers.0.self_attn.v_proj.weight" [id=22, type=nncf_model_const]; -"23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=23, type=decompress_symmetric]; -"24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0" [id=24, type=type]; +"23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=23, type=decompress_symmetric]; +"24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=24, type=type]; "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0" [id=25, type=linear]; "26 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_0" [id=26, type=view]; "27 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/transpose_0" [id=27, type=transpose]; @@ -70,8 +70,8 @@ strict digraph { "68 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/contiguous_0" [id=68, type=contiguous]; "69 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/reshape_2" [id=69, type=reshape]; "70 model.layers.0.self_attn.o_proj.weight" [id=70, type=nncf_model_const]; -"71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=71, type=decompress_symmetric]; -"72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0" [id=72, type=type]; +"71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=71, type=decompress_symmetric]; +"72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=72, type=type]; "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0" [id=73, type=linear]; "74 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/__add___0" [id=74, type=__add__]; "75 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/to_0" [id=75, type=to]; @@ -84,24 +84,24 @@ strict digraph { "82 model.layers.0.post_attention_layernorm.weight" [id=82, type=nncf_model_const]; "83 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" [id=83, type=__mul__]; "84 model.layers.0.mlp.gate_proj.weight" [id=84, type=nncf_model_const]; -"85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=85, type=decompress_symmetric]; -"86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0" [id=86, type=type]; +"85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=85, type=decompress_symmetric]; +"86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=86, type=type]; "87 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/abs_0" [id=87, type=abs]; "88 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0" [id=88, type=le]; "89 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0" [id=89, type=masked_fill]; "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0" [id=90, type=linear]; "91 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0" [id=91, type=silu]; "92 model.layers.0.mlp.up_proj.weight" [id=92, type=nncf_model_const]; -"93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=93, type=decompress_symmetric]; -"94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0" [id=94, type=type]; +"93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=93, type=decompress_symmetric]; +"94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=94, type=type]; "95 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0" [id=95, type=abs]; "96 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0" [id=96, type=le]; "97 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0" [id=97, type=masked_fill]; "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0" [id=98, type=linear]; "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0" [id=99, type=__mul__]; "100 model.layers.0.mlp.down_proj.weight" [id=100, type=nncf_model_const]; -"101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=101, type=decompress_symmetric]; -"102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0" [id=102, type=type]; +"101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=101, type=decompress_symmetric]; +"102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=102, type=type]; "103 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0" [id=103, type=abs]; "104 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0" [id=104, type=le]; "105 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0" [id=105, type=masked_fill]; @@ -117,16 +117,16 @@ strict digraph { "115 model.layers.1.input_layernorm.weight" [id=115, type=nncf_model_const]; "116 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[input_layernorm]/__mul___1" [id=116, type=__mul__]; "117 model.layers.1.self_attn.q_proj.weight" [id=117, type=nncf_model_const]; -"118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=118, type=decompress_symmetric]; -"119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0" [id=119, type=type]; +"118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=118, type=decompress_symmetric]; +"119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=119, type=type]; "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0" [id=120, type=linear]; "121 model.layers.1.self_attn.k_proj.weight" [id=121, type=nncf_model_const]; -"122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=122, type=decompress_symmetric]; -"123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0" [id=123, type=type]; +"122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=122, type=decompress_symmetric]; +"123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=123, type=type]; "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0" [id=124, type=linear]; "125 model.layers.1.self_attn.v_proj.weight" [id=125, type=nncf_model_const]; -"126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=126, type=decompress_symmetric]; -"127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0" [id=127, type=type]; +"126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=126, type=decompress_symmetric]; +"127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=127, type=type]; "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0" [id=128, type=linear]; "129 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_0" [id=129, type=view]; "130 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/transpose_0" [id=130, type=transpose]; @@ -173,8 +173,8 @@ strict digraph { "171 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/contiguous_0" [id=171, type=contiguous]; "172 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/reshape_2" [id=172, type=reshape]; "173 model.layers.1.self_attn.o_proj.weight" [id=173, type=nncf_model_const]; -"174 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LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/__add___0" [id=177, type=__add__]; "178 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/to_0" [id=178, type=to]; @@ -187,24 +187,24 @@ strict digraph { "185 model.layers.1.post_attention_layernorm.weight" [id=185, type=nncf_model_const]; "186 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" [id=186, type=__mul__]; "187 model.layers.1.mlp.gate_proj.weight" [id=187, type=nncf_model_const]; -"188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=188, type=decompress_symmetric]; -"189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0" [id=189, type=type]; +"188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=188, type=decompress_symmetric]; +"189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=189, type=type]; "190 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/abs_0" [id=190, type=abs]; "191 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0" [id=191, type=le]; "192 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0" [id=192, type=masked_fill]; "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0" [id=193, type=linear]; "194 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0" [id=194, type=silu]; "195 model.layers.1.mlp.up_proj.weight" [id=195, type=nncf_model_const]; -"196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=196, type=decompress_symmetric]; -"197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0" [id=197, type=type]; +"196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=196, type=decompress_symmetric]; +"197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=197, type=type]; "198 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[id=204, type=decompress_symmetric]; -"205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0" [id=205, type=type]; +"204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=204, type=decompress_symmetric]; +"205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=205, type=type]; "206 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0" [id=206, type=abs]; "207 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0" [id=207, type=le]; "208 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0" [id=208, type=masked_fill]; @@ -220,15 +220,15 @@ strict digraph { "218 model.norm.weight" [id=218, type=nncf_model_const]; "219 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/__mul___1" [id=219, type=__mul__]; "220 lm_head.weight" [id=220, type=nncf_model_const]; -"221 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=221, type=decompress_symmetric]; -"222 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/type_0" [id=222, type=type]; +"221 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=221, type=decompress_symmetric]; +"222 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/type_0" [id=222, type=type]; "223 LlamaForCausalLM/Linear[lm_head]/linear_0" [id=223, type=linear]; "224 LlamaForCausalLM/float_0" [id=224, type=float]; "225 /nncf_model_output_0" [id=225, type=nncf_model_output]; "0 /nncf_model_input_0" -> "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0"; -"1 model.embed_tokens.weight" -> "2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/type_0"; -"3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/type_0" -> "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0"; +"1 model.embed_tokens.weight" -> "2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/type_0"; +"3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/type_0" -> "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0"; "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0" -> "5 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/to_0"; "5 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/to_0" -> "6 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/pow_0"; "5 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/to_0" -> "10 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___0"; @@ -243,17 +243,17 @@ strict digraph { "13 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0"; "13 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0"; "13 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0"; -"14 model.layers.0.self_attn.q_proj.weight" -> "15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0"; -"16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0" -> "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0"; +"14 model.layers.0.self_attn.q_proj.weight" -> "15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0"; "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0" -> "26 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_0"; -"18 model.layers.0.self_attn.k_proj.weight" -> "19 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LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0"; "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0" -> "28 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_1"; -"22 model.layers.0.self_attn.v_proj.weight" -> "23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0"; -"24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0" -> "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0"; +"22 model.layers.0.self_attn.v_proj.weight" -> "23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0"; "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0" -> "30 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_2"; "26 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_0" -> "27 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/transpose_0"; "27 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/transpose_0" -> "39 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/__mul___0"; @@ -306,9 +306,9 @@ strict digraph { "67 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/transpose_4" -> "68 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/contiguous_0"; "68 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/contiguous_0" -> "69 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/reshape_2"; "69 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/reshape_2" -> "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0"; -"70 model.layers.0.self_attn.o_proj.weight" -> "71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0"; -"72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0" -> "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0"; +"70 model.layers.0.self_attn.o_proj.weight" -> "71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0"; "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0" -> "74 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/__add___0"; "74 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/__add___0" -> "75 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/to_0"; "75 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/to_0" -> "76 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/pow_0"; @@ -325,26 +325,26 @@ strict digraph { "83 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "89 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0"; "83 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "95 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0"; "83 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "97 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0"; -"84 model.layers.0.mlp.gate_proj.weight" -> "85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0"; -"86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0" -> "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0"; +"84 model.layers.0.mlp.gate_proj.weight" -> "85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0"; "87 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/abs_0" -> "88 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0"; "88 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0" -> "89 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0"; "89 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0" -> "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0"; "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0" -> "91 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0"; "91 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0" -> "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0"; -"92 model.layers.0.mlp.up_proj.weight" -> "93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0"; -"94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0" -> "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0"; +"92 model.layers.0.mlp.up_proj.weight" -> "93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0"; "95 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0" -> "96 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0"; "96 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0" -> "97 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0"; "97 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0" -> "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0"; "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0" -> "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0"; "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0" -> "103 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0"; "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0" -> "105 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0"; -"100 model.layers.0.mlp.down_proj.weight" -> "101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0"; -"102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0" -> "106 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/linear_0"; +"100 model.layers.0.mlp.down_proj.weight" -> "101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "106 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/linear_0"; "103 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0" -> "104 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0"; "104 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0" -> "105 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0"; "105 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0" -> "106 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/linear_0"; @@ -363,17 +363,17 @@ strict digraph { "116 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0"; "116 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0"; "116 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0"; -"117 model.layers.1.self_attn.q_proj.weight" -> "118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0"; -"119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0" -> "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0"; +"117 model.layers.1.self_attn.q_proj.weight" -> "118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0"; "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0" -> "129 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_0"; -"121 model.layers.1.self_attn.k_proj.weight" -> "122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0"; -"123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0" -> "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0"; +"121 model.layers.1.self_attn.k_proj.weight" -> "122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0"; "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0" -> "131 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_1"; -"125 model.layers.1.self_attn.v_proj.weight" -> "126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0"; -"127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0" -> "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0"; +"125 model.layers.1.self_attn.v_proj.weight" -> "126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0"; "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0" -> "133 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_2"; "129 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_0" -> "130 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/transpose_0"; "130 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/transpose_0" -> "142 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/__mul___0"; @@ -426,9 +426,9 @@ strict digraph { "170 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/transpose_4" -> "171 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/contiguous_0"; "171 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/contiguous_0" -> "172 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/reshape_2"; "172 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/reshape_2" -> "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0"; -"173 model.layers.1.self_attn.o_proj.weight" -> "174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0"; -"175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0" -> "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0"; +"173 model.layers.1.self_attn.o_proj.weight" -> "174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0"; "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0" -> "177 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/__add___0"; "177 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/__add___0" -> "178 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/to_0"; "178 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/to_0" -> "179 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/pow_0"; @@ -445,26 +445,26 @@ strict digraph { "186 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "192 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0"; "186 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "198 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0"; "186 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "200 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0"; -"187 model.layers.1.mlp.gate_proj.weight" -> "188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0"; -"189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0" -> "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0"; +"187 model.layers.1.mlp.gate_proj.weight" -> "188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0"; "190 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/abs_0" -> "191 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0"; "191 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0" -> "192 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0"; "192 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0" -> "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0"; "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0" -> "194 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0"; "194 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0" -> "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0"; -"195 model.layers.1.mlp.up_proj.weight" -> "196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0"; -"197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0" -> "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0"; +"195 model.layers.1.mlp.up_proj.weight" -> "196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0"; "198 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0" -> "199 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0"; "199 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0" -> "200 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0"; "200 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0" -> "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0"; "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0" -> "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0"; "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0" -> "206 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0"; "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0" -> "208 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0"; -"203 model.layers.1.mlp.down_proj.weight" -> "204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0"; -"205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0" -> "209 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/linear_0"; +"203 model.layers.1.mlp.down_proj.weight" -> "204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0"; +"205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "209 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/linear_0"; "206 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0" -> "207 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0"; "207 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0" -> "208 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0"; "208 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0" -> "209 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/linear_0"; @@ -480,9 +480,9 @@ strict digraph { "217 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/to_1" -> "219 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/__mul___1"; "218 model.norm.weight" -> "219 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/__mul___1"; "219 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/__mul___1" -> "223 LlamaForCausalLM/Linear[lm_head]/linear_0"; -"220 lm_head.weight" -> "221 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"221 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "222 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/type_0"; -"222 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/type_0" -> "223 LlamaForCausalLM/Linear[lm_head]/linear_0"; +"220 lm_head.weight" -> "221 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"221 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "222 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/type_0"; +"222 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/type_0" -> "223 LlamaForCausalLM/Linear[lm_head]/linear_0"; "223 LlamaForCausalLM/Linear[lm_head]/linear_0" -> "224 LlamaForCausalLM/float_0"; "224 LlamaForCausalLM/float_0" -> "225 /nncf_model_output_0"; } diff --git a/tests/torch/data/experimental/sparsify_activations/linear_int8_sym_weights_sparse_activations.dot b/tests/torch/data/experimental/sparsify_activations/linear_int8_sym_weights_sparse_activations.dot index aa24d54a2e0..c433410067b 100644 --- a/tests/torch/data/experimental/sparsify_activations/linear_int8_sym_weights_sparse_activations.dot +++ b/tests/torch/data/experimental/sparsify_activations/linear_int8_sym_weights_sparse_activations.dot @@ -1,8 +1,8 @@ strict digraph { "0 /nncf_model_input_0" [id=0, type=nncf_model_input]; "1 weight" [id=1, type=nncf_model_const]; -"2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0" [id=2, type=decompress_symmetric]; -"3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0" [id=3, type=type]; +"2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0" [id=2, type=decompress_symmetric]; +"3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0" [id=3, type=type]; "4 bias" [id=4, type=nncf_model_const]; "5 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/abs_0" [id=5, type=abs]; "6 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/le_0" [id=6, type=le]; @@ -11,9 +11,9 @@ strict digraph { "9 /nncf_model_output_0" [id=9, type=nncf_model_output]; "0 /nncf_model_input_0" -> "5 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/abs_0"; "0 /nncf_model_input_0" -> "7 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/masked_fill_0"; -"1 weight" -> "2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0"; -"2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0" -> "3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0"; -"3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0" -> "8 Linear/linear_0"; +"1 weight" -> "2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0"; +"2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0" -> "3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0"; +"3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0" -> "8 Linear/linear_0"; "4 bias" -> "8 Linear/linear_0"; "5 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/abs_0" -> "6 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/le_0"; "6 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/le_0" -> "7 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/masked_fill_0"; diff --git a/tests/torch/data/experimental/sparsify_activations/three_linear_ignore1_int8_sym_weights_sparse_activations.dot b/tests/torch/data/experimental/sparsify_activations/three_linear_ignore1_int8_sym_weights_sparse_activations.dot index ae3f667ff3a..5d2d15ae64d 100644 --- a/tests/torch/data/experimental/sparsify_activations/three_linear_ignore1_int8_sym_weights_sparse_activations.dot +++ b/tests/torch/data/experimental/sparsify_activations/three_linear_ignore1_int8_sym_weights_sparse_activations.dot @@ -1,25 +1,25 @@ strict digraph { "0 /nncf_model_input_0" [id=0, type=nncf_model_input]; "1 embedding.weight" [id=1, type=nncf_model_const]; -"2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric]; -"3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0" [id=3, type=type]; +"2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric]; +"3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0" [id=3, type=type]; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" [id=4, type=embedding]; "5 linear1.weight" [id=5, type=nncf_model_const]; -"6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=6, type=decompress_symmetric]; -"7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0" [id=7, type=type]; +"6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=6, type=decompress_symmetric]; +"7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0" [id=7, type=type]; "8 linear1.bias" [id=8, type=nncf_model_const]; "9 ThreeLinearModel/Linear[linear1]/linear_0" [id=9, type=linear]; "10 linear3.weight" [id=10, type=nncf_model_const]; -"11 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=11, type=decompress_symmetric]; -"12 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0" [id=12, type=type]; +"11 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=11, type=decompress_symmetric]; +"12 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0" [id=12, type=type]; "13 linear3.bias" [id=13, type=nncf_model_const]; "14 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0" [id=14, type=abs]; "15 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0" [id=15, type=le]; "16 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0" [id=16, type=masked_fill]; "17 ThreeLinearModel/Linear[linear3]/linear_0" [id=17, type=linear]; "18 linear2.weight" [id=18, type=nncf_model_const]; -"19 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=19, type=decompress_symmetric]; -"20 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0" [id=20, type=type]; +"19 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=19, type=decompress_symmetric]; +"20 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0" [id=20, type=type]; "21 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0" [id=21, type=abs]; "22 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0" [id=22, type=le]; "23 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0" [id=23, type=masked_fill]; @@ -27,29 +27,29 @@ strict digraph { "25 /nncf_model_output_0" [id=25, type=nncf_model_output]; "26 /nncf_model_output_1" [id=26, type=nncf_model_output]; "0 /nncf_model_input_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0"; -"1 embedding.weight" -> "2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0"; -"3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0"; +"1 embedding.weight" -> "2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0"; +"3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0"; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "9 ThreeLinearModel/Linear[linear1]/linear_0"; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "21 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0"; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "23 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0"; -"5 linear1.weight" -> "6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0"; -"7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0" -> "9 ThreeLinearModel/Linear[linear1]/linear_0"; +"5 linear1.weight" -> "6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0"; +"7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0" -> "9 ThreeLinearModel/Linear[linear1]/linear_0"; "8 linear1.bias" -> "9 ThreeLinearModel/Linear[linear1]/linear_0"; "9 ThreeLinearModel/Linear[linear1]/linear_0" -> "14 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0"; "9 ThreeLinearModel/Linear[linear1]/linear_0" -> "16 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0"; -"10 linear3.weight" -> "11 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"11 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "12 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0"; -"12 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0" -> "17 ThreeLinearModel/Linear[linear3]/linear_0"; +"10 linear3.weight" -> "11 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"11 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "12 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0"; +"12 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0" -> "17 ThreeLinearModel/Linear[linear3]/linear_0"; "13 linear3.bias" -> "17 ThreeLinearModel/Linear[linear3]/linear_0"; "14 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0" -> "15 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0"; "15 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0" -> "16 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0"; "16 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0" -> "17 ThreeLinearModel/Linear[linear3]/linear_0"; "17 ThreeLinearModel/Linear[linear3]/linear_0" -> "25 /nncf_model_output_0"; -"18 linear2.weight" -> "19 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"19 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "20 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0"; -"20 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0" -> "24 ThreeLinearModel/Linear[linear2]/linear_0"; +"18 linear2.weight" -> "19 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"19 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "20 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0"; +"20 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0" -> "24 ThreeLinearModel/Linear[linear2]/linear_0"; "21 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0" -> "22 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0"; "22 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0" -> "23 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0"; "23 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0" -> "24 ThreeLinearModel/Linear[linear2]/linear_0"; diff --git a/tests/torch/data/experimental/sparsify_activations/three_linear_int8_sym_weights_sparse_activations.dot b/tests/torch/data/experimental/sparsify_activations/three_linear_int8_sym_weights_sparse_activations.dot index c6488f1131b..437a8407d37 100644 --- a/tests/torch/data/experimental/sparsify_activations/three_linear_int8_sym_weights_sparse_activations.dot +++ b/tests/torch/data/experimental/sparsify_activations/three_linear_int8_sym_weights_sparse_activations.dot @@ -1,28 +1,28 @@ strict digraph { "0 /nncf_model_input_0" [id=0, type=nncf_model_input]; "1 embedding.weight" [id=1, type=nncf_model_const]; -"2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric]; -"3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0" [id=3, type=type]; +"2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric]; +"3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0" [id=3, type=type]; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" [id=4, type=embedding]; "5 linear1.weight" [id=5, type=nncf_model_const]; -"6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=6, type=decompress_symmetric]; -"7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0" [id=7, type=type]; +"6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=6, type=decompress_symmetric]; +"7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0" [id=7, type=type]; "8 linear1.bias" [id=8, type=nncf_model_const]; "9 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/abs_0" [id=9, type=abs]; "10 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/le_0" [id=10, type=le]; "11 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/masked_fill_0" [id=11, type=masked_fill]; "12 ThreeLinearModel/Linear[linear1]/linear_0" [id=12, type=linear]; "13 linear3.weight" [id=13, type=nncf_model_const]; -"14 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=14, type=decompress_symmetric]; -"15 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0" [id=15, type=type]; +"14 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=14, type=decompress_symmetric]; +"15 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0" [id=15, type=type]; "16 linear3.bias" [id=16, type=nncf_model_const]; "17 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0" [id=17, type=abs]; "18 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0" [id=18, type=le]; "19 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0" [id=19, type=masked_fill]; "20 ThreeLinearModel/Linear[linear3]/linear_0" [id=20, type=linear]; "21 linear2.weight" [id=21, type=nncf_model_const]; -"22 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=22, type=decompress_symmetric]; -"23 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0" [id=23, type=type]; +"22 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=22, type=decompress_symmetric]; +"23 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0" [id=23, type=type]; "24 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0" [id=24, type=abs]; "25 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0" [id=25, type=le]; "26 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0" [id=26, type=masked_fill]; @@ -30,33 +30,33 @@ strict digraph { "28 /nncf_model_output_0" [id=28, type=nncf_model_output]; "29 /nncf_model_output_1" [id=29, type=nncf_model_output]; "0 /nncf_model_input_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0"; -"1 embedding.weight" -> "2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0"; -"3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0"; +"1 embedding.weight" -> "2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0"; +"3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0"; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "9 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/abs_0"; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "11 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/masked_fill_0"; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "24 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0"; "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "26 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0"; -"5 linear1.weight" -> "6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0"; -"7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0" -> "12 ThreeLinearModel/Linear[linear1]/linear_0"; +"5 linear1.weight" -> "6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0"; +"7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0" -> "12 ThreeLinearModel/Linear[linear1]/linear_0"; "8 linear1.bias" -> "12 ThreeLinearModel/Linear[linear1]/linear_0"; "9 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/abs_0" -> "10 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/le_0"; "10 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/le_0" -> "11 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/masked_fill_0"; "11 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/masked_fill_0" -> "12 ThreeLinearModel/Linear[linear1]/linear_0"; "12 ThreeLinearModel/Linear[linear1]/linear_0" -> "17 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0"; "12 ThreeLinearModel/Linear[linear1]/linear_0" -> "19 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0"; -"13 linear3.weight" -> "14 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"14 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "15 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0"; -"15 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0" -> "20 ThreeLinearModel/Linear[linear3]/linear_0"; +"13 linear3.weight" -> "14 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"14 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "15 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0"; +"15 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0" -> "20 ThreeLinearModel/Linear[linear3]/linear_0"; "16 linear3.bias" -> "20 ThreeLinearModel/Linear[linear3]/linear_0"; "17 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0" -> "18 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0"; "18 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0" -> "19 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0"; "19 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0" -> "20 ThreeLinearModel/Linear[linear3]/linear_0"; "20 ThreeLinearModel/Linear[linear3]/linear_0" -> "28 /nncf_model_output_0"; -"21 linear2.weight" -> "22 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0"; -"22 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "23 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0"; -"23 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0" -> "27 ThreeLinearModel/Linear[linear2]/linear_0"; +"21 linear2.weight" -> "22 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0"; +"22 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "23 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0"; +"23 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0" -> "27 ThreeLinearModel/Linear[linear2]/linear_0"; "24 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0" -> "25 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0"; "25 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0" -> "26 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0"; "26 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0" -> "27 ThreeLinearModel/Linear[linear2]/linear_0"; diff --git a/tests/torch/data/ref_generated_data.json b/tests/torch/data/ref_generated_data.json new file mode 100644 index 00000000000..4f925032c61 --- /dev/null +++ b/tests/torch/data/ref_generated_data.json @@ -0,0 +1,90 @@ +[ + [ + 863, + 297, + 50, + 718, + 570, + 806, + 681, + 226, + 964, + 350, + 686, + 780 + ], + [ + 227, + 878, + 47, + 780, + 473, + 242, + 347, + 799, + 157, + 123, + 121, + 362, + 992, + 477 + ], + [ + 84, + 815, + 503, + 200, + 906, + 261, + 80, + 211, + 76, + 27, + 638, + 157, + 123, + 121 + ], + [ + 468, + 468, + 939, + 829, + 769, + 769, + 353, + 425, + 712, + 687, + 686, + 780 + ], + [ + 438, + 932, + 664, + 39, + 932, + 54, + 536, + 641, + 33, + 433, + 926, + 711 + ], + [ + 371, + 836, + 836, + 895, + 308, + 811, + 64, + 690, + 737, + 218, + 242, + 897 + ] +] \ No newline at end of file diff --git a/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithAllConstantInputsModel.dot b/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithAllConstantInputsModel.dot deleted file mode 100644 index 0cfbbf5840b..00000000000 --- a/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithAllConstantInputsModel.dot +++ /dev/null @@ -1,11 +0,0 @@ -strict digraph { -"0 _conv_w" [id=0, type=get_attr]; -"1 add" [id=1, type=add]; -"2 placeholder_graph_node" [id=2, type=input]; -"3 conv2d" [id=3, type=conv2d]; -"4 output" [id=4, type=output]; -"0 _conv_w" -> "1 add"; -"1 add" -> "3 conv2d"; -"2 placeholder_graph_node" -> "3 conv2d"; -"3 conv2d" -> "4 output"; -} diff --git a/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithAllConstantInputsModelconv2d_conv2d.dot b/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithAllConstantInputsModelconv2d_conv2d.dot new file mode 100644 index 00000000000..2132db4c1bc --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithAllConstantInputsModelconv2d_conv2d.dot @@ -0,0 +1,11 @@ +strict digraph { +"0 _conv_w" [id=0, type=get_attr]; +"1 add" [id=1, type=add]; +"2 conv2d_input" [id=2, type=input]; +"3 conv2d" [id=3, type=conv2d]; +"4 output" [id=4, type=output]; +"0 _conv_w" -> "1 add" [label="(1, 1, 1, 1)", style=solid]; +"1 add" -> "3 conv2d" [label="(1, 1, 1, 1)", style=solid]; +"2 conv2d_input" -> "3 conv2d" [label=None, style=solid]; +"3 conv2d" -> "4 output" [label="(1, 1, 1, 1)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithNotTensorBiasModel.dot b/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithNotTensorBiasModel.dot deleted file mode 100644 index 47edd99d8c9..00000000000 --- a/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithNotTensorBiasModel.dot +++ /dev/null @@ -1,11 +0,0 @@ -strict digraph { -"0 _conv_w" [id=0, type=get_attr]; -"1 add" [id=1, type=add]; -"2 arg0_1" [id=2, type=input]; -"3 conv2d" [id=3, type=conv2d]; -"4 output" [id=4, type=output]; -"0 _conv_w" -> "1 add"; -"1 add" -> "3 conv2d"; -"2 arg0_1" -> "3 conv2d"; -"3 conv2d" -> "4 output"; -} diff --git a/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithNotTensorBiasModelconv2d_conv2d.dot b/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithNotTensorBiasModelconv2d_conv2d.dot new file mode 100644 index 00000000000..8fcafac75d0 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/extracted/ConvolutionWithNotTensorBiasModelconv2d_conv2d.dot @@ -0,0 +1,11 @@ +strict digraph { +"0 _conv_w" [id=0, type=get_attr]; +"1 add" [id=1, type=add]; +"2 conv2d_input" [id=2, type=input]; +"3 conv2d" [id=3, type=conv2d]; +"4 output" [id=4, type=output]; +"0 _conv_w" -> "1 add" [label="(1, 1, 1, 1)", style=solid]; +"1 add" -> "3 conv2d" [label="(1, 1, 1, 1)", style=solid]; +"2 conv2d_input" -> "3 conv2d" [label=None, style=solid]; +"3 conv2d" -> "4 output" [label="(1, 1, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_1_add__1.dot b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_1_add__1.dot new file mode 100644 index 00000000000..a646adf8f37 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_1_add__1.dot @@ -0,0 +1,15 @@ +strict digraph { +"0 conv_b_weight" [id=0, type=get_attr]; +"1 conv_b_bias" [id=1, type=get_attr]; +"2 bias" [id=2, type=get_attr]; +"3 conv2d_1_input" [id=3, type=input]; +"4 conv2d_1" [id=4, type=conv2d]; +"5 add__1" [id=5, type=add_]; +"6 output" [id=6, type=output]; +"0 conv_b_weight" -> "4 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_b_bias" -> "4 conv2d_1" [label="(3,)", style=solid]; +"2 bias" -> "5 add__1" [label="(1,)", style=solid]; +"3 conv2d_1_input" -> "4 conv2d_1" [label=None, style=solid]; +"4 conv2d_1" -> "5 add__1" [label="(1, 3, 3, 3)", style=solid]; +"5 add__1" -> "6 output" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_add__1.dot b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_add__1.dot new file mode 100644 index 00000000000..f22a6293e89 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_add__1.dot @@ -0,0 +1,36 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 conv2d_input" [id=7, type=input]; +"8 conv2d" [id=8, type=conv2d]; +"9 conv2d_1" [id=9, type=conv2d]; +"10 add_" [id=10, type=add_]; +"11 add__1" [id=11, type=add_]; +"12 add" [id=12, type=add]; +"13 conv2d_2" [id=13, type=conv2d]; +"14 add_1" [id=14, type=add]; +"15 output" [id=15, type=output]; +"0 conv_a_weight" -> "8 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "8 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "9 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "9 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "13 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "13 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "10 add_" [label="(1,)", style=solid]; +"6 bias" -> "11 add__1" [label="(1,)", style=solid]; +"6 bias" -> "14 add_1" [label="(1,)", style=solid]; +"7 conv2d_input" -> "8 conv2d" [label=None, style=solid]; +"8 conv2d" -> "9 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "10 add_" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d_1" -> "11 add__1" [label="(1, 3, 3, 3)", style=solid]; +"10 add_" -> "12 add" [label="(1, 3, 3, 3)", style=solid]; +"11 add__1" -> "12 add" [label="(1, 3, 3, 3)", style=solid]; +"11 add__1" -> "15 output" [label="(1, 3, 3, 3)", style=solid]; +"12 add" -> "13 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"13 conv2d_2" -> "14 add_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_add__add.dot b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_add__add.dot new file mode 100644 index 00000000000..2b81cd6e8cf --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_add__add.dot @@ -0,0 +1,28 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 bias" [id=4, type=get_attr]; +"5 conv2d_input" [id=5, type=input]; +"6 conv2d" [id=6, type=conv2d]; +"7 conv2d_1" [id=7, type=conv2d]; +"8 add_" [id=8, type=add_]; +"9 add__1" [id=9, type=add_]; +"10 add" [id=10, type=add]; +"11 output" [id=11, type=output]; +"0 conv_a_weight" -> "6 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "6 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "7 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "7 conv2d_1" [label="(3,)", style=solid]; +"4 bias" -> "8 add_" [label="(1,)", style=solid]; +"4 bias" -> "9 add__1" [label="(1,)", style=solid]; +"5 conv2d_input" -> "6 conv2d" [label=None, style=solid]; +"6 conv2d" -> "7 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"6 conv2d" -> "8 add_" [label="(1, 3, 3, 3)", style=solid]; +"7 conv2d_1" -> "9 add__1" [label="(1, 3, 3, 3)", style=solid]; +"8 add_" -> "10 add" [label="(1, 3, 3, 3)", style=solid]; +"8 add_" -> "11 output" [label="(1, 3, 3, 3)", style=solid]; +"9 add__1" -> "10 add" [label="(1, 3, 3, 3)", style=solid]; +"10 add" -> "11 output" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_conv2d_1_add__add__1.dot b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_conv2d_1_add__add__1.dot new file mode 100644 index 00000000000..5b73eb3f774 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_conv2d_1_add__add__1.dot @@ -0,0 +1,26 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 bias" [id=4, type=get_attr]; +"5 conv2d_input" [id=5, type=input]; +"6 conv2d" [id=6, type=conv2d]; +"7 conv2d_1_input" [id=7, type=input]; +"8 conv2d_1" [id=8, type=conv2d]; +"9 add_" [id=9, type=add_]; +"10 add__1" [id=10, type=add_]; +"11 output" [id=11, type=output]; +"0 conv_a_weight" -> "6 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "6 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "8 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "8 conv2d_1" [label="(3,)", style=solid]; +"4 bias" -> "9 add_" [label="(1,)", style=solid]; +"4 bias" -> "10 add__1" [label="(1,)", style=solid]; +"5 conv2d_input" -> "6 conv2d" [label=None, style=solid]; +"6 conv2d" -> "9 add_" [label="(1, 3, 3, 3)", style=solid]; +"7 conv2d_1_input" -> "8 conv2d_1" [label=None, style=solid]; +"8 conv2d_1" -> "10 add__1" [label="(1, 3, 3, 3)", style=solid]; +"9 add_" -> "11 output" [label="(1, 3, 3, 3)", style=solid]; +"10 add__1" -> "11 output" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_conv2d_2.dot b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_conv2d_2.dot new file mode 100644 index 00000000000..bc231147555 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/extracted/MultiBranchesConnectedModelconv2d_conv2d_2.dot @@ -0,0 +1,33 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 conv2d_input" [id=7, type=input]; +"8 conv2d" [id=8, type=conv2d]; +"9 conv2d_1" [id=9, type=conv2d]; +"10 add_" [id=10, type=add_]; +"11 add__1" [id=11, type=add_]; +"12 add" [id=12, type=add]; +"13 conv2d_2" [id=13, type=conv2d]; +"14 output" [id=14, type=output]; +"0 conv_a_weight" -> "8 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "8 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "9 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "9 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "13 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "13 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "10 add_" [label="(1,)", style=solid]; +"6 bias" -> "11 add__1" [label="(1,)", style=solid]; +"7 conv2d_input" -> "8 conv2d" [label=None, style=solid]; +"8 conv2d" -> "9 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "10 add_" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d_1" -> "11 add__1" [label="(1, 3, 3, 3)", style=solid]; +"10 add_" -> "12 add" [label="(1, 3, 3, 3)", style=solid]; +"11 add__1" -> "12 add" [label="(1, 3, 3, 3)", style=solid]; +"12 add" -> "13 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"13 conv2d_2" -> "14 output" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/mobilenet_v3_small.dot b/tests/torch/data/reference_graphs/fx/mobilenet_v3_small.dot index 449076359ce..7d74485f281 100644 --- a/tests/torch/data/reference_graphs/fx/mobilenet_v3_small.dot +++ b/tests/torch/data/reference_graphs/fx/mobilenet_v3_small.dot @@ -1,992 +1,788 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; -"1 _param_constant0" [id=1, type=get_attr]; -"2 conv2d" [id=2, type=conv2d]; -"3 empty" [id=3, type=empty]; -"4 _param_constant1" [id=4, type=get_attr]; -"5 _param_constant2" [id=5, type=get_attr]; -"6 _tensor_constant0" [id=6, type=get_attr]; -"7 _tensor_constant1" [id=7, type=get_attr]; -"8 _native_batch_norm_legit_no_training" [id=8, type=_native_batch_norm_legit_no_training]; -"9 getitem" [id=9, type=__getitem__]; -"10 getitem_1" [id=10, type=__getitem__]; -"11 getitem_2" [id=11, type=__getitem__]; -"12 hardswish_" [id=12, type=hardswish_]; -"13 _param_constant3" [id=13, type=get_attr]; -"14 conv2d_1" [id=14, type=conv2d]; -"15 empty_1" [id=15, type=empty]; -"16 _param_constant4" [id=16, type=get_attr]; -"17 _param_constant5" [id=17, type=get_attr]; -"18 _tensor_constant2" [id=18, type=get_attr]; -"19 _tensor_constant3" [id=19, type=get_attr]; -"20 _native_batch_norm_legit_no_training_1" [id=20, type=_native_batch_norm_legit_no_training]; -"21 getitem_3" [id=21, type=__getitem__]; -"22 getitem_4" [id=22, type=__getitem__]; -"23 getitem_5" [id=23, type=__getitem__]; -"24 relu_" [id=24, type=relu_]; -"25 adaptive_avg_pool2d" [id=25, type=adaptive_avg_pool2d]; -"26 _param_constant6" [id=26, type=get_attr]; -"27 _param_constant7" [id=27, type=get_attr]; -"28 conv2d_2" [id=28, type=conv2d]; -"29 relu" [id=29, type=relu]; -"30 _param_constant8" [id=30, type=get_attr]; -"31 _param_constant9" [id=31, type=get_attr]; -"32 conv2d_3" [id=32, type=conv2d]; -"33 hardsigmoid" [id=33, type=hardsigmoid]; -"34 mul" [id=34, type=mul]; -"35 _param_constant10" [id=35, type=get_attr]; -"36 conv2d_4" [id=36, type=conv2d]; -"37 empty_2" [id=37, type=empty]; -"38 _param_constant11" [id=38, type=get_attr]; -"39 _param_constant12" [id=39, type=get_attr]; -"40 _tensor_constant4" [id=40, type=get_attr]; -"41 _tensor_constant5" [id=41, type=get_attr]; -"42 _native_batch_norm_legit_no_training_2" [id=42, type=_native_batch_norm_legit_no_training]; -"43 getitem_6" [id=43, type=__getitem__]; -"44 getitem_7" [id=44, type=__getitem__]; -"45 getitem_8" [id=45, type=__getitem__]; -"46 _param_constant13" [id=46, type=get_attr]; -"47 conv2d_5" [id=47, type=conv2d]; -"48 empty_3" [id=48, type=empty]; -"49 _param_constant14" [id=49, type=get_attr]; -"50 _param_constant15" [id=50, type=get_attr]; -"51 _tensor_constant6" [id=51, type=get_attr]; -"52 _tensor_constant7" [id=52, type=get_attr]; -"53 _native_batch_norm_legit_no_training_3" [id=53, type=_native_batch_norm_legit_no_training]; -"54 getitem_9" [id=54, type=__getitem__]; -"55 getitem_10" [id=55, type=__getitem__]; -"56 getitem_11" [id=56, type=__getitem__]; -"57 relu__1" [id=57, type=relu_]; -"58 _param_constant16" [id=58, type=get_attr]; -"59 conv2d_6" [id=59, type=conv2d]; -"60 empty_4" [id=60, type=empty]; -"61 _param_constant17" [id=61, type=get_attr]; -"62 _param_constant18" [id=62, type=get_attr]; -"63 _tensor_constant8" [id=63, type=get_attr]; -"64 _tensor_constant9" [id=64, type=get_attr]; -"65 _native_batch_norm_legit_no_training_4" [id=65, type=_native_batch_norm_legit_no_training]; -"66 getitem_12" [id=66, type=__getitem__]; -"67 getitem_13" [id=67, type=__getitem__]; -"68 getitem_14" [id=68, type=__getitem__]; -"69 relu__2" [id=69, type=relu_]; -"70 _param_constant19" [id=70, type=get_attr]; -"71 conv2d_7" [id=71, type=conv2d]; -"72 empty_5" [id=72, type=empty]; -"73 _param_constant20" [id=73, type=get_attr]; -"74 _param_constant21" [id=74, type=get_attr]; -"75 _tensor_constant10" [id=75, type=get_attr]; -"76 _tensor_constant11" [id=76, type=get_attr]; -"77 _native_batch_norm_legit_no_training_5" [id=77, type=_native_batch_norm_legit_no_training]; -"78 getitem_15" [id=78, type=__getitem__]; -"79 getitem_16" [id=79, type=__getitem__]; -"80 getitem_17" [id=80, type=__getitem__]; -"81 _param_constant22" [id=81, type=get_attr]; -"82 conv2d_8" [id=82, type=conv2d]; -"83 empty_6" [id=83, type=empty]; -"84 _param_constant23" [id=84, type=get_attr]; -"85 _param_constant24" [id=85, type=get_attr]; -"86 _tensor_constant12" [id=86, type=get_attr]; -"87 _tensor_constant13" [id=87, type=get_attr]; -"88 _native_batch_norm_legit_no_training_6" [id=88, type=_native_batch_norm_legit_no_training]; -"89 getitem_18" [id=89, type=__getitem__]; -"90 getitem_19" [id=90, type=__getitem__]; -"91 getitem_20" [id=91, type=__getitem__]; -"92 relu__3" [id=92, type=relu_]; -"93 _param_constant25" [id=93, type=get_attr]; -"94 conv2d_9" [id=94, type=conv2d]; -"95 empty_7" [id=95, type=empty]; -"96 _param_constant26" [id=96, type=get_attr]; -"97 _param_constant27" [id=97, type=get_attr]; -"98 _tensor_constant14" [id=98, type=get_attr]; -"99 _tensor_constant15" [id=99, type=get_attr]; -"100 _native_batch_norm_legit_no_training_7" [id=100, type=_native_batch_norm_legit_no_training]; -"101 getitem_21" [id=101, type=__getitem__]; -"102 getitem_22" [id=102, type=__getitem__]; -"103 getitem_23" [id=103, type=__getitem__]; -"104 relu__4" [id=104, type=relu_]; -"105 _param_constant28" [id=105, type=get_attr]; -"106 conv2d_10" [id=106, type=conv2d]; -"107 empty_8" [id=107, type=empty]; -"108 _param_constant29" [id=108, type=get_attr]; -"109 _param_constant30" [id=109, type=get_attr]; -"110 _tensor_constant16" [id=110, type=get_attr]; -"111 _tensor_constant17" [id=111, type=get_attr]; -"112 _native_batch_norm_legit_no_training_8" [id=112, type=_native_batch_norm_legit_no_training]; -"113 getitem_24" [id=113, type=__getitem__]; -"114 getitem_25" [id=114, type=__getitem__]; -"115 getitem_26" [id=115, type=__getitem__]; -"116 add_" [id=116, type=add_]; -"117 _param_constant31" [id=117, type=get_attr]; -"118 conv2d_11" [id=118, type=conv2d]; -"119 empty_9" [id=119, type=empty]; -"120 _param_constant32" [id=120, type=get_attr]; -"121 _param_constant33" [id=121, type=get_attr]; -"122 _tensor_constant18" [id=122, type=get_attr]; -"123 _tensor_constant19" [id=123, type=get_attr]; -"124 _native_batch_norm_legit_no_training_9" [id=124, type=_native_batch_norm_legit_no_training]; -"125 getitem_27" [id=125, type=__getitem__]; -"126 getitem_28" [id=126, type=__getitem__]; -"127 getitem_29" [id=127, type=__getitem__]; -"128 hardswish__1" [id=128, type=hardswish_]; -"129 _param_constant34" [id=129, type=get_attr]; -"130 conv2d_12" [id=130, type=conv2d]; -"131 empty_10" [id=131, type=empty]; -"132 _param_constant35" [id=132, type=get_attr]; -"133 _param_constant36" [id=133, type=get_attr]; -"134 _tensor_constant20" [id=134, type=get_attr]; -"135 _tensor_constant21" [id=135, type=get_attr]; -"136 _native_batch_norm_legit_no_training_10" [id=136, type=_native_batch_norm_legit_no_training]; -"137 getitem_30" [id=137, type=__getitem__]; -"138 getitem_31" [id=138, type=__getitem__]; -"139 getitem_32" [id=139, type=__getitem__]; -"140 hardswish__2" [id=140, type=hardswish_]; -"141 adaptive_avg_pool2d_1" [id=141, type=adaptive_avg_pool2d]; -"142 _param_constant37" [id=142, type=get_attr]; -"143 _param_constant38" [id=143, type=get_attr]; -"144 conv2d_13" [id=144, type=conv2d]; -"145 relu_1" [id=145, type=relu]; -"146 _param_constant39" [id=146, type=get_attr]; -"147 _param_constant40" [id=147, type=get_attr]; -"148 conv2d_14" [id=148, type=conv2d]; -"149 hardsigmoid_1" [id=149, type=hardsigmoid]; -"150 mul_1" [id=150, type=mul]; -"151 _param_constant41" [id=151, type=get_attr]; -"152 conv2d_15" [id=152, type=conv2d]; -"153 empty_11" [id=153, type=empty]; -"154 _param_constant42" [id=154, type=get_attr]; -"155 _param_constant43" [id=155, type=get_attr]; -"156 _tensor_constant22" [id=156, type=get_attr]; -"157 _tensor_constant23" [id=157, type=get_attr]; -"158 _native_batch_norm_legit_no_training_11" [id=158, type=_native_batch_norm_legit_no_training]; -"159 getitem_33" [id=159, type=__getitem__]; -"160 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hardswish_"; -"12 hardswish_" -> "14 conv2d_1"; -"13 _param_constant3" -> "14 conv2d_1"; -"14 conv2d_1" -> "20 _native_batch_norm_legit_no_training_1"; -"16 _param_constant4" -> "20 _native_batch_norm_legit_no_training_1"; -"17 _param_constant5" -> "20 _native_batch_norm_legit_no_training_1"; -"18 _tensor_constant2" -> "20 _native_batch_norm_legit_no_training_1"; -"19 _tensor_constant3" -> "20 _native_batch_norm_legit_no_training_1"; -"20 _native_batch_norm_legit_no_training_1" -> "21 getitem_3"; -"20 _native_batch_norm_legit_no_training_1" -> "22 getitem_4"; -"20 _native_batch_norm_legit_no_training_1" -> "23 getitem_5"; -"21 getitem_3" -> "24 relu_"; -"24 relu_" -> "25 adaptive_avg_pool2d"; -"24 relu_" -> "34 mul"; -"25 adaptive_avg_pool2d" -> "28 conv2d_2"; -"26 _param_constant6" -> "28 conv2d_2"; -"27 _param_constant7" -> "28 conv2d_2"; -"28 conv2d_2" -> "29 relu"; -"29 relu" -> "32 conv2d_3"; -"30 _param_constant8" -> "32 conv2d_3"; -"31 _param_constant9" -> "32 conv2d_3"; -"32 conv2d_3" -> "33 hardsigmoid"; -"33 hardsigmoid" -> "34 mul"; -"34 mul" -> "36 conv2d_4"; -"35 _param_constant10" -> "36 conv2d_4"; -"36 conv2d_4" -> "42 _native_batch_norm_legit_no_training_2"; -"38 _param_constant11" -> "42 _native_batch_norm_legit_no_training_2"; -"39 _param_constant12" -> "42 _native_batch_norm_legit_no_training_2"; -"40 _tensor_constant4" -> "42 _native_batch_norm_legit_no_training_2"; -"41 _tensor_constant5" -> "42 _native_batch_norm_legit_no_training_2"; -"42 _native_batch_norm_legit_no_training_2" -> "43 getitem_6"; -"42 _native_batch_norm_legit_no_training_2" -> "44 getitem_7"; -"42 _native_batch_norm_legit_no_training_2" -> "45 getitem_8"; -"43 getitem_6" -> "47 conv2d_5"; -"46 _param_constant13" -> "47 conv2d_5"; -"47 conv2d_5" -> "53 _native_batch_norm_legit_no_training_3"; -"49 _param_constant14" -> "53 _native_batch_norm_legit_no_training_3"; -"50 _param_constant15" -> "53 _native_batch_norm_legit_no_training_3"; -"51 _tensor_constant6" -> "53 _native_batch_norm_legit_no_training_3"; -"52 _tensor_constant7" -> "53 _native_batch_norm_legit_no_training_3"; -"53 _native_batch_norm_legit_no_training_3" -> "54 getitem_9"; -"53 _native_batch_norm_legit_no_training_3" -> "55 getitem_10"; -"53 _native_batch_norm_legit_no_training_3" -> "56 getitem_11"; -"54 getitem_9" -> "57 relu__1"; -"57 relu__1" -> "59 conv2d_6"; -"58 _param_constant16" -> "59 conv2d_6"; -"59 conv2d_6" -> "65 _native_batch_norm_legit_no_training_4"; -"61 _param_constant17" -> "65 _native_batch_norm_legit_no_training_4"; -"62 _param_constant18" -> "65 _native_batch_norm_legit_no_training_4"; -"63 _tensor_constant8" -> "65 _native_batch_norm_legit_no_training_4"; -"64 _tensor_constant9" -> "65 _native_batch_norm_legit_no_training_4"; -"65 _native_batch_norm_legit_no_training_4" -> "66 getitem_12"; -"65 _native_batch_norm_legit_no_training_4" -> "67 getitem_13"; -"65 _native_batch_norm_legit_no_training_4" -> "68 getitem_14"; -"66 getitem_12" -> "69 relu__2"; -"69 relu__2" -> "71 conv2d_7"; -"70 _param_constant19" -> "71 conv2d_7"; -"71 conv2d_7" -> "77 _native_batch_norm_legit_no_training_5"; -"73 _param_constant20" -> "77 _native_batch_norm_legit_no_training_5"; -"74 _param_constant21" -> "77 _native_batch_norm_legit_no_training_5"; -"75 _tensor_constant10" -> "77 _native_batch_norm_legit_no_training_5"; -"76 _tensor_constant11" -> "77 _native_batch_norm_legit_no_training_5"; -"77 _native_batch_norm_legit_no_training_5" -> "78 getitem_15"; -"77 _native_batch_norm_legit_no_training_5" -> "79 getitem_16"; -"77 _native_batch_norm_legit_no_training_5" -> "80 getitem_17"; -"78 getitem_15" -> "82 conv2d_8"; -"78 getitem_15" -> "116 add_"; -"81 _param_constant22" -> "82 conv2d_8"; -"82 conv2d_8" -> "88 _native_batch_norm_legit_no_training_6"; -"84 _param_constant23" -> "88 _native_batch_norm_legit_no_training_6"; -"85 _param_constant24" -> "88 _native_batch_norm_legit_no_training_6"; -"86 _tensor_constant12" -> "88 _native_batch_norm_legit_no_training_6"; -"87 _tensor_constant13" -> "88 _native_batch_norm_legit_no_training_6"; -"88 _native_batch_norm_legit_no_training_6" -> "89 getitem_18"; -"88 _native_batch_norm_legit_no_training_6" -> "90 getitem_19"; -"88 _native_batch_norm_legit_no_training_6" -> "91 getitem_20"; -"89 getitem_18" -> "92 relu__3"; -"92 relu__3" -> "94 conv2d_9"; -"93 _param_constant25" -> "94 conv2d_9"; -"94 conv2d_9" -> "100 _native_batch_norm_legit_no_training_7"; -"96 _param_constant26" -> "100 _native_batch_norm_legit_no_training_7"; -"97 _param_constant27" -> "100 _native_batch_norm_legit_no_training_7"; -"98 _tensor_constant14" -> "100 _native_batch_norm_legit_no_training_7"; -"99 _tensor_constant15" -> "100 _native_batch_norm_legit_no_training_7"; -"100 _native_batch_norm_legit_no_training_7" -> "101 getitem_21"; -"100 _native_batch_norm_legit_no_training_7" -> "102 getitem_22"; -"100 _native_batch_norm_legit_no_training_7" -> "103 getitem_23"; -"101 getitem_21" -> "104 relu__4"; -"104 relu__4" -> "106 conv2d_10"; -"105 _param_constant28" -> "106 conv2d_10"; -"106 conv2d_10" -> "112 _native_batch_norm_legit_no_training_8"; -"108 _param_constant29" -> "112 _native_batch_norm_legit_no_training_8"; -"109 _param_constant30" -> "112 _native_batch_norm_legit_no_training_8"; -"110 _tensor_constant16" -> "112 _native_batch_norm_legit_no_training_8"; -"111 _tensor_constant17" -> "112 _native_batch_norm_legit_no_training_8"; -"112 _native_batch_norm_legit_no_training_8" -> "113 getitem_24"; -"112 _native_batch_norm_legit_no_training_8" -> "114 getitem_25"; -"112 _native_batch_norm_legit_no_training_8" -> "115 getitem_26"; -"113 getitem_24" -> "116 add_"; -"116 add_" -> "118 conv2d_11"; -"117 _param_constant31" -> "118 conv2d_11"; -"118 conv2d_11" -> "124 _native_batch_norm_legit_no_training_9"; -"120 _param_constant32" -> "124 _native_batch_norm_legit_no_training_9"; -"121 _param_constant33" -> "124 _native_batch_norm_legit_no_training_9"; -"122 _tensor_constant18" -> "124 _native_batch_norm_legit_no_training_9"; -"123 _tensor_constant19" -> "124 _native_batch_norm_legit_no_training_9"; -"124 _native_batch_norm_legit_no_training_9" -> "125 getitem_27"; -"124 _native_batch_norm_legit_no_training_9" -> "126 getitem_28"; -"124 _native_batch_norm_legit_no_training_9" -> "127 getitem_29"; -"125 getitem_27" -> "128 hardswish__1"; -"128 hardswish__1" -> "130 conv2d_12"; -"129 _param_constant34" -> "130 conv2d_12"; -"130 conv2d_12" -> "136 _native_batch_norm_legit_no_training_10"; -"132 _param_constant35" -> "136 _native_batch_norm_legit_no_training_10"; -"133 _param_constant36" -> "136 _native_batch_norm_legit_no_training_10"; -"134 _tensor_constant20" -> "136 _native_batch_norm_legit_no_training_10"; -"135 _tensor_constant21" -> "136 _native_batch_norm_legit_no_training_10"; -"136 _native_batch_norm_legit_no_training_10" -> "137 getitem_30"; -"136 _native_batch_norm_legit_no_training_10" -> "138 getitem_31"; -"136 _native_batch_norm_legit_no_training_10" -> "139 getitem_32"; -"137 getitem_30" -> "140 hardswish__2"; -"140 hardswish__2" -> "141 adaptive_avg_pool2d_1"; -"140 hardswish__2" -> "150 mul_1"; -"141 adaptive_avg_pool2d_1" -> "144 conv2d_13"; -"142 _param_constant37" -> "144 conv2d_13"; -"143 _param_constant38" -> "144 conv2d_13"; -"144 conv2d_13" -> "145 relu_1"; -"145 relu_1" -> "148 conv2d_14"; -"146 _param_constant39" -> "148 conv2d_14"; -"147 _param_constant40" -> "148 conv2d_14"; -"148 conv2d_14" -> "149 hardsigmoid_1"; -"149 hardsigmoid_1" -> "150 mul_1"; -"150 mul_1" -> "152 conv2d_15"; -"151 _param_constant41" -> "152 conv2d_15"; -"152 conv2d_15" -> "158 _native_batch_norm_legit_no_training_11"; -"154 _param_constant42" -> "158 _native_batch_norm_legit_no_training_11"; -"155 _param_constant43" -> "158 _native_batch_norm_legit_no_training_11"; -"156 _tensor_constant22" -> "158 _native_batch_norm_legit_no_training_11"; -"157 _tensor_constant23" -> "158 _native_batch_norm_legit_no_training_11"; -"158 _native_batch_norm_legit_no_training_11" -> "159 getitem_33"; -"158 _native_batch_norm_legit_no_training_11" -> "160 getitem_34"; -"158 _native_batch_norm_legit_no_training_11" -> "161 getitem_35"; -"159 getitem_33" -> "163 conv2d_16"; -"159 getitem_33" -> "207 add__1"; -"162 _param_constant44" -> "163 conv2d_16"; -"163 conv2d_16" -> "169 _native_batch_norm_legit_no_training_12"; -"165 _param_constant45" -> "169 _native_batch_norm_legit_no_training_12"; -"166 _param_constant46" -> "169 _native_batch_norm_legit_no_training_12"; -"167 _tensor_constant24" -> "169 _native_batch_norm_legit_no_training_12"; -"168 _tensor_constant25" -> "169 _native_batch_norm_legit_no_training_12"; -"169 _native_batch_norm_legit_no_training_12" -> "170 getitem_36"; -"169 _native_batch_norm_legit_no_training_12" -> "171 getitem_37"; -"169 _native_batch_norm_legit_no_training_12" -> "172 getitem_38"; -"170 getitem_36" -> "173 hardswish__3"; -"173 hardswish__3" -> "175 conv2d_17"; -"174 _param_constant47" -> "175 conv2d_17"; -"175 conv2d_17" -> "181 _native_batch_norm_legit_no_training_13"; -"177 _param_constant48" -> "181 _native_batch_norm_legit_no_training_13"; -"178 _param_constant49" -> "181 _native_batch_norm_legit_no_training_13"; -"179 _tensor_constant26" -> "181 _native_batch_norm_legit_no_training_13"; -"180 _tensor_constant27" -> "181 _native_batch_norm_legit_no_training_13"; -"181 _native_batch_norm_legit_no_training_13" -> "182 getitem_39"; -"181 _native_batch_norm_legit_no_training_13" -> "183 getitem_40"; -"181 _native_batch_norm_legit_no_training_13" -> "184 getitem_41"; -"182 getitem_39" -> "185 hardswish__4"; -"185 hardswish__4" -> "186 adaptive_avg_pool2d_2"; -"185 hardswish__4" -> "195 mul_2"; -"186 adaptive_avg_pool2d_2" -> "189 conv2d_18"; -"187 _param_constant50" -> "189 conv2d_18"; -"188 _param_constant51" -> "189 conv2d_18"; -"189 conv2d_18" -> "190 relu_2"; -"190 relu_2" -> "193 conv2d_19"; -"191 _param_constant52" -> "193 conv2d_19"; -"192 _param_constant53" -> "193 conv2d_19"; -"193 conv2d_19" -> "194 hardsigmoid_2"; -"194 hardsigmoid_2" -> "195 mul_2"; -"195 mul_2" -> "197 conv2d_20"; -"196 _param_constant54" -> "197 conv2d_20"; -"197 conv2d_20" -> "203 _native_batch_norm_legit_no_training_14"; -"199 _param_constant55" -> "203 _native_batch_norm_legit_no_training_14"; -"200 _param_constant56" -> "203 _native_batch_norm_legit_no_training_14"; -"201 _tensor_constant28" -> "203 _native_batch_norm_legit_no_training_14"; -"202 _tensor_constant29" -> "203 _native_batch_norm_legit_no_training_14"; -"203 _native_batch_norm_legit_no_training_14" -> "204 getitem_42"; -"203 _native_batch_norm_legit_no_training_14" -> "205 getitem_43"; -"203 _native_batch_norm_legit_no_training_14" -> "206 getitem_44"; -"204 getitem_42" -> "207 add__1"; -"207 add__1" -> "209 conv2d_21"; -"207 add__1" -> "253 add__2"; -"208 _param_constant57" -> "209 conv2d_21"; -"209 conv2d_21" -> "215 _native_batch_norm_legit_no_training_15"; -"211 _param_constant58" -> "215 _native_batch_norm_legit_no_training_15"; -"212 _param_constant59" -> "215 _native_batch_norm_legit_no_training_15"; -"213 _tensor_constant30" -> "215 _native_batch_norm_legit_no_training_15"; -"214 _tensor_constant31" -> "215 _native_batch_norm_legit_no_training_15"; -"215 _native_batch_norm_legit_no_training_15" -> "216 getitem_45"; -"215 _native_batch_norm_legit_no_training_15" -> "217 getitem_46"; -"215 _native_batch_norm_legit_no_training_15" -> "218 getitem_47"; -"216 getitem_45" -> "219 hardswish__5"; -"219 hardswish__5" -> "221 conv2d_22"; -"220 _param_constant60" -> "221 conv2d_22"; -"221 conv2d_22" -> "227 _native_batch_norm_legit_no_training_16"; -"223 _param_constant61" -> "227 _native_batch_norm_legit_no_training_16"; -"224 _param_constant62" -> "227 _native_batch_norm_legit_no_training_16"; -"225 _tensor_constant32" -> "227 _native_batch_norm_legit_no_training_16"; -"226 _tensor_constant33" -> "227 _native_batch_norm_legit_no_training_16"; -"227 _native_batch_norm_legit_no_training_16" -> "228 getitem_48"; -"227 _native_batch_norm_legit_no_training_16" -> "229 getitem_49"; -"227 _native_batch_norm_legit_no_training_16" -> "230 getitem_50"; -"228 getitem_48" -> "231 hardswish__6"; -"231 hardswish__6" -> "232 adaptive_avg_pool2d_3"; -"231 hardswish__6" -> "241 mul_3"; -"232 adaptive_avg_pool2d_3" -> "235 conv2d_23"; -"233 _param_constant63" -> "235 conv2d_23"; -"234 _param_constant64" -> "235 conv2d_23"; -"235 conv2d_23" -> "236 relu_3"; -"236 relu_3" -> "239 conv2d_24"; -"237 _param_constant65" -> "239 conv2d_24"; -"238 _param_constant66" -> "239 conv2d_24"; -"239 conv2d_24" -> "240 hardsigmoid_3"; -"240 hardsigmoid_3" -> "241 mul_3"; -"241 mul_3" -> "243 conv2d_25"; -"242 _param_constant67" -> "243 conv2d_25"; -"243 conv2d_25" -> "249 _native_batch_norm_legit_no_training_17"; -"245 _param_constant68" -> "249 _native_batch_norm_legit_no_training_17"; -"246 _param_constant69" -> "249 _native_batch_norm_legit_no_training_17"; -"247 _tensor_constant34" -> "249 _native_batch_norm_legit_no_training_17"; -"248 _tensor_constant35" -> "249 _native_batch_norm_legit_no_training_17"; -"249 _native_batch_norm_legit_no_training_17" -> "250 getitem_51"; -"249 _native_batch_norm_legit_no_training_17" -> "251 getitem_52"; -"249 _native_batch_norm_legit_no_training_17" -> "252 getitem_53"; -"250 getitem_51" -> "253 add__2"; -"253 add__2" -> "255 conv2d_26"; -"254 _param_constant70" -> "255 conv2d_26"; -"255 conv2d_26" -> "261 _native_batch_norm_legit_no_training_18"; -"257 _param_constant71" -> "261 _native_batch_norm_legit_no_training_18"; -"258 _param_constant72" -> "261 _native_batch_norm_legit_no_training_18"; -"259 _tensor_constant36" -> "261 _native_batch_norm_legit_no_training_18"; -"260 _tensor_constant37" -> "261 _native_batch_norm_legit_no_training_18"; -"261 _native_batch_norm_legit_no_training_18" -> "262 getitem_54"; -"261 _native_batch_norm_legit_no_training_18" -> "263 getitem_55"; -"261 _native_batch_norm_legit_no_training_18" -> "264 getitem_56"; -"262 getitem_54" -> "265 hardswish__7"; -"265 hardswish__7" -> "267 conv2d_27"; -"266 _param_constant73" -> "267 conv2d_27"; -"267 conv2d_27" -> "273 _native_batch_norm_legit_no_training_19"; -"269 _param_constant74" -> "273 _native_batch_norm_legit_no_training_19"; -"270 _param_constant75" -> "273 _native_batch_norm_legit_no_training_19"; -"271 _tensor_constant38" -> "273 _native_batch_norm_legit_no_training_19"; -"272 _tensor_constant39" -> "273 _native_batch_norm_legit_no_training_19"; -"273 _native_batch_norm_legit_no_training_19" -> "274 getitem_57"; -"273 _native_batch_norm_legit_no_training_19" -> "275 getitem_58"; -"273 _native_batch_norm_legit_no_training_19" -> "276 getitem_59"; -"274 getitem_57" -> "277 hardswish__8"; -"277 hardswish__8" -> "278 adaptive_avg_pool2d_4"; -"277 hardswish__8" -> "287 mul_4"; -"278 adaptive_avg_pool2d_4" -> "281 conv2d_28"; -"279 _param_constant76" -> "281 conv2d_28"; -"280 _param_constant77" -> "281 conv2d_28"; -"281 conv2d_28" -> "282 relu_4"; -"282 relu_4" -> "285 conv2d_29"; -"283 _param_constant78" -> "285 conv2d_29"; -"284 _param_constant79" -> "285 conv2d_29"; -"285 conv2d_29" -> "286 hardsigmoid_4"; -"286 hardsigmoid_4" -> "287 mul_4"; -"287 mul_4" -> "289 conv2d_30"; -"288 _param_constant80" -> "289 conv2d_30"; -"289 conv2d_30" -> "295 _native_batch_norm_legit_no_training_20"; -"291 _param_constant81" -> "295 _native_batch_norm_legit_no_training_20"; -"292 _param_constant82" -> "295 _native_batch_norm_legit_no_training_20"; -"293 _tensor_constant40" -> "295 _native_batch_norm_legit_no_training_20"; -"294 _tensor_constant41" -> "295 _native_batch_norm_legit_no_training_20"; -"295 _native_batch_norm_legit_no_training_20" -> "296 getitem_60"; -"295 _native_batch_norm_legit_no_training_20" -> "297 getitem_61"; -"295 _native_batch_norm_legit_no_training_20" -> "298 getitem_62"; -"296 getitem_60" -> "300 conv2d_31"; -"296 getitem_60" -> "344 add__3"; -"299 _param_constant83" -> "300 conv2d_31"; -"300 conv2d_31" -> "306 _native_batch_norm_legit_no_training_21"; -"302 _param_constant84" -> "306 _native_batch_norm_legit_no_training_21"; -"303 _param_constant85" -> "306 _native_batch_norm_legit_no_training_21"; -"304 _tensor_constant42" -> "306 _native_batch_norm_legit_no_training_21"; -"305 _tensor_constant43" -> "306 _native_batch_norm_legit_no_training_21"; -"306 _native_batch_norm_legit_no_training_21" -> "307 getitem_63"; -"306 _native_batch_norm_legit_no_training_21" -> "308 getitem_64"; -"306 _native_batch_norm_legit_no_training_21" -> "309 getitem_65"; -"307 getitem_63" -> "310 hardswish__9"; -"310 hardswish__9" -> "312 conv2d_32"; -"311 _param_constant86" -> "312 conv2d_32"; -"312 conv2d_32" -> "318 _native_batch_norm_legit_no_training_22"; -"314 _param_constant87" -> "318 _native_batch_norm_legit_no_training_22"; -"315 _param_constant88" -> "318 _native_batch_norm_legit_no_training_22"; -"316 _tensor_constant44" -> "318 _native_batch_norm_legit_no_training_22"; -"317 _tensor_constant45" -> "318 _native_batch_norm_legit_no_training_22"; -"318 _native_batch_norm_legit_no_training_22" -> "319 getitem_66"; -"318 _native_batch_norm_legit_no_training_22" -> "320 getitem_67"; -"318 _native_batch_norm_legit_no_training_22" -> "321 getitem_68"; -"319 getitem_66" -> "322 hardswish__10"; -"322 hardswish__10" -> "323 adaptive_avg_pool2d_5"; -"322 hardswish__10" -> "332 mul_5"; -"323 adaptive_avg_pool2d_5" -> "326 conv2d_33"; -"324 _param_constant89" -> "326 conv2d_33"; -"325 _param_constant90" -> "326 conv2d_33"; -"326 conv2d_33" -> "327 relu_5"; -"327 relu_5" -> "330 conv2d_34"; -"328 _param_constant91" -> "330 conv2d_34"; -"329 _param_constant92" -> "330 conv2d_34"; -"330 conv2d_34" -> "331 hardsigmoid_5"; -"331 hardsigmoid_5" -> "332 mul_5"; -"332 mul_5" -> "334 conv2d_35"; -"333 _param_constant93" -> "334 conv2d_35"; -"334 conv2d_35" -> "340 _native_batch_norm_legit_no_training_23"; -"336 _param_constant94" -> "340 _native_batch_norm_legit_no_training_23"; -"337 _param_constant95" -> "340 _native_batch_norm_legit_no_training_23"; -"338 _tensor_constant46" -> "340 _native_batch_norm_legit_no_training_23"; -"339 _tensor_constant47" -> "340 _native_batch_norm_legit_no_training_23"; -"340 _native_batch_norm_legit_no_training_23" -> "341 getitem_69"; -"340 _native_batch_norm_legit_no_training_23" -> "342 getitem_70"; -"340 _native_batch_norm_legit_no_training_23" -> "343 getitem_71"; -"341 getitem_69" -> "344 add__3"; -"344 add__3" -> "346 conv2d_36"; -"345 _param_constant96" -> "346 conv2d_36"; -"346 conv2d_36" -> "352 _native_batch_norm_legit_no_training_24"; -"348 _param_constant97" -> "352 _native_batch_norm_legit_no_training_24"; -"349 _param_constant98" -> "352 _native_batch_norm_legit_no_training_24"; -"350 _tensor_constant48" -> "352 _native_batch_norm_legit_no_training_24"; -"351 _tensor_constant49" -> "352 _native_batch_norm_legit_no_training_24"; -"352 _native_batch_norm_legit_no_training_24" -> "353 getitem_72"; -"352 _native_batch_norm_legit_no_training_24" -> "354 getitem_73"; -"352 _native_batch_norm_legit_no_training_24" -> "355 getitem_74"; -"353 getitem_72" -> "356 hardswish__11"; -"356 hardswish__11" -> "358 conv2d_37"; -"357 _param_constant99" -> "358 conv2d_37"; -"358 conv2d_37" -> "364 _native_batch_norm_legit_no_training_25"; -"360 _param_constant100" -> "364 _native_batch_norm_legit_no_training_25"; -"361 _param_constant101" -> "364 _native_batch_norm_legit_no_training_25"; -"362 _tensor_constant50" -> "364 _native_batch_norm_legit_no_training_25"; -"363 _tensor_constant51" -> "364 _native_batch_norm_legit_no_training_25"; -"364 _native_batch_norm_legit_no_training_25" -> "365 getitem_75"; -"364 _native_batch_norm_legit_no_training_25" -> "366 getitem_76"; -"364 _native_batch_norm_legit_no_training_25" -> "367 getitem_77"; -"365 getitem_75" -> "368 hardswish__12"; -"368 hardswish__12" -> "369 adaptive_avg_pool2d_6"; -"368 hardswish__12" -> "378 mul_6"; -"369 adaptive_avg_pool2d_6" -> "372 conv2d_38"; -"370 _param_constant102" -> "372 conv2d_38"; -"371 _param_constant103" -> "372 conv2d_38"; -"372 conv2d_38" -> "373 relu_6"; -"373 relu_6" -> "376 conv2d_39"; -"374 _param_constant104" -> "376 conv2d_39"; -"375 _param_constant105" -> "376 conv2d_39"; -"376 conv2d_39" -> "377 hardsigmoid_6"; -"377 hardsigmoid_6" -> "378 mul_6"; -"378 mul_6" -> "380 conv2d_40"; -"379 _param_constant106" -> "380 conv2d_40"; -"380 conv2d_40" -> "386 _native_batch_norm_legit_no_training_26"; -"382 _param_constant107" -> "386 _native_batch_norm_legit_no_training_26"; -"383 _param_constant108" -> "386 _native_batch_norm_legit_no_training_26"; -"384 _tensor_constant52" -> "386 _native_batch_norm_legit_no_training_26"; -"385 _tensor_constant53" -> "386 _native_batch_norm_legit_no_training_26"; -"386 _native_batch_norm_legit_no_training_26" -> "387 getitem_78"; -"386 _native_batch_norm_legit_no_training_26" -> "388 getitem_79"; -"386 _native_batch_norm_legit_no_training_26" -> "389 getitem_80"; -"387 getitem_78" -> "391 conv2d_41"; -"387 getitem_78" -> "435 add__4"; -"390 _param_constant109" -> "391 conv2d_41"; -"391 conv2d_41" -> "397 _native_batch_norm_legit_no_training_27"; -"393 _param_constant110" -> "397 _native_batch_norm_legit_no_training_27"; -"394 _param_constant111" -> "397 _native_batch_norm_legit_no_training_27"; -"395 _tensor_constant54" -> "397 _native_batch_norm_legit_no_training_27"; -"396 _tensor_constant55" -> "397 _native_batch_norm_legit_no_training_27"; -"397 _native_batch_norm_legit_no_training_27" -> "398 getitem_81"; -"397 _native_batch_norm_legit_no_training_27" -> "399 getitem_82"; -"397 _native_batch_norm_legit_no_training_27" -> "400 getitem_83"; -"398 getitem_81" -> "401 hardswish__13"; -"401 hardswish__13" -> "403 conv2d_42"; -"402 _param_constant112" -> "403 conv2d_42"; -"403 conv2d_42" -> "409 _native_batch_norm_legit_no_training_28"; -"405 _param_constant113" -> "409 _native_batch_norm_legit_no_training_28"; -"406 _param_constant114" -> "409 _native_batch_norm_legit_no_training_28"; -"407 _tensor_constant56" -> "409 _native_batch_norm_legit_no_training_28"; -"408 _tensor_constant57" -> "409 _native_batch_norm_legit_no_training_28"; -"409 _native_batch_norm_legit_no_training_28" -> "410 getitem_84"; -"409 _native_batch_norm_legit_no_training_28" -> "411 getitem_85"; -"409 _native_batch_norm_legit_no_training_28" -> "412 getitem_86"; -"410 getitem_84" -> "413 hardswish__14"; -"413 hardswish__14" -> "414 adaptive_avg_pool2d_7"; -"413 hardswish__14" -> "423 mul_7"; -"414 adaptive_avg_pool2d_7" -> "417 conv2d_43"; -"415 _param_constant115" -> "417 conv2d_43"; -"416 _param_constant116" -> "417 conv2d_43"; -"417 conv2d_43" -> "418 relu_7"; -"418 relu_7" -> "421 conv2d_44"; -"419 _param_constant117" -> "421 conv2d_44"; -"420 _param_constant118" -> "421 conv2d_44"; -"421 conv2d_44" -> "422 hardsigmoid_7"; -"422 hardsigmoid_7" -> "423 mul_7"; -"423 mul_7" -> "425 conv2d_45"; -"424 _param_constant119" -> "425 conv2d_45"; -"425 conv2d_45" -> "431 _native_batch_norm_legit_no_training_29"; -"427 _param_constant120" -> "431 _native_batch_norm_legit_no_training_29"; -"428 _param_constant121" -> "431 _native_batch_norm_legit_no_training_29"; -"429 _tensor_constant58" -> "431 _native_batch_norm_legit_no_training_29"; -"430 _tensor_constant59" -> "431 _native_batch_norm_legit_no_training_29"; -"431 _native_batch_norm_legit_no_training_29" -> "432 getitem_87"; -"431 _native_batch_norm_legit_no_training_29" -> "433 getitem_88"; -"431 _native_batch_norm_legit_no_training_29" -> "434 getitem_89"; -"432 getitem_87" -> "435 add__4"; -"435 add__4" -> "437 conv2d_46"; -"435 add__4" -> "481 add__5"; -"436 _param_constant122" -> "437 conv2d_46"; -"437 conv2d_46" -> "443 _native_batch_norm_legit_no_training_30"; -"439 _param_constant123" -> "443 _native_batch_norm_legit_no_training_30"; -"440 _param_constant124" -> "443 _native_batch_norm_legit_no_training_30"; -"441 _tensor_constant60" -> "443 _native_batch_norm_legit_no_training_30"; -"442 _tensor_constant61" -> "443 _native_batch_norm_legit_no_training_30"; -"443 _native_batch_norm_legit_no_training_30" -> "444 getitem_90"; -"443 _native_batch_norm_legit_no_training_30" -> "445 getitem_91"; -"443 _native_batch_norm_legit_no_training_30" -> "446 getitem_92"; -"444 getitem_90" -> "447 hardswish__15"; -"447 hardswish__15" -> "449 conv2d_47"; -"448 _param_constant125" -> "449 conv2d_47"; -"449 conv2d_47" -> "455 _native_batch_norm_legit_no_training_31"; -"451 _param_constant126" -> "455 _native_batch_norm_legit_no_training_31"; -"452 _param_constant127" -> "455 _native_batch_norm_legit_no_training_31"; -"453 _tensor_constant62" -> "455 _native_batch_norm_legit_no_training_31"; -"454 _tensor_constant63" -> "455 _native_batch_norm_legit_no_training_31"; -"455 _native_batch_norm_legit_no_training_31" -> "456 getitem_93"; -"455 _native_batch_norm_legit_no_training_31" -> "457 getitem_94"; -"455 _native_batch_norm_legit_no_training_31" -> "458 getitem_95"; -"456 getitem_93" -> "459 hardswish__16"; -"459 hardswish__16" -> "460 adaptive_avg_pool2d_8"; -"459 hardswish__16" -> "469 mul_8"; -"460 adaptive_avg_pool2d_8" -> "463 conv2d_48"; -"461 _param_constant128" -> "463 conv2d_48"; -"462 _param_constant129" -> "463 conv2d_48"; -"463 conv2d_48" -> "464 relu_8"; -"464 relu_8" -> "467 conv2d_49"; -"465 _param_constant130" -> "467 conv2d_49"; -"466 _param_constant131" -> "467 conv2d_49"; -"467 conv2d_49" -> "468 hardsigmoid_8"; -"468 hardsigmoid_8" -> "469 mul_8"; -"469 mul_8" -> "471 conv2d_50"; -"470 _param_constant132" -> "471 conv2d_50"; -"471 conv2d_50" -> "477 _native_batch_norm_legit_no_training_32"; -"473 _param_constant133" -> "477 _native_batch_norm_legit_no_training_32"; -"474 _param_constant134" -> "477 _native_batch_norm_legit_no_training_32"; -"475 _tensor_constant64" -> "477 _native_batch_norm_legit_no_training_32"; -"476 _tensor_constant65" -> "477 _native_batch_norm_legit_no_training_32"; -"477 _native_batch_norm_legit_no_training_32" -> "478 getitem_96"; -"477 _native_batch_norm_legit_no_training_32" -> "479 getitem_97"; -"477 _native_batch_norm_legit_no_training_32" -> "480 getitem_98"; -"478 getitem_96" -> "481 add__5"; -"481 add__5" -> "483 conv2d_51"; -"482 _param_constant135" -> "483 conv2d_51"; -"483 conv2d_51" -> "489 _native_batch_norm_legit_no_training_33"; -"485 _param_constant136" -> "489 _native_batch_norm_legit_no_training_33"; -"486 _param_constant137" -> "489 _native_batch_norm_legit_no_training_33"; -"487 _tensor_constant66" -> "489 _native_batch_norm_legit_no_training_33"; -"488 _tensor_constant67" -> "489 _native_batch_norm_legit_no_training_33"; -"489 _native_batch_norm_legit_no_training_33" -> "490 getitem_99"; -"489 _native_batch_norm_legit_no_training_33" -> "491 getitem_100"; -"489 _native_batch_norm_legit_no_training_33" -> "492 getitem_101"; -"490 getitem_99" -> "493 hardswish__17"; -"493 hardswish__17" -> "494 adaptive_avg_pool2d_9"; -"494 adaptive_avg_pool2d_9" -> "495 flatten"; -"495 flatten" -> "498 linear"; -"496 _param_constant138" -> "498 linear"; -"497 _param_constant139" -> "498 linear"; -"498 linear" -> "499 hardswish__18"; -"499 hardswish__18" -> "500 dropout_"; -"500 dropout_" -> "503 linear_1"; -"501 _param_constant140" -> "503 linear_1"; -"502 _param_constant141" -> "503 linear_1"; -"503 linear_1" -> "504 output"; +"0 features_0_0_weight" [id=0, type=get_attr]; +"1 features_0_1_weight" [id=1, type=get_attr]; +"2 features_0_1_bias" [id=2, type=get_attr]; +"3 features_1_block_0_0_weight" [id=3, type=get_attr]; +"4 features_1_block_0_1_weight" [id=4, type=get_attr]; +"5 features_1_block_0_1_bias" [id=5, type=get_attr]; +"6 features_1_block_1_fc1_weight" [id=6, type=get_attr]; +"7 features_1_block_1_fc1_bias" [id=7, 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batch_norm_29" [label="(96,)", style=solid]; +"230 features_10_block_3_1_running_var" -> "376 batch_norm_29" [label="(96,)", style=solid]; +"232 features_11_block_0_1_running_mean" -> "379 batch_norm_30" [label="(576,)", style=solid]; +"233 features_11_block_0_1_running_var" -> "379 batch_norm_30" [label="(576,)", style=solid]; +"235 features_11_block_1_1_running_mean" -> "382 batch_norm_31" [label="(576,)", style=solid]; +"236 features_11_block_1_1_running_var" -> "382 batch_norm_31" [label="(576,)", style=solid]; +"238 features_11_block_3_1_running_mean" -> "391 batch_norm_32" [label="(96,)", style=solid]; +"239 features_11_block_3_1_running_var" -> "391 batch_norm_32" [label="(96,)", style=solid]; +"241 features_12_1_running_mean" -> "394 batch_norm_33" [label="(576,)", style=solid]; +"242 features_12_1_running_var" -> "394 batch_norm_33" [label="(576,)", style=solid]; +"244 x" -> "245 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"245 conv2d" -> "246 batch_norm" [label="(1, 16, 112, 112)", style=solid]; +"246 batch_norm" -> "247 hardswish_" [label="(1, 16, 112, 112)", style=solid]; +"247 hardswish_" -> "248 conv2d_1" [label="(1, 16, 112, 112)", style=solid]; +"248 conv2d_1" -> "249 batch_norm_1" [label="(1, 16, 56, 56)", style=solid]; +"249 batch_norm_1" -> "250 relu_" [label="(1, 16, 56, 56)", style=solid]; +"250 relu_" -> "251 adaptive_avg_pool2d" [label="(1, 16, 56, 56)", style=solid]; +"250 relu_" -> "256 mul" [label="(1, 16, 56, 56)", style=solid]; +"251 adaptive_avg_pool2d" -> "252 conv2d_2" [label="(1, 16, 1, 1)", style=solid]; +"252 conv2d_2" -> "253 relu" [label="(1, 8, 1, 1)", style=solid]; +"253 relu" -> "254 conv2d_3" [label="(1, 8, 1, 1)", style=solid]; +"254 conv2d_3" -> "255 hardsigmoid" [label="(1, 16, 1, 1)", style=solid]; +"255 hardsigmoid" -> "256 mul" [label="(1, 16, 1, 1)", style=solid]; +"256 mul" -> "257 conv2d_4" [label="(1, 16, 56, 56)", style=solid]; +"257 conv2d_4" -> "258 batch_norm_2" [label="(1, 16, 56, 56)", style=solid]; +"258 batch_norm_2" -> "259 conv2d_5" [label="(1, 16, 56, 56)", style=solid]; +"259 conv2d_5" -> "260 batch_norm_3" [label="(1, 72, 56, 56)", style=solid]; +"260 batch_norm_3" -> "261 relu__1" [label="(1, 72, 56, 56)", style=solid]; +"261 relu__1" -> "262 conv2d_6" [label="(1, 72, 56, 56)", style=solid]; +"262 conv2d_6" -> "263 batch_norm_4" [label="(1, 72, 28, 28)", style=solid]; +"263 batch_norm_4" -> "264 relu__2" [label="(1, 72, 28, 28)", style=solid]; +"264 relu__2" -> "265 conv2d_7" [label="(1, 72, 28, 28)", style=solid]; +"265 conv2d_7" -> "266 batch_norm_5" [label="(1, 24, 28, 28)", style=solid]; +"266 batch_norm_5" -> "267 conv2d_8" [label="(1, 24, 28, 28)", style=solid]; +"266 batch_norm_5" -> "275 add_" [label="(1, 24, 28, 28)", style=solid]; +"267 conv2d_8" -> "268 batch_norm_6" [label="(1, 88, 28, 28)", style=solid]; +"268 batch_norm_6" -> "269 relu__3" [label="(1, 88, 28, 28)", style=solid]; +"269 relu__3" -> "270 conv2d_9" [label="(1, 88, 28, 28)", style=solid]; +"270 conv2d_9" -> "271 batch_norm_7" [label="(1, 88, 28, 28)", style=solid]; +"271 batch_norm_7" -> "272 relu__4" [label="(1, 88, 28, 28)", style=solid]; +"272 relu__4" -> "273 conv2d_10" [label="(1, 88, 28, 28)", style=solid]; +"273 conv2d_10" -> "274 batch_norm_8" [label="(1, 24, 28, 28)", style=solid]; +"274 batch_norm_8" -> "275 add_" [label="(1, 24, 28, 28)", style=solid]; +"275 add_" -> "276 conv2d_11" [label="(1, 24, 28, 28)", style=solid]; +"276 conv2d_11" -> "277 batch_norm_9" [label="(1, 96, 28, 28)", style=solid]; +"277 batch_norm_9" -> "278 hardswish__1" [label="(1, 96, 28, 28)", style=solid]; +"278 hardswish__1" -> "279 conv2d_12" [label="(1, 96, 28, 28)", style=solid]; +"279 conv2d_12" -> "280 batch_norm_10" [label="(1, 96, 14, 14)", style=solid]; +"280 batch_norm_10" -> "281 hardswish__2" [label="(1, 96, 14, 14)", style=solid]; +"281 hardswish__2" -> "282 adaptive_avg_pool2d_1" [label="(1, 96, 14, 14)", style=solid]; +"281 hardswish__2" -> "287 mul_1" [label="(1, 96, 14, 14)", style=solid]; +"282 adaptive_avg_pool2d_1" -> "283 conv2d_13" [label="(1, 96, 1, 1)", style=solid]; +"283 conv2d_13" -> "284 relu_1" [label="(1, 24, 1, 1)", style=solid]; +"284 relu_1" -> "285 conv2d_14" [label="(1, 24, 1, 1)", style=solid]; +"285 conv2d_14" -> "286 hardsigmoid_1" [label="(1, 96, 1, 1)", style=solid]; +"286 hardsigmoid_1" -> "287 mul_1" [label="(1, 96, 1, 1)", style=solid]; +"287 mul_1" -> "288 conv2d_15" [label="(1, 96, 14, 14)", style=solid]; +"288 conv2d_15" -> "289 batch_norm_11" [label="(1, 40, 14, 14)", style=solid]; +"289 batch_norm_11" -> "290 conv2d_16" [label="(1, 40, 14, 14)", style=solid]; +"289 batch_norm_11" -> "304 add__1" [label="(1, 40, 14, 14)", style=solid]; +"290 conv2d_16" -> "291 batch_norm_12" [label="(1, 240, 14, 14)", style=solid]; +"291 batch_norm_12" -> "292 hardswish__3" [label="(1, 240, 14, 14)", style=solid]; +"292 hardswish__3" -> "293 conv2d_17" [label="(1, 240, 14, 14)", style=solid]; +"293 conv2d_17" -> "294 batch_norm_13" [label="(1, 240, 14, 14)", style=solid]; +"294 batch_norm_13" -> "295 hardswish__4" [label="(1, 240, 14, 14)", style=solid]; +"295 hardswish__4" -> "296 adaptive_avg_pool2d_2" [label="(1, 240, 14, 14)", style=solid]; +"295 hardswish__4" -> "301 mul_2" [label="(1, 240, 14, 14)", style=solid]; +"296 adaptive_avg_pool2d_2" -> "297 conv2d_18" [label="(1, 240, 1, 1)", style=solid]; +"297 conv2d_18" -> "298 relu_2" [label="(1, 64, 1, 1)", style=solid]; +"298 relu_2" -> "299 conv2d_19" [label="(1, 64, 1, 1)", style=solid]; +"299 conv2d_19" -> "300 hardsigmoid_2" [label="(1, 240, 1, 1)", style=solid]; +"300 hardsigmoid_2" -> "301 mul_2" [label="(1, 240, 1, 1)", style=solid]; +"301 mul_2" -> "302 conv2d_20" [label="(1, 240, 14, 14)", style=solid]; +"302 conv2d_20" -> "303 batch_norm_14" [label="(1, 40, 14, 14)", style=solid]; +"303 batch_norm_14" -> "304 add__1" [label="(1, 40, 14, 14)", style=solid]; +"304 add__1" -> "305 conv2d_21" [label="(1, 40, 14, 14)", style=solid]; +"304 add__1" -> "319 add__2" [label="(1, 40, 14, 14)", style=solid]; +"305 conv2d_21" -> "306 batch_norm_15" [label="(1, 240, 14, 14)", style=solid]; +"306 batch_norm_15" -> "307 hardswish__5" [label="(1, 240, 14, 14)", style=solid]; +"307 hardswish__5" -> "308 conv2d_22" [label="(1, 240, 14, 14)", style=solid]; +"308 conv2d_22" -> "309 batch_norm_16" [label="(1, 240, 14, 14)", style=solid]; +"309 batch_norm_16" -> "310 hardswish__6" [label="(1, 240, 14, 14)", style=solid]; +"310 hardswish__6" -> "311 adaptive_avg_pool2d_3" [label="(1, 240, 14, 14)", style=solid]; +"310 hardswish__6" -> "316 mul_3" [label="(1, 240, 14, 14)", style=solid]; +"311 adaptive_avg_pool2d_3" -> "312 conv2d_23" [label="(1, 240, 1, 1)", style=solid]; +"312 conv2d_23" -> "313 relu_3" [label="(1, 64, 1, 1)", style=solid]; +"313 relu_3" -> "314 conv2d_24" [label="(1, 64, 1, 1)", style=solid]; +"314 conv2d_24" -> "315 hardsigmoid_3" [label="(1, 240, 1, 1)", style=solid]; +"315 hardsigmoid_3" -> "316 mul_3" [label="(1, 240, 1, 1)", style=solid]; +"316 mul_3" -> "317 conv2d_25" [label="(1, 240, 14, 14)", style=solid]; +"317 conv2d_25" -> "318 batch_norm_17" [label="(1, 40, 14, 14)", style=solid]; +"318 batch_norm_17" -> "319 add__2" [label="(1, 40, 14, 14)", style=solid]; +"319 add__2" -> "320 conv2d_26" [label="(1, 40, 14, 14)", style=solid]; +"320 conv2d_26" -> "321 batch_norm_18" [label="(1, 120, 14, 14)", style=solid]; +"321 batch_norm_18" -> "322 hardswish__7" [label="(1, 120, 14, 14)", style=solid]; +"322 hardswish__7" -> "323 conv2d_27" [label="(1, 120, 14, 14)", style=solid]; +"323 conv2d_27" -> "324 batch_norm_19" [label="(1, 120, 14, 14)", style=solid]; +"324 batch_norm_19" -> "325 hardswish__8" [label="(1, 120, 14, 14)", style=solid]; +"325 hardswish__8" -> "326 adaptive_avg_pool2d_4" [label="(1, 120, 14, 14)", style=solid]; +"325 hardswish__8" -> "331 mul_4" [label="(1, 120, 14, 14)", style=solid]; +"326 adaptive_avg_pool2d_4" -> "327 conv2d_28" [label="(1, 120, 1, 1)", style=solid]; +"327 conv2d_28" -> "328 relu_4" [label="(1, 32, 1, 1)", style=solid]; +"328 relu_4" -> "329 conv2d_29" [label="(1, 32, 1, 1)", style=solid]; +"329 conv2d_29" -> "330 hardsigmoid_4" [label="(1, 120, 1, 1)", style=solid]; +"330 hardsigmoid_4" -> "331 mul_4" [label="(1, 120, 1, 1)", style=solid]; +"331 mul_4" -> "332 conv2d_30" [label="(1, 120, 14, 14)", style=solid]; +"332 conv2d_30" -> "333 batch_norm_20" [label="(1, 48, 14, 14)", style=solid]; +"333 batch_norm_20" -> "334 conv2d_31" [label="(1, 48, 14, 14)", style=solid]; +"333 batch_norm_20" -> "348 add__3" [label="(1, 48, 14, 14)", style=solid]; +"334 conv2d_31" -> "335 batch_norm_21" [label="(1, 144, 14, 14)", style=solid]; +"335 batch_norm_21" -> "336 hardswish__9" [label="(1, 144, 14, 14)", style=solid]; +"336 hardswish__9" -> "337 conv2d_32" [label="(1, 144, 14, 14)", style=solid]; +"337 conv2d_32" -> "338 batch_norm_22" [label="(1, 144, 14, 14)", style=solid]; +"338 batch_norm_22" -> "339 hardswish__10" [label="(1, 144, 14, 14)", style=solid]; +"339 hardswish__10" -> "340 adaptive_avg_pool2d_5" [label="(1, 144, 14, 14)", style=solid]; +"339 hardswish__10" -> "345 mul_5" [label="(1, 144, 14, 14)", style=solid]; +"340 adaptive_avg_pool2d_5" -> "341 conv2d_33" [label="(1, 144, 1, 1)", style=solid]; +"341 conv2d_33" -> "342 relu_5" [label="(1, 40, 1, 1)", style=solid]; +"342 relu_5" -> "343 conv2d_34" [label="(1, 40, 1, 1)", style=solid]; +"343 conv2d_34" -> "344 hardsigmoid_5" [label="(1, 144, 1, 1)", style=solid]; +"344 hardsigmoid_5" -> "345 mul_5" [label="(1, 144, 1, 1)", style=solid]; +"345 mul_5" -> "346 conv2d_35" [label="(1, 144, 14, 14)", style=solid]; +"346 conv2d_35" -> "347 batch_norm_23" [label="(1, 48, 14, 14)", style=solid]; +"347 batch_norm_23" -> "348 add__3" [label="(1, 48, 14, 14)", style=solid]; +"348 add__3" -> "349 conv2d_36" [label="(1, 48, 14, 14)", style=solid]; +"349 conv2d_36" -> "350 batch_norm_24" [label="(1, 288, 14, 14)", style=solid]; +"350 batch_norm_24" -> "351 hardswish__11" [label="(1, 288, 14, 14)", style=solid]; +"351 hardswish__11" -> "352 conv2d_37" [label="(1, 288, 14, 14)", style=solid]; +"352 conv2d_37" -> "353 batch_norm_25" [label="(1, 288, 7, 7)", style=solid]; +"353 batch_norm_25" -> "354 hardswish__12" [label="(1, 288, 7, 7)", style=solid]; +"354 hardswish__12" -> "355 adaptive_avg_pool2d_6" [label="(1, 288, 7, 7)", style=solid]; +"354 hardswish__12" -> "360 mul_6" [label="(1, 288, 7, 7)", style=solid]; +"355 adaptive_avg_pool2d_6" -> "356 conv2d_38" [label="(1, 288, 1, 1)", style=solid]; +"356 conv2d_38" -> "357 relu_6" [label="(1, 72, 1, 1)", style=solid]; +"357 relu_6" -> "358 conv2d_39" [label="(1, 72, 1, 1)", style=solid]; +"358 conv2d_39" -> "359 hardsigmoid_6" [label="(1, 288, 1, 1)", style=solid]; +"359 hardsigmoid_6" -> "360 mul_6" [label="(1, 288, 1, 1)", style=solid]; +"360 mul_6" -> "361 conv2d_40" [label="(1, 288, 7, 7)", style=solid]; +"361 conv2d_40" -> "362 batch_norm_26" [label="(1, 96, 7, 7)", style=solid]; +"362 batch_norm_26" -> "363 conv2d_41" [label="(1, 96, 7, 7)", style=solid]; +"362 batch_norm_26" -> "377 add__4" [label="(1, 96, 7, 7)", style=solid]; +"363 conv2d_41" -> "364 batch_norm_27" [label="(1, 576, 7, 7)", style=solid]; +"364 batch_norm_27" -> "365 hardswish__13" [label="(1, 576, 7, 7)", style=solid]; +"365 hardswish__13" -> "366 conv2d_42" [label="(1, 576, 7, 7)", style=solid]; +"366 conv2d_42" -> "367 batch_norm_28" [label="(1, 576, 7, 7)", style=solid]; +"367 batch_norm_28" -> "368 hardswish__14" [label="(1, 576, 7, 7)", style=solid]; +"368 hardswish__14" -> "369 adaptive_avg_pool2d_7" [label="(1, 576, 7, 7)", style=solid]; +"368 hardswish__14" -> "374 mul_7" [label="(1, 576, 7, 7)", style=solid]; +"369 adaptive_avg_pool2d_7" -> "370 conv2d_43" [label="(1, 576, 1, 1)", style=solid]; +"370 conv2d_43" -> "371 relu_7" [label="(1, 144, 1, 1)", style=solid]; +"371 relu_7" -> "372 conv2d_44" [label="(1, 144, 1, 1)", style=solid]; +"372 conv2d_44" -> "373 hardsigmoid_7" [label="(1, 576, 1, 1)", style=solid]; +"373 hardsigmoid_7" -> "374 mul_7" [label="(1, 576, 1, 1)", style=solid]; +"374 mul_7" -> "375 conv2d_45" [label="(1, 576, 7, 7)", style=solid]; +"375 conv2d_45" -> "376 batch_norm_29" [label="(1, 96, 7, 7)", style=solid]; +"376 batch_norm_29" -> "377 add__4" [label="(1, 96, 7, 7)", style=solid]; +"377 add__4" -> "378 conv2d_46" [label="(1, 96, 7, 7)", style=solid]; +"377 add__4" -> "392 add__5" [label="(1, 96, 7, 7)", style=solid]; +"378 conv2d_46" -> "379 batch_norm_30" [label="(1, 576, 7, 7)", style=solid]; +"379 batch_norm_30" -> "380 hardswish__15" [label="(1, 576, 7, 7)", style=solid]; +"380 hardswish__15" -> "381 conv2d_47" [label="(1, 576, 7, 7)", style=solid]; +"381 conv2d_47" -> "382 batch_norm_31" [label="(1, 576, 7, 7)", style=solid]; +"382 batch_norm_31" -> "383 hardswish__16" [label="(1, 576, 7, 7)", style=solid]; +"383 hardswish__16" -> "384 adaptive_avg_pool2d_8" [label="(1, 576, 7, 7)", style=solid]; +"383 hardswish__16" -> "389 mul_8" [label="(1, 576, 7, 7)", style=solid]; +"384 adaptive_avg_pool2d_8" -> "385 conv2d_48" [label="(1, 576, 1, 1)", style=solid]; +"385 conv2d_48" -> "386 relu_8" [label="(1, 144, 1, 1)", style=solid]; +"386 relu_8" -> "387 conv2d_49" [label="(1, 144, 1, 1)", style=solid]; +"387 conv2d_49" -> "388 hardsigmoid_8" [label="(1, 576, 1, 1)", style=solid]; +"388 hardsigmoid_8" -> "389 mul_8" [label="(1, 576, 1, 1)", style=solid]; +"389 mul_8" -> "390 conv2d_50" [label="(1, 576, 7, 7)", style=solid]; +"390 conv2d_50" -> "391 batch_norm_32" [label="(1, 96, 7, 7)", style=solid]; +"391 batch_norm_32" -> "392 add__5" [label="(1, 96, 7, 7)", style=solid]; +"392 add__5" -> "393 conv2d_51" [label="(1, 96, 7, 7)", style=solid]; +"393 conv2d_51" -> "394 batch_norm_33" [label="(1, 576, 7, 7)", style=solid]; +"394 batch_norm_33" -> "395 hardswish__17" [label="(1, 576, 7, 7)", style=solid]; +"395 hardswish__17" -> "396 adaptive_avg_pool2d_9" [label="(1, 576, 7, 7)", style=solid]; +"396 adaptive_avg_pool2d_9" -> "397 flatten" [label="(1, 576, 1, 1)", style=solid]; +"397 flatten" -> "398 linear" [label="(1, 576)", style=solid]; +"398 linear" -> "399 hardswish__18" [label="(1, 1024)", style=solid]; +"399 hardswish__18" -> "400 dropout_" [label="(1, 1024)", style=solid]; +"400 dropout_" -> "401 linear_1" [label="(1, 1024)", style=solid]; +"401 linear_1" -> "402 output_1" [label="(1, 1000)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/post_quantization_compressed/mobilenet_v3_small.dot b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/mobilenet_v3_small.dot new file mode 100644 index 00000000000..47c6390f606 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/mobilenet_v3_small.dot @@ -0,0 +1,1110 @@ +strict digraph { +"0 features_1_block_1_fc1_weight" [id=0, type=get_attr]; +"1 features_1_block_1_fc2_weight" [id=1, type=get_attr]; +"2 features_4_block_2_fc1_weight" [id=2, type=get_attr]; +"3 features_4_block_2_fc2_weight" [id=3, type=get_attr]; +"4 features_5_block_2_fc1_weight" [id=4, type=get_attr]; +"5 features_5_block_2_fc2_weight" [id=5, type=get_attr]; +"6 features_6_block_2_fc1_weight" [id=6, type=get_attr]; +"7 features_6_block_2_fc2_weight" [id=7, type=get_attr]; +"8 features_7_block_2_fc1_weight" [id=8, type=get_attr]; +"9 features_7_block_2_fc2_weight" [id=9, type=get_attr]; +"10 features_8_block_2_fc1_weight" [id=10, type=get_attr]; +"11 features_8_block_2_fc2_weight" [id=11, type=get_attr]; +"12 features_9_block_2_fc1_weight" [id=12, type=get_attr]; +"13 features_9_block_2_fc2_weight" [id=13, type=get_attr]; +"14 features_10_block_2_fc1_weight" [id=14, type=get_attr]; +"15 features_10_block_2_fc2_weight" [id=15, type=get_attr]; +"16 features_11_block_2_fc1_weight" [id=16, type=get_attr]; +"17 features_11_block_2_fc2_weight" [id=17, type=get_attr]; +"18 x" [id=18, type=input]; +"19 quantize_per_tensor_default" [id=19, type=quantize_per_tensor]; +"20 dequantize_per_tensor_default" [id=20, type=dequantize_per_tensor]; +"21 features_0_0_weight_bias_0_0" [id=21, type=get_attr]; +"22 scale_updated_constant0" [id=22, type=get_attr]; +"23 compressed_weight_updated_constant0" [id=23, type=get_attr]; +"24 mul_tensor" [id=24, type=mul]; +"25 zero_point_updated_constant0" [id=25, type=get_attr]; +"26 sub_tensor" [id=26, type=sub]; +"27 conv2d" [id=27, type=conv2d]; +"28 hardswish__scale_0" [id=28, type=get_attr]; +"29 hardswish__zero_point_0" [id=29, type=get_attr]; +"30 hardswish_" [id=30, type=hardswish_]; +"31 quantize_per_channel_default_1" [id=31, type=quantize_per_channel]; +"32 dequantize_per_channel_default_1" [id=32, type=dequantize_per_channel]; +"33 features_1_block_0_0_weight_bias_0_0" [id=33, type=get_attr]; +"34 scale_updated_constant1" [id=34, type=get_attr]; +"35 compressed_weight_updated_constant1" [id=35, type=get_attr]; +"36 mul_tensor_1" [id=36, type=mul]; +"37 zero_point_updated_constant1" [id=37, type=get_attr]; +"38 sub_tensor_1" [id=38, type=sub]; +"39 conv2d_1" [id=39, type=conv2d]; +"40 relu_" [id=40, type=relu_]; +"41 quantize_per_tensor_default_1" [id=41, type=quantize_per_tensor]; +"42 dequantize_per_tensor_default_2" [id=42, type=dequantize_per_tensor]; +"43 dequantize_per_tensor_default_1" [id=43, type=dequantize_per_tensor]; +"44 adaptive_avg_pool2d" [id=44, type=adaptive_avg_pool2d]; +"45 features_1_block_1_fc1_bias_0_0" [id=45, type=get_attr]; +"46 conv2d_2" [id=46, type=conv2d]; +"47 relu" [id=47, type=relu]; +"48 features_1_block_1_fc2_bias_0_0" [id=48, type=get_attr]; +"49 conv2d_3" [id=49, type=conv2d]; +"50 hardsigmoid" [id=50, type=hardsigmoid]; +"51 quantize_per_tensor_default_2" [id=51, type=quantize_per_tensor]; +"52 dequantize_per_tensor_default_3" [id=52, type=dequantize_per_tensor]; +"53 mul" [id=53, type=mul]; +"54 quantize_per_tensor_default_3" [id=54, type=quantize_per_tensor]; +"55 dequantize_per_tensor_default_4" [id=55, type=dequantize_per_tensor]; +"56 features_1_block_2_0_weight_bias_0_0" [id=56, type=get_attr]; +"57 scale_updated_constant2" [id=57, type=get_attr]; +"58 compressed_weight_updated_constant2" [id=58, type=get_attr]; +"59 mul_tensor_2" [id=59, type=mul]; +"60 zero_point_updated_constant2" [id=60, type=get_attr]; +"61 sub_tensor_2" [id=61, type=sub]; +"62 conv2d_4" [id=62, type=conv2d]; +"63 quantize_per_tensor_default_4" [id=63, type=quantize_per_tensor]; +"64 dequantize_per_tensor_default_5" [id=64, type=dequantize_per_tensor]; +"65 features_2_block_0_0_weight_bias_0_0" [id=65, type=get_attr]; +"66 scale_updated_constant3" [id=66, type=get_attr]; +"67 compressed_weight_updated_constant3" [id=67, type=get_attr]; +"68 mul_tensor_3" [id=68, type=mul]; +"69 zero_point_updated_constant3" [id=69, type=get_attr]; +"70 sub_tensor_3" [id=70, type=sub]; +"71 conv2d_5" [id=71, type=conv2d]; +"72 relu__1_scale_0" [id=72, type=get_attr]; +"73 relu__1_zero_point_0" [id=73, type=get_attr]; +"74 relu__1" [id=74, type=relu_]; +"75 quantize_per_channel_default_5" [id=75, type=quantize_per_channel]; +"76 dequantize_per_channel_default_5" [id=76, type=dequantize_per_channel]; +"77 features_2_block_1_0_weight_bias_0_0" [id=77, type=get_attr]; +"78 scale_updated_constant4" [id=78, type=get_attr]; +"79 compressed_weight_updated_constant4" [id=79, type=get_attr]; +"80 mul_tensor_4" [id=80, type=mul]; +"81 zero_point_updated_constant4" [id=81, type=get_attr]; +"82 sub_tensor_4" [id=82, type=sub]; +"83 conv2d_6" [id=83, type=conv2d]; +"84 relu__2" [id=84, type=relu_]; +"85 quantize_per_tensor_default_5" [id=85, type=quantize_per_tensor]; +"86 dequantize_per_tensor_default_6" [id=86, type=dequantize_per_tensor]; +"87 features_2_block_2_0_weight_bias_0_0" [id=87, type=get_attr]; +"88 scale_updated_constant5" [id=88, type=get_attr]; +"89 compressed_weight_updated_constant5" [id=89, type=get_attr]; +"90 mul_tensor_5" [id=90, type=mul]; +"91 zero_point_updated_constant5" [id=91, type=get_attr]; +"92 sub_tensor_5" [id=92, type=sub]; +"93 conv2d_7" [id=93, type=conv2d]; +"94 quantize_per_tensor_default_6" [id=94, type=quantize_per_tensor]; +"95 dequantize_per_tensor_default_8" [id=95, type=dequantize_per_tensor]; +"96 dequantize_per_tensor_default_7" [id=96, type=dequantize_per_tensor]; +"97 features_3_block_0_0_weight_bias_0_0" [id=97, type=get_attr]; +"98 scale_updated_constant6" [id=98, type=get_attr]; +"99 compressed_weight_updated_constant6" [id=99, type=get_attr]; +"100 mul_tensor_6" [id=100, type=mul]; +"101 zero_point_updated_constant6" [id=101, type=get_attr]; +"102 sub_tensor_6" [id=102, type=sub]; +"103 conv2d_8" [id=103, type=conv2d]; +"104 relu__3_scale_0" [id=104, type=get_attr]; +"105 relu__3_zero_point_0" [id=105, type=get_attr]; +"106 relu__3" [id=106, type=relu_]; +"107 quantize_per_channel_default_9" [id=107, type=quantize_per_channel]; +"108 dequantize_per_channel_default_9" [id=108, type=dequantize_per_channel]; +"109 features_3_block_1_0_weight_bias_0_0" [id=109, type=get_attr]; +"110 scale_updated_constant7" [id=110, type=get_attr]; +"111 compressed_weight_updated_constant7" [id=111, type=get_attr]; +"112 mul_tensor_7" [id=112, type=mul]; +"113 zero_point_updated_constant7" [id=113, type=get_attr]; +"114 sub_tensor_7" [id=114, type=sub]; +"115 conv2d_9" [id=115, type=conv2d]; +"116 relu__4" [id=116, type=relu_]; +"117 quantize_per_tensor_default_7" [id=117, type=quantize_per_tensor]; +"118 dequantize_per_tensor_default_9" [id=118, type=dequantize_per_tensor]; +"119 features_3_block_2_0_weight_bias_0_0" [id=119, type=get_attr]; +"120 scale_updated_constant8" [id=120, type=get_attr]; +"121 compressed_weight_updated_constant8" [id=121, type=get_attr]; +"122 mul_tensor_8" [id=122, type=mul]; +"123 zero_point_updated_constant8" [id=123, type=get_attr]; +"124 sub_tensor_8" [id=124, type=sub]; +"125 conv2d_10" [id=125, type=conv2d]; +"126 quantize_per_tensor_default_8" [id=126, type=quantize_per_tensor]; +"127 dequantize_per_tensor_default_10" [id=127, type=dequantize_per_tensor]; +"128 add_" [id=128, type=add_]; +"129 quantize_per_tensor_default_9" [id=129, type=quantize_per_tensor]; +"130 dequantize_per_tensor_default_11" [id=130, type=dequantize_per_tensor]; +"131 features_4_block_0_0_weight_bias_0_0" [id=131, type=get_attr]; +"132 scale_updated_constant9" [id=132, type=get_attr]; +"133 compressed_weight_updated_constant9" [id=133, type=get_attr]; +"134 mul_tensor_9" [id=134, type=mul]; +"135 zero_point_updated_constant9" [id=135, type=get_attr]; +"136 sub_tensor_9" [id=136, type=sub]; +"137 conv2d_11" [id=137, type=conv2d]; +"138 hardswish__1_scale_0" [id=138, type=get_attr]; +"139 hardswish__1_zero_point_0" [id=139, type=get_attr]; +"140 hardswish__1" [id=140, type=hardswish_]; +"141 quantize_per_channel_default_13" [id=141, type=quantize_per_channel]; +"142 dequantize_per_channel_default_13" [id=142, type=dequantize_per_channel]; +"143 features_4_block_1_0_weight_bias_0_0" [id=143, type=get_attr]; +"144 scale_updated_constant10" [id=144, type=get_attr]; +"145 compressed_weight_updated_constant10" [id=145, type=get_attr]; +"146 mul_tensor_10" [id=146, type=mul]; +"147 zero_point_updated_constant10" [id=147, type=get_attr]; +"148 sub_tensor_10" [id=148, type=sub]; +"149 conv2d_12" [id=149, type=conv2d]; +"150 hardswish__2" [id=150, type=hardswish_]; +"151 quantize_per_tensor_default_10" [id=151, type=quantize_per_tensor]; +"152 dequantize_per_tensor_default_13" [id=152, type=dequantize_per_tensor]; +"153 dequantize_per_tensor_default_12" [id=153, type=dequantize_per_tensor]; +"154 adaptive_avg_pool2d_1" [id=154, type=adaptive_avg_pool2d]; +"155 features_4_block_2_fc1_bias_0_0" [id=155, type=get_attr]; +"156 conv2d_13" [id=156, type=conv2d]; +"157 relu_1" [id=157, type=relu]; +"158 features_4_block_2_fc2_bias_0_0" [id=158, type=get_attr]; +"159 conv2d_14" [id=159, type=conv2d]; +"160 hardsigmoid_1" [id=160, type=hardsigmoid]; +"161 quantize_per_tensor_default_11" [id=161, type=quantize_per_tensor]; +"162 dequantize_per_tensor_default_14" [id=162, type=dequantize_per_tensor]; +"163 mul_1" [id=163, type=mul]; +"164 quantize_per_tensor_default_12" [id=164, type=quantize_per_tensor]; +"165 dequantize_per_tensor_default_15" [id=165, type=dequantize_per_tensor]; +"166 features_4_block_3_0_weight_bias_0_0" [id=166, type=get_attr]; +"167 scale_updated_constant11" [id=167, type=get_attr]; +"168 compressed_weight_updated_constant11" [id=168, type=get_attr]; +"169 mul_tensor_11" [id=169, type=mul]; +"170 zero_point_updated_constant11" [id=170, type=get_attr]; +"171 sub_tensor_11" [id=171, type=sub]; +"172 conv2d_15" [id=172, type=conv2d]; +"173 quantize_per_tensor_default_13" [id=173, type=quantize_per_tensor]; +"174 dequantize_per_tensor_default_17" [id=174, type=dequantize_per_tensor]; +"175 dequantize_per_tensor_default_16" [id=175, type=dequantize_per_tensor]; +"176 features_5_block_0_0_weight_bias_0_0" [id=176, type=get_attr]; +"177 scale_updated_constant12" [id=177, type=get_attr]; +"178 compressed_weight_updated_constant12" [id=178, type=get_attr]; +"179 mul_tensor_12" [id=179, type=mul]; +"180 zero_point_updated_constant12" [id=180, type=get_attr]; +"181 sub_tensor_12" [id=181, type=sub]; +"182 conv2d_16" [id=182, type=conv2d]; +"183 hardswish__3_scale_0" [id=183, type=get_attr]; +"184 hardswish__3_zero_point_0" [id=184, type=get_attr]; +"185 hardswish__3" [id=185, type=hardswish_]; +"186 quantize_per_channel_default_17" [id=186, type=quantize_per_channel]; +"187 dequantize_per_channel_default_17" [id=187, type=dequantize_per_channel]; +"188 features_5_block_1_0_weight_bias_0_0" [id=188, type=get_attr]; +"189 scale_updated_constant13" [id=189, type=get_attr]; +"190 compressed_weight_updated_constant13" [id=190, type=get_attr]; +"191 mul_tensor_13" [id=191, type=mul]; +"192 zero_point_updated_constant13" [id=192, type=get_attr]; +"193 sub_tensor_13" [id=193, type=sub]; +"194 conv2d_17" [id=194, type=conv2d]; +"195 hardswish__4" [id=195, type=hardswish_]; +"196 quantize_per_tensor_default_14" [id=196, type=quantize_per_tensor]; +"197 dequantize_per_tensor_default_19" [id=197, type=dequantize_per_tensor]; +"198 dequantize_per_tensor_default_18" [id=198, type=dequantize_per_tensor]; +"199 adaptive_avg_pool2d_2" [id=199, type=adaptive_avg_pool2d]; +"200 features_5_block_2_fc1_bias_0_0" [id=200, type=get_attr]; +"201 conv2d_18" [id=201, type=conv2d]; +"202 relu_2" [id=202, type=relu]; +"203 features_5_block_2_fc2_bias_0_0" [id=203, type=get_attr]; +"204 conv2d_19" [id=204, type=conv2d]; +"205 hardsigmoid_2" [id=205, type=hardsigmoid]; +"206 quantize_per_tensor_default_15" [id=206, type=quantize_per_tensor]; +"207 dequantize_per_tensor_default_20" [id=207, type=dequantize_per_tensor]; +"208 mul_2" [id=208, type=mul]; +"209 quantize_per_tensor_default_16" [id=209, type=quantize_per_tensor]; +"210 dequantize_per_tensor_default_21" [id=210, type=dequantize_per_tensor]; +"211 features_5_block_3_0_weight_bias_0_0" [id=211, type=get_attr]; +"212 scale_updated_constant14" [id=212, type=get_attr]; +"213 compressed_weight_updated_constant14" [id=213, type=get_attr]; +"214 mul_tensor_14" [id=214, type=mul]; +"215 zero_point_updated_constant14" [id=215, type=get_attr]; +"216 sub_tensor_14" [id=216, type=sub]; +"217 conv2d_20" [id=217, type=conv2d]; +"218 quantize_per_tensor_default_17" [id=218, type=quantize_per_tensor]; +"219 dequantize_per_tensor_default_22" [id=219, type=dequantize_per_tensor]; +"220 add__1" [id=220, type=add_]; +"221 quantize_per_tensor_default_18" [id=221, type=quantize_per_tensor]; +"222 dequantize_per_tensor_default_24" [id=222, type=dequantize_per_tensor]; +"223 dequantize_per_tensor_default_23" [id=223, type=dequantize_per_tensor]; +"224 features_6_block_0_0_weight_bias_0_0" [id=224, type=get_attr]; +"225 scale_updated_constant15" [id=225, type=get_attr]; +"226 compressed_weight_updated_constant15" [id=226, type=get_attr]; +"227 mul_tensor_15" [id=227, type=mul]; +"228 zero_point_updated_constant15" [id=228, type=get_attr]; +"229 sub_tensor_15" [id=229, type=sub]; +"230 conv2d_21" [id=230, type=conv2d]; +"231 hardswish__5_scale_0" [id=231, type=get_attr]; +"232 hardswish__5_zero_point_0" [id=232, type=get_attr]; +"233 hardswish__5" [id=233, type=hardswish_]; +"234 quantize_per_channel_default_21" [id=234, type=quantize_per_channel]; +"235 dequantize_per_channel_default_21" [id=235, type=dequantize_per_channel]; +"236 features_6_block_1_0_weight_bias_0_0" [id=236, type=get_attr]; +"237 scale_updated_constant16" [id=237, type=get_attr]; +"238 compressed_weight_updated_constant16" [id=238, type=get_attr]; +"239 mul_tensor_16" [id=239, type=mul]; +"240 zero_point_updated_constant16" [id=240, type=get_attr]; +"241 sub_tensor_16" [id=241, type=sub]; +"242 conv2d_22" [id=242, type=conv2d]; +"243 hardswish__6" [id=243, type=hardswish_]; +"244 quantize_per_tensor_default_19" [id=244, type=quantize_per_tensor]; +"245 dequantize_per_tensor_default_26" [id=245, type=dequantize_per_tensor]; +"246 dequantize_per_tensor_default_25" [id=246, type=dequantize_per_tensor]; +"247 adaptive_avg_pool2d_3" [id=247, type=adaptive_avg_pool2d]; +"248 features_6_block_2_fc1_bias_0_0" [id=248, type=get_attr]; +"249 conv2d_23" [id=249, type=conv2d]; +"250 relu_3" [id=250, type=relu]; +"251 features_6_block_2_fc2_bias_0_0" [id=251, type=get_attr]; +"252 conv2d_24" [id=252, type=conv2d]; +"253 hardsigmoid_3" [id=253, type=hardsigmoid]; +"254 quantize_per_tensor_default_20" [id=254, type=quantize_per_tensor]; +"255 dequantize_per_tensor_default_27" [id=255, type=dequantize_per_tensor]; +"256 mul_3" [id=256, type=mul]; +"257 quantize_per_tensor_default_21" [id=257, type=quantize_per_tensor]; +"258 dequantize_per_tensor_default_28" [id=258, type=dequantize_per_tensor]; +"259 features_6_block_3_0_weight_bias_0_0" [id=259, type=get_attr]; +"260 scale_updated_constant17" [id=260, type=get_attr]; +"261 compressed_weight_updated_constant17" [id=261, type=get_attr]; +"262 mul_tensor_17" [id=262, type=mul]; +"263 zero_point_updated_constant17" [id=263, type=get_attr]; +"264 sub_tensor_17" [id=264, type=sub]; +"265 conv2d_25" [id=265, type=conv2d]; +"266 quantize_per_tensor_default_22" [id=266, type=quantize_per_tensor]; +"267 dequantize_per_tensor_default_29" [id=267, type=dequantize_per_tensor]; +"268 add__2" [id=268, type=add_]; +"269 quantize_per_tensor_default_23" [id=269, type=quantize_per_tensor]; +"270 dequantize_per_tensor_default_30" [id=270, type=dequantize_per_tensor]; +"271 features_7_block_0_0_weight_bias_0_0" [id=271, type=get_attr]; +"272 scale_updated_constant18" [id=272, type=get_attr]; +"273 compressed_weight_updated_constant18" [id=273, type=get_attr]; +"274 mul_tensor_18" [id=274, type=mul]; +"275 zero_point_updated_constant18" [id=275, type=get_attr]; +"276 sub_tensor_18" [id=276, type=sub]; +"277 conv2d_26" [id=277, type=conv2d]; +"278 hardswish__7_scale_0" [id=278, type=get_attr]; +"279 hardswish__7_zero_point_0" [id=279, type=get_attr]; +"280 hardswish__7" [id=280, type=hardswish_]; +"281 quantize_per_channel_default_25" [id=281, type=quantize_per_channel]; +"282 dequantize_per_channel_default_25" [id=282, type=dequantize_per_channel]; +"283 features_7_block_1_0_weight_bias_0_0" [id=283, type=get_attr]; +"284 scale_updated_constant19" [id=284, type=get_attr]; +"285 compressed_weight_updated_constant19" [id=285, type=get_attr]; +"286 mul_tensor_19" [id=286, type=mul]; +"287 zero_point_updated_constant19" [id=287, type=get_attr]; +"288 sub_tensor_19" [id=288, type=sub]; +"289 conv2d_27" [id=289, type=conv2d]; +"290 hardswish__8" [id=290, type=hardswish_]; +"291 quantize_per_tensor_default_24" [id=291, type=quantize_per_tensor]; +"292 dequantize_per_tensor_default_32" [id=292, type=dequantize_per_tensor]; +"293 dequantize_per_tensor_default_31" [id=293, type=dequantize_per_tensor]; +"294 adaptive_avg_pool2d_4" [id=294, type=adaptive_avg_pool2d]; +"295 features_7_block_2_fc1_bias_0_0" [id=295, type=get_attr]; +"296 conv2d_28" [id=296, type=conv2d]; +"297 relu_4" [id=297, type=relu]; +"298 features_7_block_2_fc2_bias_0_0" [id=298, type=get_attr]; +"299 conv2d_29" [id=299, type=conv2d]; +"300 hardsigmoid_4" [id=300, type=hardsigmoid]; +"301 quantize_per_tensor_default_25" [id=301, type=quantize_per_tensor]; +"302 dequantize_per_tensor_default_33" [id=302, type=dequantize_per_tensor]; +"303 mul_4" [id=303, type=mul]; +"304 quantize_per_tensor_default_26" [id=304, type=quantize_per_tensor]; +"305 dequantize_per_tensor_default_34" [id=305, type=dequantize_per_tensor]; +"306 features_7_block_3_0_weight_bias_0_0" [id=306, type=get_attr]; +"307 scale_updated_constant20" [id=307, type=get_attr]; +"308 compressed_weight_updated_constant20" [id=308, type=get_attr]; +"309 mul_tensor_20" [id=309, type=mul]; +"310 zero_point_updated_constant20" [id=310, type=get_attr]; +"311 sub_tensor_20" [id=311, type=sub]; +"312 conv2d_30" [id=312, type=conv2d]; +"313 quantize_per_tensor_default_27" [id=313, type=quantize_per_tensor]; +"314 dequantize_per_tensor_default_36" [id=314, type=dequantize_per_tensor]; +"315 dequantize_per_tensor_default_35" [id=315, type=dequantize_per_tensor]; +"316 features_8_block_0_0_weight_bias_0_0" [id=316, type=get_attr]; +"317 scale_updated_constant21" [id=317, type=get_attr]; +"318 compressed_weight_updated_constant21" [id=318, type=get_attr]; +"319 mul_tensor_21" [id=319, type=mul]; +"320 zero_point_updated_constant21" [id=320, type=get_attr]; +"321 sub_tensor_21" [id=321, type=sub]; +"322 conv2d_31" [id=322, type=conv2d]; +"323 hardswish__9_scale_0" [id=323, type=get_attr]; +"324 hardswish__9_zero_point_0" [id=324, type=get_attr]; +"325 hardswish__9" [id=325, type=hardswish_]; +"326 quantize_per_channel_default_29" [id=326, type=quantize_per_channel]; +"327 dequantize_per_channel_default_29" [id=327, type=dequantize_per_channel]; +"328 features_8_block_1_0_weight_bias_0_0" [id=328, type=get_attr]; +"329 scale_updated_constant22" [id=329, type=get_attr]; +"330 compressed_weight_updated_constant22" [id=330, type=get_attr]; +"331 mul_tensor_22" [id=331, type=mul]; +"332 zero_point_updated_constant22" [id=332, type=get_attr]; +"333 sub_tensor_22" [id=333, type=sub]; +"334 conv2d_32" [id=334, type=conv2d]; +"335 hardswish__10" [id=335, type=hardswish_]; +"336 quantize_per_tensor_default_28" [id=336, type=quantize_per_tensor]; +"337 dequantize_per_tensor_default_38" [id=337, type=dequantize_per_tensor]; +"338 dequantize_per_tensor_default_37" [id=338, type=dequantize_per_tensor]; +"339 adaptive_avg_pool2d_5" [id=339, type=adaptive_avg_pool2d]; +"340 features_8_block_2_fc1_bias_0_0" [id=340, type=get_attr]; +"341 conv2d_33" [id=341, type=conv2d]; +"342 relu_5" [id=342, type=relu]; +"343 features_8_block_2_fc2_bias_0_0" [id=343, type=get_attr]; +"344 conv2d_34" [id=344, type=conv2d]; +"345 hardsigmoid_5" [id=345, type=hardsigmoid]; +"346 quantize_per_tensor_default_29" [id=346, type=quantize_per_tensor]; +"347 dequantize_per_tensor_default_39" [id=347, type=dequantize_per_tensor]; +"348 mul_5" [id=348, type=mul]; +"349 quantize_per_tensor_default_30" [id=349, type=quantize_per_tensor]; +"350 dequantize_per_tensor_default_40" [id=350, type=dequantize_per_tensor]; +"351 features_8_block_3_0_weight_bias_0_0" [id=351, type=get_attr]; +"352 scale_updated_constant23" [id=352, type=get_attr]; +"353 compressed_weight_updated_constant23" [id=353, type=get_attr]; +"354 mul_tensor_23" [id=354, type=mul]; +"355 zero_point_updated_constant23" [id=355, type=get_attr]; +"356 sub_tensor_23" [id=356, type=sub]; +"357 conv2d_35" [id=357, type=conv2d]; +"358 quantize_per_tensor_default_31" [id=358, type=quantize_per_tensor]; +"359 dequantize_per_tensor_default_41" [id=359, type=dequantize_per_tensor]; +"360 add__3" [id=360, type=add_]; +"361 quantize_per_tensor_default_32" [id=361, type=quantize_per_tensor]; +"362 dequantize_per_tensor_default_42" [id=362, type=dequantize_per_tensor]; +"363 features_9_block_0_0_weight_bias_0_0" [id=363, type=get_attr]; +"364 scale_updated_constant24" [id=364, type=get_attr]; +"365 compressed_weight_updated_constant24" [id=365, type=get_attr]; +"366 mul_tensor_24" [id=366, type=mul]; +"367 zero_point_updated_constant24" [id=367, type=get_attr]; +"368 sub_tensor_24" [id=368, type=sub]; +"369 conv2d_36" [id=369, type=conv2d]; +"370 hardswish__11_scale_0" [id=370, type=get_attr]; +"371 hardswish__11_zero_point_0" [id=371, type=get_attr]; +"372 hardswish__11" [id=372, type=hardswish_]; +"373 quantize_per_channel_default_33" [id=373, type=quantize_per_channel]; +"374 dequantize_per_channel_default_33" [id=374, type=dequantize_per_channel]; +"375 features_9_block_1_0_weight_bias_0_0" [id=375, type=get_attr]; +"376 scale_updated_constant25" [id=376, type=get_attr]; +"377 compressed_weight_updated_constant25" [id=377, type=get_attr]; +"378 mul_tensor_25" [id=378, type=mul]; +"379 zero_point_updated_constant25" [id=379, type=get_attr]; +"380 sub_tensor_25" [id=380, type=sub]; +"381 conv2d_37" [id=381, type=conv2d]; +"382 hardswish__12" [id=382, type=hardswish_]; +"383 quantize_per_tensor_default_33" [id=383, type=quantize_per_tensor]; +"384 dequantize_per_tensor_default_44" [id=384, type=dequantize_per_tensor]; +"385 dequantize_per_tensor_default_43" [id=385, type=dequantize_per_tensor]; +"386 adaptive_avg_pool2d_6" [id=386, type=adaptive_avg_pool2d]; +"387 features_9_block_2_fc1_bias_0_0" [id=387, type=get_attr]; +"388 conv2d_38" [id=388, type=conv2d]; +"389 relu_6" [id=389, type=relu]; +"390 features_9_block_2_fc2_bias_0_0" [id=390, type=get_attr]; +"391 conv2d_39" [id=391, type=conv2d]; +"392 hardsigmoid_6" [id=392, type=hardsigmoid]; +"393 quantize_per_tensor_default_34" [id=393, type=quantize_per_tensor]; +"394 dequantize_per_tensor_default_45" [id=394, type=dequantize_per_tensor]; +"395 mul_6" [id=395, type=mul]; +"396 quantize_per_tensor_default_35" [id=396, type=quantize_per_tensor]; +"397 dequantize_per_tensor_default_46" [id=397, type=dequantize_per_tensor]; +"398 features_9_block_3_0_weight_bias_0_0" [id=398, type=get_attr]; +"399 scale_updated_constant26" [id=399, type=get_attr]; +"400 compressed_weight_updated_constant26" [id=400, type=get_attr]; +"401 mul_tensor_26" [id=401, type=mul]; +"402 zero_point_updated_constant26" [id=402, type=get_attr]; +"403 sub_tensor_26" [id=403, type=sub]; +"404 conv2d_40" [id=404, type=conv2d]; +"405 quantize_per_tensor_default_36" [id=405, type=quantize_per_tensor]; +"406 dequantize_per_tensor_default_48" [id=406, type=dequantize_per_tensor]; +"407 dequantize_per_tensor_default_47" [id=407, type=dequantize_per_tensor]; +"408 features_10_block_0_0_weight_bias_0_0" [id=408, type=get_attr]; +"409 scale_updated_constant27" [id=409, type=get_attr]; +"410 compressed_weight_updated_constant27" [id=410, type=get_attr]; +"411 mul_tensor_27" [id=411, type=mul]; +"412 zero_point_updated_constant27" [id=412, type=get_attr]; +"413 sub_tensor_27" [id=413, type=sub]; +"414 conv2d_41" [id=414, type=conv2d]; +"415 hardswish__13_scale_0" [id=415, type=get_attr]; +"416 hardswish__13_zero_point_0" [id=416, type=get_attr]; +"417 hardswish__13" [id=417, type=hardswish_]; +"418 quantize_per_channel_default_37" [id=418, type=quantize_per_channel]; +"419 dequantize_per_channel_default_37" [id=419, type=dequantize_per_channel]; +"420 features_10_block_1_0_weight_bias_0_0" [id=420, type=get_attr]; +"421 scale_updated_constant28" [id=421, type=get_attr]; +"422 compressed_weight_updated_constant28" [id=422, type=get_attr]; +"423 mul_tensor_28" [id=423, type=mul]; +"424 zero_point_updated_constant28" [id=424, type=get_attr]; +"425 sub_tensor_28" [id=425, type=sub]; +"426 conv2d_42" [id=426, type=conv2d]; +"427 hardswish__14" [id=427, type=hardswish_]; +"428 quantize_per_tensor_default_37" [id=428, type=quantize_per_tensor]; +"429 dequantize_per_tensor_default_50" [id=429, type=dequantize_per_tensor]; +"430 dequantize_per_tensor_default_49" [id=430, type=dequantize_per_tensor]; +"431 adaptive_avg_pool2d_7" [id=431, type=adaptive_avg_pool2d]; +"432 features_10_block_2_fc1_bias_0_0" [id=432, type=get_attr]; +"433 conv2d_43" [id=433, type=conv2d]; +"434 relu_7" [id=434, type=relu]; +"435 features_10_block_2_fc2_bias_0_0" [id=435, type=get_attr]; +"436 conv2d_44" [id=436, type=conv2d]; +"437 hardsigmoid_7" [id=437, type=hardsigmoid]; +"438 quantize_per_tensor_default_38" [id=438, type=quantize_per_tensor]; +"439 dequantize_per_tensor_default_51" [id=439, type=dequantize_per_tensor]; +"440 mul_7" [id=440, type=mul]; +"441 quantize_per_tensor_default_39" [id=441, type=quantize_per_tensor]; +"442 dequantize_per_tensor_default_52" [id=442, type=dequantize_per_tensor]; +"443 features_10_block_3_0_weight_bias_0_0" [id=443, type=get_attr]; +"444 scale_updated_constant29" [id=444, type=get_attr]; +"445 compressed_weight_updated_constant29" [id=445, type=get_attr]; +"446 mul_tensor_29" [id=446, type=mul]; +"447 zero_point_updated_constant29" [id=447, type=get_attr]; +"448 sub_tensor_29" [id=448, type=sub]; +"449 conv2d_45" [id=449, type=conv2d]; +"450 quantize_per_tensor_default_40" [id=450, type=quantize_per_tensor]; +"451 dequantize_per_tensor_default_53" [id=451, type=dequantize_per_tensor]; +"452 add__4" [id=452, type=add_]; +"453 quantize_per_tensor_default_41" [id=453, type=quantize_per_tensor]; +"454 dequantize_per_tensor_default_55" [id=454, type=dequantize_per_tensor]; +"455 dequantize_per_tensor_default_54" [id=455, type=dequantize_per_tensor]; +"456 features_11_block_0_0_weight_bias_0_0" [id=456, type=get_attr]; +"457 scale_updated_constant30" [id=457, type=get_attr]; +"458 compressed_weight_updated_constant30" [id=458, type=get_attr]; +"459 mul_tensor_30" [id=459, type=mul]; +"460 zero_point_updated_constant30" [id=460, type=get_attr]; +"461 sub_tensor_30" [id=461, type=sub]; +"462 conv2d_46" [id=462, type=conv2d]; +"463 hardswish__15_scale_0" [id=463, type=get_attr]; +"464 hardswish__15_zero_point_0" [id=464, type=get_attr]; +"465 hardswish__15" [id=465, type=hardswish_]; +"466 quantize_per_channel_default_41" [id=466, type=quantize_per_channel]; +"467 dequantize_per_channel_default_41" [id=467, type=dequantize_per_channel]; +"468 features_11_block_1_0_weight_bias_0_0" [id=468, type=get_attr]; +"469 scale_updated_constant31" [id=469, type=get_attr]; +"470 compressed_weight_updated_constant31" [id=470, type=get_attr]; +"471 mul_tensor_31" [id=471, type=mul]; +"472 zero_point_updated_constant31" [id=472, type=get_attr]; +"473 sub_tensor_31" [id=473, type=sub]; +"474 conv2d_47" [id=474, type=conv2d]; +"475 hardswish__16" [id=475, type=hardswish_]; +"476 quantize_per_tensor_default_42" [id=476, type=quantize_per_tensor]; +"477 dequantize_per_tensor_default_57" [id=477, type=dequantize_per_tensor]; +"478 dequantize_per_tensor_default_56" [id=478, type=dequantize_per_tensor]; +"479 adaptive_avg_pool2d_8" [id=479, type=adaptive_avg_pool2d]; +"480 features_11_block_2_fc1_bias_0_0" [id=480, type=get_attr]; +"481 conv2d_48" [id=481, type=conv2d]; +"482 relu_8" [id=482, type=relu]; +"483 features_11_block_2_fc2_bias_0_0" [id=483, type=get_attr]; +"484 conv2d_49" [id=484, type=conv2d]; +"485 hardsigmoid_8" [id=485, type=hardsigmoid]; +"486 quantize_per_tensor_default_43" [id=486, type=quantize_per_tensor]; +"487 dequantize_per_tensor_default_58" [id=487, type=dequantize_per_tensor]; +"488 mul_8" [id=488, type=mul]; +"489 quantize_per_tensor_default_44" [id=489, type=quantize_per_tensor]; +"490 dequantize_per_tensor_default_59" [id=490, type=dequantize_per_tensor]; +"491 features_11_block_3_0_weight_bias_0_0" [id=491, type=get_attr]; +"492 scale_updated_constant32" [id=492, type=get_attr]; +"493 compressed_weight_updated_constant32" [id=493, type=get_attr]; +"494 mul_tensor_32" [id=494, type=mul]; +"495 zero_point_updated_constant32" [id=495, type=get_attr]; +"496 sub_tensor_32" [id=496, type=sub]; +"497 conv2d_50" [id=497, type=conv2d]; +"498 quantize_per_tensor_default_45" [id=498, type=quantize_per_tensor]; +"499 dequantize_per_tensor_default_60" [id=499, type=dequantize_per_tensor]; +"500 add__5" [id=500, type=add_]; +"501 quantize_per_tensor_default_46" [id=501, type=quantize_per_tensor]; +"502 dequantize_per_tensor_default_61" [id=502, type=dequantize_per_tensor]; +"503 features_12_0_weight_bias_0_0" [id=503, type=get_attr]; +"504 scale_updated_constant33" [id=504, type=get_attr]; +"505 compressed_weight_updated_constant33" [id=505, type=get_attr]; +"506 mul_tensor_33" [id=506, type=mul]; +"507 zero_point_updated_constant33" [id=507, type=get_attr]; +"508 sub_tensor_33" [id=508, type=sub]; +"509 conv2d_51" [id=509, type=conv2d]; +"510 hardswish__17" [id=510, type=hardswish_]; +"511 quantize_per_tensor_default_47" [id=511, type=quantize_per_tensor]; +"512 dequantize_per_tensor_default_62" [id=512, type=dequantize_per_tensor]; +"513 adaptive_avg_pool2d_9" [id=513, type=adaptive_avg_pool2d]; +"514 quantize_per_tensor_default_48" [id=514, type=quantize_per_tensor]; +"515 dequantize_per_tensor_default_63" [id=515, type=dequantize_per_tensor]; +"516 flatten" [id=516, type=flatten]; +"517 classifier_0_bias_0_0" [id=517, type=get_attr]; +"518 scale_updated_constant34" [id=518, type=get_attr]; +"519 compressed_weight_updated_constant34" [id=519, type=get_attr]; +"520 mul_tensor_34" [id=520, type=mul]; +"521 zero_point_updated_constant34" [id=521, type=get_attr]; +"522 sub_tensor_34" [id=522, type=sub]; +"523 linear" [id=523, type=linear]; +"524 hardswish__18" [id=524, type=hardswish_]; +"525 quantize_per_tensor_default_49" [id=525, type=quantize_per_tensor]; +"526 dequantize_per_tensor_default_64" [id=526, type=dequantize_per_tensor]; +"527 dropout_" [id=527, type=dropout_]; +"528 classifier_3_bias_0_0" [id=528, type=get_attr]; +"529 scale_updated_constant35" [id=529, type=get_attr]; +"530 compressed_weight_updated_constant35" [id=530, type=get_attr]; +"531 mul_tensor_35" [id=531, type=mul]; +"532 zero_point_updated_constant35" [id=532, type=get_attr]; +"533 sub_tensor_35" [id=533, type=sub]; +"534 linear_1" [id=534, type=linear]; +"535 output" [id=535, type=output]; +"0 features_1_block_1_fc1_weight" -> "46 conv2d_2" [label="(8, 16, 1, 1)", style=solid]; +"1 features_1_block_1_fc2_weight" -> "49 conv2d_3" [label="(16, 8, 1, 1)", style=solid]; +"2 features_4_block_2_fc1_weight" -> "156 conv2d_13" [label="(24, 96, 1, 1)", style=solid]; +"3 features_4_block_2_fc2_weight" -> "159 conv2d_14" [label="(96, 24, 1, 1)", style=solid]; +"4 features_5_block_2_fc1_weight" -> "201 conv2d_18" [label="(64, 240, 1, 1)", style=solid]; +"5 features_5_block_2_fc2_weight" -> "204 conv2d_19" [label="(240, 64, 1, 1)", style=solid]; +"6 features_6_block_2_fc1_weight" -> "249 conv2d_23" [label="(64, 240, 1, 1)", style=solid]; +"7 features_6_block_2_fc2_weight" -> "252 conv2d_24" [label="(240, 64, 1, 1)", style=solid]; +"8 features_7_block_2_fc1_weight" -> "296 conv2d_28" [label="(32, 120, 1, 1)", style=solid]; +"9 features_7_block_2_fc2_weight" -> "299 conv2d_29" [label="(120, 32, 1, 1)", style=solid]; +"10 features_8_block_2_fc1_weight" -> "341 conv2d_33" [label="(40, 144, 1, 1)", style=solid]; +"11 features_8_block_2_fc2_weight" -> "344 conv2d_34" [label="(144, 40, 1, 1)", style=solid]; +"12 features_9_block_2_fc1_weight" -> "388 conv2d_38" [label="(72, 288, 1, 1)", style=solid]; +"13 features_9_block_2_fc2_weight" -> "391 conv2d_39" [label="(288, 72, 1, 1)", style=solid]; +"14 features_10_block_2_fc1_weight" -> "433 conv2d_43" [label="(144, 576, 1, 1)", style=solid]; +"15 features_10_block_2_fc2_weight" -> "436 conv2d_44" [label="(576, 144, 1, 1)", style=solid]; +"16 features_11_block_2_fc1_weight" -> "481 conv2d_48" [label="(144, 576, 1, 1)", style=solid]; +"17 features_11_block_2_fc2_weight" -> "484 conv2d_49" [label="(576, 144, 1, 1)", style=solid]; +"18 x" -> "19 quantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"19 quantize_per_tensor_default" -> "20 dequantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"20 dequantize_per_tensor_default" -> "27 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"21 features_0_0_weight_bias_0_0" -> "27 conv2d" [label="(16,)", style=solid]; +"22 scale_updated_constant0" -> "24 mul_tensor" [label="(16, 1, 1, 1)", style=solid]; +"23 compressed_weight_updated_constant0" -> "24 mul_tensor" [label="(16, 3, 3, 3)", style=solid]; +"24 mul_tensor" -> "26 sub_tensor" [label="(16, 3, 3, 3)", style=solid]; +"25 zero_point_updated_constant0" -> "26 sub_tensor" [label="(16, 1, 1, 1)", style=solid]; +"26 sub_tensor" -> "27 conv2d" [label="(16, 3, 3, 3)", style=solid]; +"27 conv2d" -> "30 hardswish_" [label="(1, 16, 112, 112)", style=solid]; +"28 hardswish__scale_0" -> "31 quantize_per_channel_default_1" [label="(16,)", style=solid]; +"28 hardswish__scale_0" -> "32 dequantize_per_channel_default_1" [label="(16,)", style=solid]; +"29 hardswish__zero_point_0" -> "31 quantize_per_channel_default_1" [label="(16,)", style=solid]; +"29 hardswish__zero_point_0" -> "32 dequantize_per_channel_default_1" [label="(16,)", style=solid]; +"30 hardswish_" -> "31 quantize_per_channel_default_1" [label="(1, 16, 112, 112)", style=solid]; +"31 quantize_per_channel_default_1" -> "32 dequantize_per_channel_default_1" [label="(1, 16, 112, 112)", style=solid]; +"32 dequantize_per_channel_default_1" -> "39 conv2d_1" [label="(1, 16, 112, 112)", style=solid]; +"33 features_1_block_0_0_weight_bias_0_0" -> "39 conv2d_1" [label="(16,)", style=solid]; +"34 scale_updated_constant1" -> "36 mul_tensor_1" [label="(16, 1, 1, 1)", style=solid]; +"35 compressed_weight_updated_constant1" -> "36 mul_tensor_1" [label="(16, 1, 3, 3)", style=solid]; +"36 mul_tensor_1" -> "38 sub_tensor_1" [label="(16, 1, 3, 3)", style=solid]; +"37 zero_point_updated_constant1" -> "38 sub_tensor_1" [label="(16, 1, 1, 1)", style=solid]; +"38 sub_tensor_1" -> "39 conv2d_1" [label="(16, 1, 3, 3)", style=solid]; +"39 conv2d_1" -> "40 relu_" [label="(1, 16, 56, 56)", style=solid]; +"40 relu_" -> "41 quantize_per_tensor_default_1" [label="(1, 16, 56, 56)", style=solid]; +"41 quantize_per_tensor_default_1" -> "42 dequantize_per_tensor_default_2" [label="(1, 16, 56, 56)", style=solid]; +"41 quantize_per_tensor_default_1" -> "43 dequantize_per_tensor_default_1" [label="(1, 16, 56, 56)", style=solid]; +"42 dequantize_per_tensor_default_2" -> "53 mul" [label="(1, 16, 56, 56)", style=solid]; +"43 dequantize_per_tensor_default_1" -> "44 adaptive_avg_pool2d" [label="(1, 16, 56, 56)", style=solid]; +"44 adaptive_avg_pool2d" -> "46 conv2d_2" [label="(1, 16, 1, 1)", style=solid]; +"45 features_1_block_1_fc1_bias_0_0" -> "46 conv2d_2" [label="(8,)", style=solid]; +"46 conv2d_2" -> "47 relu" [label="(1, 8, 1, 1)", style=solid]; +"47 relu" -> "49 conv2d_3" [label="(1, 8, 1, 1)", style=solid]; +"48 features_1_block_1_fc2_bias_0_0" -> "49 conv2d_3" [label="(16,)", style=solid]; +"49 conv2d_3" -> "50 hardsigmoid" [label="(1, 16, 1, 1)", style=solid]; +"50 hardsigmoid" -> "51 quantize_per_tensor_default_2" [label="(1, 16, 1, 1)", style=solid]; +"51 quantize_per_tensor_default_2" -> "52 dequantize_per_tensor_default_3" [label="(1, 16, 1, 1)", style=solid]; +"52 dequantize_per_tensor_default_3" -> "53 mul" [label="(1, 16, 1, 1)", style=solid]; +"53 mul" -> "54 quantize_per_tensor_default_3" [label="(1, 16, 56, 56)", style=solid]; +"54 quantize_per_tensor_default_3" -> "55 dequantize_per_tensor_default_4" [label="(1, 16, 56, 56)", style=solid]; +"55 dequantize_per_tensor_default_4" -> "62 conv2d_4" [label="(1, 16, 56, 56)", style=solid]; +"56 features_1_block_2_0_weight_bias_0_0" -> "62 conv2d_4" [label="(16,)", style=solid]; +"57 scale_updated_constant2" -> "59 mul_tensor_2" [label="(16, 1, 1, 1)", style=solid]; +"58 compressed_weight_updated_constant2" -> "59 mul_tensor_2" [label="(16, 16, 1, 1)", style=solid]; +"59 mul_tensor_2" -> "61 sub_tensor_2" [label="(16, 16, 1, 1)", style=solid]; +"60 zero_point_updated_constant2" -> "61 sub_tensor_2" [label="(16, 1, 1, 1)", style=solid]; +"61 sub_tensor_2" -> "62 conv2d_4" [label="(16, 16, 1, 1)", style=solid]; +"62 conv2d_4" -> "63 quantize_per_tensor_default_4" [label="(1, 16, 56, 56)", style=solid]; +"63 quantize_per_tensor_default_4" -> "64 dequantize_per_tensor_default_5" [label="(1, 16, 56, 56)", style=solid]; +"64 dequantize_per_tensor_default_5" -> "71 conv2d_5" [label="(1, 16, 56, 56)", style=solid]; +"65 features_2_block_0_0_weight_bias_0_0" -> "71 conv2d_5" [label="(72,)", style=solid]; +"66 scale_updated_constant3" -> "68 mul_tensor_3" [label="(72, 1, 1, 1)", style=solid]; +"67 compressed_weight_updated_constant3" -> "68 mul_tensor_3" [label="(72, 16, 1, 1)", style=solid]; +"68 mul_tensor_3" -> "70 sub_tensor_3" [label="(72, 16, 1, 1)", style=solid]; +"69 zero_point_updated_constant3" -> "70 sub_tensor_3" [label="(72, 1, 1, 1)", style=solid]; +"70 sub_tensor_3" -> "71 conv2d_5" [label="(72, 16, 1, 1)", style=solid]; +"71 conv2d_5" -> "74 relu__1" [label="(1, 72, 56, 56)", style=solid]; +"72 relu__1_scale_0" -> "75 quantize_per_channel_default_5" [label="(72,)", style=solid]; +"72 relu__1_scale_0" -> "76 dequantize_per_channel_default_5" [label="(72,)", style=solid]; +"73 relu__1_zero_point_0" -> "75 quantize_per_channel_default_5" [label="(72,)", style=solid]; +"73 relu__1_zero_point_0" -> "76 dequantize_per_channel_default_5" [label="(72,)", style=solid]; +"74 relu__1" -> "75 quantize_per_channel_default_5" [label="(1, 72, 56, 56)", style=solid]; +"75 quantize_per_channel_default_5" -> "76 dequantize_per_channel_default_5" [label="(1, 72, 56, 56)", style=solid]; +"76 dequantize_per_channel_default_5" -> "83 conv2d_6" [label="(1, 72, 56, 56)", style=solid]; +"77 features_2_block_1_0_weight_bias_0_0" -> "83 conv2d_6" [label="(72,)", style=solid]; +"78 scale_updated_constant4" -> "80 mul_tensor_4" [label="(72, 1, 1, 1)", style=solid]; +"79 compressed_weight_updated_constant4" -> "80 mul_tensor_4" [label="(72, 1, 3, 3)", style=solid]; +"80 mul_tensor_4" -> "82 sub_tensor_4" [label="(72, 1, 3, 3)", style=solid]; +"81 zero_point_updated_constant4" -> "82 sub_tensor_4" [label="(72, 1, 1, 1)", style=solid]; +"82 sub_tensor_4" -> "83 conv2d_6" [label="(72, 1, 3, 3)", style=solid]; +"83 conv2d_6" -> "84 relu__2" [label="(1, 72, 28, 28)", style=solid]; +"84 relu__2" -> "85 quantize_per_tensor_default_5" [label="(1, 72, 28, 28)", style=solid]; +"85 quantize_per_tensor_default_5" -> "86 dequantize_per_tensor_default_6" [label="(1, 72, 28, 28)", style=solid]; +"86 dequantize_per_tensor_default_6" -> "93 conv2d_7" [label="(1, 72, 28, 28)", style=solid]; +"87 features_2_block_2_0_weight_bias_0_0" -> "93 conv2d_7" [label="(24,)", style=solid]; +"88 scale_updated_constant5" -> "90 mul_tensor_5" [label="(24, 1, 1, 1)", style=solid]; +"89 compressed_weight_updated_constant5" -> "90 mul_tensor_5" [label="(24, 72, 1, 1)", style=solid]; +"90 mul_tensor_5" -> "92 sub_tensor_5" [label="(24, 72, 1, 1)", style=solid]; +"91 zero_point_updated_constant5" -> "92 sub_tensor_5" [label="(24, 1, 1, 1)", style=solid]; +"92 sub_tensor_5" -> "93 conv2d_7" [label="(24, 72, 1, 1)", style=solid]; +"93 conv2d_7" -> "94 quantize_per_tensor_default_6" [label="(1, 24, 28, 28)", style=solid]; +"94 quantize_per_tensor_default_6" -> "95 dequantize_per_tensor_default_8" [label="(1, 24, 28, 28)", style=solid]; +"94 quantize_per_tensor_default_6" -> "96 dequantize_per_tensor_default_7" [label="(1, 24, 28, 28)", style=solid]; +"95 dequantize_per_tensor_default_8" -> "128 add_" [label="(1, 24, 28, 28)", style=solid]; +"96 dequantize_per_tensor_default_7" -> "103 conv2d_8" [label="(1, 24, 28, 28)", style=solid]; +"97 features_3_block_0_0_weight_bias_0_0" -> "103 conv2d_8" [label="(88,)", style=solid]; +"98 scale_updated_constant6" -> "100 mul_tensor_6" [label="(88, 1, 1, 1)", style=solid]; +"99 compressed_weight_updated_constant6" -> "100 mul_tensor_6" [label="(88, 24, 1, 1)", style=solid]; +"100 mul_tensor_6" -> "102 sub_tensor_6" [label="(88, 24, 1, 1)", style=solid]; +"101 zero_point_updated_constant6" -> "102 sub_tensor_6" [label="(88, 1, 1, 1)", style=solid]; +"102 sub_tensor_6" -> "103 conv2d_8" [label="(88, 24, 1, 1)", style=solid]; +"103 conv2d_8" -> "106 relu__3" [label="(1, 88, 28, 28)", style=solid]; +"104 relu__3_scale_0" -> "107 quantize_per_channel_default_9" [label="(88,)", style=solid]; +"104 relu__3_scale_0" -> "108 dequantize_per_channel_default_9" [label="(88,)", style=solid]; +"105 relu__3_zero_point_0" -> "107 quantize_per_channel_default_9" [label="(88,)", style=solid]; +"105 relu__3_zero_point_0" -> "108 dequantize_per_channel_default_9" [label="(88,)", style=solid]; +"106 relu__3" -> "107 quantize_per_channel_default_9" [label="(1, 88, 28, 28)", style=solid]; +"107 quantize_per_channel_default_9" -> "108 dequantize_per_channel_default_9" [label="(1, 88, 28, 28)", style=solid]; +"108 dequantize_per_channel_default_9" -> "115 conv2d_9" [label="(1, 88, 28, 28)", style=solid]; +"109 features_3_block_1_0_weight_bias_0_0" -> "115 conv2d_9" [label="(88,)", style=solid]; +"110 scale_updated_constant7" -> "112 mul_tensor_7" [label="(88, 1, 1, 1)", style=solid]; +"111 compressed_weight_updated_constant7" -> "112 mul_tensor_7" [label="(88, 1, 3, 3)", style=solid]; +"112 mul_tensor_7" -> "114 sub_tensor_7" [label="(88, 1, 3, 3)", style=solid]; +"113 zero_point_updated_constant7" -> "114 sub_tensor_7" [label="(88, 1, 1, 1)", style=solid]; +"114 sub_tensor_7" -> "115 conv2d_9" [label="(88, 1, 3, 3)", style=solid]; +"115 conv2d_9" -> "116 relu__4" [label="(1, 88, 28, 28)", style=solid]; +"116 relu__4" -> "117 quantize_per_tensor_default_7" [label="(1, 88, 28, 28)", style=solid]; +"117 quantize_per_tensor_default_7" -> "118 dequantize_per_tensor_default_9" [label="(1, 88, 28, 28)", style=solid]; +"118 dequantize_per_tensor_default_9" -> "125 conv2d_10" [label="(1, 88, 28, 28)", style=solid]; +"119 features_3_block_2_0_weight_bias_0_0" -> "125 conv2d_10" [label="(24,)", style=solid]; +"120 scale_updated_constant8" -> "122 mul_tensor_8" [label="(24, 1, 1, 1)", style=solid]; +"121 compressed_weight_updated_constant8" -> "122 mul_tensor_8" [label="(24, 88, 1, 1)", style=solid]; +"122 mul_tensor_8" -> "124 sub_tensor_8" [label="(24, 88, 1, 1)", style=solid]; +"123 zero_point_updated_constant8" -> "124 sub_tensor_8" [label="(24, 1, 1, 1)", style=solid]; +"124 sub_tensor_8" -> "125 conv2d_10" [label="(24, 88, 1, 1)", style=solid]; +"125 conv2d_10" -> "126 quantize_per_tensor_default_8" [label="(1, 24, 28, 28)", style=solid]; +"126 quantize_per_tensor_default_8" -> "127 dequantize_per_tensor_default_10" [label="(1, 24, 28, 28)", style=solid]; +"127 dequantize_per_tensor_default_10" -> "128 add_" [label="(1, 24, 28, 28)", style=solid]; +"128 add_" -> "129 quantize_per_tensor_default_9" [label="(1, 24, 28, 28)", style=solid]; +"129 quantize_per_tensor_default_9" -> "130 dequantize_per_tensor_default_11" [label="(1, 24, 28, 28)", style=solid]; +"130 dequantize_per_tensor_default_11" -> "137 conv2d_11" [label="(1, 24, 28, 28)", style=solid]; +"131 features_4_block_0_0_weight_bias_0_0" -> "137 conv2d_11" [label="(96,)", style=solid]; +"132 scale_updated_constant9" -> "134 mul_tensor_9" [label="(96, 1, 1, 1)", style=solid]; +"133 compressed_weight_updated_constant9" -> "134 mul_tensor_9" [label="(96, 24, 1, 1)", style=solid]; +"134 mul_tensor_9" -> "136 sub_tensor_9" [label="(96, 24, 1, 1)", style=solid]; +"135 zero_point_updated_constant9" -> "136 sub_tensor_9" [label="(96, 1, 1, 1)", style=solid]; +"136 sub_tensor_9" -> "137 conv2d_11" [label="(96, 24, 1, 1)", style=solid]; +"137 conv2d_11" -> "140 hardswish__1" [label="(1, 96, 28, 28)", style=solid]; +"138 hardswish__1_scale_0" -> "141 quantize_per_channel_default_13" [label="(96,)", style=solid]; +"138 hardswish__1_scale_0" -> "142 dequantize_per_channel_default_13" [label="(96,)", style=solid]; +"139 hardswish__1_zero_point_0" -> "141 quantize_per_channel_default_13" [label="(96,)", style=solid]; +"139 hardswish__1_zero_point_0" -> "142 dequantize_per_channel_default_13" [label="(96,)", style=solid]; +"140 hardswish__1" -> "141 quantize_per_channel_default_13" [label="(1, 96, 28, 28)", style=solid]; +"141 quantize_per_channel_default_13" -> "142 dequantize_per_channel_default_13" [label="(1, 96, 28, 28)", style=solid]; +"142 dequantize_per_channel_default_13" -> "149 conv2d_12" [label="(1, 96, 28, 28)", style=solid]; +"143 features_4_block_1_0_weight_bias_0_0" -> "149 conv2d_12" [label="(96,)", style=solid]; +"144 scale_updated_constant10" -> "146 mul_tensor_10" [label="(96, 1, 1, 1)", style=solid]; +"145 compressed_weight_updated_constant10" -> "146 mul_tensor_10" [label="(96, 1, 5, 5)", style=solid]; +"146 mul_tensor_10" -> "148 sub_tensor_10" [label="(96, 1, 5, 5)", style=solid]; +"147 zero_point_updated_constant10" -> "148 sub_tensor_10" [label="(96, 1, 1, 1)", style=solid]; +"148 sub_tensor_10" -> "149 conv2d_12" [label="(96, 1, 5, 5)", style=solid]; +"149 conv2d_12" -> "150 hardswish__2" [label="(1, 96, 14, 14)", style=solid]; +"150 hardswish__2" -> "151 quantize_per_tensor_default_10" [label="(1, 96, 14, 14)", style=solid]; +"151 quantize_per_tensor_default_10" -> "152 dequantize_per_tensor_default_13" [label="(1, 96, 14, 14)", style=solid]; +"151 quantize_per_tensor_default_10" -> "153 dequantize_per_tensor_default_12" [label="(1, 96, 14, 14)", style=solid]; +"152 dequantize_per_tensor_default_13" -> "163 mul_1" [label="(1, 96, 14, 14)", style=solid]; +"153 dequantize_per_tensor_default_12" -> "154 adaptive_avg_pool2d_1" [label="(1, 96, 14, 14)", style=solid]; +"154 adaptive_avg_pool2d_1" -> "156 conv2d_13" [label="(1, 96, 1, 1)", style=solid]; +"155 features_4_block_2_fc1_bias_0_0" -> "156 conv2d_13" [label="(24,)", style=solid]; +"156 conv2d_13" -> "157 relu_1" [label="(1, 24, 1, 1)", style=solid]; +"157 relu_1" -> "159 conv2d_14" [label="(1, 24, 1, 1)", style=solid]; +"158 features_4_block_2_fc2_bias_0_0" -> "159 conv2d_14" [label="(96,)", style=solid]; +"159 conv2d_14" -> "160 hardsigmoid_1" [label="(1, 96, 1, 1)", style=solid]; +"160 hardsigmoid_1" -> "161 quantize_per_tensor_default_11" [label="(1, 96, 1, 1)", style=solid]; +"161 quantize_per_tensor_default_11" -> "162 dequantize_per_tensor_default_14" [label="(1, 96, 1, 1)", style=solid]; +"162 dequantize_per_tensor_default_14" -> "163 mul_1" [label="(1, 96, 1, 1)", style=solid]; +"163 mul_1" -> "164 quantize_per_tensor_default_12" [label="(1, 96, 14, 14)", style=solid]; +"164 quantize_per_tensor_default_12" -> "165 dequantize_per_tensor_default_15" [label="(1, 96, 14, 14)", style=solid]; +"165 dequantize_per_tensor_default_15" -> "172 conv2d_15" [label="(1, 96, 14, 14)", style=solid]; +"166 features_4_block_3_0_weight_bias_0_0" -> "172 conv2d_15" [label="(40,)", style=solid]; +"167 scale_updated_constant11" -> "169 mul_tensor_11" [label="(40, 1, 1, 1)", style=solid]; +"168 compressed_weight_updated_constant11" -> "169 mul_tensor_11" [label="(40, 96, 1, 1)", style=solid]; +"169 mul_tensor_11" -> "171 sub_tensor_11" [label="(40, 96, 1, 1)", style=solid]; +"170 zero_point_updated_constant11" -> "171 sub_tensor_11" [label="(40, 1, 1, 1)", style=solid]; +"171 sub_tensor_11" -> "172 conv2d_15" [label="(40, 96, 1, 1)", style=solid]; +"172 conv2d_15" -> "173 quantize_per_tensor_default_13" [label="(1, 40, 14, 14)", style=solid]; +"173 quantize_per_tensor_default_13" -> "174 dequantize_per_tensor_default_17" [label="(1, 40, 14, 14)", style=solid]; +"173 quantize_per_tensor_default_13" -> "175 dequantize_per_tensor_default_16" [label="(1, 40, 14, 14)", style=solid]; +"174 dequantize_per_tensor_default_17" -> "220 add__1" [label="(1, 40, 14, 14)", style=solid]; +"175 dequantize_per_tensor_default_16" -> "182 conv2d_16" [label="(1, 40, 14, 14)", style=solid]; +"176 features_5_block_0_0_weight_bias_0_0" -> "182 conv2d_16" [label="(240,)", style=solid]; +"177 scale_updated_constant12" -> "179 mul_tensor_12" [label="(240, 1, 1, 1)", style=solid]; +"178 compressed_weight_updated_constant12" -> "179 mul_tensor_12" [label="(240, 40, 1, 1)", style=solid]; +"179 mul_tensor_12" -> "181 sub_tensor_12" [label="(240, 40, 1, 1)", style=solid]; +"180 zero_point_updated_constant12" -> "181 sub_tensor_12" [label="(240, 1, 1, 1)", style=solid]; +"181 sub_tensor_12" -> "182 conv2d_16" [label="(240, 40, 1, 1)", style=solid]; +"182 conv2d_16" -> "185 hardswish__3" [label="(1, 240, 14, 14)", style=solid]; +"183 hardswish__3_scale_0" -> "186 quantize_per_channel_default_17" [label="(240,)", style=solid]; +"183 hardswish__3_scale_0" -> "187 dequantize_per_channel_default_17" [label="(240,)", style=solid]; +"184 hardswish__3_zero_point_0" -> "186 quantize_per_channel_default_17" [label="(240,)", style=solid]; +"184 hardswish__3_zero_point_0" -> "187 dequantize_per_channel_default_17" [label="(240,)", style=solid]; +"185 hardswish__3" -> "186 quantize_per_channel_default_17" [label="(1, 240, 14, 14)", style=solid]; +"186 quantize_per_channel_default_17" -> "187 dequantize_per_channel_default_17" [label="(1, 240, 14, 14)", style=solid]; +"187 dequantize_per_channel_default_17" -> "194 conv2d_17" [label="(1, 240, 14, 14)", style=solid]; +"188 features_5_block_1_0_weight_bias_0_0" -> "194 conv2d_17" [label="(240,)", style=solid]; +"189 scale_updated_constant13" -> "191 mul_tensor_13" [label="(240, 1, 1, 1)", style=solid]; +"190 compressed_weight_updated_constant13" -> "191 mul_tensor_13" [label="(240, 1, 5, 5)", style=solid]; +"191 mul_tensor_13" -> "193 sub_tensor_13" [label="(240, 1, 5, 5)", style=solid]; +"192 zero_point_updated_constant13" -> "193 sub_tensor_13" [label="(240, 1, 1, 1)", style=solid]; +"193 sub_tensor_13" -> "194 conv2d_17" [label="(240, 1, 5, 5)", style=solid]; +"194 conv2d_17" -> "195 hardswish__4" [label="(1, 240, 14, 14)", style=solid]; +"195 hardswish__4" -> "196 quantize_per_tensor_default_14" [label="(1, 240, 14, 14)", style=solid]; +"196 quantize_per_tensor_default_14" -> "197 dequantize_per_tensor_default_19" [label="(1, 240, 14, 14)", style=solid]; +"196 quantize_per_tensor_default_14" -> "198 dequantize_per_tensor_default_18" [label="(1, 240, 14, 14)", style=solid]; +"197 dequantize_per_tensor_default_19" -> "208 mul_2" [label="(1, 240, 14, 14)", style=solid]; +"198 dequantize_per_tensor_default_18" -> "199 adaptive_avg_pool2d_2" [label="(1, 240, 14, 14)", style=solid]; +"199 adaptive_avg_pool2d_2" -> "201 conv2d_18" [label="(1, 240, 1, 1)", style=solid]; +"200 features_5_block_2_fc1_bias_0_0" -> "201 conv2d_18" [label="(64,)", style=solid]; +"201 conv2d_18" -> "202 relu_2" [label="(1, 64, 1, 1)", style=solid]; +"202 relu_2" -> "204 conv2d_19" [label="(1, 64, 1, 1)", style=solid]; +"203 features_5_block_2_fc2_bias_0_0" -> "204 conv2d_19" [label="(240,)", style=solid]; +"204 conv2d_19" -> "205 hardsigmoid_2" [label="(1, 240, 1, 1)", style=solid]; +"205 hardsigmoid_2" -> "206 quantize_per_tensor_default_15" [label="(1, 240, 1, 1)", style=solid]; +"206 quantize_per_tensor_default_15" -> "207 dequantize_per_tensor_default_20" [label="(1, 240, 1, 1)", style=solid]; +"207 dequantize_per_tensor_default_20" -> "208 mul_2" [label="(1, 240, 1, 1)", style=solid]; +"208 mul_2" -> "209 quantize_per_tensor_default_16" [label="(1, 240, 14, 14)", style=solid]; +"209 quantize_per_tensor_default_16" -> "210 dequantize_per_tensor_default_21" [label="(1, 240, 14, 14)", style=solid]; +"210 dequantize_per_tensor_default_21" -> "217 conv2d_20" [label="(1, 240, 14, 14)", style=solid]; +"211 features_5_block_3_0_weight_bias_0_0" -> "217 conv2d_20" [label="(40,)", style=solid]; +"212 scale_updated_constant14" -> "214 mul_tensor_14" [label="(40, 1, 1, 1)", style=solid]; +"213 compressed_weight_updated_constant14" -> "214 mul_tensor_14" [label="(40, 240, 1, 1)", style=solid]; +"214 mul_tensor_14" -> "216 sub_tensor_14" [label="(40, 240, 1, 1)", style=solid]; +"215 zero_point_updated_constant14" -> "216 sub_tensor_14" [label="(40, 1, 1, 1)", style=solid]; +"216 sub_tensor_14" -> "217 conv2d_20" [label="(40, 240, 1, 1)", style=solid]; +"217 conv2d_20" -> "218 quantize_per_tensor_default_17" [label="(1, 40, 14, 14)", style=solid]; +"218 quantize_per_tensor_default_17" -> "219 dequantize_per_tensor_default_22" [label="(1, 40, 14, 14)", style=solid]; +"219 dequantize_per_tensor_default_22" -> "220 add__1" [label="(1, 40, 14, 14)", style=solid]; +"220 add__1" -> "221 quantize_per_tensor_default_18" [label="(1, 40, 14, 14)", style=solid]; +"221 quantize_per_tensor_default_18" -> "222 dequantize_per_tensor_default_24" [label="(1, 40, 14, 14)", style=solid]; +"221 quantize_per_tensor_default_18" -> "223 dequantize_per_tensor_default_23" [label="(1, 40, 14, 14)", style=solid]; +"222 dequantize_per_tensor_default_24" -> "268 add__2" [label="(1, 40, 14, 14)", style=solid]; +"223 dequantize_per_tensor_default_23" -> "230 conv2d_21" [label="(1, 40, 14, 14)", style=solid]; +"224 features_6_block_0_0_weight_bias_0_0" -> "230 conv2d_21" [label="(240,)", style=solid]; +"225 scale_updated_constant15" -> "227 mul_tensor_15" [label="(240, 1, 1, 1)", style=solid]; +"226 compressed_weight_updated_constant15" -> "227 mul_tensor_15" [label="(240, 40, 1, 1)", style=solid]; +"227 mul_tensor_15" -> "229 sub_tensor_15" [label="(240, 40, 1, 1)", style=solid]; +"228 zero_point_updated_constant15" -> "229 sub_tensor_15" [label="(240, 1, 1, 1)", style=solid]; +"229 sub_tensor_15" -> "230 conv2d_21" [label="(240, 40, 1, 1)", style=solid]; +"230 conv2d_21" -> "233 hardswish__5" [label="(1, 240, 14, 14)", style=solid]; +"231 hardswish__5_scale_0" -> "234 quantize_per_channel_default_21" [label="(240,)", style=solid]; +"231 hardswish__5_scale_0" -> "235 dequantize_per_channel_default_21" [label="(240,)", style=solid]; +"232 hardswish__5_zero_point_0" -> "234 quantize_per_channel_default_21" [label="(240,)", style=solid]; +"232 hardswish__5_zero_point_0" -> "235 dequantize_per_channel_default_21" [label="(240,)", style=solid]; +"233 hardswish__5" -> "234 quantize_per_channel_default_21" [label="(1, 240, 14, 14)", style=solid]; +"234 quantize_per_channel_default_21" -> "235 dequantize_per_channel_default_21" [label="(1, 240, 14, 14)", style=solid]; +"235 dequantize_per_channel_default_21" -> "242 conv2d_22" [label="(1, 240, 14, 14)", style=solid]; +"236 features_6_block_1_0_weight_bias_0_0" -> "242 conv2d_22" [label="(240,)", style=solid]; +"237 scale_updated_constant16" -> "239 mul_tensor_16" [label="(240, 1, 1, 1)", style=solid]; +"238 compressed_weight_updated_constant16" -> "239 mul_tensor_16" [label="(240, 1, 5, 5)", style=solid]; +"239 mul_tensor_16" -> "241 sub_tensor_16" [label="(240, 1, 5, 5)", style=solid]; +"240 zero_point_updated_constant16" -> "241 sub_tensor_16" [label="(240, 1, 1, 1)", style=solid]; +"241 sub_tensor_16" -> "242 conv2d_22" [label="(240, 1, 5, 5)", style=solid]; +"242 conv2d_22" -> "243 hardswish__6" [label="(1, 240, 14, 14)", style=solid]; +"243 hardswish__6" -> "244 quantize_per_tensor_default_19" [label="(1, 240, 14, 14)", style=solid]; +"244 quantize_per_tensor_default_19" -> "245 dequantize_per_tensor_default_26" [label="(1, 240, 14, 14)", style=solid]; +"244 quantize_per_tensor_default_19" -> "246 dequantize_per_tensor_default_25" [label="(1, 240, 14, 14)", style=solid]; +"245 dequantize_per_tensor_default_26" -> "256 mul_3" [label="(1, 240, 14, 14)", style=solid]; +"246 dequantize_per_tensor_default_25" -> "247 adaptive_avg_pool2d_3" [label="(1, 240, 14, 14)", style=solid]; +"247 adaptive_avg_pool2d_3" -> "249 conv2d_23" [label="(1, 240, 1, 1)", style=solid]; +"248 features_6_block_2_fc1_bias_0_0" -> "249 conv2d_23" [label="(64,)", style=solid]; +"249 conv2d_23" -> "250 relu_3" [label="(1, 64, 1, 1)", style=solid]; +"250 relu_3" -> "252 conv2d_24" [label="(1, 64, 1, 1)", style=solid]; +"251 features_6_block_2_fc2_bias_0_0" -> "252 conv2d_24" [label="(240,)", style=solid]; +"252 conv2d_24" -> "253 hardsigmoid_3" [label="(1, 240, 1, 1)", style=solid]; +"253 hardsigmoid_3" -> "254 quantize_per_tensor_default_20" [label="(1, 240, 1, 1)", style=solid]; +"254 quantize_per_tensor_default_20" -> "255 dequantize_per_tensor_default_27" [label="(1, 240, 1, 1)", style=solid]; +"255 dequantize_per_tensor_default_27" -> "256 mul_3" [label="(1, 240, 1, 1)", style=solid]; +"256 mul_3" -> "257 quantize_per_tensor_default_21" [label="(1, 240, 14, 14)", style=solid]; +"257 quantize_per_tensor_default_21" -> "258 dequantize_per_tensor_default_28" [label="(1, 240, 14, 14)", style=solid]; +"258 dequantize_per_tensor_default_28" -> "265 conv2d_25" [label="(1, 240, 14, 14)", style=solid]; +"259 features_6_block_3_0_weight_bias_0_0" -> "265 conv2d_25" [label="(40,)", style=solid]; +"260 scale_updated_constant17" -> "262 mul_tensor_17" [label="(40, 1, 1, 1)", style=solid]; +"261 compressed_weight_updated_constant17" -> "262 mul_tensor_17" [label="(40, 240, 1, 1)", style=solid]; +"262 mul_tensor_17" -> "264 sub_tensor_17" [label="(40, 240, 1, 1)", style=solid]; +"263 zero_point_updated_constant17" -> "264 sub_tensor_17" [label="(40, 1, 1, 1)", style=solid]; +"264 sub_tensor_17" -> "265 conv2d_25" [label="(40, 240, 1, 1)", style=solid]; +"265 conv2d_25" -> "266 quantize_per_tensor_default_22" [label="(1, 40, 14, 14)", style=solid]; +"266 quantize_per_tensor_default_22" -> "267 dequantize_per_tensor_default_29" [label="(1, 40, 14, 14)", style=solid]; +"267 dequantize_per_tensor_default_29" -> "268 add__2" [label="(1, 40, 14, 14)", style=solid]; +"268 add__2" -> "269 quantize_per_tensor_default_23" [label="(1, 40, 14, 14)", style=solid]; +"269 quantize_per_tensor_default_23" -> "270 dequantize_per_tensor_default_30" [label="(1, 40, 14, 14)", style=solid]; +"270 dequantize_per_tensor_default_30" -> "277 conv2d_26" [label="(1, 40, 14, 14)", style=solid]; +"271 features_7_block_0_0_weight_bias_0_0" -> "277 conv2d_26" [label="(120,)", style=solid]; +"272 scale_updated_constant18" -> "274 mul_tensor_18" [label="(120, 1, 1, 1)", style=solid]; +"273 compressed_weight_updated_constant18" -> "274 mul_tensor_18" [label="(120, 40, 1, 1)", style=solid]; +"274 mul_tensor_18" -> "276 sub_tensor_18" [label="(120, 40, 1, 1)", style=solid]; +"275 zero_point_updated_constant18" -> "276 sub_tensor_18" [label="(120, 1, 1, 1)", style=solid]; +"276 sub_tensor_18" -> "277 conv2d_26" [label="(120, 40, 1, 1)", style=solid]; +"277 conv2d_26" -> "280 hardswish__7" [label="(1, 120, 14, 14)", style=solid]; +"278 hardswish__7_scale_0" -> "281 quantize_per_channel_default_25" [label="(120,)", style=solid]; +"278 hardswish__7_scale_0" -> "282 dequantize_per_channel_default_25" [label="(120,)", style=solid]; +"279 hardswish__7_zero_point_0" -> "281 quantize_per_channel_default_25" [label="(120,)", style=solid]; +"279 hardswish__7_zero_point_0" -> "282 dequantize_per_channel_default_25" [label="(120,)", style=solid]; +"280 hardswish__7" -> "281 quantize_per_channel_default_25" [label="(1, 120, 14, 14)", style=solid]; +"281 quantize_per_channel_default_25" -> "282 dequantize_per_channel_default_25" [label="(1, 120, 14, 14)", style=solid]; +"282 dequantize_per_channel_default_25" -> "289 conv2d_27" [label="(1, 120, 14, 14)", style=solid]; +"283 features_7_block_1_0_weight_bias_0_0" -> "289 conv2d_27" [label="(120,)", style=solid]; +"284 scale_updated_constant19" -> "286 mul_tensor_19" [label="(120, 1, 1, 1)", style=solid]; +"285 compressed_weight_updated_constant19" -> "286 mul_tensor_19" [label="(120, 1, 5, 5)", style=solid]; +"286 mul_tensor_19" -> "288 sub_tensor_19" [label="(120, 1, 5, 5)", style=solid]; +"287 zero_point_updated_constant19" -> "288 sub_tensor_19" [label="(120, 1, 1, 1)", style=solid]; +"288 sub_tensor_19" -> "289 conv2d_27" [label="(120, 1, 5, 5)", style=solid]; +"289 conv2d_27" -> "290 hardswish__8" [label="(1, 120, 14, 14)", style=solid]; +"290 hardswish__8" -> "291 quantize_per_tensor_default_24" [label="(1, 120, 14, 14)", style=solid]; +"291 quantize_per_tensor_default_24" -> "292 dequantize_per_tensor_default_32" [label="(1, 120, 14, 14)", style=solid]; +"291 quantize_per_tensor_default_24" -> "293 dequantize_per_tensor_default_31" [label="(1, 120, 14, 14)", style=solid]; +"292 dequantize_per_tensor_default_32" -> "303 mul_4" [label="(1, 120, 14, 14)", style=solid]; +"293 dequantize_per_tensor_default_31" -> "294 adaptive_avg_pool2d_4" [label="(1, 120, 14, 14)", style=solid]; +"294 adaptive_avg_pool2d_4" -> "296 conv2d_28" [label="(1, 120, 1, 1)", style=solid]; +"295 features_7_block_2_fc1_bias_0_0" -> "296 conv2d_28" [label="(32,)", style=solid]; +"296 conv2d_28" -> "297 relu_4" [label="(1, 32, 1, 1)", style=solid]; +"297 relu_4" -> "299 conv2d_29" [label="(1, 32, 1, 1)", style=solid]; +"298 features_7_block_2_fc2_bias_0_0" -> "299 conv2d_29" [label="(120,)", style=solid]; +"299 conv2d_29" -> "300 hardsigmoid_4" [label="(1, 120, 1, 1)", style=solid]; +"300 hardsigmoid_4" -> "301 quantize_per_tensor_default_25" [label="(1, 120, 1, 1)", style=solid]; +"301 quantize_per_tensor_default_25" -> "302 dequantize_per_tensor_default_33" [label="(1, 120, 1, 1)", style=solid]; +"302 dequantize_per_tensor_default_33" -> "303 mul_4" [label="(1, 120, 1, 1)", style=solid]; +"303 mul_4" -> "304 quantize_per_tensor_default_26" [label="(1, 120, 14, 14)", style=solid]; +"304 quantize_per_tensor_default_26" -> "305 dequantize_per_tensor_default_34" [label="(1, 120, 14, 14)", style=solid]; +"305 dequantize_per_tensor_default_34" -> "312 conv2d_30" [label="(1, 120, 14, 14)", style=solid]; +"306 features_7_block_3_0_weight_bias_0_0" -> "312 conv2d_30" [label="(48,)", style=solid]; +"307 scale_updated_constant20" -> "309 mul_tensor_20" [label="(48, 1, 1, 1)", style=solid]; +"308 compressed_weight_updated_constant20" -> "309 mul_tensor_20" [label="(48, 120, 1, 1)", style=solid]; +"309 mul_tensor_20" -> "311 sub_tensor_20" [label="(48, 120, 1, 1)", style=solid]; +"310 zero_point_updated_constant20" -> "311 sub_tensor_20" [label="(48, 1, 1, 1)", style=solid]; +"311 sub_tensor_20" -> "312 conv2d_30" [label="(48, 120, 1, 1)", style=solid]; +"312 conv2d_30" -> "313 quantize_per_tensor_default_27" [label="(1, 48, 14, 14)", style=solid]; +"313 quantize_per_tensor_default_27" -> "314 dequantize_per_tensor_default_36" [label="(1, 48, 14, 14)", style=solid]; +"313 quantize_per_tensor_default_27" -> "315 dequantize_per_tensor_default_35" [label="(1, 48, 14, 14)", style=solid]; +"314 dequantize_per_tensor_default_36" -> "360 add__3" [label="(1, 48, 14, 14)", style=solid]; +"315 dequantize_per_tensor_default_35" -> "322 conv2d_31" [label="(1, 48, 14, 14)", style=solid]; +"316 features_8_block_0_0_weight_bias_0_0" -> "322 conv2d_31" [label="(144,)", style=solid]; +"317 scale_updated_constant21" -> "319 mul_tensor_21" [label="(144, 1, 1, 1)", style=solid]; +"318 compressed_weight_updated_constant21" -> "319 mul_tensor_21" [label="(144, 48, 1, 1)", style=solid]; +"319 mul_tensor_21" -> "321 sub_tensor_21" [label="(144, 48, 1, 1)", style=solid]; +"320 zero_point_updated_constant21" -> "321 sub_tensor_21" [label="(144, 1, 1, 1)", style=solid]; +"321 sub_tensor_21" -> "322 conv2d_31" [label="(144, 48, 1, 1)", style=solid]; +"322 conv2d_31" -> "325 hardswish__9" [label="(1, 144, 14, 14)", style=solid]; +"323 hardswish__9_scale_0" -> "326 quantize_per_channel_default_29" [label="(144,)", style=solid]; +"323 hardswish__9_scale_0" -> "327 dequantize_per_channel_default_29" [label="(144,)", style=solid]; +"324 hardswish__9_zero_point_0" -> "326 quantize_per_channel_default_29" [label="(144,)", style=solid]; +"324 hardswish__9_zero_point_0" -> "327 dequantize_per_channel_default_29" [label="(144,)", style=solid]; +"325 hardswish__9" -> "326 quantize_per_channel_default_29" [label="(1, 144, 14, 14)", style=solid]; +"326 quantize_per_channel_default_29" -> "327 dequantize_per_channel_default_29" [label="(1, 144, 14, 14)", style=solid]; +"327 dequantize_per_channel_default_29" -> "334 conv2d_32" [label="(1, 144, 14, 14)", style=solid]; +"328 features_8_block_1_0_weight_bias_0_0" -> "334 conv2d_32" [label="(144,)", style=solid]; +"329 scale_updated_constant22" -> "331 mul_tensor_22" [label="(144, 1, 1, 1)", style=solid]; +"330 compressed_weight_updated_constant22" -> "331 mul_tensor_22" [label="(144, 1, 5, 5)", style=solid]; +"331 mul_tensor_22" -> "333 sub_tensor_22" [label="(144, 1, 5, 5)", style=solid]; +"332 zero_point_updated_constant22" -> "333 sub_tensor_22" [label="(144, 1, 1, 1)", style=solid]; +"333 sub_tensor_22" -> "334 conv2d_32" [label="(144, 1, 5, 5)", style=solid]; +"334 conv2d_32" -> "335 hardswish__10" [label="(1, 144, 14, 14)", style=solid]; +"335 hardswish__10" -> "336 quantize_per_tensor_default_28" [label="(1, 144, 14, 14)", style=solid]; +"336 quantize_per_tensor_default_28" -> "337 dequantize_per_tensor_default_38" [label="(1, 144, 14, 14)", style=solid]; +"336 quantize_per_tensor_default_28" -> "338 dequantize_per_tensor_default_37" [label="(1, 144, 14, 14)", style=solid]; +"337 dequantize_per_tensor_default_38" -> "348 mul_5" [label="(1, 144, 14, 14)", style=solid]; +"338 dequantize_per_tensor_default_37" -> "339 adaptive_avg_pool2d_5" [label="(1, 144, 14, 14)", style=solid]; +"339 adaptive_avg_pool2d_5" -> "341 conv2d_33" [label="(1, 144, 1, 1)", style=solid]; +"340 features_8_block_2_fc1_bias_0_0" -> "341 conv2d_33" [label="(40,)", style=solid]; +"341 conv2d_33" -> "342 relu_5" [label="(1, 40, 1, 1)", style=solid]; +"342 relu_5" -> "344 conv2d_34" [label="(1, 40, 1, 1)", style=solid]; +"343 features_8_block_2_fc2_bias_0_0" -> "344 conv2d_34" [label="(144,)", style=solid]; +"344 conv2d_34" -> "345 hardsigmoid_5" [label="(1, 144, 1, 1)", style=solid]; +"345 hardsigmoid_5" -> "346 quantize_per_tensor_default_29" [label="(1, 144, 1, 1)", style=solid]; +"346 quantize_per_tensor_default_29" -> "347 dequantize_per_tensor_default_39" [label="(1, 144, 1, 1)", style=solid]; +"347 dequantize_per_tensor_default_39" -> "348 mul_5" [label="(1, 144, 1, 1)", style=solid]; +"348 mul_5" -> "349 quantize_per_tensor_default_30" [label="(1, 144, 14, 14)", style=solid]; +"349 quantize_per_tensor_default_30" -> "350 dequantize_per_tensor_default_40" [label="(1, 144, 14, 14)", style=solid]; +"350 dequantize_per_tensor_default_40" -> "357 conv2d_35" [label="(1, 144, 14, 14)", style=solid]; +"351 features_8_block_3_0_weight_bias_0_0" -> "357 conv2d_35" [label="(48,)", style=solid]; +"352 scale_updated_constant23" -> "354 mul_tensor_23" [label="(48, 1, 1, 1)", style=solid]; +"353 compressed_weight_updated_constant23" -> "354 mul_tensor_23" [label="(48, 144, 1, 1)", style=solid]; +"354 mul_tensor_23" -> "356 sub_tensor_23" [label="(48, 144, 1, 1)", style=solid]; +"355 zero_point_updated_constant23" -> "356 sub_tensor_23" [label="(48, 1, 1, 1)", style=solid]; +"356 sub_tensor_23" -> "357 conv2d_35" [label="(48, 144, 1, 1)", style=solid]; +"357 conv2d_35" -> "358 quantize_per_tensor_default_31" [label="(1, 48, 14, 14)", style=solid]; +"358 quantize_per_tensor_default_31" -> "359 dequantize_per_tensor_default_41" [label="(1, 48, 14, 14)", style=solid]; +"359 dequantize_per_tensor_default_41" -> "360 add__3" [label="(1, 48, 14, 14)", style=solid]; +"360 add__3" -> "361 quantize_per_tensor_default_32" [label="(1, 48, 14, 14)", style=solid]; +"361 quantize_per_tensor_default_32" -> "362 dequantize_per_tensor_default_42" [label="(1, 48, 14, 14)", style=solid]; +"362 dequantize_per_tensor_default_42" -> "369 conv2d_36" [label="(1, 48, 14, 14)", style=solid]; +"363 features_9_block_0_0_weight_bias_0_0" -> "369 conv2d_36" [label="(288,)", style=solid]; +"364 scale_updated_constant24" -> "366 mul_tensor_24" [label="(288, 1, 1, 1)", style=solid]; +"365 compressed_weight_updated_constant24" -> "366 mul_tensor_24" [label="(288, 48, 1, 1)", style=solid]; +"366 mul_tensor_24" -> "368 sub_tensor_24" [label="(288, 48, 1, 1)", style=solid]; +"367 zero_point_updated_constant24" -> "368 sub_tensor_24" [label="(288, 1, 1, 1)", style=solid]; +"368 sub_tensor_24" -> "369 conv2d_36" [label="(288, 48, 1, 1)", style=solid]; +"369 conv2d_36" -> "372 hardswish__11" [label="(1, 288, 14, 14)", style=solid]; +"370 hardswish__11_scale_0" -> "373 quantize_per_channel_default_33" [label="(288,)", style=solid]; +"370 hardswish__11_scale_0" -> "374 dequantize_per_channel_default_33" [label="(288,)", style=solid]; +"371 hardswish__11_zero_point_0" -> "373 quantize_per_channel_default_33" [label="(288,)", style=solid]; +"371 hardswish__11_zero_point_0" -> "374 dequantize_per_channel_default_33" [label="(288,)", style=solid]; +"372 hardswish__11" -> "373 quantize_per_channel_default_33" [label="(1, 288, 14, 14)", style=solid]; +"373 quantize_per_channel_default_33" -> "374 dequantize_per_channel_default_33" [label="(1, 288, 14, 14)", style=solid]; +"374 dequantize_per_channel_default_33" -> "381 conv2d_37" [label="(1, 288, 14, 14)", style=solid]; +"375 features_9_block_1_0_weight_bias_0_0" -> "381 conv2d_37" [label="(288,)", style=solid]; +"376 scale_updated_constant25" -> "378 mul_tensor_25" [label="(288, 1, 1, 1)", style=solid]; +"377 compressed_weight_updated_constant25" -> "378 mul_tensor_25" [label="(288, 1, 5, 5)", style=solid]; +"378 mul_tensor_25" -> "380 sub_tensor_25" [label="(288, 1, 5, 5)", style=solid]; +"379 zero_point_updated_constant25" -> "380 sub_tensor_25" [label="(288, 1, 1, 1)", style=solid]; +"380 sub_tensor_25" -> "381 conv2d_37" [label="(288, 1, 5, 5)", style=solid]; +"381 conv2d_37" -> "382 hardswish__12" [label="(1, 288, 7, 7)", style=solid]; +"382 hardswish__12" -> "383 quantize_per_tensor_default_33" [label="(1, 288, 7, 7)", style=solid]; +"383 quantize_per_tensor_default_33" -> "384 dequantize_per_tensor_default_44" [label="(1, 288, 7, 7)", style=solid]; +"383 quantize_per_tensor_default_33" -> "385 dequantize_per_tensor_default_43" [label="(1, 288, 7, 7)", style=solid]; +"384 dequantize_per_tensor_default_44" -> "395 mul_6" [label="(1, 288, 7, 7)", style=solid]; +"385 dequantize_per_tensor_default_43" -> "386 adaptive_avg_pool2d_6" [label="(1, 288, 7, 7)", style=solid]; +"386 adaptive_avg_pool2d_6" -> "388 conv2d_38" [label="(1, 288, 1, 1)", style=solid]; +"387 features_9_block_2_fc1_bias_0_0" -> "388 conv2d_38" [label="(72,)", style=solid]; +"388 conv2d_38" -> "389 relu_6" [label="(1, 72, 1, 1)", style=solid]; +"389 relu_6" -> "391 conv2d_39" [label="(1, 72, 1, 1)", style=solid]; +"390 features_9_block_2_fc2_bias_0_0" -> "391 conv2d_39" [label="(288,)", style=solid]; +"391 conv2d_39" -> "392 hardsigmoid_6" [label="(1, 288, 1, 1)", style=solid]; +"392 hardsigmoid_6" -> "393 quantize_per_tensor_default_34" [label="(1, 288, 1, 1)", style=solid]; +"393 quantize_per_tensor_default_34" -> "394 dequantize_per_tensor_default_45" [label="(1, 288, 1, 1)", style=solid]; +"394 dequantize_per_tensor_default_45" -> "395 mul_6" [label="(1, 288, 1, 1)", style=solid]; +"395 mul_6" -> "396 quantize_per_tensor_default_35" [label="(1, 288, 7, 7)", style=solid]; +"396 quantize_per_tensor_default_35" -> "397 dequantize_per_tensor_default_46" [label="(1, 288, 7, 7)", style=solid]; +"397 dequantize_per_tensor_default_46" -> "404 conv2d_40" [label="(1, 288, 7, 7)", style=solid]; +"398 features_9_block_3_0_weight_bias_0_0" -> "404 conv2d_40" [label="(96,)", style=solid]; +"399 scale_updated_constant26" -> "401 mul_tensor_26" [label="(96, 1, 1, 1)", style=solid]; +"400 compressed_weight_updated_constant26" -> "401 mul_tensor_26" [label="(96, 288, 1, 1)", style=solid]; +"401 mul_tensor_26" -> "403 sub_tensor_26" [label="(96, 288, 1, 1)", style=solid]; +"402 zero_point_updated_constant26" -> "403 sub_tensor_26" [label="(96, 1, 1, 1)", style=solid]; +"403 sub_tensor_26" -> "404 conv2d_40" [label="(96, 288, 1, 1)", style=solid]; +"404 conv2d_40" -> "405 quantize_per_tensor_default_36" [label="(1, 96, 7, 7)", style=solid]; +"405 quantize_per_tensor_default_36" -> "406 dequantize_per_tensor_default_48" [label="(1, 96, 7, 7)", style=solid]; +"405 quantize_per_tensor_default_36" -> "407 dequantize_per_tensor_default_47" [label="(1, 96, 7, 7)", style=solid]; +"406 dequantize_per_tensor_default_48" -> "452 add__4" [label="(1, 96, 7, 7)", style=solid]; +"407 dequantize_per_tensor_default_47" -> "414 conv2d_41" [label="(1, 96, 7, 7)", style=solid]; +"408 features_10_block_0_0_weight_bias_0_0" -> "414 conv2d_41" [label="(576,)", style=solid]; +"409 scale_updated_constant27" -> "411 mul_tensor_27" [label="(576, 1, 1, 1)", style=solid]; +"410 compressed_weight_updated_constant27" -> "411 mul_tensor_27" [label="(576, 96, 1, 1)", style=solid]; +"411 mul_tensor_27" -> "413 sub_tensor_27" [label="(576, 96, 1, 1)", style=solid]; +"412 zero_point_updated_constant27" -> "413 sub_tensor_27" [label="(576, 1, 1, 1)", style=solid]; +"413 sub_tensor_27" -> "414 conv2d_41" [label="(576, 96, 1, 1)", style=solid]; +"414 conv2d_41" -> "417 hardswish__13" [label="(1, 576, 7, 7)", style=solid]; +"415 hardswish__13_scale_0" -> "418 quantize_per_channel_default_37" [label="(576,)", style=solid]; +"415 hardswish__13_scale_0" -> "419 dequantize_per_channel_default_37" [label="(576,)", style=solid]; +"416 hardswish__13_zero_point_0" -> "418 quantize_per_channel_default_37" [label="(576,)", style=solid]; +"416 hardswish__13_zero_point_0" -> "419 dequantize_per_channel_default_37" [label="(576,)", style=solid]; +"417 hardswish__13" -> "418 quantize_per_channel_default_37" [label="(1, 576, 7, 7)", style=solid]; +"418 quantize_per_channel_default_37" -> "419 dequantize_per_channel_default_37" [label="(1, 576, 7, 7)", style=solid]; +"419 dequantize_per_channel_default_37" -> "426 conv2d_42" [label="(1, 576, 7, 7)", style=solid]; +"420 features_10_block_1_0_weight_bias_0_0" -> "426 conv2d_42" [label="(576,)", style=solid]; +"421 scale_updated_constant28" -> "423 mul_tensor_28" [label="(576, 1, 1, 1)", style=solid]; +"422 compressed_weight_updated_constant28" -> "423 mul_tensor_28" [label="(576, 1, 5, 5)", style=solid]; +"423 mul_tensor_28" -> "425 sub_tensor_28" [label="(576, 1, 5, 5)", style=solid]; +"424 zero_point_updated_constant28" -> "425 sub_tensor_28" [label="(576, 1, 1, 1)", style=solid]; +"425 sub_tensor_28" -> "426 conv2d_42" [label="(576, 1, 5, 5)", style=solid]; +"426 conv2d_42" -> "427 hardswish__14" [label="(1, 576, 7, 7)", style=solid]; +"427 hardswish__14" -> "428 quantize_per_tensor_default_37" [label="(1, 576, 7, 7)", style=solid]; +"428 quantize_per_tensor_default_37" -> "429 dequantize_per_tensor_default_50" [label="(1, 576, 7, 7)", style=solid]; +"428 quantize_per_tensor_default_37" -> "430 dequantize_per_tensor_default_49" [label="(1, 576, 7, 7)", style=solid]; +"429 dequantize_per_tensor_default_50" -> "440 mul_7" [label="(1, 576, 7, 7)", style=solid]; +"430 dequantize_per_tensor_default_49" -> "431 adaptive_avg_pool2d_7" [label="(1, 576, 7, 7)", style=solid]; +"431 adaptive_avg_pool2d_7" -> "433 conv2d_43" [label="(1, 576, 1, 1)", style=solid]; +"432 features_10_block_2_fc1_bias_0_0" -> "433 conv2d_43" [label="(144,)", style=solid]; +"433 conv2d_43" -> "434 relu_7" [label="(1, 144, 1, 1)", style=solid]; +"434 relu_7" -> "436 conv2d_44" [label="(1, 144, 1, 1)", style=solid]; +"435 features_10_block_2_fc2_bias_0_0" -> "436 conv2d_44" [label="(576,)", style=solid]; +"436 conv2d_44" -> "437 hardsigmoid_7" [label="(1, 576, 1, 1)", style=solid]; +"437 hardsigmoid_7" -> "438 quantize_per_tensor_default_38" [label="(1, 576, 1, 1)", style=solid]; +"438 quantize_per_tensor_default_38" -> "439 dequantize_per_tensor_default_51" [label="(1, 576, 1, 1)", style=solid]; +"439 dequantize_per_tensor_default_51" -> "440 mul_7" [label="(1, 576, 1, 1)", style=solid]; +"440 mul_7" -> "441 quantize_per_tensor_default_39" [label="(1, 576, 7, 7)", style=solid]; +"441 quantize_per_tensor_default_39" -> "442 dequantize_per_tensor_default_52" [label="(1, 576, 7, 7)", style=solid]; +"442 dequantize_per_tensor_default_52" -> "449 conv2d_45" [label="(1, 576, 7, 7)", style=solid]; +"443 features_10_block_3_0_weight_bias_0_0" -> "449 conv2d_45" [label="(96,)", style=solid]; +"444 scale_updated_constant29" -> "446 mul_tensor_29" [label="(96, 1, 1, 1)", style=solid]; +"445 compressed_weight_updated_constant29" -> "446 mul_tensor_29" [label="(96, 576, 1, 1)", style=solid]; +"446 mul_tensor_29" -> "448 sub_tensor_29" [label="(96, 576, 1, 1)", style=solid]; +"447 zero_point_updated_constant29" -> "448 sub_tensor_29" [label="(96, 1, 1, 1)", style=solid]; +"448 sub_tensor_29" -> "449 conv2d_45" [label="(96, 576, 1, 1)", style=solid]; +"449 conv2d_45" -> "450 quantize_per_tensor_default_40" [label="(1, 96, 7, 7)", style=solid]; +"450 quantize_per_tensor_default_40" -> "451 dequantize_per_tensor_default_53" [label="(1, 96, 7, 7)", style=solid]; +"451 dequantize_per_tensor_default_53" -> "452 add__4" [label="(1, 96, 7, 7)", style=solid]; +"452 add__4" -> "453 quantize_per_tensor_default_41" [label="(1, 96, 7, 7)", style=solid]; +"453 quantize_per_tensor_default_41" -> "454 dequantize_per_tensor_default_55" [label="(1, 96, 7, 7)", style=solid]; +"453 quantize_per_tensor_default_41" -> "455 dequantize_per_tensor_default_54" [label="(1, 96, 7, 7)", style=solid]; +"454 dequantize_per_tensor_default_55" -> "500 add__5" [label="(1, 96, 7, 7)", style=solid]; +"455 dequantize_per_tensor_default_54" -> "462 conv2d_46" [label="(1, 96, 7, 7)", style=solid]; +"456 features_11_block_0_0_weight_bias_0_0" -> "462 conv2d_46" [label="(576,)", style=solid]; +"457 scale_updated_constant30" -> "459 mul_tensor_30" [label="(576, 1, 1, 1)", style=solid]; +"458 compressed_weight_updated_constant30" -> "459 mul_tensor_30" [label="(576, 96, 1, 1)", style=solid]; +"459 mul_tensor_30" -> "461 sub_tensor_30" [label="(576, 96, 1, 1)", style=solid]; +"460 zero_point_updated_constant30" -> "461 sub_tensor_30" [label="(576, 1, 1, 1)", style=solid]; +"461 sub_tensor_30" -> "462 conv2d_46" [label="(576, 96, 1, 1)", style=solid]; +"462 conv2d_46" -> "465 hardswish__15" [label="(1, 576, 7, 7)", style=solid]; +"463 hardswish__15_scale_0" -> "466 quantize_per_channel_default_41" [label="(576,)", style=solid]; +"463 hardswish__15_scale_0" -> "467 dequantize_per_channel_default_41" [label="(576,)", style=solid]; +"464 hardswish__15_zero_point_0" -> "466 quantize_per_channel_default_41" [label="(576,)", style=solid]; +"464 hardswish__15_zero_point_0" -> "467 dequantize_per_channel_default_41" [label="(576,)", style=solid]; +"465 hardswish__15" -> "466 quantize_per_channel_default_41" [label="(1, 576, 7, 7)", style=solid]; +"466 quantize_per_channel_default_41" -> "467 dequantize_per_channel_default_41" [label="(1, 576, 7, 7)", style=solid]; +"467 dequantize_per_channel_default_41" -> "474 conv2d_47" [label="(1, 576, 7, 7)", style=solid]; +"468 features_11_block_1_0_weight_bias_0_0" -> "474 conv2d_47" [label="(576,)", style=solid]; +"469 scale_updated_constant31" -> "471 mul_tensor_31" [label="(576, 1, 1, 1)", style=solid]; +"470 compressed_weight_updated_constant31" -> "471 mul_tensor_31" [label="(576, 1, 5, 5)", style=solid]; +"471 mul_tensor_31" -> "473 sub_tensor_31" [label="(576, 1, 5, 5)", style=solid]; +"472 zero_point_updated_constant31" -> "473 sub_tensor_31" [label="(576, 1, 1, 1)", style=solid]; +"473 sub_tensor_31" -> "474 conv2d_47" [label="(576, 1, 5, 5)", style=solid]; +"474 conv2d_47" -> "475 hardswish__16" [label="(1, 576, 7, 7)", style=solid]; +"475 hardswish__16" -> "476 quantize_per_tensor_default_42" [label="(1, 576, 7, 7)", style=solid]; +"476 quantize_per_tensor_default_42" -> "477 dequantize_per_tensor_default_57" [label="(1, 576, 7, 7)", style=solid]; +"476 quantize_per_tensor_default_42" -> "478 dequantize_per_tensor_default_56" [label="(1, 576, 7, 7)", style=solid]; +"477 dequantize_per_tensor_default_57" -> "488 mul_8" [label="(1, 576, 7, 7)", style=solid]; +"478 dequantize_per_tensor_default_56" -> "479 adaptive_avg_pool2d_8" [label="(1, 576, 7, 7)", style=solid]; +"479 adaptive_avg_pool2d_8" -> "481 conv2d_48" [label="(1, 576, 1, 1)", style=solid]; +"480 features_11_block_2_fc1_bias_0_0" -> "481 conv2d_48" [label="(144,)", style=solid]; +"481 conv2d_48" -> "482 relu_8" [label="(1, 144, 1, 1)", style=solid]; +"482 relu_8" -> "484 conv2d_49" [label="(1, 144, 1, 1)", style=solid]; +"483 features_11_block_2_fc2_bias_0_0" -> "484 conv2d_49" [label="(576,)", style=solid]; +"484 conv2d_49" -> "485 hardsigmoid_8" [label="(1, 576, 1, 1)", style=solid]; +"485 hardsigmoid_8" -> "486 quantize_per_tensor_default_43" [label="(1, 576, 1, 1)", style=solid]; +"486 quantize_per_tensor_default_43" -> "487 dequantize_per_tensor_default_58" [label="(1, 576, 1, 1)", style=solid]; +"487 dequantize_per_tensor_default_58" -> "488 mul_8" [label="(1, 576, 1, 1)", style=solid]; +"488 mul_8" -> "489 quantize_per_tensor_default_44" [label="(1, 576, 7, 7)", style=solid]; +"489 quantize_per_tensor_default_44" -> "490 dequantize_per_tensor_default_59" [label="(1, 576, 7, 7)", style=solid]; +"490 dequantize_per_tensor_default_59" -> "497 conv2d_50" [label="(1, 576, 7, 7)", style=solid]; +"491 features_11_block_3_0_weight_bias_0_0" -> "497 conv2d_50" [label="(96,)", style=solid]; +"492 scale_updated_constant32" -> "494 mul_tensor_32" [label="(96, 1, 1, 1)", style=solid]; +"493 compressed_weight_updated_constant32" -> "494 mul_tensor_32" [label="(96, 576, 1, 1)", style=solid]; +"494 mul_tensor_32" -> "496 sub_tensor_32" [label="(96, 576, 1, 1)", style=solid]; +"495 zero_point_updated_constant32" -> "496 sub_tensor_32" [label="(96, 1, 1, 1)", style=solid]; +"496 sub_tensor_32" -> "497 conv2d_50" [label="(96, 576, 1, 1)", style=solid]; +"497 conv2d_50" -> "498 quantize_per_tensor_default_45" [label="(1, 96, 7, 7)", style=solid]; +"498 quantize_per_tensor_default_45" -> "499 dequantize_per_tensor_default_60" [label="(1, 96, 7, 7)", style=solid]; +"499 dequantize_per_tensor_default_60" -> "500 add__5" [label="(1, 96, 7, 7)", style=solid]; +"500 add__5" -> "501 quantize_per_tensor_default_46" [label="(1, 96, 7, 7)", style=solid]; +"501 quantize_per_tensor_default_46" -> "502 dequantize_per_tensor_default_61" [label="(1, 96, 7, 7)", style=solid]; +"502 dequantize_per_tensor_default_61" -> "509 conv2d_51" [label="(1, 96, 7, 7)", style=solid]; +"503 features_12_0_weight_bias_0_0" -> "509 conv2d_51" [label="(576,)", style=solid]; +"504 scale_updated_constant33" -> "506 mul_tensor_33" [label="(576, 1, 1, 1)", style=solid]; +"505 compressed_weight_updated_constant33" -> "506 mul_tensor_33" [label="(576, 96, 1, 1)", style=solid]; +"506 mul_tensor_33" -> "508 sub_tensor_33" [label="(576, 96, 1, 1)", style=solid]; +"507 zero_point_updated_constant33" -> "508 sub_tensor_33" [label="(576, 1, 1, 1)", style=solid]; +"508 sub_tensor_33" -> "509 conv2d_51" [label="(576, 96, 1, 1)", style=solid]; +"509 conv2d_51" -> "510 hardswish__17" [label="(1, 576, 7, 7)", style=solid]; +"510 hardswish__17" -> "511 quantize_per_tensor_default_47" [label="(1, 576, 7, 7)", style=solid]; +"511 quantize_per_tensor_default_47" -> "512 dequantize_per_tensor_default_62" [label="(1, 576, 7, 7)", style=solid]; +"512 dequantize_per_tensor_default_62" -> "513 adaptive_avg_pool2d_9" [label="(1, 576, 7, 7)", style=solid]; +"513 adaptive_avg_pool2d_9" -> "514 quantize_per_tensor_default_48" [label="(1, 576, 1, 1)", style=solid]; +"514 quantize_per_tensor_default_48" -> "515 dequantize_per_tensor_default_63" [label="(1, 576, 1, 1)", style=solid]; +"515 dequantize_per_tensor_default_63" -> "516 flatten" [label="(1, 576, 1, 1)", style=solid]; +"516 flatten" -> "523 linear" [label="(1, 576)", style=solid]; +"517 classifier_0_bias_0_0" -> "523 linear" [label="(1024,)", style=solid]; +"518 scale_updated_constant34" -> "520 mul_tensor_34" [label="(1024, 1)", style=solid]; +"519 compressed_weight_updated_constant34" -> "520 mul_tensor_34" [label="(1024, 576)", style=solid]; +"520 mul_tensor_34" -> "522 sub_tensor_34" [label="(1024, 576)", style=solid]; +"521 zero_point_updated_constant34" -> "522 sub_tensor_34" [label="(1024, 1)", style=solid]; +"522 sub_tensor_34" -> "523 linear" [label="(1024, 576)", style=solid]; +"523 linear" -> "524 hardswish__18" [label="(1, 1024)", style=solid]; +"524 hardswish__18" -> "525 quantize_per_tensor_default_49" [label="(1, 1024)", style=solid]; +"525 quantize_per_tensor_default_49" -> "526 dequantize_per_tensor_default_64" [label="(1, 1024)", style=solid]; +"526 dequantize_per_tensor_default_64" -> "527 dropout_" [label="(1, 1024)", style=solid]; +"527 dropout_" -> "534 linear_1" [label="(1, 1024)", style=solid]; +"528 classifier_3_bias_0_0" -> "534 linear_1" [label="(1000,)", style=solid]; +"529 scale_updated_constant35" -> "531 mul_tensor_35" [label="(1000, 1)", style=solid]; +"530 compressed_weight_updated_constant35" -> "531 mul_tensor_35" [label="(1000, 1024)", style=solid]; +"531 mul_tensor_35" -> "533 sub_tensor_35" [label="(1000, 1024)", style=solid]; +"532 zero_point_updated_constant35" -> "533 sub_tensor_35" [label="(1000, 1)", style=solid]; +"533 sub_tensor_35" -> "534 linear_1" [label="(1000, 1024)", style=solid]; +"534 linear_1" -> "535 output" [label="(1, 1000)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/post_quantization_compressed/resnet18.dot b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/resnet18.dot new file mode 100644 index 00000000000..27ed6dbd832 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/resnet18.dot @@ -0,0 +1,497 @@ +strict digraph { +"0 x" [id=0, type=input]; +"1 quantize_per_tensor_default" [id=1, type=quantize_per_tensor]; +"2 dequantize_per_tensor_default" [id=2, type=dequantize_per_tensor]; +"3 conv1_weight_bias_0_0" [id=3, type=get_attr]; +"4 scale_updated_constant0" [id=4, type=get_attr]; +"5 compressed_weight_updated_constant0" [id=5, type=get_attr]; +"6 mul_tensor" [id=6, type=mul]; +"7 zero_point_updated_constant0" [id=7, type=get_attr]; +"8 sub_tensor" [id=8, type=sub]; +"9 conv2d" [id=9, type=conv2d]; +"10 relu_" [id=10, type=relu_]; +"11 quantize_per_tensor_default_1" [id=11, type=quantize_per_tensor]; +"12 dequantize_per_tensor_default_1" [id=12, type=dequantize_per_tensor]; +"13 max_pool2d" [id=13, type=max_pool2d]; +"14 layer1_0_conv1_weight_bias_0_0" [id=14, type=get_attr]; +"15 scale_updated_constant1" [id=15, type=get_attr]; +"16 compressed_weight_updated_constant1" [id=16, type=get_attr]; +"17 mul_tensor_1" [id=17, type=mul]; +"18 zero_point_updated_constant1" [id=18, type=get_attr]; +"19 sub_tensor_1" [id=19, type=sub]; +"20 conv2d_1" [id=20, type=conv2d]; +"21 relu__1" [id=21, type=relu_]; +"22 quantize_per_tensor_default_2" [id=22, type=quantize_per_tensor]; +"23 dequantize_per_tensor_default_2" [id=23, type=dequantize_per_tensor]; +"24 layer1_0_conv2_weight_bias_0_0" [id=24, type=get_attr]; +"25 scale_updated_constant2" [id=25, type=get_attr]; +"26 compressed_weight_updated_constant2" [id=26, type=get_attr]; +"27 mul_tensor_2" [id=27, type=mul]; +"28 zero_point_updated_constant2" [id=28, type=get_attr]; +"29 sub_tensor_2" [id=29, type=sub]; +"30 conv2d_2" [id=30, type=conv2d]; +"31 quantize_per_tensor_default_3" [id=31, type=quantize_per_tensor]; +"32 dequantize_per_tensor_default_3" [id=32, type=dequantize_per_tensor]; +"33 add_" [id=33, type=add_]; +"34 relu__2" [id=34, type=relu_]; +"35 quantize_per_tensor_default_4" [id=35, type=quantize_per_tensor]; +"36 dequantize_per_tensor_default_5" [id=36, type=dequantize_per_tensor]; +"37 dequantize_per_tensor_default_4" [id=37, type=dequantize_per_tensor]; +"38 layer1_1_conv1_weight_bias_0_0" [id=38, type=get_attr]; +"39 scale_updated_constant3" [id=39, type=get_attr]; +"40 compressed_weight_updated_constant3" [id=40, type=get_attr]; +"41 mul_tensor_3" [id=41, type=mul]; +"42 zero_point_updated_constant3" [id=42, type=get_attr]; +"43 sub_tensor_3" [id=43, type=sub]; +"44 conv2d_3" [id=44, type=conv2d]; +"45 relu__3" [id=45, type=relu_]; +"46 quantize_per_tensor_default_5" [id=46, type=quantize_per_tensor]; +"47 dequantize_per_tensor_default_6" [id=47, type=dequantize_per_tensor]; +"48 layer1_1_conv2_weight_bias_0_0" [id=48, type=get_attr]; +"49 scale_updated_constant4" [id=49, type=get_attr]; +"50 compressed_weight_updated_constant4" [id=50, type=get_attr]; +"51 mul_tensor_4" [id=51, type=mul]; +"52 zero_point_updated_constant4" [id=52, type=get_attr]; +"53 sub_tensor_4" [id=53, type=sub]; +"54 conv2d_4" [id=54, type=conv2d]; +"55 quantize_per_tensor_default_6" [id=55, type=quantize_per_tensor]; +"56 dequantize_per_tensor_default_7" [id=56, type=dequantize_per_tensor]; +"57 add__1" [id=57, type=add_]; +"58 relu__4" [id=58, type=relu_]; +"59 quantize_per_tensor_default_7" [id=59, type=quantize_per_tensor]; +"60 dequantize_per_tensor_default_9" [id=60, type=dequantize_per_tensor]; +"61 dequantize_per_tensor_default_8" [id=61, type=dequantize_per_tensor]; +"62 layer2_0_conv1_weight_bias_0_0" [id=62, type=get_attr]; +"63 scale_updated_constant5" [id=63, type=get_attr]; +"64 compressed_weight_updated_constant5" [id=64, type=get_attr]; +"65 mul_tensor_5" [id=65, type=mul]; +"66 zero_point_updated_constant5" [id=66, type=get_attr]; +"67 sub_tensor_5" [id=67, type=sub]; +"68 conv2d_5" [id=68, type=conv2d]; +"69 relu__5" [id=69, type=relu_]; +"70 quantize_per_tensor_default_8" [id=70, type=quantize_per_tensor]; +"71 dequantize_per_tensor_default_10" [id=71, type=dequantize_per_tensor]; +"72 layer2_0_conv2_weight_bias_0_0" [id=72, type=get_attr]; +"73 scale_updated_constant6" [id=73, type=get_attr]; +"74 compressed_weight_updated_constant6" [id=74, type=get_attr]; +"75 mul_tensor_6" [id=75, type=mul]; +"76 zero_point_updated_constant6" [id=76, type=get_attr]; +"77 sub_tensor_6" [id=77, type=sub]; +"78 conv2d_6" [id=78, type=conv2d]; +"79 quantize_per_tensor_default_9" [id=79, type=quantize_per_tensor]; +"80 dequantize_per_tensor_default_11" [id=80, type=dequantize_per_tensor]; +"81 layer2_0_downsample_0_weight_bias_0_0" [id=81, type=get_attr]; +"82 scale_updated_constant7" [id=82, type=get_attr]; +"83 compressed_weight_updated_constant7" [id=83, type=get_attr]; +"84 mul_tensor_7" [id=84, type=mul]; +"85 zero_point_updated_constant7" [id=85, type=get_attr]; +"86 sub_tensor_7" [id=86, type=sub]; +"87 conv2d_7" [id=87, type=conv2d]; +"88 quantize_per_tensor_default_10" [id=88, type=quantize_per_tensor]; +"89 dequantize_per_tensor_default_12" [id=89, type=dequantize_per_tensor]; +"90 add__2" [id=90, type=add_]; +"91 relu__6" [id=91, type=relu_]; +"92 quantize_per_tensor_default_11" [id=92, type=quantize_per_tensor]; +"93 dequantize_per_tensor_default_14" [id=93, type=dequantize_per_tensor]; +"94 dequantize_per_tensor_default_13" [id=94, type=dequantize_per_tensor]; +"95 layer2_1_conv1_weight_bias_0_0" [id=95, type=get_attr]; +"96 scale_updated_constant8" [id=96, type=get_attr]; +"97 compressed_weight_updated_constant8" [id=97, type=get_attr]; +"98 mul_tensor_8" [id=98, type=mul]; +"99 zero_point_updated_constant8" [id=99, type=get_attr]; +"100 sub_tensor_8" [id=100, type=sub]; +"101 conv2d_8" [id=101, type=conv2d]; +"102 relu__7" [id=102, type=relu_]; +"103 quantize_per_tensor_default_12" [id=103, type=quantize_per_tensor]; +"104 dequantize_per_tensor_default_15" [id=104, type=dequantize_per_tensor]; +"105 layer2_1_conv2_weight_bias_0_0" [id=105, type=get_attr]; +"106 scale_updated_constant9" [id=106, type=get_attr]; +"107 compressed_weight_updated_constant9" [id=107, type=get_attr]; +"108 mul_tensor_9" [id=108, type=mul]; +"109 zero_point_updated_constant9" [id=109, type=get_attr]; +"110 sub_tensor_9" [id=110, type=sub]; +"111 conv2d_9" [id=111, type=conv2d]; +"112 quantize_per_tensor_default_13" [id=112, type=quantize_per_tensor]; +"113 dequantize_per_tensor_default_16" [id=113, type=dequantize_per_tensor]; +"114 add__3" [id=114, type=add_]; +"115 relu__8" [id=115, type=relu_]; +"116 quantize_per_tensor_default_14" [id=116, type=quantize_per_tensor]; +"117 dequantize_per_tensor_default_18" [id=117, type=dequantize_per_tensor]; +"118 dequantize_per_tensor_default_17" [id=118, type=dequantize_per_tensor]; +"119 layer3_0_conv1_weight_bias_0_0" [id=119, type=get_attr]; +"120 scale_updated_constant10" [id=120, type=get_attr]; +"121 compressed_weight_updated_constant10" [id=121, type=get_attr]; +"122 mul_tensor_10" [id=122, type=mul]; +"123 zero_point_updated_constant10" [id=123, type=get_attr]; +"124 sub_tensor_10" [id=124, type=sub]; +"125 conv2d_10" [id=125, type=conv2d]; +"126 relu__9" [id=126, type=relu_]; +"127 quantize_per_tensor_default_15" [id=127, type=quantize_per_tensor]; +"128 dequantize_per_tensor_default_19" [id=128, type=dequantize_per_tensor]; +"129 layer3_0_conv2_weight_bias_0_0" [id=129, type=get_attr]; +"130 scale_updated_constant11" [id=130, type=get_attr]; +"131 compressed_weight_updated_constant11" [id=131, type=get_attr]; +"132 mul_tensor_11" [id=132, type=mul]; +"133 zero_point_updated_constant11" [id=133, type=get_attr]; +"134 sub_tensor_11" [id=134, type=sub]; +"135 conv2d_11" [id=135, type=conv2d]; +"136 quantize_per_tensor_default_16" [id=136, type=quantize_per_tensor]; +"137 dequantize_per_tensor_default_20" [id=137, type=dequantize_per_tensor]; +"138 layer3_0_downsample_0_weight_bias_0_0" [id=138, type=get_attr]; +"139 scale_updated_constant12" [id=139, type=get_attr]; +"140 compressed_weight_updated_constant12" [id=140, type=get_attr]; +"141 mul_tensor_12" [id=141, type=mul]; +"142 zero_point_updated_constant12" [id=142, type=get_attr]; +"143 sub_tensor_12" [id=143, type=sub]; +"144 conv2d_12" [id=144, type=conv2d]; +"145 quantize_per_tensor_default_17" [id=145, type=quantize_per_tensor]; +"146 dequantize_per_tensor_default_21" [id=146, type=dequantize_per_tensor]; +"147 add__4" [id=147, type=add_]; +"148 relu__10" [id=148, type=relu_]; +"149 quantize_per_tensor_default_18" [id=149, type=quantize_per_tensor]; +"150 dequantize_per_tensor_default_23" [id=150, type=dequantize_per_tensor]; +"151 dequantize_per_tensor_default_22" [id=151, type=dequantize_per_tensor]; +"152 layer3_1_conv1_weight_bias_0_0" [id=152, type=get_attr]; +"153 scale_updated_constant13" [id=153, type=get_attr]; +"154 compressed_weight_updated_constant13" [id=154, type=get_attr]; +"155 mul_tensor_13" [id=155, type=mul]; +"156 zero_point_updated_constant13" [id=156, type=get_attr]; +"157 sub_tensor_13" [id=157, type=sub]; +"158 conv2d_13" [id=158, type=conv2d]; +"159 relu__11" [id=159, type=relu_]; +"160 quantize_per_tensor_default_19" [id=160, type=quantize_per_tensor]; +"161 dequantize_per_tensor_default_24" [id=161, type=dequantize_per_tensor]; +"162 layer3_1_conv2_weight_bias_0_0" [id=162, type=get_attr]; +"163 scale_updated_constant14" [id=163, type=get_attr]; +"164 compressed_weight_updated_constant14" [id=164, type=get_attr]; +"165 mul_tensor_14" [id=165, type=mul]; +"166 zero_point_updated_constant14" [id=166, type=get_attr]; +"167 sub_tensor_14" [id=167, type=sub]; +"168 conv2d_14" [id=168, type=conv2d]; +"169 quantize_per_tensor_default_20" [id=169, type=quantize_per_tensor]; +"170 dequantize_per_tensor_default_25" [id=170, type=dequantize_per_tensor]; +"171 add__5" [id=171, type=add_]; +"172 relu__12" [id=172, type=relu_]; +"173 quantize_per_tensor_default_21" [id=173, type=quantize_per_tensor]; +"174 dequantize_per_tensor_default_27" [id=174, type=dequantize_per_tensor]; +"175 dequantize_per_tensor_default_26" [id=175, type=dequantize_per_tensor]; +"176 layer4_0_conv1_weight_bias_0_0" [id=176, type=get_attr]; +"177 scale_updated_constant15" [id=177, type=get_attr]; +"178 compressed_weight_updated_constant15" [id=178, type=get_attr]; +"179 mul_tensor_15" [id=179, type=mul]; +"180 zero_point_updated_constant15" [id=180, type=get_attr]; +"181 sub_tensor_15" [id=181, type=sub]; +"182 conv2d_15" [id=182, type=conv2d]; +"183 relu__13" [id=183, type=relu_]; +"184 quantize_per_tensor_default_22" [id=184, type=quantize_per_tensor]; +"185 dequantize_per_tensor_default_28" [id=185, type=dequantize_per_tensor]; +"186 layer4_0_conv2_weight_bias_0_0" [id=186, type=get_attr]; +"187 scale_updated_constant16" [id=187, type=get_attr]; +"188 compressed_weight_updated_constant16" [id=188, type=get_attr]; +"189 mul_tensor_16" [id=189, type=mul]; +"190 zero_point_updated_constant16" [id=190, type=get_attr]; +"191 sub_tensor_16" [id=191, type=sub]; +"192 conv2d_16" [id=192, type=conv2d]; +"193 quantize_per_tensor_default_23" [id=193, type=quantize_per_tensor]; +"194 dequantize_per_tensor_default_29" [id=194, type=dequantize_per_tensor]; +"195 layer4_0_downsample_0_weight_bias_0_0" [id=195, type=get_attr]; +"196 scale_updated_constant17" [id=196, type=get_attr]; +"197 compressed_weight_updated_constant17" [id=197, type=get_attr]; +"198 mul_tensor_17" [id=198, type=mul]; +"199 zero_point_updated_constant17" [id=199, type=get_attr]; +"200 sub_tensor_17" [id=200, type=sub]; +"201 conv2d_17" [id=201, type=conv2d]; +"202 quantize_per_tensor_default_24" [id=202, type=quantize_per_tensor]; +"203 dequantize_per_tensor_default_30" [id=203, type=dequantize_per_tensor]; +"204 add__6" [id=204, type=add_]; +"205 relu__14" [id=205, type=relu_]; +"206 quantize_per_tensor_default_25" [id=206, type=quantize_per_tensor]; +"207 dequantize_per_tensor_default_32" [id=207, type=dequantize_per_tensor]; +"208 dequantize_per_tensor_default_31" [id=208, type=dequantize_per_tensor]; +"209 layer4_1_conv1_weight_bias_0_0" [id=209, type=get_attr]; +"210 scale_updated_constant18" [id=210, type=get_attr]; +"211 compressed_weight_updated_constant18" [id=211, type=get_attr]; +"212 mul_tensor_18" [id=212, type=mul]; +"213 zero_point_updated_constant18" [id=213, type=get_attr]; +"214 sub_tensor_18" [id=214, type=sub]; +"215 conv2d_18" [id=215, type=conv2d]; +"216 relu__15" [id=216, type=relu_]; +"217 quantize_per_tensor_default_26" [id=217, type=quantize_per_tensor]; +"218 dequantize_per_tensor_default_33" [id=218, type=dequantize_per_tensor]; +"219 layer4_1_conv2_weight_bias_0_0" [id=219, type=get_attr]; +"220 scale_updated_constant19" [id=220, type=get_attr]; +"221 compressed_weight_updated_constant19" [id=221, type=get_attr]; +"222 mul_tensor_19" [id=222, type=mul]; +"223 zero_point_updated_constant19" [id=223, type=get_attr]; +"224 sub_tensor_19" [id=224, type=sub]; +"225 conv2d_19" [id=225, type=conv2d]; +"226 quantize_per_tensor_default_27" [id=226, type=quantize_per_tensor]; +"227 dequantize_per_tensor_default_34" [id=227, type=dequantize_per_tensor]; +"228 add__7" [id=228, type=add_]; +"229 relu__16" [id=229, type=relu_]; +"230 quantize_per_tensor_default_28" [id=230, type=quantize_per_tensor]; +"231 dequantize_per_tensor_default_35" [id=231, type=dequantize_per_tensor]; +"232 adaptive_avg_pool2d" [id=232, type=adaptive_avg_pool2d]; +"233 quantize_per_tensor_default_29" [id=233, type=quantize_per_tensor]; +"234 dequantize_per_tensor_default_36" [id=234, type=dequantize_per_tensor]; +"235 flatten" [id=235, type=flatten]; +"236 fc_bias_0_0" [id=236, type=get_attr]; +"237 scale_updated_constant20" [id=237, type=get_attr]; +"238 compressed_weight_updated_constant20" [id=238, type=get_attr]; +"239 mul_tensor_20" [id=239, type=mul]; +"240 zero_point_updated_constant20" [id=240, type=get_attr]; +"241 sub_tensor_20" [id=241, type=sub]; +"242 linear" [id=242, type=linear]; +"243 output" [id=243, type=output]; +"0 x" -> "1 quantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"1 quantize_per_tensor_default" -> "2 dequantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"2 dequantize_per_tensor_default" -> "9 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"3 conv1_weight_bias_0_0" -> "9 conv2d" [label="(64,)", style=solid]; +"4 scale_updated_constant0" -> "6 mul_tensor" [label="(64, 1, 1, 1)", style=solid]; +"5 compressed_weight_updated_constant0" -> "6 mul_tensor" [label="(64, 3, 7, 7)", style=solid]; +"6 mul_tensor" -> "8 sub_tensor" [label="(64, 3, 7, 7)", style=solid]; +"7 zero_point_updated_constant0" -> "8 sub_tensor" [label="(64, 1, 1, 1)", style=solid]; +"8 sub_tensor" -> "9 conv2d" [label="(64, 3, 7, 7)", style=solid]; +"9 conv2d" -> "10 relu_" [label="(1, 64, 112, 112)", style=solid]; +"10 relu_" -> "11 quantize_per_tensor_default_1" [label="(1, 64, 112, 112)", style=solid]; +"11 quantize_per_tensor_default_1" -> "12 dequantize_per_tensor_default_1" [label="(1, 64, 112, 112)", style=solid]; +"12 dequantize_per_tensor_default_1" -> "13 max_pool2d" [label="(1, 64, 112, 112)", style=solid]; +"13 max_pool2d" -> "20 conv2d_1" [label="(1, 64, 56, 56)", style=solid]; +"13 max_pool2d" -> "33 add_" [label="(1, 64, 56, 56)", style=solid]; +"14 layer1_0_conv1_weight_bias_0_0" -> "20 conv2d_1" [label="(64,)", style=solid]; +"15 scale_updated_constant1" -> "17 mul_tensor_1" [label="(64, 1, 1, 1)", style=solid]; +"16 compressed_weight_updated_constant1" -> "17 mul_tensor_1" [label="(64, 64, 3, 3)", style=solid]; +"17 mul_tensor_1" -> "19 sub_tensor_1" [label="(64, 64, 3, 3)", style=solid]; +"18 zero_point_updated_constant1" -> "19 sub_tensor_1" [label="(64, 1, 1, 1)", style=solid]; +"19 sub_tensor_1" -> "20 conv2d_1" [label="(64, 64, 3, 3)", style=solid]; +"20 conv2d_1" -> "21 relu__1" [label="(1, 64, 56, 56)", style=solid]; +"21 relu__1" -> "22 quantize_per_tensor_default_2" [label="(1, 64, 56, 56)", style=solid]; +"22 quantize_per_tensor_default_2" -> "23 dequantize_per_tensor_default_2" [label="(1, 64, 56, 56)", style=solid]; +"23 dequantize_per_tensor_default_2" -> "30 conv2d_2" [label="(1, 64, 56, 56)", style=solid]; +"24 layer1_0_conv2_weight_bias_0_0" -> "30 conv2d_2" [label="(64,)", style=solid]; +"25 scale_updated_constant2" -> "27 mul_tensor_2" [label="(64, 1, 1, 1)", style=solid]; +"26 compressed_weight_updated_constant2" -> "27 mul_tensor_2" [label="(64, 64, 3, 3)", style=solid]; +"27 mul_tensor_2" -> "29 sub_tensor_2" [label="(64, 64, 3, 3)", style=solid]; +"28 zero_point_updated_constant2" -> "29 sub_tensor_2" [label="(64, 1, 1, 1)", style=solid]; +"29 sub_tensor_2" -> "30 conv2d_2" [label="(64, 64, 3, 3)", style=solid]; +"30 conv2d_2" -> "31 quantize_per_tensor_default_3" [label="(1, 64, 56, 56)", style=solid]; +"31 quantize_per_tensor_default_3" -> "32 dequantize_per_tensor_default_3" [label="(1, 64, 56, 56)", style=solid]; +"32 dequantize_per_tensor_default_3" -> "33 add_" [label="(1, 64, 56, 56)", style=solid]; +"33 add_" -> "34 relu__2" [label="(1, 64, 56, 56)", style=solid]; +"34 relu__2" -> "35 quantize_per_tensor_default_4" [label="(1, 64, 56, 56)", style=solid]; +"35 quantize_per_tensor_default_4" -> "36 dequantize_per_tensor_default_5" [label="(1, 64, 56, 56)", style=solid]; +"35 quantize_per_tensor_default_4" -> "37 dequantize_per_tensor_default_4" [label="(1, 64, 56, 56)", style=solid]; +"36 dequantize_per_tensor_default_5" -> "57 add__1" [label="(1, 64, 56, 56)", style=solid]; +"37 dequantize_per_tensor_default_4" -> "44 conv2d_3" [label="(1, 64, 56, 56)", style=solid]; +"38 layer1_1_conv1_weight_bias_0_0" -> "44 conv2d_3" [label="(64,)", style=solid]; +"39 scale_updated_constant3" -> "41 mul_tensor_3" [label="(64, 1, 1, 1)", style=solid]; +"40 compressed_weight_updated_constant3" -> "41 mul_tensor_3" [label="(64, 64, 3, 3)", style=solid]; +"41 mul_tensor_3" -> "43 sub_tensor_3" [label="(64, 64, 3, 3)", style=solid]; +"42 zero_point_updated_constant3" -> "43 sub_tensor_3" [label="(64, 1, 1, 1)", style=solid]; +"43 sub_tensor_3" -> "44 conv2d_3" [label="(64, 64, 3, 3)", style=solid]; +"44 conv2d_3" -> "45 relu__3" [label="(1, 64, 56, 56)", style=solid]; +"45 relu__3" -> "46 quantize_per_tensor_default_5" [label="(1, 64, 56, 56)", style=solid]; +"46 quantize_per_tensor_default_5" -> "47 dequantize_per_tensor_default_6" [label="(1, 64, 56, 56)", style=solid]; +"47 dequantize_per_tensor_default_6" -> "54 conv2d_4" [label="(1, 64, 56, 56)", style=solid]; +"48 layer1_1_conv2_weight_bias_0_0" -> "54 conv2d_4" [label="(64,)", style=solid]; +"49 scale_updated_constant4" -> "51 mul_tensor_4" [label="(64, 1, 1, 1)", style=solid]; +"50 compressed_weight_updated_constant4" -> "51 mul_tensor_4" [label="(64, 64, 3, 3)", style=solid]; +"51 mul_tensor_4" -> "53 sub_tensor_4" [label="(64, 64, 3, 3)", style=solid]; +"52 zero_point_updated_constant4" -> "53 sub_tensor_4" [label="(64, 1, 1, 1)", style=solid]; +"53 sub_tensor_4" -> "54 conv2d_4" [label="(64, 64, 3, 3)", style=solid]; +"54 conv2d_4" -> "55 quantize_per_tensor_default_6" [label="(1, 64, 56, 56)", style=solid]; +"55 quantize_per_tensor_default_6" -> "56 dequantize_per_tensor_default_7" [label="(1, 64, 56, 56)", style=solid]; +"56 dequantize_per_tensor_default_7" -> "57 add__1" [label="(1, 64, 56, 56)", style=solid]; +"57 add__1" -> "58 relu__4" [label="(1, 64, 56, 56)", style=solid]; +"58 relu__4" -> "59 quantize_per_tensor_default_7" [label="(1, 64, 56, 56)", style=solid]; +"59 quantize_per_tensor_default_7" -> "60 dequantize_per_tensor_default_9" [label="(1, 64, 56, 56)", style=solid]; +"59 quantize_per_tensor_default_7" -> "61 dequantize_per_tensor_default_8" [label="(1, 64, 56, 56)", style=solid]; +"60 dequantize_per_tensor_default_9" -> "87 conv2d_7" [label="(1, 64, 56, 56)", style=solid]; +"61 dequantize_per_tensor_default_8" -> "68 conv2d_5" [label="(1, 64, 56, 56)", style=solid]; +"62 layer2_0_conv1_weight_bias_0_0" -> "68 conv2d_5" [label="(128,)", style=solid]; +"63 scale_updated_constant5" -> "65 mul_tensor_5" [label="(128, 1, 1, 1)", style=solid]; +"64 compressed_weight_updated_constant5" -> "65 mul_tensor_5" [label="(128, 64, 3, 3)", style=solid]; +"65 mul_tensor_5" -> "67 sub_tensor_5" [label="(128, 64, 3, 3)", style=solid]; +"66 zero_point_updated_constant5" -> "67 sub_tensor_5" [label="(128, 1, 1, 1)", style=solid]; +"67 sub_tensor_5" -> "68 conv2d_5" [label="(128, 64, 3, 3)", style=solid]; +"68 conv2d_5" -> "69 relu__5" [label="(1, 128, 28, 28)", style=solid]; +"69 relu__5" -> "70 quantize_per_tensor_default_8" [label="(1, 128, 28, 28)", style=solid]; +"70 quantize_per_tensor_default_8" -> "71 dequantize_per_tensor_default_10" [label="(1, 128, 28, 28)", style=solid]; +"71 dequantize_per_tensor_default_10" -> "78 conv2d_6" [label="(1, 128, 28, 28)", style=solid]; +"72 layer2_0_conv2_weight_bias_0_0" -> "78 conv2d_6" [label="(128,)", style=solid]; +"73 scale_updated_constant6" -> "75 mul_tensor_6" [label="(128, 1, 1, 1)", style=solid]; +"74 compressed_weight_updated_constant6" -> "75 mul_tensor_6" [label="(128, 128, 3, 3)", style=solid]; +"75 mul_tensor_6" -> "77 sub_tensor_6" [label="(128, 128, 3, 3)", style=solid]; +"76 zero_point_updated_constant6" -> "77 sub_tensor_6" [label="(128, 1, 1, 1)", style=solid]; +"77 sub_tensor_6" -> "78 conv2d_6" [label="(128, 128, 3, 3)", style=solid]; +"78 conv2d_6" -> "79 quantize_per_tensor_default_9" [label="(1, 128, 28, 28)", style=solid]; +"79 quantize_per_tensor_default_9" -> "80 dequantize_per_tensor_default_11" [label="(1, 128, 28, 28)", style=solid]; +"80 dequantize_per_tensor_default_11" -> "90 add__2" [label="(1, 128, 28, 28)", style=solid]; +"81 layer2_0_downsample_0_weight_bias_0_0" -> "87 conv2d_7" [label="(128,)", style=solid]; +"82 scale_updated_constant7" -> "84 mul_tensor_7" [label="(128, 1, 1, 1)", style=solid]; +"83 compressed_weight_updated_constant7" -> "84 mul_tensor_7" [label="(128, 64, 1, 1)", style=solid]; +"84 mul_tensor_7" -> "86 sub_tensor_7" [label="(128, 64, 1, 1)", style=solid]; +"85 zero_point_updated_constant7" -> "86 sub_tensor_7" [label="(128, 1, 1, 1)", style=solid]; +"86 sub_tensor_7" -> "87 conv2d_7" [label="(128, 64, 1, 1)", style=solid]; +"87 conv2d_7" -> "88 quantize_per_tensor_default_10" [label="(1, 128, 28, 28)", style=solid]; +"88 quantize_per_tensor_default_10" -> "89 dequantize_per_tensor_default_12" [label="(1, 128, 28, 28)", style=solid]; +"89 dequantize_per_tensor_default_12" -> "90 add__2" [label="(1, 128, 28, 28)", style=solid]; +"90 add__2" -> "91 relu__6" [label="(1, 128, 28, 28)", style=solid]; +"91 relu__6" -> "92 quantize_per_tensor_default_11" [label="(1, 128, 28, 28)", style=solid]; +"92 quantize_per_tensor_default_11" -> "93 dequantize_per_tensor_default_14" [label="(1, 128, 28, 28)", style=solid]; +"92 quantize_per_tensor_default_11" -> "94 dequantize_per_tensor_default_13" [label="(1, 128, 28, 28)", style=solid]; +"93 dequantize_per_tensor_default_14" -> "114 add__3" [label="(1, 128, 28, 28)", style=solid]; +"94 dequantize_per_tensor_default_13" -> "101 conv2d_8" [label="(1, 128, 28, 28)", style=solid]; +"95 layer2_1_conv1_weight_bias_0_0" -> "101 conv2d_8" [label="(128,)", style=solid]; +"96 scale_updated_constant8" -> "98 mul_tensor_8" [label="(128, 1, 1, 1)", style=solid]; +"97 compressed_weight_updated_constant8" -> "98 mul_tensor_8" [label="(128, 128, 3, 3)", style=solid]; +"98 mul_tensor_8" -> "100 sub_tensor_8" [label="(128, 128, 3, 3)", style=solid]; +"99 zero_point_updated_constant8" -> "100 sub_tensor_8" [label="(128, 1, 1, 1)", style=solid]; +"100 sub_tensor_8" -> "101 conv2d_8" [label="(128, 128, 3, 3)", style=solid]; +"101 conv2d_8" -> "102 relu__7" [label="(1, 128, 28, 28)", style=solid]; +"102 relu__7" -> "103 quantize_per_tensor_default_12" [label="(1, 128, 28, 28)", style=solid]; +"103 quantize_per_tensor_default_12" -> "104 dequantize_per_tensor_default_15" [label="(1, 128, 28, 28)", style=solid]; +"104 dequantize_per_tensor_default_15" -> "111 conv2d_9" [label="(1, 128, 28, 28)", style=solid]; +"105 layer2_1_conv2_weight_bias_0_0" -> "111 conv2d_9" [label="(128,)", style=solid]; +"106 scale_updated_constant9" -> "108 mul_tensor_9" [label="(128, 1, 1, 1)", style=solid]; +"107 compressed_weight_updated_constant9" -> "108 mul_tensor_9" [label="(128, 128, 3, 3)", style=solid]; +"108 mul_tensor_9" -> "110 sub_tensor_9" [label="(128, 128, 3, 3)", style=solid]; +"109 zero_point_updated_constant9" -> "110 sub_tensor_9" [label="(128, 1, 1, 1)", style=solid]; +"110 sub_tensor_9" -> "111 conv2d_9" [label="(128, 128, 3, 3)", style=solid]; +"111 conv2d_9" -> "112 quantize_per_tensor_default_13" [label="(1, 128, 28, 28)", style=solid]; +"112 quantize_per_tensor_default_13" -> "113 dequantize_per_tensor_default_16" [label="(1, 128, 28, 28)", style=solid]; +"113 dequantize_per_tensor_default_16" -> "114 add__3" [label="(1, 128, 28, 28)", style=solid]; +"114 add__3" -> "115 relu__8" [label="(1, 128, 28, 28)", style=solid]; +"115 relu__8" -> "116 quantize_per_tensor_default_14" [label="(1, 128, 28, 28)", style=solid]; +"116 quantize_per_tensor_default_14" -> "117 dequantize_per_tensor_default_18" [label="(1, 128, 28, 28)", style=solid]; +"116 quantize_per_tensor_default_14" -> "118 dequantize_per_tensor_default_17" [label="(1, 128, 28, 28)", style=solid]; +"117 dequantize_per_tensor_default_18" -> "144 conv2d_12" [label="(1, 128, 28, 28)", style=solid]; +"118 dequantize_per_tensor_default_17" -> "125 conv2d_10" [label="(1, 128, 28, 28)", style=solid]; +"119 layer3_0_conv1_weight_bias_0_0" -> "125 conv2d_10" [label="(256,)", style=solid]; +"120 scale_updated_constant10" -> "122 mul_tensor_10" [label="(256, 1, 1, 1)", style=solid]; +"121 compressed_weight_updated_constant10" -> "122 mul_tensor_10" [label="(256, 128, 3, 3)", style=solid]; +"122 mul_tensor_10" -> "124 sub_tensor_10" [label="(256, 128, 3, 3)", style=solid]; +"123 zero_point_updated_constant10" -> "124 sub_tensor_10" [label="(256, 1, 1, 1)", style=solid]; +"124 sub_tensor_10" -> "125 conv2d_10" [label="(256, 128, 3, 3)", style=solid]; +"125 conv2d_10" -> "126 relu__9" [label="(1, 256, 14, 14)", style=solid]; +"126 relu__9" -> "127 quantize_per_tensor_default_15" [label="(1, 256, 14, 14)", style=solid]; +"127 quantize_per_tensor_default_15" -> "128 dequantize_per_tensor_default_19" [label="(1, 256, 14, 14)", style=solid]; +"128 dequantize_per_tensor_default_19" -> "135 conv2d_11" [label="(1, 256, 14, 14)", style=solid]; +"129 layer3_0_conv2_weight_bias_0_0" -> "135 conv2d_11" [label="(256,)", style=solid]; +"130 scale_updated_constant11" -> "132 mul_tensor_11" [label="(256, 1, 1, 1)", style=solid]; +"131 compressed_weight_updated_constant11" -> "132 mul_tensor_11" [label="(256, 256, 3, 3)", style=solid]; +"132 mul_tensor_11" -> "134 sub_tensor_11" [label="(256, 256, 3, 3)", style=solid]; +"133 zero_point_updated_constant11" -> "134 sub_tensor_11" [label="(256, 1, 1, 1)", style=solid]; +"134 sub_tensor_11" -> "135 conv2d_11" [label="(256, 256, 3, 3)", style=solid]; +"135 conv2d_11" -> "136 quantize_per_tensor_default_16" [label="(1, 256, 14, 14)", style=solid]; +"136 quantize_per_tensor_default_16" -> "137 dequantize_per_tensor_default_20" [label="(1, 256, 14, 14)", style=solid]; +"137 dequantize_per_tensor_default_20" -> "147 add__4" [label="(1, 256, 14, 14)", style=solid]; +"138 layer3_0_downsample_0_weight_bias_0_0" -> "144 conv2d_12" [label="(256,)", style=solid]; +"139 scale_updated_constant12" -> "141 mul_tensor_12" [label="(256, 1, 1, 1)", style=solid]; +"140 compressed_weight_updated_constant12" -> "141 mul_tensor_12" [label="(256, 128, 1, 1)", style=solid]; +"141 mul_tensor_12" -> "143 sub_tensor_12" [label="(256, 128, 1, 1)", style=solid]; +"142 zero_point_updated_constant12" -> "143 sub_tensor_12" [label="(256, 1, 1, 1)", style=solid]; +"143 sub_tensor_12" -> "144 conv2d_12" [label="(256, 128, 1, 1)", style=solid]; +"144 conv2d_12" -> "145 quantize_per_tensor_default_17" [label="(1, 256, 14, 14)", style=solid]; +"145 quantize_per_tensor_default_17" -> "146 dequantize_per_tensor_default_21" [label="(1, 256, 14, 14)", style=solid]; +"146 dequantize_per_tensor_default_21" -> "147 add__4" [label="(1, 256, 14, 14)", style=solid]; +"147 add__4" -> "148 relu__10" [label="(1, 256, 14, 14)", style=solid]; +"148 relu__10" -> "149 quantize_per_tensor_default_18" [label="(1, 256, 14, 14)", style=solid]; +"149 quantize_per_tensor_default_18" -> "150 dequantize_per_tensor_default_23" [label="(1, 256, 14, 14)", style=solid]; +"149 quantize_per_tensor_default_18" -> "151 dequantize_per_tensor_default_22" [label="(1, 256, 14, 14)", style=solid]; +"150 dequantize_per_tensor_default_23" -> "171 add__5" [label="(1, 256, 14, 14)", style=solid]; +"151 dequantize_per_tensor_default_22" -> "158 conv2d_13" [label="(1, 256, 14, 14)", style=solid]; +"152 layer3_1_conv1_weight_bias_0_0" -> "158 conv2d_13" [label="(256,)", style=solid]; +"153 scale_updated_constant13" -> "155 mul_tensor_13" [label="(256, 1, 1, 1)", style=solid]; +"154 compressed_weight_updated_constant13" -> "155 mul_tensor_13" [label="(256, 256, 3, 3)", style=solid]; +"155 mul_tensor_13" -> "157 sub_tensor_13" [label="(256, 256, 3, 3)", style=solid]; +"156 zero_point_updated_constant13" -> "157 sub_tensor_13" [label="(256, 1, 1, 1)", style=solid]; +"157 sub_tensor_13" -> "158 conv2d_13" [label="(256, 256, 3, 3)", style=solid]; +"158 conv2d_13" -> "159 relu__11" [label="(1, 256, 14, 14)", style=solid]; +"159 relu__11" -> "160 quantize_per_tensor_default_19" [label="(1, 256, 14, 14)", style=solid]; +"160 quantize_per_tensor_default_19" -> "161 dequantize_per_tensor_default_24" [label="(1, 256, 14, 14)", style=solid]; +"161 dequantize_per_tensor_default_24" -> "168 conv2d_14" [label="(1, 256, 14, 14)", style=solid]; +"162 layer3_1_conv2_weight_bias_0_0" -> "168 conv2d_14" [label="(256,)", style=solid]; +"163 scale_updated_constant14" -> "165 mul_tensor_14" [label="(256, 1, 1, 1)", style=solid]; +"164 compressed_weight_updated_constant14" -> "165 mul_tensor_14" [label="(256, 256, 3, 3)", style=solid]; +"165 mul_tensor_14" -> "167 sub_tensor_14" [label="(256, 256, 3, 3)", style=solid]; +"166 zero_point_updated_constant14" -> "167 sub_tensor_14" [label="(256, 1, 1, 1)", style=solid]; +"167 sub_tensor_14" -> "168 conv2d_14" [label="(256, 256, 3, 3)", style=solid]; +"168 conv2d_14" -> "169 quantize_per_tensor_default_20" [label="(1, 256, 14, 14)", style=solid]; +"169 quantize_per_tensor_default_20" -> "170 dequantize_per_tensor_default_25" [label="(1, 256, 14, 14)", style=solid]; +"170 dequantize_per_tensor_default_25" -> "171 add__5" [label="(1, 256, 14, 14)", style=solid]; +"171 add__5" -> "172 relu__12" [label="(1, 256, 14, 14)", style=solid]; +"172 relu__12" -> "173 quantize_per_tensor_default_21" [label="(1, 256, 14, 14)", style=solid]; +"173 quantize_per_tensor_default_21" -> "174 dequantize_per_tensor_default_27" [label="(1, 256, 14, 14)", style=solid]; +"173 quantize_per_tensor_default_21" -> "175 dequantize_per_tensor_default_26" [label="(1, 256, 14, 14)", style=solid]; +"174 dequantize_per_tensor_default_27" -> "201 conv2d_17" [label="(1, 256, 14, 14)", style=solid]; +"175 dequantize_per_tensor_default_26" -> "182 conv2d_15" [label="(1, 256, 14, 14)", style=solid]; +"176 layer4_0_conv1_weight_bias_0_0" -> "182 conv2d_15" [label="(512,)", style=solid]; +"177 scale_updated_constant15" -> "179 mul_tensor_15" [label="(512, 1, 1, 1)", style=solid]; +"178 compressed_weight_updated_constant15" -> "179 mul_tensor_15" [label="(512, 256, 3, 3)", style=solid]; +"179 mul_tensor_15" -> "181 sub_tensor_15" [label="(512, 256, 3, 3)", style=solid]; +"180 zero_point_updated_constant15" -> "181 sub_tensor_15" [label="(512, 1, 1, 1)", style=solid]; +"181 sub_tensor_15" -> "182 conv2d_15" [label="(512, 256, 3, 3)", style=solid]; +"182 conv2d_15" -> "183 relu__13" [label="(1, 512, 7, 7)", style=solid]; +"183 relu__13" -> "184 quantize_per_tensor_default_22" [label="(1, 512, 7, 7)", style=solid]; +"184 quantize_per_tensor_default_22" -> "185 dequantize_per_tensor_default_28" [label="(1, 512, 7, 7)", style=solid]; +"185 dequantize_per_tensor_default_28" -> "192 conv2d_16" [label="(1, 512, 7, 7)", style=solid]; +"186 layer4_0_conv2_weight_bias_0_0" -> "192 conv2d_16" [label="(512,)", style=solid]; +"187 scale_updated_constant16" -> "189 mul_tensor_16" [label="(512, 1, 1, 1)", style=solid]; +"188 compressed_weight_updated_constant16" -> "189 mul_tensor_16" [label="(512, 512, 3, 3)", style=solid]; +"189 mul_tensor_16" -> "191 sub_tensor_16" [label="(512, 512, 3, 3)", style=solid]; +"190 zero_point_updated_constant16" -> "191 sub_tensor_16" [label="(512, 1, 1, 1)", style=solid]; +"191 sub_tensor_16" -> "192 conv2d_16" [label="(512, 512, 3, 3)", style=solid]; +"192 conv2d_16" -> "193 quantize_per_tensor_default_23" [label="(1, 512, 7, 7)", style=solid]; +"193 quantize_per_tensor_default_23" -> "194 dequantize_per_tensor_default_29" [label="(1, 512, 7, 7)", style=solid]; +"194 dequantize_per_tensor_default_29" -> "204 add__6" [label="(1, 512, 7, 7)", style=solid]; +"195 layer4_0_downsample_0_weight_bias_0_0" -> "201 conv2d_17" [label="(512,)", style=solid]; +"196 scale_updated_constant17" -> "198 mul_tensor_17" [label="(512, 1, 1, 1)", style=solid]; +"197 compressed_weight_updated_constant17" -> "198 mul_tensor_17" [label="(512, 256, 1, 1)", style=solid]; +"198 mul_tensor_17" -> "200 sub_tensor_17" [label="(512, 256, 1, 1)", style=solid]; +"199 zero_point_updated_constant17" -> "200 sub_tensor_17" [label="(512, 1, 1, 1)", style=solid]; +"200 sub_tensor_17" -> "201 conv2d_17" [label="(512, 256, 1, 1)", style=solid]; +"201 conv2d_17" -> "202 quantize_per_tensor_default_24" [label="(1, 512, 7, 7)", style=solid]; +"202 quantize_per_tensor_default_24" -> "203 dequantize_per_tensor_default_30" [label="(1, 512, 7, 7)", style=solid]; +"203 dequantize_per_tensor_default_30" -> "204 add__6" [label="(1, 512, 7, 7)", style=solid]; +"204 add__6" -> "205 relu__14" [label="(1, 512, 7, 7)", style=solid]; +"205 relu__14" -> "206 quantize_per_tensor_default_25" [label="(1, 512, 7, 7)", style=solid]; +"206 quantize_per_tensor_default_25" -> "207 dequantize_per_tensor_default_32" [label="(1, 512, 7, 7)", style=solid]; +"206 quantize_per_tensor_default_25" -> "208 dequantize_per_tensor_default_31" [label="(1, 512, 7, 7)", style=solid]; +"207 dequantize_per_tensor_default_32" -> "228 add__7" [label="(1, 512, 7, 7)", style=solid]; +"208 dequantize_per_tensor_default_31" -> "215 conv2d_18" [label="(1, 512, 7, 7)", style=solid]; +"209 layer4_1_conv1_weight_bias_0_0" -> "215 conv2d_18" [label="(512,)", style=solid]; +"210 scale_updated_constant18" -> "212 mul_tensor_18" [label="(512, 1, 1, 1)", style=solid]; +"211 compressed_weight_updated_constant18" -> "212 mul_tensor_18" [label="(512, 512, 3, 3)", style=solid]; +"212 mul_tensor_18" -> "214 sub_tensor_18" [label="(512, 512, 3, 3)", style=solid]; +"213 zero_point_updated_constant18" -> "214 sub_tensor_18" [label="(512, 1, 1, 1)", style=solid]; +"214 sub_tensor_18" -> "215 conv2d_18" [label="(512, 512, 3, 3)", style=solid]; +"215 conv2d_18" -> "216 relu__15" [label="(1, 512, 7, 7)", style=solid]; +"216 relu__15" -> "217 quantize_per_tensor_default_26" [label="(1, 512, 7, 7)", style=solid]; +"217 quantize_per_tensor_default_26" -> "218 dequantize_per_tensor_default_33" [label="(1, 512, 7, 7)", style=solid]; +"218 dequantize_per_tensor_default_33" -> "225 conv2d_19" [label="(1, 512, 7, 7)", style=solid]; +"219 layer4_1_conv2_weight_bias_0_0" -> "225 conv2d_19" [label="(512,)", style=solid]; +"220 scale_updated_constant19" -> "222 mul_tensor_19" [label="(512, 1, 1, 1)", style=solid]; +"221 compressed_weight_updated_constant19" -> "222 mul_tensor_19" [label="(512, 512, 3, 3)", style=solid]; +"222 mul_tensor_19" -> "224 sub_tensor_19" [label="(512, 512, 3, 3)", style=solid]; +"223 zero_point_updated_constant19" -> "224 sub_tensor_19" [label="(512, 1, 1, 1)", style=solid]; +"224 sub_tensor_19" -> "225 conv2d_19" [label="(512, 512, 3, 3)", style=solid]; +"225 conv2d_19" -> "226 quantize_per_tensor_default_27" [label="(1, 512, 7, 7)", style=solid]; +"226 quantize_per_tensor_default_27" -> "227 dequantize_per_tensor_default_34" [label="(1, 512, 7, 7)", style=solid]; +"227 dequantize_per_tensor_default_34" -> "228 add__7" [label="(1, 512, 7, 7)", style=solid]; +"228 add__7" -> "229 relu__16" [label="(1, 512, 7, 7)", style=solid]; +"229 relu__16" -> "230 quantize_per_tensor_default_28" [label="(1, 512, 7, 7)", style=solid]; +"230 quantize_per_tensor_default_28" -> "231 dequantize_per_tensor_default_35" [label="(1, 512, 7, 7)", style=solid]; +"231 dequantize_per_tensor_default_35" -> "232 adaptive_avg_pool2d" [label="(1, 512, 7, 7)", style=solid]; +"232 adaptive_avg_pool2d" -> "233 quantize_per_tensor_default_29" [label="(1, 512, 1, 1)", style=solid]; +"233 quantize_per_tensor_default_29" -> "234 dequantize_per_tensor_default_36" [label="(1, 512, 1, 1)", style=solid]; +"234 dequantize_per_tensor_default_36" -> "235 flatten" [label="(1, 512, 1, 1)", style=solid]; +"235 flatten" -> "242 linear" [label="(1, 512)", style=solid]; +"236 fc_bias_0_0" -> "242 linear" [label="(1000,)", style=solid]; +"237 scale_updated_constant20" -> "239 mul_tensor_20" [label="(1000, 1)", style=solid]; +"238 compressed_weight_updated_constant20" -> "239 mul_tensor_20" [label="(1000, 512)", style=solid]; +"239 mul_tensor_20" -> "241 sub_tensor_20" [label="(1000, 512)", style=solid]; +"240 zero_point_updated_constant20" -> "241 sub_tensor_20" [label="(1000, 1)", style=solid]; +"241 sub_tensor_20" -> "242 linear" [label="(1000, 512)", style=solid]; +"242 linear" -> "243 output" [label="(1, 1000)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/post_quantization_compressed/swin_v2_s.dot b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/swin_v2_s.dot new file mode 100644 index 00000000000..74e0e2c334c --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/swin_v2_s.dot @@ -0,0 +1,5264 @@ +strict digraph { +"0 features_0_2_weight" [id=0, type=get_attr]; +"1 features_0_2_bias" [id=1, type=get_attr]; +"2 features_1_0_norm1_weight" [id=2, type=get_attr]; +"3 features_1_0_norm1_bias" [id=3, type=get_attr]; +"4 features_1_0_norm2_weight" [id=4, type=get_attr]; +"5 features_1_0_norm2_bias" [id=5, type=get_attr]; +"6 features_1_1_norm1_weight" [id=6, 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dropout_62_0_0_nncf_smooth_quant_0" [id=1699, type=call_module]; +"1700 quantize_per_tensor_default_98" [id=1700, type=quantize_per_tensor]; +"1701 dequantize_per_tensor_default_98" [id=1701, type=dequantize_per_tensor]; +"1702 features_5_11_mlp_3_bias_0_0" [id=1702, type=get_attr]; +"1703 scale_updated_constant66" [id=1703, type=get_attr]; +"1704 compressed_weight_updated_constant66" [id=1704, type=get_attr]; +"1705 mul_tensor_66" [id=1705, type=mul]; +"1706 zero_point_updated_constant66" [id=1706, type=get_attr]; +"1707 sub_tensor_66" [id=1707, type=sub]; +"1708 linear_97" [id=1708, type=linear]; +"1709 dropout_63" [id=1709, type=dropout]; +"1710 layer_norm_34" [id=1710, type=layer_norm]; +"1711 add_55" [id=1711, type=add]; +"1712 _frozen_param136" [id=1712, type=get_attr]; +"1713 pad_18" [id=1713, type=pad]; +"1714 view_90" [id=1714, type=view]; +"1715 permute_74" [id=1715, type=permute]; +"1716 reshape_72" [id=1716, type=reshape]; +"1717 reshape_72_0_0_nncf_smooth_quant_0" [id=1717, type=call_module]; +"1718 quantize_per_tensor_default_99" [id=1718, type=quantize_per_tensor]; +"1719 dequantize_per_tensor_default_99" [id=1719, type=dequantize_per_tensor]; +"1720 _frozen_param137_0_0" [id=1720, type=get_attr]; +"1721 scale_updated_constant67" [id=1721, type=get_attr]; +"1722 compressed_weight_updated_constant67" [id=1722, type=get_attr]; +"1723 mul_tensor_67" [id=1723, type=mul]; +"1724 zero_point_updated_constant67" [id=1724, type=get_attr]; +"1725 sub_tensor_67" [id=1725, type=sub]; +"1726 linear_100" [id=1726, type=linear]; +"1727 reshape_73" [id=1727, type=reshape]; +"1728 permute_75" [id=1728, type=permute]; +"1729 select_48" [id=1729, type=select]; +"1730 select_49" [id=1730, type=select]; +"1731 select_50" [id=1731, type=select]; +"1732 linalg_vector_norm_32" [id=1732, type=linalg_vector_norm]; +"1733 clamp_min_32" [id=1733, type=clamp_min]; +"1734 expand_as_32" [id=1734, type=expand_as]; +"1735 div_32" [id=1735, type=div]; +"1736 quantize_per_tensor_default_100" [id=1736, type=quantize_per_tensor]; +"1737 dequantize_per_tensor_default_100" [id=1737, type=dequantize_per_tensor]; +"1738 linalg_vector_norm_33" [id=1738, type=linalg_vector_norm]; +"1739 clamp_min_33" [id=1739, type=clamp_min]; +"1740 expand_as_33" [id=1740, type=expand_as]; +"1741 div_33" [id=1741, type=div]; +"1742 quantize_per_tensor_default_101" [id=1742, type=quantize_per_tensor]; +"1743 dequantize_per_tensor_default_101" [id=1743, type=dequantize_per_tensor]; +"1744 transpose_32" [id=1744, type=transpose]; +"1745 matmul_32" [id=1745, type=matmul]; +"1746 _frozen_param139" [id=1746, type=get_attr]; +"1747 mul_33" [id=1747, type=mul]; +"1748 add_56" [id=1748, type=add]; +"1749 softmax_16" [id=1749, type=softmax]; +"1750 dropout_64" [id=1750, type=dropout]; +"1751 matmul_33" [id=1751, type=matmul]; +"1752 transpose_33" [id=1752, type=transpose]; +"1753 reshape_74" [id=1753, type=reshape]; +"1754 reshape_74_0_0_nncf_smooth_quant_0" [id=1754, type=call_module]; +"1755 quantize_per_tensor_default_102" [id=1755, type=quantize_per_tensor]; +"1756 dequantize_per_tensor_default_102" [id=1756, type=dequantize_per_tensor]; +"1757 features_5_12_attn_proj_bias_0_0" [id=1757, type=get_attr]; +"1758 scale_updated_constant68" [id=1758, type=get_attr]; +"1759 compressed_weight_updated_constant68" [id=1759, type=get_attr]; +"1760 mul_tensor_68" [id=1760, type=mul]; +"1761 zero_point_updated_constant68" [id=1761, type=get_attr]; +"1762 sub_tensor_68" [id=1762, type=sub]; +"1763 linear_101" [id=1763, type=linear]; +"1764 dropout_65" [id=1764, type=dropout]; +"1765 view_91" [id=1765, type=view]; +"1766 permute_76" [id=1766, type=permute]; +"1767 reshape_75" [id=1767, type=reshape]; +"1768 slice_246" [id=1768, type=slice]; +"1769 slice_247" [id=1769, type=slice]; +"1770 slice_248" [id=1770, type=slice]; +"1771 slice_249" [id=1771, type=slice]; +"1772 contiguous_31" [id=1772, type=contiguous]; +"1773 layer_norm_35" [id=1773, type=layer_norm]; 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[id=1790, type=get_attr]; +"1791 scale_updated_constant70" [id=1791, type=get_attr]; +"1792 compressed_weight_updated_constant70" [id=1792, type=get_attr]; +"1793 mul_tensor_70" [id=1793, type=mul]; +"1794 zero_point_updated_constant70" [id=1794, type=get_attr]; +"1795 sub_tensor_70" [id=1795, type=sub]; +"1796 linear_103" [id=1796, type=linear]; +"1797 dropout_67" [id=1797, type=dropout]; +"1798 layer_norm_36" [id=1798, type=layer_norm]; +"1799 add_58" [id=1799, type=add]; +"1800 _frozen_param140" [id=1800, type=get_attr]; +"1801 pad_19" [id=1801, type=pad]; +"1802 roll_16" [id=1802, type=roll]; +"1803 view_94" [id=1803, type=view]; +"1804 permute_78" [id=1804, type=permute]; +"1805 reshape_76" [id=1805, type=reshape]; +"1806 reshape_76_0_0_nncf_smooth_quant_0" [id=1806, type=call_module]; +"1807 quantize_per_tensor_default_105" [id=1807, type=quantize_per_tensor]; +"1808 dequantize_per_tensor_default_105" [id=1808, type=dequantize_per_tensor]; +"1809 _frozen_param141_0_0" [id=1809, type=get_attr]; +"1810 scale_updated_constant71" [id=1810, type=get_attr]; +"1811 compressed_weight_updated_constant71" [id=1811, type=get_attr]; +"1812 mul_tensor_71" [id=1812, type=mul]; +"1813 zero_point_updated_constant71" [id=1813, type=get_attr]; +"1814 sub_tensor_71" [id=1814, type=sub]; +"1815 linear_106" [id=1815, type=linear]; +"1816 reshape_77" [id=1816, type=reshape]; +"1817 permute_79" [id=1817, type=permute]; +"1818 select_51" [id=1818, type=select]; +"1819 select_52" [id=1819, type=select]; +"1820 select_53" [id=1820, type=select]; +"1821 linalg_vector_norm_34" [id=1821, type=linalg_vector_norm]; +"1822 clamp_min_34" [id=1822, type=clamp_min]; +"1823 expand_as_34" [id=1823, type=expand_as]; +"1824 div_34" [id=1824, type=div]; +"1825 quantize_per_tensor_default_106" [id=1825, type=quantize_per_tensor]; +"1826 dequantize_per_tensor_default_106" [id=1826, type=dequantize_per_tensor]; +"1827 linalg_vector_norm_35" [id=1827, type=linalg_vector_norm]; +"1828 clamp_min_35" [id=1828, type=clamp_min]; +"1829 expand_as_35" [id=1829, type=expand_as]; +"1830 div_35" [id=1830, type=div]; +"1831 quantize_per_tensor_default_107" [id=1831, type=quantize_per_tensor]; +"1832 dequantize_per_tensor_default_107" [id=1832, type=dequantize_per_tensor]; +"1833 transpose_34" [id=1833, type=transpose]; +"1834 matmul_34" [id=1834, type=matmul]; +"1835 _frozen_param143" [id=1835, type=get_attr]; +"1836 mul_35" [id=1836, type=mul]; +"1837 add_59" [id=1837, type=add]; +"1838 new_zeros_8" [id=1838, type=new_zeros]; +"1839 view_95" [id=1839, type=view]; +"1840 permute_80" [id=1840, type=permute]; +"1841 reshape_78" [id=1841, type=reshape]; +"1842 unsqueeze_50" [id=1842, type=unsqueeze]; +"1843 unsqueeze_51" [id=1843, type=unsqueeze]; +"1844 sub_8" [id=1844, type=sub]; +"1845 ne_8" [id=1845, type=ne]; +"1846 masked_fill_16" [id=1846, type=masked_fill]; +"1847 eq_8" [id=1847, type=eq]; +"1848 masked_fill_17" [id=1848, type=masked_fill]; +"1849 view_96" [id=1849, type=view]; +"1850 unsqueeze_52" [id=1850, type=unsqueeze]; +"1851 unsqueeze_53" [id=1851, type=unsqueeze]; +"1852 add_60" [id=1852, type=add]; +"1853 view_97" [id=1853, type=view]; +"1854 softmax_17" [id=1854, type=softmax]; +"1855 dropout_68" [id=1855, type=dropout]; +"1856 matmul_35" [id=1856, type=matmul]; +"1857 transpose_35" [id=1857, type=transpose]; +"1858 reshape_79" [id=1858, type=reshape]; +"1859 reshape_79_0_0_nncf_smooth_quant_0" [id=1859, type=call_module]; +"1860 quantize_per_tensor_default_108" [id=1860, type=quantize_per_tensor]; +"1861 dequantize_per_tensor_default_108" [id=1861, type=dequantize_per_tensor]; +"1862 features_5_13_attn_proj_bias_0_0" [id=1862, type=get_attr]; +"1863 scale_updated_constant72" [id=1863, type=get_attr]; +"1864 compressed_weight_updated_constant72" [id=1864, type=get_attr]; +"1865 mul_tensor_72" [id=1865, type=mul]; +"1866 zero_point_updated_constant72" [id=1866, type=get_attr]; +"1867 sub_tensor_72" [id=1867, type=sub]; +"1868 linear_107" [id=1868, type=linear]; +"1869 dropout_69" [id=1869, type=dropout]; +"1870 view_98" [id=1870, type=view]; +"1871 permute_81" [id=1871, type=permute]; +"1872 reshape_80" [id=1872, type=reshape]; +"1873 roll_17" [id=1873, type=roll]; +"1874 slice_269" [id=1874, type=slice]; +"1875 slice_270" [id=1875, type=slice]; +"1876 slice_271" [id=1876, type=slice]; +"1877 slice_272" [id=1877, type=slice]; +"1878 contiguous_33" [id=1878, type=contiguous]; +"1879 layer_norm_37" [id=1879, type=layer_norm]; +"1880 add_61" [id=1880, type=add]; +"1881 add_61_0_0_nncf_smooth_quant_0" [id=1881, type=call_module]; +"1882 quantize_per_tensor_default_109" [id=1882, type=quantize_per_tensor]; +"1883 dequantize_per_tensor_default_109" [id=1883, type=dequantize_per_tensor]; +"1884 features_5_13_mlp_0_bias_0_0" [id=1884, type=get_attr]; +"1885 scale_updated_constant73" [id=1885, type=get_attr]; +"1886 compressed_weight_updated_constant73" [id=1886, type=get_attr]; +"1887 mul_tensor_73" [id=1887, type=mul]; +"1888 zero_point_updated_constant73" [id=1888, type=get_attr]; +"1889 sub_tensor_73" [id=1889, type=sub]; +"1890 linear_108" [id=1890, type=linear]; +"1891 gelu_17" [id=1891, type=gelu]; +"1892 dropout_70" [id=1892, type=dropout]; +"1893 dropout_70_0_0_nncf_smooth_quant_0" [id=1893, type=call_module]; +"1894 quantize_per_tensor_default_110" [id=1894, type=quantize_per_tensor]; +"1895 dequantize_per_tensor_default_110" [id=1895, type=dequantize_per_tensor]; +"1896 features_5_13_mlp_3_bias_0_0" [id=1896, type=get_attr]; +"1897 scale_updated_constant74" [id=1897, type=get_attr]; +"1898 compressed_weight_updated_constant74" [id=1898, type=get_attr]; +"1899 mul_tensor_74" [id=1899, type=mul]; +"1900 zero_point_updated_constant74" [id=1900, type=get_attr]; +"1901 sub_tensor_74" [id=1901, type=sub]; +"1902 linear_109" [id=1902, type=linear]; +"1903 dropout_71" [id=1903, type=dropout]; +"1904 layer_norm_38" [id=1904, type=layer_norm]; +"1905 add_62" [id=1905, type=add]; +"1906 _frozen_param153" [id=1906, type=get_attr]; +"1907 pad_20" [id=1907, type=pad]; +"1908 view_101" [id=1908, type=view]; +"1909 permute_83" [id=1909, type=permute]; +"1910 reshape_81" [id=1910, type=reshape]; +"1911 reshape_81_0_0_nncf_smooth_quant_0" [id=1911, type=call_module]; +"1912 quantize_per_tensor_default_111" [id=1912, type=quantize_per_tensor]; +"1913 dequantize_per_tensor_default_111" [id=1913, type=dequantize_per_tensor]; +"1914 _frozen_param154_0_0" [id=1914, type=get_attr]; +"1915 scale_updated_constant75" [id=1915, type=get_attr]; +"1916 compressed_weight_updated_constant75" [id=1916, type=get_attr]; +"1917 mul_tensor_75" [id=1917, type=mul]; +"1918 zero_point_updated_constant75" [id=1918, type=get_attr]; +"1919 sub_tensor_75" [id=1919, type=sub]; +"1920 linear_112" [id=1920, type=linear]; +"1921 reshape_82" [id=1921, type=reshape]; +"1922 permute_84" [id=1922, type=permute]; +"1923 select_54" [id=1923, type=select]; +"1924 select_55" [id=1924, type=select]; +"1925 select_56" [id=1925, type=select]; +"1926 linalg_vector_norm_36" [id=1926, type=linalg_vector_norm]; +"1927 clamp_min_36" [id=1927, type=clamp_min]; +"1928 expand_as_36" [id=1928, type=expand_as]; +"1929 div_36" [id=1929, type=div]; +"1930 quantize_per_tensor_default_112" [id=1930, type=quantize_per_tensor]; +"1931 dequantize_per_tensor_default_112" [id=1931, type=dequantize_per_tensor]; +"1932 linalg_vector_norm_37" [id=1932, type=linalg_vector_norm]; +"1933 clamp_min_37" [id=1933, type=clamp_min]; +"1934 expand_as_37" [id=1934, type=expand_as]; +"1935 div_37" [id=1935, type=div]; +"1936 quantize_per_tensor_default_113" [id=1936, type=quantize_per_tensor]; +"1937 dequantize_per_tensor_default_113" [id=1937, type=dequantize_per_tensor]; +"1938 transpose_36" [id=1938, type=transpose]; +"1939 matmul_36" [id=1939, type=matmul]; +"1940 _frozen_param156" [id=1940, type=get_attr]; +"1941 mul_37" [id=1941, type=mul]; +"1942 add_63" [id=1942, type=add]; +"1943 softmax_18" [id=1943, type=softmax]; +"1944 dropout_72" [id=1944, type=dropout]; +"1945 matmul_37" [id=1945, type=matmul]; +"1946 transpose_37" [id=1946, type=transpose]; +"1947 reshape_83" [id=1947, type=reshape]; +"1948 reshape_83_0_0_nncf_smooth_quant_0" [id=1948, type=call_module]; +"1949 quantize_per_tensor_default_114" [id=1949, type=quantize_per_tensor]; +"1950 dequantize_per_tensor_default_114" [id=1950, type=dequantize_per_tensor]; +"1951 features_5_14_attn_proj_bias_0_0" [id=1951, type=get_attr]; +"1952 scale_updated_constant76" [id=1952, type=get_attr]; +"1953 compressed_weight_updated_constant76" [id=1953, type=get_attr]; +"1954 mul_tensor_76" [id=1954, type=mul]; +"1955 zero_point_updated_constant76" [id=1955, type=get_attr]; +"1956 sub_tensor_76" [id=1956, type=sub]; +"1957 linear_113" [id=1957, type=linear]; +"1958 dropout_73" [id=1958, type=dropout]; +"1959 view_102" [id=1959, type=view]; +"1960 permute_85" [id=1960, type=permute]; +"1961 reshape_84" [id=1961, type=reshape]; +"1962 slice_274" [id=1962, type=slice]; +"1963 slice_275" [id=1963, type=slice]; +"1964 slice_276" [id=1964, type=slice]; +"1965 slice_277" [id=1965, type=slice]; +"1966 contiguous_35" [id=1966, type=contiguous]; +"1967 layer_norm_39" [id=1967, type=layer_norm]; +"1968 add_64" [id=1968, type=add]; +"1969 add_64_0_0_nncf_smooth_quant_0" [id=1969, type=call_module]; +"1970 quantize_per_tensor_default_115" [id=1970, type=quantize_per_tensor]; +"1971 dequantize_per_tensor_default_115" [id=1971, type=dequantize_per_tensor]; +"1972 features_5_14_mlp_0_bias_0_0" [id=1972, type=get_attr]; +"1973 scale_updated_constant77" [id=1973, type=get_attr]; +"1974 compressed_weight_updated_constant77" [id=1974, type=get_attr]; +"1975 mul_tensor_77" [id=1975, type=mul]; +"1976 zero_point_updated_constant77" [id=1976, type=get_attr]; +"1977 sub_tensor_77" [id=1977, type=sub]; +"1978 linear_114" [id=1978, type=linear]; +"1979 gelu_18" [id=1979, type=gelu]; +"1980 dropout_74" [id=1980, type=dropout]; +"1981 dropout_74_0_0_nncf_smooth_quant_0" [id=1981, type=call_module]; +"1982 quantize_per_tensor_default_116" [id=1982, type=quantize_per_tensor]; +"1983 dequantize_per_tensor_default_116" [id=1983, type=dequantize_per_tensor]; +"1984 features_5_14_mlp_3_bias_0_0" [id=1984, type=get_attr]; +"1985 scale_updated_constant78" [id=1985, type=get_attr]; +"1986 compressed_weight_updated_constant78" [id=1986, type=get_attr]; +"1987 mul_tensor_78" [id=1987, type=mul]; +"1988 zero_point_updated_constant78" [id=1988, type=get_attr]; +"1989 sub_tensor_78" [id=1989, type=sub]; +"1990 linear_115" [id=1990, type=linear]; +"1991 dropout_75" [id=1991, type=dropout]; +"1992 layer_norm_40" [id=1992, type=layer_norm]; +"1993 add_65" [id=1993, type=add]; +"1994 _frozen_param157" [id=1994, type=get_attr]; +"1995 pad_21" [id=1995, type=pad]; +"1996 roll_18" [id=1996, type=roll]; +"1997 view_105" [id=1997, type=view]; +"1998 permute_87" [id=1998, type=permute]; +"1999 reshape_85" [id=1999, type=reshape]; +"2000 reshape_85_0_0_nncf_smooth_quant_0" [id=2000, type=call_module]; +"2001 quantize_per_tensor_default_117" [id=2001, type=quantize_per_tensor]; +"2002 dequantize_per_tensor_default_117" [id=2002, type=dequantize_per_tensor]; +"2003 _frozen_param158_0_0" [id=2003, type=get_attr]; +"2004 scale_updated_constant79" [id=2004, type=get_attr]; +"2005 compressed_weight_updated_constant79" [id=2005, type=get_attr]; +"2006 mul_tensor_79" [id=2006, type=mul]; +"2007 zero_point_updated_constant79" [id=2007, type=get_attr]; +"2008 sub_tensor_79" [id=2008, type=sub]; +"2009 linear_118" [id=2009, type=linear]; +"2010 reshape_86" [id=2010, type=reshape]; +"2011 permute_88" [id=2011, type=permute]; +"2012 select_57" [id=2012, type=select]; +"2013 select_58" [id=2013, type=select]; +"2014 select_59" [id=2014, type=select]; +"2015 linalg_vector_norm_38" [id=2015, type=linalg_vector_norm]; +"2016 clamp_min_38" [id=2016, type=clamp_min]; +"2017 expand_as_38" [id=2017, type=expand_as]; +"2018 div_38" [id=2018, type=div]; +"2019 quantize_per_tensor_default_118" [id=2019, type=quantize_per_tensor]; +"2020 dequantize_per_tensor_default_118" [id=2020, type=dequantize_per_tensor]; +"2021 linalg_vector_norm_39" [id=2021, type=linalg_vector_norm]; +"2022 clamp_min_39" [id=2022, type=clamp_min]; +"2023 expand_as_39" [id=2023, type=expand_as]; +"2024 div_39" [id=2024, type=div]; +"2025 quantize_per_tensor_default_119" [id=2025, type=quantize_per_tensor]; +"2026 dequantize_per_tensor_default_119" [id=2026, type=dequantize_per_tensor]; +"2027 transpose_38" [id=2027, type=transpose]; +"2028 matmul_38" [id=2028, type=matmul]; +"2029 _frozen_param160" [id=2029, type=get_attr]; +"2030 mul_39" [id=2030, type=mul]; +"2031 add_66" [id=2031, type=add]; +"2032 new_zeros_9" [id=2032, type=new_zeros]; +"2033 view_106" [id=2033, type=view]; +"2034 permute_89" [id=2034, type=permute]; +"2035 reshape_87" [id=2035, type=reshape]; +"2036 unsqueeze_56" [id=2036, type=unsqueeze]; +"2037 unsqueeze_57" [id=2037, type=unsqueeze]; +"2038 sub_9" [id=2038, type=sub]; +"2039 ne_9" [id=2039, type=ne]; +"2040 masked_fill_18" [id=2040, type=masked_fill]; +"2041 eq_9" [id=2041, type=eq]; +"2042 masked_fill_19" [id=2042, type=masked_fill]; +"2043 view_107" [id=2043, type=view]; +"2044 unsqueeze_58" [id=2044, type=unsqueeze]; +"2045 unsqueeze_59" [id=2045, type=unsqueeze]; +"2046 add_67" [id=2046, type=add]; +"2047 view_108" [id=2047, type=view]; +"2048 softmax_19" [id=2048, type=softmax]; +"2049 dropout_76" [id=2049, type=dropout]; +"2050 matmul_39" [id=2050, type=matmul]; +"2051 transpose_39" [id=2051, type=transpose]; +"2052 reshape_88" [id=2052, type=reshape]; +"2053 reshape_88_0_0_nncf_smooth_quant_0" [id=2053, type=call_module]; +"2054 quantize_per_tensor_default_120" [id=2054, type=quantize_per_tensor]; +"2055 dequantize_per_tensor_default_120" [id=2055, type=dequantize_per_tensor]; +"2056 features_5_15_attn_proj_bias_0_0" [id=2056, type=get_attr]; +"2057 scale_updated_constant80" [id=2057, type=get_attr]; +"2058 compressed_weight_updated_constant80" [id=2058, type=get_attr]; +"2059 mul_tensor_80" [id=2059, type=mul]; +"2060 zero_point_updated_constant80" [id=2060, type=get_attr]; +"2061 sub_tensor_80" [id=2061, type=sub]; +"2062 linear_119" [id=2062, type=linear]; +"2063 dropout_77" [id=2063, type=dropout]; +"2064 view_109" [id=2064, type=view]; +"2065 permute_90" [id=2065, type=permute]; +"2066 reshape_89" [id=2066, type=reshape]; +"2067 roll_19" [id=2067, type=roll]; +"2068 slice_297" [id=2068, type=slice]; +"2069 slice_298" [id=2069, type=slice]; +"2070 slice_299" [id=2070, type=slice]; +"2071 slice_300" [id=2071, type=slice]; +"2072 contiguous_37" [id=2072, type=contiguous]; +"2073 layer_norm_41" [id=2073, type=layer_norm]; +"2074 add_68" [id=2074, type=add]; +"2075 add_68_0_0_nncf_smooth_quant_0" [id=2075, type=call_module]; +"2076 quantize_per_tensor_default_121" [id=2076, type=quantize_per_tensor]; +"2077 dequantize_per_tensor_default_121" [id=2077, type=dequantize_per_tensor]; +"2078 features_5_15_mlp_0_bias_0_0" [id=2078, type=get_attr]; +"2079 scale_updated_constant81" [id=2079, type=get_attr]; +"2080 compressed_weight_updated_constant81" [id=2080, type=get_attr]; +"2081 mul_tensor_81" [id=2081, type=mul]; +"2082 zero_point_updated_constant81" [id=2082, type=get_attr]; +"2083 sub_tensor_81" [id=2083, type=sub]; +"2084 linear_120" [id=2084, type=linear]; +"2085 gelu_19" [id=2085, type=gelu]; +"2086 dropout_78" [id=2086, type=dropout]; +"2087 dropout_78_0_0_nncf_smooth_quant_0" [id=2087, type=call_module]; +"2088 quantize_per_tensor_default_122" [id=2088, type=quantize_per_tensor]; +"2089 dequantize_per_tensor_default_122" [id=2089, type=dequantize_per_tensor]; +"2090 features_5_15_mlp_3_bias_0_0" [id=2090, type=get_attr]; +"2091 scale_updated_constant82" [id=2091, type=get_attr]; +"2092 compressed_weight_updated_constant82" [id=2092, type=get_attr]; +"2093 mul_tensor_82" [id=2093, type=mul]; +"2094 zero_point_updated_constant82" 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[id=2113, type=sub]; +"2114 linear_124" [id=2114, type=linear]; +"2115 reshape_91" [id=2115, type=reshape]; +"2116 permute_93" [id=2116, type=permute]; +"2117 select_60" [id=2117, type=select]; +"2118 select_61" [id=2118, type=select]; +"2119 select_62" [id=2119, type=select]; +"2120 linalg_vector_norm_40" [id=2120, type=linalg_vector_norm]; +"2121 clamp_min_40" [id=2121, type=clamp_min]; +"2122 expand_as_40" [id=2122, type=expand_as]; +"2123 div_40" [id=2123, type=div]; +"2124 quantize_per_tensor_default_124" [id=2124, type=quantize_per_tensor]; +"2125 dequantize_per_tensor_default_124" [id=2125, type=dequantize_per_tensor]; +"2126 linalg_vector_norm_41" [id=2126, type=linalg_vector_norm]; +"2127 clamp_min_41" [id=2127, type=clamp_min]; +"2128 expand_as_41" [id=2128, type=expand_as]; +"2129 div_41" [id=2129, type=div]; +"2130 quantize_per_tensor_default_125" [id=2130, type=quantize_per_tensor]; +"2131 dequantize_per_tensor_default_125" [id=2131, type=dequantize_per_tensor]; +"2132 transpose_40" [id=2132, type=transpose]; +"2133 matmul_40" [id=2133, type=matmul]; +"2134 _frozen_param173" [id=2134, type=get_attr]; +"2135 mul_41" [id=2135, type=mul]; +"2136 add_70" [id=2136, type=add]; +"2137 softmax_20" [id=2137, type=softmax]; +"2138 dropout_80" [id=2138, type=dropout]; +"2139 matmul_41" [id=2139, type=matmul]; +"2140 transpose_41" [id=2140, type=transpose]; +"2141 reshape_92" [id=2141, type=reshape]; +"2142 reshape_92_0_0_nncf_smooth_quant_0" [id=2142, type=call_module]; +"2143 quantize_per_tensor_default_126" [id=2143, type=quantize_per_tensor]; +"2144 dequantize_per_tensor_default_126" [id=2144, type=dequantize_per_tensor]; +"2145 features_5_16_attn_proj_bias_0_0" [id=2145, type=get_attr]; +"2146 scale_updated_constant84" [id=2146, type=get_attr]; +"2147 compressed_weight_updated_constant84" [id=2147, type=get_attr]; +"2148 mul_tensor_84" [id=2148, type=mul]; +"2149 zero_point_updated_constant84" [id=2149, type=get_attr]; +"2150 sub_tensor_84" [id=2150, type=sub]; +"2151 linear_125" [id=2151, type=linear]; +"2152 dropout_81" [id=2152, type=dropout]; +"2153 view_113" [id=2153, type=view]; +"2154 permute_94" [id=2154, type=permute]; +"2155 reshape_93" [id=2155, type=reshape]; +"2156 slice_302" [id=2156, type=slice]; +"2157 slice_303" [id=2157, type=slice]; +"2158 slice_304" [id=2158, type=slice]; +"2159 slice_305" [id=2159, type=slice]; +"2160 contiguous_39" [id=2160, type=contiguous]; +"2161 layer_norm_43" [id=2161, type=layer_norm]; +"2162 add_71" [id=2162, type=add]; +"2163 add_71_0_0_nncf_smooth_quant_0" [id=2163, type=call_module]; +"2164 quantize_per_tensor_default_127" [id=2164, type=quantize_per_tensor]; +"2165 dequantize_per_tensor_default_127" [id=2165, type=dequantize_per_tensor]; +"2166 features_5_16_mlp_0_bias_0_0" [id=2166, type=get_attr]; +"2167 scale_updated_constant85" [id=2167, type=get_attr]; +"2168 compressed_weight_updated_constant85" [id=2168, type=get_attr]; +"2169 mul_tensor_85" [id=2169, type=mul]; +"2170 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style=solid]; +"81 features_5_14_norm2_bias" -> "1992 layer_norm_40" [label="(384,)", style=solid]; +"82 features_5_15_norm1_weight" -> "2073 layer_norm_41" [label="(384,)", style=solid]; +"83 features_5_15_norm1_bias" -> "2073 layer_norm_41" [label="(384,)", style=solid]; +"84 features_5_15_norm2_weight" -> "2098 layer_norm_42" [label="(384,)", style=solid]; +"85 features_5_15_norm2_bias" -> "2098 layer_norm_42" [label="(384,)", style=solid]; +"86 features_5_16_norm1_weight" -> "2161 layer_norm_43" [label="(384,)", style=solid]; +"87 features_5_16_norm1_bias" -> "2161 layer_norm_43" [label="(384,)", style=solid]; +"88 features_5_16_norm2_weight" -> "2186 layer_norm_44" [label="(384,)", style=solid]; +"89 features_5_16_norm2_bias" -> "2186 layer_norm_44" [label="(384,)", style=solid]; +"90 features_5_17_norm1_weight" -> "2267 layer_norm_45" [label="(384,)", style=solid]; +"91 features_5_17_norm1_bias" -> "2267 layer_norm_45" [label="(384,)", style=solid]; +"92 features_5_17_norm2_weight" -> "2292 layer_norm_46" [label="(384,)", style=solid]; +"93 features_5_17_norm2_bias" -> "2292 layer_norm_46" [label="(384,)", style=solid]; +"94 features_6_norm_weight" -> "2317 layer_norm_47" [label="(768,)", style=solid]; +"95 features_6_norm_bias" -> "2317 layer_norm_47" [label="(768,)", style=solid]; +"96 features_7_0_norm1_weight" -> "2379 layer_norm_48" [label="(768,)", style=solid]; +"97 features_7_0_norm1_bias" -> "2379 layer_norm_48" [label="(768,)", style=solid]; +"98 features_7_0_norm2_weight" -> "2404 layer_norm_49" [label="(768,)", style=solid]; +"99 features_7_0_norm2_bias" -> "2404 layer_norm_49" [label="(768,)", style=solid]; +"100 features_7_1_norm1_weight" -> "2467 layer_norm_50" [label="(768,)", style=solid]; +"101 features_7_1_norm1_bias" -> "2467 layer_norm_50" [label="(768,)", style=solid]; +"102 features_7_1_norm2_weight" -> "2492 layer_norm_51" [label="(768,)", style=solid]; +"103 features_7_1_norm2_bias" -> "2492 layer_norm_51" [label="(768,)", style=solid]; +"104 norm_weight" -> "2494 layer_norm_52" [label="(768,)", style=solid]; +"105 norm_bias" -> "2494 layer_norm_52" [label="(768,)", style=solid]; +"106 x" -> "107 quantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"107 quantize_per_tensor_default" -> "108 dequantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"108 dequantize_per_tensor_default" -> "115 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"109 features_0_0_bias_0_0" -> "115 conv2d" [label="(96,)", style=solid]; +"110 scale_updated_constant0" -> "112 mul_tensor" [label="(96, 1, 1, 1)", style=solid]; +"111 compressed_weight_updated_constant0" -> "112 mul_tensor" [label="(96, 3, 4, 4)", style=solid]; +"112 mul_tensor" -> "114 sub_tensor" [label="(96, 3, 4, 4)", style=solid]; +"113 zero_point_updated_constant0" -> "114 sub_tensor" [label="(96, 1, 1, 1)", style=solid]; +"114 sub_tensor" -> "115 conv2d" [label="(96, 3, 4, 4)", style=solid]; +"115 conv2d" -> "116 permute" [label="(1, 96, 56, 56)", style=solid]; +"116 permute" -> "117 layer_norm" [label="(1, 56, 56, 96)", style=solid]; +"117 layer_norm" -> "119 pad" [label="(1, 56, 56, 96)", style=solid]; +"117 layer_norm" -> "177 add_1" [label="(1, 56, 56, 96)", style=solid]; +"118 _frozen_param0" -> "154 add" [label="(1, 3, 64, 64)", style=solid]; +"119 pad" -> "120 view_2" [label="(1, 56, 56, 96)", style=solid]; +"120 view_2" -> "121 permute_2" [label="(1, 7, 8, 7, 8, 96)", style=solid]; +"121 permute_2" -> "122 reshape" [label="(1, 7, 7, 8, 8, 96)", style=solid]; +"122 reshape" -> "123 reshape_0_0_nncf_smooth_quant_0" [label="(49, 64, 96)", style=solid]; +"123 reshape_0_0_nncf_smooth_quant_0" -> "124 quantize_per_tensor_default_1" [label="(49, 64, 96)", style=solid]; +"124 quantize_per_tensor_default_1" -> "125 dequantize_per_tensor_default_1" [label="(49, 64, 96)", style=solid]; +"125 dequantize_per_tensor_default_1" -> "132 linear_2" [label="(49, 64, 96)", style=solid]; +"126 _frozen_param1_0_0" -> "132 linear_2" [label="(288,)", style=solid]; +"127 scale_updated_constant1" -> "129 mul_tensor_1" [label="(288, 1)", style=solid]; +"128 compressed_weight_updated_constant1" -> "129 mul_tensor_1" [label="(288, 96)", style=solid]; +"129 mul_tensor_1" -> "131 sub_tensor_1" [label="(288, 96)", style=solid]; +"130 zero_point_updated_constant1" -> "131 sub_tensor_1" [label="(288, 1)", style=solid]; +"131 sub_tensor_1" -> "132 linear_2" [label="(288, 96)", style=solid]; +"132 linear_2" -> "133 reshape_1" [label="(49, 64, 288)", style=solid]; +"133 reshape_1" -> "134 permute_3" [label="(49, 64, 3, 3, 32)", style=solid]; +"134 permute_3" -> "135 select" [label="(3, 49, 3, 64, 32)", style=solid]; +"134 permute_3" -> "136 select_1" [label="(3, 49, 3, 64, 32)", style=solid]; +"134 permute_3" -> "137 select_2" [label="(3, 49, 3, 64, 32)", style=solid]; +"135 select" -> "138 linalg_vector_norm" [label="(49, 3, 64, 32)", style=solid]; +"135 select" -> "140 expand_as" [label="(49, 3, 64, 32)", style=solid]; +"135 select" -> "141 div" [label="(49, 3, 64, 32)", style=solid]; +"136 select_1" -> "144 linalg_vector_norm_1" [label="(49, 3, 64, 32)", style=solid]; +"136 select_1" -> "146 expand_as_1" [label="(49, 3, 64, 32)", style=solid]; +"136 select_1" -> "147 div_1" [label="(49, 3, 64, 32)", style=solid]; +"137 select_2" -> "157 matmul_1" [label="(49, 3, 64, 32)", style=solid]; +"138 linalg_vector_norm" -> "139 clamp_min" [label="(49, 3, 64, 1)", style=solid]; +"139 clamp_min" -> "140 expand_as" [label="(49, 3, 64, 1)", style=solid]; +"140 expand_as" -> "141 div" [label="(49, 3, 64, 32)", style=solid]; +"141 div" -> "142 quantize_per_tensor_default_2" [label="(49, 3, 64, 32)", style=solid]; +"142 quantize_per_tensor_default_2" -> "143 dequantize_per_tensor_default_2" [label="(49, 3, 64, 32)", style=solid]; +"143 dequantize_per_tensor_default_2" -> "151 matmul" [label="(49, 3, 64, 32)", style=solid]; +"144 linalg_vector_norm_1" -> "145 clamp_min_1" [label="(49, 3, 64, 1)", style=solid]; +"145 clamp_min_1" -> "146 expand_as_1" [label="(49, 3, 64, 1)", style=solid]; +"146 expand_as_1" -> "147 div_1" [label="(49, 3, 64, 32)", style=solid]; +"147 div_1" -> "148 quantize_per_tensor_default_3" [label="(49, 3, 64, 32)", style=solid]; +"148 quantize_per_tensor_default_3" -> "149 dequantize_per_tensor_default_3" [label="(49, 3, 64, 32)", style=solid]; +"149 dequantize_per_tensor_default_3" -> "150 transpose" [label="(49, 3, 64, 32)", style=solid]; +"150 transpose" -> "151 matmul" [label="(49, 3, 32, 64)", style=solid]; +"151 matmul" -> "153 mul_1" [label="(49, 3, 64, 64)", style=solid]; +"152 _frozen_param3" -> "153 mul_1" [label="(3, 1, 1)", style=solid]; +"153 mul_1" -> "154 add" [label="(49, 3, 64, 64)", style=solid]; +"154 add" -> "155 softmax" [label="(49, 3, 64, 64)", style=solid]; +"155 softmax" -> "156 dropout" [label="(49, 3, 64, 64)", style=solid]; +"156 dropout" -> "157 matmul_1" [label="(49, 3, 64, 64)", style=solid]; +"157 matmul_1" -> "158 transpose_1" [label="(49, 3, 64, 32)", style=solid]; +"158 transpose_1" -> "159 reshape_2" [label="(49, 64, 3, 32)", style=solid]; +"159 reshape_2" -> "160 reshape_2_0_0_nncf_smooth_quant_0" [label="(49, 64, 96)", style=solid]; +"160 reshape_2_0_0_nncf_smooth_quant_0" -> "161 quantize_per_tensor_default_4" [label="(49, 64, 96)", style=solid]; +"161 quantize_per_tensor_default_4" -> "162 dequantize_per_tensor_default_4" [label="(49, 64, 96)", style=solid]; +"162 dequantize_per_tensor_default_4" -> "169 linear_3" [label="(49, 64, 96)", style=solid]; +"163 features_1_0_attn_proj_bias_0_0" -> "169 linear_3" [label="(96,)", style=solid]; +"164 scale_updated_constant2" -> "166 mul_tensor_2" [label="(96, 1)", style=solid]; +"165 compressed_weight_updated_constant2" -> "166 mul_tensor_2" [label="(96, 96)", style=solid]; +"166 mul_tensor_2" -> "168 sub_tensor_2" [label="(96, 96)", style=solid]; +"167 zero_point_updated_constant2" -> "168 sub_tensor_2" [label="(96, 1)", style=solid]; +"168 sub_tensor_2" -> "169 linear_3" [label="(96, 96)", style=solid]; +"169 linear_3" -> "170 dropout_1" [label="(49, 64, 96)", style=solid]; +"170 dropout_1" -> "171 view_3" [label="(49, 64, 96)", style=solid]; +"171 view_3" -> "172 permute_4" [label="(1, 7, 7, 8, 8, 96)", style=solid]; +"172 permute_4" -> "173 reshape_3" [label="(1, 7, 8, 7, 8, 96)", style=solid]; +"173 reshape_3" -> "174 slice_2" [label="(1, 56, 56, 96)", style=solid]; +"174 slice_2" -> "175 slice_3" [label="(1, 56, 56, 96)", style=solid]; +"175 slice_3" -> "176 layer_norm_1" [label="(1, 56, 56, 96)", style=solid]; +"176 layer_norm_1" -> "177 add_1" [label="(1, 56, 56, 96)", style=solid]; +"177 add_1" -> "178 add_1_0_0_nncf_smooth_quant_0" [label="(1, 56, 56, 96)", style=solid]; +"177 add_1" -> "202 add_2" [label="(1, 56, 56, 96)", style=solid]; +"178 add_1_0_0_nncf_smooth_quant_0" -> "179 quantize_per_tensor_default_5" [label="(1, 56, 56, 96)", style=solid]; +"179 quantize_per_tensor_default_5" -> "180 dequantize_per_tensor_default_5" [label="(1, 56, 56, 96)", style=solid]; +"180 dequantize_per_tensor_default_5" -> "187 linear_4" [label="(1, 56, 56, 96)", style=solid]; +"181 features_1_0_mlp_0_bias_0_0" -> "187 linear_4" [label="(384,)", style=solid]; +"182 scale_updated_constant3" -> "184 mul_tensor_3" [label="(384, 1)", style=solid]; +"183 compressed_weight_updated_constant3" -> "184 mul_tensor_3" [label="(384, 96)", style=solid]; +"184 mul_tensor_3" -> "186 sub_tensor_3" [label="(384, 96)", style=solid]; +"185 zero_point_updated_constant3" -> "186 sub_tensor_3" [label="(384, 1)", style=solid]; +"186 sub_tensor_3" -> "187 linear_4" [label="(384, 96)", style=solid]; +"187 linear_4" -> "188 gelu" [label="(1, 56, 56, 384)", style=solid]; +"188 gelu" -> "189 dropout_2" [label="(1, 56, 56, 384)", style=solid]; +"189 dropout_2" -> "190 dropout_2_0_0_nncf_smooth_quant_0" [label="(1, 56, 56, 384)", style=solid]; +"190 dropout_2_0_0_nncf_smooth_quant_0" -> "191 quantize_per_tensor_default_6" [label="(1, 56, 56, 384)", style=solid]; +"191 quantize_per_tensor_default_6" -> "192 dequantize_per_tensor_default_6" [label="(1, 56, 56, 384)", style=solid]; +"192 dequantize_per_tensor_default_6" -> "199 linear_5" [label="(1, 56, 56, 384)", style=solid]; +"193 features_1_0_mlp_3_bias_0_0" -> "199 linear_5" [label="(96,)", style=solid]; +"194 scale_updated_constant4" -> "196 mul_tensor_4" [label="(96, 1)", style=solid]; +"195 compressed_weight_updated_constant4" -> "196 mul_tensor_4" [label="(96, 384)", style=solid]; +"196 mul_tensor_4" -> "198 sub_tensor_4" [label="(96, 384)", style=solid]; +"197 zero_point_updated_constant4" -> "198 sub_tensor_4" [label="(96, 1)", style=solid]; +"198 sub_tensor_4" -> "199 linear_5" [label="(96, 384)", style=solid]; +"199 linear_5" -> "200 dropout_3" [label="(1, 56, 56, 96)", style=solid]; +"200 dropout_3" -> "201 layer_norm_2" [label="(1, 56, 56, 96)", style=solid]; +"201 layer_norm_2" -> "202 add_2" [label="(1, 56, 56, 96)", style=solid]; +"202 add_2" -> "204 pad_1" [label="(1, 56, 56, 96)", style=solid]; +"202 add_2" -> "280 add_5" [label="(1, 56, 56, 96)", style=solid]; +"203 _frozen_param4" -> "240 add_3" [label="(1, 3, 64, 64)", style=solid]; +"204 pad_1" -> "205 roll" [label="(1, 56, 56, 96)", style=solid]; +"205 roll" -> "206 view_6" [label="(1, 56, 56, 96)", style=solid]; +"206 view_6" -> "207 permute_6" [label="(1, 7, 8, 7, 8, 96)", style=solid]; +"207 permute_6" -> "208 reshape_4" [label="(1, 7, 7, 8, 8, 96)", style=solid]; +"208 reshape_4" -> "209 reshape_4_0_0_nncf_smooth_quant_0" [label="(49, 64, 96)", style=solid]; +"208 reshape_4" -> "241 new_zeros" [label="(49, 64, 96)", style=solid]; +"209 reshape_4_0_0_nncf_smooth_quant_0" -> "210 quantize_per_tensor_default_7" [label="(49, 64, 96)", style=solid]; +"210 quantize_per_tensor_default_7" -> "211 dequantize_per_tensor_default_7" [label="(49, 64, 96)", style=solid]; +"211 dequantize_per_tensor_default_7" -> "218 linear_8" [label="(49, 64, 96)", style=solid]; +"212 _frozen_param5_0_0" -> "218 linear_8" [label="(288,)", style=solid]; +"213 scale_updated_constant5" -> "215 mul_tensor_5" [label="(288, 1)", style=solid]; +"214 compressed_weight_updated_constant5" -> "215 mul_tensor_5" [label="(288, 96)", style=solid]; +"215 mul_tensor_5" -> "217 sub_tensor_5" [label="(288, 96)", style=solid]; +"216 zero_point_updated_constant5" -> "217 sub_tensor_5" [label="(288, 1)", style=solid]; +"217 sub_tensor_5" -> "218 linear_8" [label="(288, 96)", style=solid]; +"218 linear_8" -> "219 reshape_5" [label="(49, 64, 288)", style=solid]; +"219 reshape_5" -> "220 permute_7" [label="(49, 64, 3, 3, 32)", style=solid]; +"220 permute_7" -> "221 select_3" [label="(3, 49, 3, 64, 32)", style=solid]; +"220 permute_7" -> "222 select_4" [label="(3, 49, 3, 64, 32)", style=solid]; +"220 permute_7" -> "223 select_5" [label="(3, 49, 3, 64, 32)", style=solid]; +"221 select_3" -> "224 linalg_vector_norm_2" [label="(49, 3, 64, 32)", style=solid]; +"221 select_3" -> "226 expand_as_2" [label="(49, 3, 64, 32)", style=solid]; +"221 select_3" -> "227 div_2" [label="(49, 3, 64, 32)", style=solid]; +"222 select_4" -> "230 linalg_vector_norm_3" [label="(49, 3, 64, 32)", style=solid]; +"222 select_4" -> "232 expand_as_3" [label="(49, 3, 64, 32)", style=solid]; +"222 select_4" -> "233 div_3" [label="(49, 3, 64, 32)", style=solid]; +"223 select_5" -> "259 matmul_3" [label="(49, 3, 64, 32)", style=solid]; +"224 linalg_vector_norm_2" -> "225 clamp_min_2" [label="(49, 3, 64, 1)", style=solid]; +"225 clamp_min_2" -> "226 expand_as_2" [label="(49, 3, 64, 1)", style=solid]; +"226 expand_as_2" -> "227 div_2" [label="(49, 3, 64, 32)", style=solid]; +"227 div_2" -> "228 quantize_per_tensor_default_8" [label="(49, 3, 64, 32)", style=solid]; +"228 quantize_per_tensor_default_8" -> "229 dequantize_per_tensor_default_8" [label="(49, 3, 64, 32)", style=solid]; +"229 dequantize_per_tensor_default_8" -> "237 matmul_2" [label="(49, 3, 64, 32)", style=solid]; 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[label="(49, 1, 64, 64)", style=solid]; +"254 unsqueeze_5" -> "255 add_4" [label="(1, 49, 1, 64, 64)", style=solid]; +"255 add_4" -> "256 view_9" [label="(1, 49, 3, 64, 64)", style=solid]; +"256 view_9" -> "257 softmax_1" [label="(49, 3, 64, 64)", style=solid]; +"257 softmax_1" -> "258 dropout_4" [label="(49, 3, 64, 64)", style=solid]; +"258 dropout_4" -> "259 matmul_3" [label="(49, 3, 64, 64)", style=solid]; +"259 matmul_3" -> "260 transpose_3" [label="(49, 3, 64, 32)", style=solid]; +"260 transpose_3" -> "261 reshape_7" [label="(49, 64, 3, 32)", style=solid]; +"261 reshape_7" -> "262 reshape_7_0_0_nncf_smooth_quant_0" [label="(49, 64, 96)", style=solid]; +"262 reshape_7_0_0_nncf_smooth_quant_0" -> "263 quantize_per_tensor_default_10" [label="(49, 64, 96)", style=solid]; +"263 quantize_per_tensor_default_10" -> "264 dequantize_per_tensor_default_10" [label="(49, 64, 96)", style=solid]; +"264 dequantize_per_tensor_default_10" -> "271 linear_9" [label="(49, 64, 96)", style=solid]; +"265 features_1_1_attn_proj_bias_0_0" -> "271 linear_9" [label="(96,)", style=solid]; +"266 scale_updated_constant6" -> "268 mul_tensor_6" [label="(96, 1)", style=solid]; +"267 compressed_weight_updated_constant6" -> "268 mul_tensor_6" [label="(96, 96)", style=solid]; +"268 mul_tensor_6" -> "270 sub_tensor_6" [label="(96, 96)", style=solid]; +"269 zero_point_updated_constant6" -> "270 sub_tensor_6" [label="(96, 1)", style=solid]; +"270 sub_tensor_6" -> "271 linear_9" [label="(96, 96)", style=solid]; +"271 linear_9" -> "272 dropout_5" [label="(49, 64, 96)", style=solid]; +"272 dropout_5" -> "273 view_10" [label="(49, 64, 96)", style=solid]; +"273 view_10" -> "274 permute_9" [label="(1, 7, 7, 8, 8, 96)", style=solid]; +"274 permute_9" -> "275 reshape_8" [label="(1, 7, 8, 7, 8, 96)", style=solid]; +"275 reshape_8" -> "276 roll_1" [label="(1, 56, 56, 96)", style=solid]; +"276 roll_1" -> "277 slice_23" [label="(1, 56, 56, 96)", style=solid]; +"277 slice_23" -> "278 slice_24" [label="(1, 56, 56, 96)", style=solid]; +"278 slice_24" -> "279 layer_norm_3" [label="(1, 56, 56, 96)", style=solid]; +"279 layer_norm_3" -> "280 add_5" [label="(1, 56, 56, 96)", style=solid]; +"280 add_5" -> "281 add_5_0_0_nncf_smooth_quant_0" [label="(1, 56, 56, 96)", style=solid]; +"280 add_5" -> "305 add_6" [label="(1, 56, 56, 96)", style=solid]; +"281 add_5_0_0_nncf_smooth_quant_0" -> "282 quantize_per_tensor_default_11" [label="(1, 56, 56, 96)", style=solid]; +"282 quantize_per_tensor_default_11" -> "283 dequantize_per_tensor_default_11" [label="(1, 56, 56, 96)", style=solid]; +"283 dequantize_per_tensor_default_11" -> "290 linear_10" [label="(1, 56, 56, 96)", style=solid]; +"284 features_1_1_mlp_0_bias_0_0" -> "290 linear_10" [label="(384,)", style=solid]; +"285 scale_updated_constant7" -> "287 mul_tensor_7" [label="(384, 1)", style=solid]; +"286 compressed_weight_updated_constant7" -> "287 mul_tensor_7" [label="(384, 96)", style=solid]; +"287 mul_tensor_7" -> "289 sub_tensor_7" [label="(384, 96)", style=solid]; +"288 zero_point_updated_constant7" -> "289 sub_tensor_7" [label="(384, 1)", style=solid]; +"289 sub_tensor_7" -> "290 linear_10" [label="(384, 96)", style=solid]; +"290 linear_10" -> "291 gelu_1" [label="(1, 56, 56, 384)", style=solid]; +"291 gelu_1" -> "292 dropout_6" [label="(1, 56, 56, 384)", style=solid]; +"292 dropout_6" -> "293 dropout_6_0_0_nncf_smooth_quant_0" [label="(1, 56, 56, 384)", style=solid]; +"293 dropout_6_0_0_nncf_smooth_quant_0" -> "294 quantize_per_tensor_default_12" [label="(1, 56, 56, 384)", style=solid]; +"294 quantize_per_tensor_default_12" -> "295 dequantize_per_tensor_default_12" [label="(1, 56, 56, 384)", style=solid]; +"295 dequantize_per_tensor_default_12" -> "302 linear_11" [label="(1, 56, 56, 384)", style=solid]; +"296 features_1_1_mlp_3_bias_0_0" -> "302 linear_11" [label="(96,)", style=solid]; +"297 scale_updated_constant8" -> "299 mul_tensor_8" [label="(96, 1)", style=solid]; +"298 compressed_weight_updated_constant8" -> "299 mul_tensor_8" [label="(96, 384)", style=solid]; +"299 mul_tensor_8" -> "301 sub_tensor_8" [label="(96, 384)", style=solid]; +"300 zero_point_updated_constant8" -> "301 sub_tensor_8" [label="(96, 1)", style=solid]; +"301 sub_tensor_8" -> "302 linear_11" [label="(96, 384)", style=solid]; +"302 linear_11" -> "303 dropout_7" [label="(1, 56, 56, 96)", style=solid]; +"303 dropout_7" -> "304 layer_norm_4" [label="(1, 56, 56, 96)", style=solid]; +"304 layer_norm_4" -> "305 add_6" [label="(1, 56, 56, 96)", style=solid]; +"305 add_6" -> "306 pad_2" [label="(1, 56, 56, 96)", style=solid]; +"306 pad_2" -> "307 slice_25" [label="(1, 56, 56, 96)", style=solid]; +"306 pad_2" -> "310 slice_28" [label="(1, 56, 56, 96)", style=solid]; +"306 pad_2" -> "313 slice_31" [label="(1, 56, 56, 96)", style=solid]; +"306 pad_2" -> "316 slice_34" [label="(1, 56, 56, 96)", style=solid]; +"307 slice_25" -> "308 slice_26" [label="(1, 28, 56, 96)", style=solid]; +"308 slice_26" -> "309 slice_27" [label="(1, 28, 28, 96)", style=solid]; +"309 slice_27" -> "319 cat" [label="(1, 28, 28, 96)", style=solid]; +"310 slice_28" -> "311 slice_29" [label="(1, 28, 56, 96)", style=solid]; +"311 slice_29" -> "312 slice_30" [label="(1, 28, 28, 96)", style=solid]; +"312 slice_30" -> "319 cat" [label="(1, 28, 28, 96)", style=solid]; +"313 slice_31" -> "314 slice_32" [label="(1, 28, 56, 96)", style=solid]; +"314 slice_32" -> "315 slice_33" [label="(1, 28, 28, 96)", style=solid]; +"315 slice_33" -> "319 cat" [label="(1, 28, 28, 96)", style=solid]; +"316 slice_34" -> "317 slice_35" [label="(1, 28, 56, 96)", style=solid]; +"317 slice_35" -> "318 slice_36" [label="(1, 28, 28, 96)", style=solid]; +"318 slice_36" -> "319 cat" [label="(1, 28, 28, 96)", style=solid]; +"319 cat" -> "320 cat_0_0_nncf_smooth_quant_0" [label="(1, 28, 28, 384)", style=solid]; +"320 cat_0_0_nncf_smooth_quant_0" -> "321 quantize_per_tensor_default_13" [label="(1, 28, 28, 384)", style=solid]; +"321 quantize_per_tensor_default_13" -> "322 dequantize_per_tensor_default_13" [label="(1, 28, 28, 384)", style=solid]; +"322 dequantize_per_tensor_default_13" -> "328 linear_12" [label="(1, 28, 28, 384)", style=solid]; +"323 scale_updated_constant9" -> "325 mul_tensor_9" [label="(192, 1)", style=solid]; +"324 compressed_weight_updated_constant9" -> "325 mul_tensor_9" [label="(192, 384)", style=solid]; +"325 mul_tensor_9" -> "327 sub_tensor_9" [label="(192, 384)", style=solid]; +"326 zero_point_updated_constant9" -> "327 sub_tensor_9" [label="(192, 1)", style=solid]; +"327 sub_tensor_9" -> "328 linear_12" [label="(192, 384)", style=solid]; +"328 linear_12" -> "329 layer_norm_5" [label="(1, 28, 28, 192)", style=solid]; +"329 layer_norm_5" -> "331 pad_3" [label="(1, 28, 28, 192)", style=solid]; +"329 layer_norm_5" -> "392 add_8" [label="(1, 28, 28, 192)", style=solid]; +"330 _frozen_param17" -> "366 add_7" [label="(1, 6, 64, 64)", style=solid]; +"331 pad_3" -> "332 view_13" [label="(1, 32, 32, 192)", style=solid]; +"332 view_13" -> "333 permute_11" [label="(1, 4, 8, 4, 8, 192)", style=solid]; +"333 permute_11" -> "334 reshape_9" [label="(1, 4, 4, 8, 8, 192)", style=solid]; +"334 reshape_9" -> "335 reshape_9_0_0_nncf_smooth_quant_0" [label="(16, 64, 192)", style=solid]; +"335 reshape_9_0_0_nncf_smooth_quant_0" -> "336 quantize_per_tensor_default_14" [label="(16, 64, 192)", style=solid]; +"336 quantize_per_tensor_default_14" -> "337 dequantize_per_tensor_default_14" [label="(16, 64, 192)", style=solid]; +"337 dequantize_per_tensor_default_14" -> "344 linear_15" [label="(16, 64, 192)", style=solid]; +"338 _frozen_param18_0_0" -> "344 linear_15" [label="(576,)", style=solid]; +"339 scale_updated_constant10" -> "341 mul_tensor_10" [label="(576, 1)", style=solid]; +"340 compressed_weight_updated_constant10" -> "341 mul_tensor_10" [label="(576, 192)", style=solid]; +"341 mul_tensor_10" -> "343 sub_tensor_10" [label="(576, 192)", style=solid]; +"342 zero_point_updated_constant10" -> "343 sub_tensor_10" [label="(576, 1)", style=solid]; +"343 sub_tensor_10" -> "344 linear_15" [label="(576, 192)", style=solid]; +"344 linear_15" -> "345 reshape_10" [label="(16, 64, 576)", style=solid]; +"345 reshape_10" -> "346 permute_12" [label="(16, 64, 3, 6, 32)", style=solid]; +"346 permute_12" -> "347 select_6" [label="(3, 16, 6, 64, 32)", style=solid]; +"346 permute_12" -> "348 select_7" [label="(3, 16, 6, 64, 32)", style=solid]; +"346 permute_12" -> "349 select_8" [label="(3, 16, 6, 64, 32)", style=solid]; +"347 select_6" -> "350 linalg_vector_norm_4" [label="(16, 6, 64, 32)", style=solid]; +"347 select_6" -> "352 expand_as_4" [label="(16, 6, 64, 32)", style=solid]; +"347 select_6" -> "353 div_4" [label="(16, 6, 64, 32)", style=solid]; +"348 select_7" -> "356 linalg_vector_norm_5" [label="(16, 6, 64, 32)", style=solid]; +"348 select_7" -> "358 expand_as_5" [label="(16, 6, 64, 32)", style=solid]; +"348 select_7" -> "359 div_5" [label="(16, 6, 64, 32)", style=solid]; +"349 select_8" -> "369 matmul_5" [label="(16, 6, 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[label="(16, 6, 64, 32)", style=solid]; +"361 dequantize_per_tensor_default_16" -> "362 transpose_4" [label="(16, 6, 64, 32)", style=solid]; +"362 transpose_4" -> "363 matmul_4" [label="(16, 6, 32, 64)", style=solid]; +"363 matmul_4" -> "365 mul_5" [label="(16, 6, 64, 64)", style=solid]; +"364 _frozen_param20" -> "365 mul_5" [label="(6, 1, 1)", style=solid]; +"365 mul_5" -> "366 add_7" [label="(16, 6, 64, 64)", style=solid]; +"366 add_7" -> "367 softmax_2" [label="(16, 6, 64, 64)", style=solid]; +"367 softmax_2" -> "368 dropout_8" [label="(16, 6, 64, 64)", style=solid]; +"368 dropout_8" -> "369 matmul_5" [label="(16, 6, 64, 64)", style=solid]; +"369 matmul_5" -> "370 transpose_5" [label="(16, 6, 64, 32)", style=solid]; +"370 transpose_5" -> "371 reshape_11" [label="(16, 64, 6, 32)", style=solid]; +"371 reshape_11" -> "372 reshape_11_0_0_nncf_smooth_quant_0" [label="(16, 64, 192)", style=solid]; +"372 reshape_11_0_0_nncf_smooth_quant_0" -> "373 quantize_per_tensor_default_17" [label="(16, 64, 192)", style=solid]; +"373 quantize_per_tensor_default_17" -> "374 dequantize_per_tensor_default_17" [label="(16, 64, 192)", style=solid]; +"374 dequantize_per_tensor_default_17" -> "381 linear_16" [label="(16, 64, 192)", style=solid]; +"375 features_3_0_attn_proj_bias_0_0" -> "381 linear_16" [label="(192,)", style=solid]; +"376 scale_updated_constant11" -> "378 mul_tensor_11" [label="(192, 1)", style=solid]; +"377 compressed_weight_updated_constant11" -> "378 mul_tensor_11" [label="(192, 192)", style=solid]; +"378 mul_tensor_11" -> "380 sub_tensor_11" [label="(192, 192)", style=solid]; +"379 zero_point_updated_constant11" -> "380 sub_tensor_11" [label="(192, 1)", style=solid]; +"380 sub_tensor_11" -> "381 linear_16" [label="(192, 192)", style=solid]; +"381 linear_16" -> "382 dropout_9" [label="(16, 64, 192)", style=solid]; +"382 dropout_9" -> "383 view_14" [label="(16, 64, 192)", style=solid]; +"383 view_14" -> "384 permute_13" [label="(1, 4, 4, 8, 8, 192)", style=solid]; +"384 permute_13" -> "385 reshape_12" [label="(1, 4, 8, 4, 8, 192)", style=solid]; +"385 reshape_12" -> "386 slice_38" [label="(1, 32, 32, 192)", style=solid]; +"386 slice_38" -> "387 slice_39" [label="(1, 32, 32, 192)", style=solid]; +"387 slice_39" -> "388 slice_40" [label="(1, 28, 32, 192)", style=solid]; +"388 slice_40" -> "389 slice_41" [label="(1, 28, 28, 192)", style=solid]; +"389 slice_41" -> "390 contiguous_3" [label="(1, 28, 28, 192)", style=solid]; +"390 contiguous_3" -> "391 layer_norm_6" [label="(1, 28, 28, 192)", style=solid]; +"391 layer_norm_6" -> "392 add_8" [label="(1, 28, 28, 192)", style=solid]; +"392 add_8" -> "393 add_8_0_0_nncf_smooth_quant_0" [label="(1, 28, 28, 192)", style=solid]; +"392 add_8" -> "417 add_9" [label="(1, 28, 28, 192)", style=solid]; +"393 add_8_0_0_nncf_smooth_quant_0" -> "394 quantize_per_tensor_default_18" [label="(1, 28, 28, 192)", style=solid]; +"394 quantize_per_tensor_default_18" -> "395 dequantize_per_tensor_default_18" [label="(1, 28, 28, 192)", style=solid]; +"395 dequantize_per_tensor_default_18" -> "402 linear_17" [label="(1, 28, 28, 192)", style=solid]; +"396 features_3_0_mlp_0_bias_0_0" -> "402 linear_17" [label="(768,)", style=solid]; +"397 scale_updated_constant12" -> "399 mul_tensor_12" [label="(768, 1)", style=solid]; +"398 compressed_weight_updated_constant12" -> "399 mul_tensor_12" [label="(768, 192)", style=solid]; +"399 mul_tensor_12" -> "401 sub_tensor_12" [label="(768, 192)", style=solid]; +"400 zero_point_updated_constant12" -> "401 sub_tensor_12" [label="(768, 1)", style=solid]; +"401 sub_tensor_12" -> "402 linear_17" [label="(768, 192)", style=solid]; +"402 linear_17" -> "403 gelu_2" [label="(1, 28, 28, 768)", style=solid]; +"403 gelu_2" -> "404 dropout_10" [label="(1, 28, 28, 768)", style=solid]; +"404 dropout_10" -> "405 dropout_10_0_0_nncf_smooth_quant_0" [label="(1, 28, 28, 768)", style=solid]; +"405 dropout_10_0_0_nncf_smooth_quant_0" -> "406 quantize_per_tensor_default_19" [label="(1, 28, 28, 768)", style=solid]; +"406 quantize_per_tensor_default_19" -> "407 dequantize_per_tensor_default_19" [label="(1, 28, 28, 768)", style=solid]; +"407 dequantize_per_tensor_default_19" -> "414 linear_18" [label="(1, 28, 28, 768)", style=solid]; +"408 features_3_0_mlp_3_bias_0_0" -> "414 linear_18" [label="(192,)", style=solid]; +"409 scale_updated_constant13" -> "411 mul_tensor_13" [label="(192, 1)", style=solid]; +"410 compressed_weight_updated_constant13" -> "411 mul_tensor_13" [label="(192, 768)", style=solid]; +"411 mul_tensor_13" -> "413 sub_tensor_13" [label="(192, 768)", style=solid]; +"412 zero_point_updated_constant13" -> "413 sub_tensor_13" [label="(192, 1)", style=solid]; +"413 sub_tensor_13" -> "414 linear_18" [label="(192, 768)", style=solid]; +"414 linear_18" -> "415 dropout_11" [label="(1, 28, 28, 192)", style=solid]; +"415 dropout_11" -> "416 layer_norm_7" [label="(1, 28, 28, 192)", style=solid]; +"416 layer_norm_7" -> "417 add_9" [label="(1, 28, 28, 192)", style=solid]; +"417 add_9" -> "419 pad_4" [label="(1, 28, 28, 192)", style=solid]; +"417 add_9" -> "498 add_12" [label="(1, 28, 28, 192)", style=solid]; +"418 _frozen_param21" -> "455 add_10" [label="(1, 6, 64, 64)", style=solid]; +"419 pad_4" -> "420 roll_2" [label="(1, 32, 32, 192)", style=solid]; +"420 roll_2" -> "421 view_17" [label="(1, 32, 32, 192)", style=solid]; +"421 view_17" -> "422 permute_15" [label="(1, 4, 8, 4, 8, 192)", style=solid]; +"422 permute_15" -> "423 reshape_13" [label="(1, 4, 4, 8, 8, 192)", style=solid]; +"423 reshape_13" -> "424 reshape_13_0_0_nncf_smooth_quant_0" [label="(16, 64, 192)", style=solid]; +"423 reshape_13" -> "456 new_zeros_1" [label="(16, 64, 192)", style=solid]; +"424 reshape_13_0_0_nncf_smooth_quant_0" -> "425 quantize_per_tensor_default_20" [label="(16, 64, 192)", style=solid]; +"425 quantize_per_tensor_default_20" -> "426 dequantize_per_tensor_default_20" [label="(16, 64, 192)", style=solid]; +"426 dequantize_per_tensor_default_20" -> 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dequantize_per_tensor_default_21" -> "452 matmul_6" [label="(16, 6, 64, 32)", style=solid]; +"445 linalg_vector_norm_7" -> "446 clamp_min_7" [label="(16, 6, 64, 1)", style=solid]; +"446 clamp_min_7" -> "447 expand_as_7" [label="(16, 6, 64, 1)", style=solid]; +"447 expand_as_7" -> "448 div_7" [label="(16, 6, 64, 32)", style=solid]; +"448 div_7" -> "449 quantize_per_tensor_default_22" [label="(16, 6, 64, 32)", style=solid]; +"449 quantize_per_tensor_default_22" -> "450 dequantize_per_tensor_default_22" [label="(16, 6, 64, 32)", style=solid]; +"450 dequantize_per_tensor_default_22" -> "451 transpose_6" [label="(16, 6, 64, 32)", style=solid]; +"451 transpose_6" -> "452 matmul_6" [label="(16, 6, 32, 64)", style=solid]; +"452 matmul_6" -> "454 mul_7" [label="(16, 6, 64, 64)", style=solid]; +"453 _frozen_param24" -> "454 mul_7" [label="(6, 1, 1)", style=solid]; +"454 mul_7" -> "455 add_10" [label="(16, 6, 64, 64)", style=solid]; +"455 add_10" -> "467 view_19" [label="(16, 6, 64, 64)", style=solid]; +"456 new_zeros_1" -> "457 view_18" [label="(32, 32)", style=solid]; +"457 view_18" -> "458 permute_17" [label="(4, 8, 4, 8)", style=solid]; +"458 permute_17" -> "459 reshape_15" [label="(4, 4, 8, 8)", style=solid]; +"459 reshape_15" -> "460 unsqueeze_8" [label="(16, 64)", style=solid]; +"459 reshape_15" -> "461 unsqueeze_9" [label="(16, 64)", style=solid]; +"460 unsqueeze_8" -> "462 sub_1" [label="(16, 1, 64)", style=solid]; +"461 unsqueeze_9" -> "462 sub_1" [label="(16, 64, 1)", style=solid]; +"462 sub_1" -> "463 ne_1" [label="(16, 64, 64)", style=solid]; +"462 sub_1" -> "464 masked_fill_2" [label="(16, 64, 64)", style=solid]; +"462 sub_1" -> "465 eq_1" [label="(16, 64, 64)", style=solid]; +"463 ne_1" -> "464 masked_fill_2" [label="(16, 64, 64)", style=solid]; +"464 masked_fill_2" -> "466 masked_fill_3" [label="(16, 64, 64)", style=solid]; +"465 eq_1" -> "466 masked_fill_3" [label="(16, 64, 64)", style=solid]; +"466 masked_fill_3" -> "468 unsqueeze_10" [label="(16, 64, 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style=solid]; +"490 reshape_17" -> "491 roll_3" [label="(1, 32, 32, 192)", style=solid]; +"491 roll_3" -> "492 slice_61" [label="(1, 32, 32, 192)", style=solid]; +"492 slice_61" -> "493 slice_62" [label="(1, 32, 32, 192)", style=solid]; +"493 slice_62" -> "494 slice_63" [label="(1, 28, 32, 192)", style=solid]; +"494 slice_63" -> "495 slice_64" [label="(1, 28, 28, 192)", style=solid]; +"495 slice_64" -> "496 contiguous_5" [label="(1, 28, 28, 192)", style=solid]; +"496 contiguous_5" -> "497 layer_norm_8" [label="(1, 28, 28, 192)", style=solid]; +"497 layer_norm_8" -> "498 add_12" [label="(1, 28, 28, 192)", style=solid]; +"498 add_12" -> "499 add_12_0_0_nncf_smooth_quant_0" [label="(1, 28, 28, 192)", style=solid]; +"498 add_12" -> "523 add_13" [label="(1, 28, 28, 192)", style=solid]; +"499 add_12_0_0_nncf_smooth_quant_0" -> "500 quantize_per_tensor_default_24" [label="(1, 28, 28, 192)", style=solid]; +"500 quantize_per_tensor_default_24" -> "501 dequantize_per_tensor_default_24" [label="(1, 28, 28, 192)", style=solid]; +"501 dequantize_per_tensor_default_24" -> "508 linear_23" [label="(1, 28, 28, 192)", style=solid]; +"502 features_3_1_mlp_0_bias_0_0" -> "508 linear_23" [label="(768,)", style=solid]; +"503 scale_updated_constant16" -> "505 mul_tensor_16" [label="(768, 1)", style=solid]; +"504 compressed_weight_updated_constant16" -> "505 mul_tensor_16" [label="(768, 192)", style=solid]; +"505 mul_tensor_16" -> "507 sub_tensor_16" [label="(768, 192)", style=solid]; +"506 zero_point_updated_constant16" -> "507 sub_tensor_16" [label="(768, 1)", style=solid]; +"507 sub_tensor_16" -> "508 linear_23" [label="(768, 192)", style=solid]; +"508 linear_23" -> "509 gelu_3" [label="(1, 28, 28, 768)", style=solid]; +"509 gelu_3" -> "510 dropout_14" [label="(1, 28, 28, 768)", style=solid]; +"510 dropout_14" -> "511 dropout_14_0_0_nncf_smooth_quant_0" [label="(1, 28, 28, 768)", style=solid]; +"511 dropout_14_0_0_nncf_smooth_quant_0" -> "512 quantize_per_tensor_default_25" [label="(1, 28, 28, 768)", style=solid]; +"512 quantize_per_tensor_default_25" -> "513 dequantize_per_tensor_default_25" [label="(1, 28, 28, 768)", style=solid]; +"513 dequantize_per_tensor_default_25" -> "520 linear_24" [label="(1, 28, 28, 768)", style=solid]; +"514 features_3_1_mlp_3_bias_0_0" -> "520 linear_24" [label="(192,)", style=solid]; +"515 scale_updated_constant17" -> "517 mul_tensor_17" [label="(192, 1)", style=solid]; +"516 compressed_weight_updated_constant17" -> "517 mul_tensor_17" [label="(192, 768)", style=solid]; +"517 mul_tensor_17" -> "519 sub_tensor_17" [label="(192, 768)", style=solid]; +"518 zero_point_updated_constant17" -> "519 sub_tensor_17" [label="(192, 1)", style=solid]; +"519 sub_tensor_17" -> "520 linear_24" [label="(192, 768)", style=solid]; +"520 linear_24" -> "521 dropout_15" [label="(1, 28, 28, 192)", style=solid]; +"521 dropout_15" -> "522 layer_norm_9" [label="(1, 28, 28, 192)", style=solid]; +"522 layer_norm_9" -> "523 add_13" [label="(1, 28, 28, 192)", style=solid]; +"523 add_13" -> "524 pad_5" [label="(1, 28, 28, 192)", style=solid]; +"524 pad_5" -> "525 slice_65" [label="(1, 28, 28, 192)", style=solid]; +"524 pad_5" -> "528 slice_68" [label="(1, 28, 28, 192)", style=solid]; +"524 pad_5" -> "531 slice_71" [label="(1, 28, 28, 192)", style=solid]; +"524 pad_5" -> "534 slice_74" [label="(1, 28, 28, 192)", style=solid]; +"525 slice_65" -> "526 slice_66" [label="(1, 14, 28, 192)", style=solid]; +"526 slice_66" -> "527 slice_67" [label="(1, 14, 14, 192)", style=solid]; +"527 slice_67" -> "537 cat_1" [label="(1, 14, 14, 192)", style=solid]; +"528 slice_68" -> "529 slice_69" [label="(1, 14, 28, 192)", style=solid]; +"529 slice_69" -> "530 slice_70" [label="(1, 14, 14, 192)", style=solid]; +"530 slice_70" -> "537 cat_1" [label="(1, 14, 14, 192)", style=solid]; +"531 slice_71" -> "532 slice_72" [label="(1, 14, 28, 192)", style=solid]; +"532 slice_72" -> "533 slice_73" [label="(1, 14, 14, 192)", style=solid]; +"533 slice_73" -> "537 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style=solid]; +"847 permute_34" -> "850 select_23" [label="(3, 4, 12, 64, 32)", style=solid]; +"848 select_21" -> "851 linalg_vector_norm_14" [label="(4, 12, 64, 32)", style=solid]; +"848 select_21" -> "853 expand_as_14" [label="(4, 12, 64, 32)", style=solid]; +"848 select_21" -> "854 div_14" [label="(4, 12, 64, 32)", style=solid]; +"849 select_22" -> "857 linalg_vector_norm_15" [label="(4, 12, 64, 32)", style=solid]; +"849 select_22" -> "859 expand_as_15" [label="(4, 12, 64, 32)", style=solid]; +"849 select_22" -> "860 div_15" [label="(4, 12, 64, 32)", style=solid]; +"850 select_23" -> "886 matmul_15" [label="(4, 12, 64, 32)", style=solid]; +"851 linalg_vector_norm_14" -> "852 clamp_min_14" [label="(4, 12, 64, 1)", style=solid]; +"852 clamp_min_14" -> "853 expand_as_14" [label="(4, 12, 64, 1)", style=solid]; +"853 expand_as_14" -> "854 div_14" [label="(4, 12, 64, 32)", style=solid]; +"854 div_14" -> "855 quantize_per_tensor_default_46" [label="(4, 12, 64, 32)", style=solid]; +"855 quantize_per_tensor_default_46" -> "856 dequantize_per_tensor_default_46" [label="(4, 12, 64, 32)", style=solid]; +"856 dequantize_per_tensor_default_46" -> "864 matmul_14" [label="(4, 12, 64, 32)", style=solid]; +"857 linalg_vector_norm_15" -> "858 clamp_min_15" [label="(4, 12, 64, 1)", style=solid]; +"858 clamp_min_15" -> "859 expand_as_15" [label="(4, 12, 64, 1)", style=solid]; +"859 expand_as_15" -> "860 div_15" [label="(4, 12, 64, 32)", style=solid]; +"860 div_15" -> "861 quantize_per_tensor_default_47" [label="(4, 12, 64, 32)", style=solid]; +"861 quantize_per_tensor_default_47" -> "862 dequantize_per_tensor_default_47" [label="(4, 12, 64, 32)", style=solid]; +"862 dequantize_per_tensor_default_47" -> "863 transpose_14" [label="(4, 12, 64, 32)", style=solid]; +"863 transpose_14" -> "864 matmul_14" [label="(4, 12, 32, 64)", style=solid]; +"864 matmul_14" -> "866 mul_15" [label="(4, 12, 64, 64)", style=solid]; +"865 _frozen_param58" -> "866 mul_15" [label="(12, 1, 1)", style=solid]; +"866 mul_15" -> "867 add_24" [label="(4, 12, 64, 64)", style=solid]; +"867 add_24" -> "879 view_41" [label="(4, 12, 64, 64)", style=solid]; +"868 new_zeros_3" -> "869 view_40" [label="(16, 16)", style=solid]; +"869 view_40" -> "870 permute_35" [label="(2, 8, 2, 8)", style=solid]; +"870 permute_35" -> "871 reshape_33" [label="(2, 2, 8, 8)", style=solid]; +"871 reshape_33" -> "872 unsqueeze_20" [label="(4, 64)", style=solid]; +"871 reshape_33" -> "873 unsqueeze_21" [label="(4, 64)", style=solid]; +"872 unsqueeze_20" -> "874 sub_3" [label="(4, 1, 64)", style=solid]; +"873 unsqueeze_21" -> "874 sub_3" [label="(4, 64, 1)", style=solid]; +"874 sub_3" -> "875 ne_3" [label="(4, 64, 64)", style=solid]; +"874 sub_3" -> "876 masked_fill_6" [label="(4, 64, 64)", style=solid]; +"874 sub_3" -> "877 eq_3" [label="(4, 64, 64)", style=solid]; +"875 ne_3" -> "876 masked_fill_6" [label="(4, 64, 64)", style=solid]; +"876 masked_fill_6" -> "878 masked_fill_7" [label="(4, 64, 64)", style=solid]; +"877 eq_3" -> "878 masked_fill_7" [label="(4, 64, 64)", style=solid]; +"878 masked_fill_7" -> "880 unsqueeze_22" [label="(4, 64, 64)", style=solid]; +"879 view_41" -> "882 add_25" [label="(1, 4, 12, 64, 64)", style=solid]; +"880 unsqueeze_22" -> "881 unsqueeze_23" [label="(4, 1, 64, 64)", style=solid]; +"881 unsqueeze_23" -> "882 add_25" [label="(1, 4, 1, 64, 64)", style=solid]; +"882 add_25" -> "883 view_42" [label="(1, 4, 12, 64, 64)", style=solid]; +"883 view_42" -> "884 softmax_7" [label="(4, 12, 64, 64)", style=solid]; +"884 softmax_7" -> "885 dropout_28" [label="(4, 12, 64, 64)", style=solid]; +"885 dropout_28" -> "886 matmul_15" [label="(4, 12, 64, 64)", style=solid]; +"886 matmul_15" -> "887 transpose_15" [label="(4, 12, 64, 32)", style=solid]; +"887 transpose_15" -> "888 reshape_34" [label="(4, 64, 12, 32)", style=solid]; +"888 reshape_34" -> "889 reshape_34_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"889 reshape_34_0_0_nncf_smooth_quant_0" -> "890 quantize_per_tensor_default_48" [label="(4, 64, 384)", style=solid]; +"890 quantize_per_tensor_default_48" -> "891 dequantize_per_tensor_default_48" [label="(4, 64, 384)", style=solid]; +"891 dequantize_per_tensor_default_48" -> "898 linear_47" [label="(4, 64, 384)", style=solid]; +"892 features_5_3_attn_proj_bias_0_0" -> "898 linear_47" [label="(384,)", style=solid]; +"893 scale_updated_constant32" -> "895 mul_tensor_32" [label="(384, 1)", style=solid]; +"894 compressed_weight_updated_constant32" -> "895 mul_tensor_32" [label="(384, 384)", style=solid]; +"895 mul_tensor_32" -> "897 sub_tensor_32" [label="(384, 384)", style=solid]; +"896 zero_point_updated_constant32" -> "897 sub_tensor_32" [label="(384, 1)", style=solid]; +"897 sub_tensor_32" -> "898 linear_47" [label="(384, 384)", style=solid]; +"898 linear_47" -> "899 dropout_29" [label="(4, 64, 384)", style=solid]; +"899 dropout_29" -> "900 view_43" [label="(4, 64, 384)", style=solid]; +"900 view_43" -> "901 permute_36" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"901 permute_36" -> "902 reshape_35" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"902 reshape_35" -> "903 roll_7" [label="(1, 16, 16, 384)", style=solid]; +"903 roll_7" -> "904 slice_129" [label="(1, 16, 16, 384)", style=solid]; +"904 slice_129" -> "905 slice_130" [label="(1, 16, 16, 384)", style=solid]; +"905 slice_130" -> "906 slice_131" [label="(1, 14, 16, 384)", style=solid]; +"906 slice_131" -> "907 slice_132" [label="(1, 14, 14, 384)", style=solid]; +"907 slice_132" -> "908 contiguous_13" [label="(1, 14, 14, 384)", style=solid]; +"908 contiguous_13" -> "909 layer_norm_17" [label="(1, 14, 14, 384)", style=solid]; +"909 layer_norm_17" -> "910 add_26" [label="(1, 14, 14, 384)", style=solid]; +"910 add_26" -> "911 add_26_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 384)", style=solid]; +"910 add_26" -> "935 add_27" [label="(1, 14, 14, 384)", style=solid]; +"911 add_26_0_0_nncf_smooth_quant_0" -> "912 quantize_per_tensor_default_49" [label="(1, 14, 14, 384)", style=solid]; +"912 quantize_per_tensor_default_49" -> "913 dequantize_per_tensor_default_49" [label="(1, 14, 14, 384)", style=solid]; +"913 dequantize_per_tensor_default_49" -> "920 linear_48" [label="(1, 14, 14, 384)", style=solid]; +"914 features_5_3_mlp_0_bias_0_0" -> "920 linear_48" [label="(1536,)", style=solid]; +"915 scale_updated_constant33" -> "917 mul_tensor_33" [label="(1536, 1)", style=solid]; +"916 compressed_weight_updated_constant33" -> "917 mul_tensor_33" [label="(1536, 384)", style=solid]; +"917 mul_tensor_33" -> "919 sub_tensor_33" [label="(1536, 384)", style=solid]; +"918 zero_point_updated_constant33" -> "919 sub_tensor_33" [label="(1536, 1)", style=solid]; +"919 sub_tensor_33" -> "920 linear_48" [label="(1536, 384)", style=solid]; +"920 linear_48" -> "921 gelu_7" [label="(1, 14, 14, 1536)", style=solid]; +"921 gelu_7" -> "922 dropout_30" [label="(1, 14, 14, 1536)", style=solid]; +"922 dropout_30" -> "923 dropout_30_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 1536)", style=solid]; +"923 dropout_30_0_0_nncf_smooth_quant_0" -> "924 quantize_per_tensor_default_50" [label="(1, 14, 14, 1536)", style=solid]; +"924 quantize_per_tensor_default_50" -> "925 dequantize_per_tensor_default_50" [label="(1, 14, 14, 1536)", style=solid]; +"925 dequantize_per_tensor_default_50" -> "932 linear_49" [label="(1, 14, 14, 1536)", style=solid]; +"926 features_5_3_mlp_3_bias_0_0" -> "932 linear_49" [label="(384,)", style=solid]; +"927 scale_updated_constant34" -> "929 mul_tensor_34" [label="(384, 1)", style=solid]; +"928 compressed_weight_updated_constant34" -> "929 mul_tensor_34" [label="(384, 1536)", style=solid]; +"929 mul_tensor_34" -> "931 sub_tensor_34" [label="(384, 1536)", style=solid]; +"930 zero_point_updated_constant34" -> "931 sub_tensor_34" [label="(384, 1)", style=solid]; +"931 sub_tensor_34" -> "932 linear_49" [label="(384, 1536)", style=solid]; +"932 linear_49" -> "933 dropout_31" [label="(1, 14, 14, 384)", style=solid]; +"933 dropout_31" -> "934 layer_norm_18" [label="(1, 14, 14, 384)", style=solid]; +"934 layer_norm_18" -> "935 add_27" [label="(1, 14, 14, 384)", style=solid]; +"935 add_27" -> "937 pad_10" [label="(1, 14, 14, 384)", style=solid]; +"935 add_27" -> "998 add_29" [label="(1, 14, 14, 384)", style=solid]; +"936 _frozen_param68" -> "972 add_28" [label="(1, 12, 64, 64)", style=solid]; +"937 pad_10" -> "938 view_46" [label="(1, 16, 16, 384)", style=solid]; +"938 view_46" -> "939 permute_38" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"939 permute_38" -> "940 reshape_36" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"940 reshape_36" -> "941 reshape_36_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"941 reshape_36_0_0_nncf_smooth_quant_0" -> "942 quantize_per_tensor_default_51" [label="(4, 64, 384)", style=solid]; +"942 quantize_per_tensor_default_51" -> "943 dequantize_per_tensor_default_51" [label="(4, 64, 384)", style=solid]; +"943 dequantize_per_tensor_default_51" -> "950 linear_52" [label="(4, 64, 384)", style=solid]; +"944 _frozen_param69_0_0" -> "950 linear_52" [label="(1152,)", style=solid]; +"945 scale_updated_constant35" -> "947 mul_tensor_35" [label="(1152, 1)", style=solid]; +"946 compressed_weight_updated_constant35" -> "947 mul_tensor_35" [label="(1152, 384)", style=solid]; +"947 mul_tensor_35" -> "949 sub_tensor_35" [label="(1152, 384)", style=solid]; +"948 zero_point_updated_constant35" -> "949 sub_tensor_35" [label="(1152, 1)", style=solid]; +"949 sub_tensor_35" -> "950 linear_52" [label="(1152, 384)", style=solid]; +"950 linear_52" -> "951 reshape_37" [label="(4, 64, 1152)", style=solid]; +"951 reshape_37" -> "952 permute_39" [label="(4, 64, 3, 12, 32)", style=solid]; +"952 permute_39" -> "953 select_24" [label="(3, 4, 12, 64, 32)", style=solid]; +"952 permute_39" -> "954 select_25" [label="(3, 4, 12, 64, 32)", style=solid]; +"952 permute_39" -> "955 select_26" [label="(3, 4, 12, 64, 32)", style=solid]; +"953 select_24" -> "956 linalg_vector_norm_16" [label="(4, 12, 64, 32)", style=solid]; +"953 select_24" -> "958 expand_as_16" [label="(4, 12, 64, 32)", style=solid]; +"953 select_24" -> "959 div_16" [label="(4, 12, 64, 32)", style=solid]; +"954 select_25" -> "962 linalg_vector_norm_17" [label="(4, 12, 64, 32)", style=solid]; +"954 select_25" -> "964 expand_as_17" [label="(4, 12, 64, 32)", style=solid]; +"954 select_25" -> "965 div_17" [label="(4, 12, 64, 32)", style=solid]; +"955 select_26" -> "975 matmul_17" [label="(4, 12, 64, 32)", style=solid]; +"956 linalg_vector_norm_16" -> "957 clamp_min_16" [label="(4, 12, 64, 1)", style=solid]; +"957 clamp_min_16" -> "958 expand_as_16" [label="(4, 12, 64, 1)", style=solid]; +"958 expand_as_16" -> "959 div_16" [label="(4, 12, 64, 32)", style=solid]; +"959 div_16" -> "960 quantize_per_tensor_default_52" [label="(4, 12, 64, 32)", style=solid]; +"960 quantize_per_tensor_default_52" -> "961 dequantize_per_tensor_default_52" [label="(4, 12, 64, 32)", style=solid]; +"961 dequantize_per_tensor_default_52" -> "969 matmul_16" [label="(4, 12, 64, 32)", style=solid]; +"962 linalg_vector_norm_17" -> "963 clamp_min_17" [label="(4, 12, 64, 1)", style=solid]; +"963 clamp_min_17" -> "964 expand_as_17" [label="(4, 12, 64, 1)", style=solid]; +"964 expand_as_17" -> "965 div_17" [label="(4, 12, 64, 32)", style=solid]; +"965 div_17" -> "966 quantize_per_tensor_default_53" [label="(4, 12, 64, 32)", style=solid]; +"966 quantize_per_tensor_default_53" -> "967 dequantize_per_tensor_default_53" [label="(4, 12, 64, 32)", style=solid]; +"967 dequantize_per_tensor_default_53" -> "968 transpose_16" [label="(4, 12, 64, 32)", style=solid]; +"968 transpose_16" -> "969 matmul_16" [label="(4, 12, 32, 64)", style=solid]; +"969 matmul_16" -> "971 mul_17" [label="(4, 12, 64, 64)", style=solid]; +"970 _frozen_param71" -> "971 mul_17" [label="(12, 1, 1)", style=solid]; +"971 mul_17" -> "972 add_28" [label="(4, 12, 64, 64)", style=solid]; +"972 add_28" -> "973 softmax_8" [label="(4, 12, 64, 64)", style=solid]; +"973 softmax_8" -> "974 dropout_32" [label="(4, 12, 64, 64)", style=solid]; +"974 dropout_32" -> "975 matmul_17" [label="(4, 12, 64, 64)", style=solid]; +"975 matmul_17" -> "976 transpose_17" [label="(4, 12, 64, 32)", style=solid]; +"976 transpose_17" -> "977 reshape_38" [label="(4, 64, 12, 32)", style=solid]; +"977 reshape_38" -> "978 reshape_38_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"978 reshape_38_0_0_nncf_smooth_quant_0" -> "979 quantize_per_tensor_default_54" [label="(4, 64, 384)", style=solid]; +"979 quantize_per_tensor_default_54" -> "980 dequantize_per_tensor_default_54" [label="(4, 64, 384)", style=solid]; +"980 dequantize_per_tensor_default_54" -> "987 linear_53" [label="(4, 64, 384)", style=solid]; +"981 features_5_4_attn_proj_bias_0_0" -> "987 linear_53" [label="(384,)", style=solid]; +"982 scale_updated_constant36" -> "984 mul_tensor_36" [label="(384, 1)", style=solid]; +"983 compressed_weight_updated_constant36" -> "984 mul_tensor_36" [label="(384, 384)", style=solid]; +"984 mul_tensor_36" -> "986 sub_tensor_36" [label="(384, 384)", style=solid]; +"985 zero_point_updated_constant36" -> "986 sub_tensor_36" [label="(384, 1)", style=solid]; +"986 sub_tensor_36" -> "987 linear_53" [label="(384, 384)", style=solid]; +"987 linear_53" -> "988 dropout_33" [label="(4, 64, 384)", style=solid]; +"988 dropout_33" -> "989 view_47" [label="(4, 64, 384)", style=solid]; +"989 view_47" -> "990 permute_40" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"990 permute_40" -> "991 reshape_39" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"991 reshape_39" -> "992 slice_134" [label="(1, 16, 16, 384)", style=solid]; +"992 slice_134" -> "993 slice_135" [label="(1, 16, 16, 384)", style=solid]; +"993 slice_135" -> "994 slice_136" [label="(1, 14, 16, 384)", style=solid]; +"994 slice_136" -> "995 slice_137" [label="(1, 14, 14, 384)", style=solid]; +"995 slice_137" -> "996 contiguous_15" [label="(1, 14, 14, 384)", style=solid]; +"996 contiguous_15" -> "997 layer_norm_19" [label="(1, 14, 14, 384)", style=solid]; +"997 layer_norm_19" -> "998 add_29" [label="(1, 14, 14, 384)", style=solid]; +"998 add_29" -> "999 add_29_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 384)", style=solid]; +"998 add_29" -> "1023 add_30" [label="(1, 14, 14, 384)", style=solid]; +"999 add_29_0_0_nncf_smooth_quant_0" -> "1000 quantize_per_tensor_default_55" [label="(1, 14, 14, 384)", style=solid]; +"1000 quantize_per_tensor_default_55" -> "1001 dequantize_per_tensor_default_55" [label="(1, 14, 14, 384)", style=solid]; +"1001 dequantize_per_tensor_default_55" -> "1008 linear_54" [label="(1, 14, 14, 384)", style=solid]; +"1002 features_5_4_mlp_0_bias_0_0" -> "1008 linear_54" [label="(1536,)", style=solid]; +"1003 scale_updated_constant37" -> "1005 mul_tensor_37" [label="(1536, 1)", style=solid]; +"1004 compressed_weight_updated_constant37" -> "1005 mul_tensor_37" [label="(1536, 384)", style=solid]; +"1005 mul_tensor_37" -> "1007 sub_tensor_37" [label="(1536, 384)", style=solid]; +"1006 zero_point_updated_constant37" -> "1007 sub_tensor_37" [label="(1536, 1)", style=solid]; +"1007 sub_tensor_37" -> "1008 linear_54" [label="(1536, 384)", style=solid]; +"1008 linear_54" -> "1009 gelu_8" [label="(1, 14, 14, 1536)", style=solid]; +"1009 gelu_8" -> "1010 dropout_34" [label="(1, 14, 14, 1536)", style=solid]; +"1010 dropout_34" -> "1011 dropout_34_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 1536)", style=solid]; +"1011 dropout_34_0_0_nncf_smooth_quant_0" -> "1012 quantize_per_tensor_default_56" [label="(1, 14, 14, 1536)", style=solid]; +"1012 quantize_per_tensor_default_56" -> "1013 dequantize_per_tensor_default_56" [label="(1, 14, 14, 1536)", style=solid]; +"1013 dequantize_per_tensor_default_56" -> "1020 linear_55" [label="(1, 14, 14, 1536)", style=solid]; +"1014 features_5_4_mlp_3_bias_0_0" -> "1020 linear_55" [label="(384,)", style=solid]; +"1015 scale_updated_constant38" -> "1017 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style=solid]; +"1886 compressed_weight_updated_constant73" -> "1887 mul_tensor_73" [label="(1536, 384)", style=solid]; +"1887 mul_tensor_73" -> "1889 sub_tensor_73" [label="(1536, 384)", style=solid]; +"1888 zero_point_updated_constant73" -> "1889 sub_tensor_73" [label="(1536, 1)", style=solid]; +"1889 sub_tensor_73" -> "1890 linear_108" [label="(1536, 384)", style=solid]; +"1890 linear_108" -> "1891 gelu_17" [label="(1, 14, 14, 1536)", style=solid]; +"1891 gelu_17" -> "1892 dropout_70" [label="(1, 14, 14, 1536)", style=solid]; +"1892 dropout_70" -> "1893 dropout_70_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 1536)", style=solid]; +"1893 dropout_70_0_0_nncf_smooth_quant_0" -> "1894 quantize_per_tensor_default_110" [label="(1, 14, 14, 1536)", style=solid]; +"1894 quantize_per_tensor_default_110" -> "1895 dequantize_per_tensor_default_110" [label="(1, 14, 14, 1536)", style=solid]; +"1895 dequantize_per_tensor_default_110" -> "1902 linear_109" [label="(1, 14, 14, 1536)", style=solid]; +"1896 features_5_13_mlp_3_bias_0_0" -> "1902 linear_109" [label="(384,)", style=solid]; +"1897 scale_updated_constant74" -> "1899 mul_tensor_74" [label="(384, 1)", style=solid]; +"1898 compressed_weight_updated_constant74" -> "1899 mul_tensor_74" [label="(384, 1536)", style=solid]; +"1899 mul_tensor_74" -> "1901 sub_tensor_74" [label="(384, 1536)", style=solid]; +"1900 zero_point_updated_constant74" -> "1901 sub_tensor_74" [label="(384, 1)", style=solid]; +"1901 sub_tensor_74" -> "1902 linear_109" [label="(384, 1536)", style=solid]; +"1902 linear_109" -> "1903 dropout_71" [label="(1, 14, 14, 384)", style=solid]; +"1903 dropout_71" -> "1904 layer_norm_38" [label="(1, 14, 14, 384)", style=solid]; +"1904 layer_norm_38" -> "1905 add_62" [label="(1, 14, 14, 384)", style=solid]; +"1905 add_62" -> "1907 pad_20" [label="(1, 14, 14, 384)", style=solid]; +"1905 add_62" -> "1968 add_64" [label="(1, 14, 14, 384)", style=solid]; +"1906 _frozen_param153" -> "1942 add_63" [label="(1, 12, 64, 64)", style=solid]; +"1907 pad_20" -> "1908 view_101" [label="(1, 16, 16, 384)", style=solid]; +"1908 view_101" -> "1909 permute_83" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1909 permute_83" -> "1910 reshape_81" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1910 reshape_81" -> "1911 reshape_81_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"1911 reshape_81_0_0_nncf_smooth_quant_0" -> "1912 quantize_per_tensor_default_111" [label="(4, 64, 384)", style=solid]; +"1912 quantize_per_tensor_default_111" -> "1913 dequantize_per_tensor_default_111" [label="(4, 64, 384)", style=solid]; +"1913 dequantize_per_tensor_default_111" -> "1920 linear_112" [label="(4, 64, 384)", style=solid]; +"1914 _frozen_param154_0_0" -> "1920 linear_112" [label="(1152,)", style=solid]; +"1915 scale_updated_constant75" -> "1917 mul_tensor_75" [label="(1152, 1)", style=solid]; +"1916 compressed_weight_updated_constant75" -> "1917 mul_tensor_75" [label="(1152, 384)", style=solid]; +"1917 mul_tensor_75" -> "1919 sub_tensor_75" [label="(1152, 384)", style=solid]; +"1918 zero_point_updated_constant75" -> "1919 sub_tensor_75" [label="(1152, 1)", style=solid]; +"1919 sub_tensor_75" -> "1920 linear_112" [label="(1152, 384)", style=solid]; +"1920 linear_112" -> "1921 reshape_82" [label="(4, 64, 1152)", style=solid]; +"1921 reshape_82" -> "1922 permute_84" [label="(4, 64, 3, 12, 32)", style=solid]; +"1922 permute_84" -> "1923 select_54" [label="(3, 4, 12, 64, 32)", style=solid]; +"1922 permute_84" -> "1924 select_55" [label="(3, 4, 12, 64, 32)", style=solid]; +"1922 permute_84" -> "1925 select_56" [label="(3, 4, 12, 64, 32)", style=solid]; +"1923 select_54" -> "1926 linalg_vector_norm_36" [label="(4, 12, 64, 32)", style=solid]; +"1923 select_54" -> "1928 expand_as_36" [label="(4, 12, 64, 32)", style=solid]; +"1923 select_54" -> "1929 div_36" [label="(4, 12, 64, 32)", style=solid]; +"1924 select_55" -> "1932 linalg_vector_norm_37" [label="(4, 12, 64, 32)", style=solid]; +"1924 select_55" -> "1934 expand_as_37" [label="(4, 12, 64, 32)", style=solid]; +"1924 select_55" -> "1935 div_37" [label="(4, 12, 64, 32)", style=solid]; +"1925 select_56" -> "1945 matmul_37" [label="(4, 12, 64, 32)", style=solid]; +"1926 linalg_vector_norm_36" -> "1927 clamp_min_36" [label="(4, 12, 64, 1)", style=solid]; +"1927 clamp_min_36" -> "1928 expand_as_36" [label="(4, 12, 64, 1)", style=solid]; +"1928 expand_as_36" -> "1929 div_36" [label="(4, 12, 64, 32)", style=solid]; +"1929 div_36" -> "1930 quantize_per_tensor_default_112" [label="(4, 12, 64, 32)", style=solid]; +"1930 quantize_per_tensor_default_112" -> "1931 dequantize_per_tensor_default_112" [label="(4, 12, 64, 32)", style=solid]; +"1931 dequantize_per_tensor_default_112" -> "1939 matmul_36" [label="(4, 12, 64, 32)", style=solid]; +"1932 linalg_vector_norm_37" -> "1933 clamp_min_37" [label="(4, 12, 64, 1)", style=solid]; +"1933 clamp_min_37" -> "1934 expand_as_37" [label="(4, 12, 64, 1)", style=solid]; +"1934 expand_as_37" -> "1935 div_37" [label="(4, 12, 64, 32)", style=solid]; +"1935 div_37" -> "1936 quantize_per_tensor_default_113" [label="(4, 12, 64, 32)", style=solid]; +"1936 quantize_per_tensor_default_113" -> "1937 dequantize_per_tensor_default_113" [label="(4, 12, 64, 32)", style=solid]; +"1937 dequantize_per_tensor_default_113" -> "1938 transpose_36" [label="(4, 12, 64, 32)", style=solid]; +"1938 transpose_36" -> "1939 matmul_36" [label="(4, 12, 32, 64)", style=solid]; +"1939 matmul_36" -> "1941 mul_37" [label="(4, 12, 64, 64)", style=solid]; +"1940 _frozen_param156" -> "1941 mul_37" [label="(12, 1, 1)", style=solid]; +"1941 mul_37" -> "1942 add_63" [label="(4, 12, 64, 64)", style=solid]; +"1942 add_63" -> "1943 softmax_18" [label="(4, 12, 64, 64)", style=solid]; +"1943 softmax_18" -> "1944 dropout_72" [label="(4, 12, 64, 64)", style=solid]; +"1944 dropout_72" -> "1945 matmul_37" [label="(4, 12, 64, 64)", style=solid]; +"1945 matmul_37" -> "1946 transpose_37" [label="(4, 12, 64, 32)", style=solid]; +"1946 transpose_37" -> "1947 reshape_83" [label="(4, 64, 12, 32)", style=solid]; +"1947 reshape_83" -> "1948 reshape_83_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"1948 reshape_83_0_0_nncf_smooth_quant_0" -> "1949 quantize_per_tensor_default_114" [label="(4, 64, 384)", style=solid]; +"1949 quantize_per_tensor_default_114" -> "1950 dequantize_per_tensor_default_114" [label="(4, 64, 384)", style=solid]; +"1950 dequantize_per_tensor_default_114" -> "1957 linear_113" [label="(4, 64, 384)", style=solid]; +"1951 features_5_14_attn_proj_bias_0_0" -> "1957 linear_113" [label="(384,)", style=solid]; +"1952 scale_updated_constant76" -> "1954 mul_tensor_76" [label="(384, 1)", style=solid]; +"1953 compressed_weight_updated_constant76" -> "1954 mul_tensor_76" [label="(384, 384)", style=solid]; +"1954 mul_tensor_76" -> "1956 sub_tensor_76" [label="(384, 384)", style=solid]; +"1955 zero_point_updated_constant76" -> "1956 sub_tensor_76" [label="(384, 1)", style=solid]; +"1956 sub_tensor_76" -> "1957 linear_113" [label="(384, 384)", style=solid]; +"1957 linear_113" -> "1958 dropout_73" [label="(4, 64, 384)", style=solid]; +"1958 dropout_73" -> "1959 view_102" [label="(4, 64, 384)", style=solid]; +"1959 view_102" -> "1960 permute_85" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1960 permute_85" -> "1961 reshape_84" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1961 reshape_84" -> "1962 slice_274" [label="(1, 16, 16, 384)", style=solid]; +"1962 slice_274" -> "1963 slice_275" [label="(1, 16, 16, 384)", style=solid]; +"1963 slice_275" -> "1964 slice_276" [label="(1, 14, 16, 384)", style=solid]; +"1964 slice_276" -> "1965 slice_277" [label="(1, 14, 14, 384)", style=solid]; +"1965 slice_277" -> "1966 contiguous_35" [label="(1, 14, 14, 384)", style=solid]; +"1966 contiguous_35" -> "1967 layer_norm_39" [label="(1, 14, 14, 384)", style=solid]; +"1967 layer_norm_39" -> "1968 add_64" [label="(1, 14, 14, 384)", style=solid]; +"1968 add_64" -> "1969 add_64_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 384)", style=solid]; +"1968 add_64" -> "1993 add_65" [label="(1, 14, 14, 384)", style=solid]; +"1969 add_64_0_0_nncf_smooth_quant_0" -> "1970 quantize_per_tensor_default_115" [label="(1, 14, 14, 384)", style=solid]; +"1970 quantize_per_tensor_default_115" -> "1971 dequantize_per_tensor_default_115" [label="(1, 14, 14, 384)", style=solid]; +"1971 dequantize_per_tensor_default_115" -> "1978 linear_114" [label="(1, 14, 14, 384)", style=solid]; +"1972 features_5_14_mlp_0_bias_0_0" -> "1978 linear_114" [label="(1536,)", style=solid]; +"1973 scale_updated_constant77" -> "1975 mul_tensor_77" [label="(1536, 1)", style=solid]; +"1974 compressed_weight_updated_constant77" -> "1975 mul_tensor_77" [label="(1536, 384)", style=solid]; +"1975 mul_tensor_77" -> "1977 sub_tensor_77" [label="(1536, 384)", style=solid]; +"1976 zero_point_updated_constant77" -> "1977 sub_tensor_77" [label="(1536, 1)", style=solid]; +"1977 sub_tensor_77" -> "1978 linear_114" [label="(1536, 384)", style=solid]; +"1978 linear_114" -> "1979 gelu_18" [label="(1, 14, 14, 1536)", style=solid]; +"1979 gelu_18" -> "1980 dropout_74" [label="(1, 14, 14, 1536)", style=solid]; +"1980 dropout_74" -> "1981 dropout_74_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 1536)", style=solid]; +"1981 dropout_74_0_0_nncf_smooth_quant_0" -> "1982 quantize_per_tensor_default_116" [label="(1, 14, 14, 1536)", style=solid]; +"1982 quantize_per_tensor_default_116" -> "1983 dequantize_per_tensor_default_116" [label="(1, 14, 14, 1536)", style=solid]; +"1983 dequantize_per_tensor_default_116" -> "1990 linear_115" [label="(1, 14, 14, 1536)", style=solid]; +"1984 features_5_14_mlp_3_bias_0_0" -> "1990 linear_115" [label="(384,)", style=solid]; +"1985 scale_updated_constant78" -> "1987 mul_tensor_78" [label="(384, 1)", style=solid]; +"1986 compressed_weight_updated_constant78" -> "1987 mul_tensor_78" [label="(384, 1536)", style=solid]; +"1987 mul_tensor_78" -> "1989 sub_tensor_78" [label="(384, 1536)", style=solid]; +"1988 zero_point_updated_constant78" -> "1989 sub_tensor_78" [label="(384, 1)", style=solid]; +"1989 sub_tensor_78" -> "1990 linear_115" [label="(384, 1536)", style=solid]; +"1990 linear_115" -> "1991 dropout_75" [label="(1, 14, 14, 384)", style=solid]; +"1991 dropout_75" -> "1992 layer_norm_40" [label="(1, 14, 14, 384)", style=solid]; +"1992 layer_norm_40" -> "1993 add_65" [label="(1, 14, 14, 384)", style=solid]; +"1993 add_65" -> "1995 pad_21" [label="(1, 14, 14, 384)", style=solid]; +"1993 add_65" -> "2074 add_68" [label="(1, 14, 14, 384)", style=solid]; +"1994 _frozen_param157" -> "2031 add_66" [label="(1, 12, 64, 64)", style=solid]; +"1995 pad_21" -> "1996 roll_18" [label="(1, 16, 16, 384)", style=solid]; +"1996 roll_18" -> "1997 view_105" [label="(1, 16, 16, 384)", style=solid]; +"1997 view_105" -> "1998 permute_87" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1998 permute_87" -> "1999 reshape_85" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1999 reshape_85" -> "2000 reshape_85_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"1999 reshape_85" -> "2032 new_zeros_9" [label="(4, 64, 384)", style=solid]; +"2000 reshape_85_0_0_nncf_smooth_quant_0" -> "2001 quantize_per_tensor_default_117" [label="(4, 64, 384)", style=solid]; +"2001 quantize_per_tensor_default_117" -> "2002 dequantize_per_tensor_default_117" [label="(4, 64, 384)", style=solid]; +"2002 dequantize_per_tensor_default_117" -> "2009 linear_118" [label="(4, 64, 384)", style=solid]; +"2003 _frozen_param158_0_0" -> "2009 linear_118" [label="(1152,)", style=solid]; +"2004 scale_updated_constant79" -> "2006 mul_tensor_79" [label="(1152, 1)", style=solid]; +"2005 compressed_weight_updated_constant79" -> "2006 mul_tensor_79" [label="(1152, 384)", style=solid]; +"2006 mul_tensor_79" -> "2008 sub_tensor_79" [label="(1152, 384)", style=solid]; +"2007 zero_point_updated_constant79" -> "2008 sub_tensor_79" [label="(1152, 1)", style=solid]; +"2008 sub_tensor_79" -> "2009 linear_118" [label="(1152, 384)", style=solid]; +"2009 linear_118" -> "2010 reshape_86" [label="(4, 64, 1152)", style=solid]; +"2010 reshape_86" -> "2011 permute_88" [label="(4, 64, 3, 12, 32)", style=solid]; +"2011 permute_88" -> "2012 select_57" [label="(3, 4, 12, 64, 32)", style=solid]; +"2011 permute_88" -> "2013 select_58" [label="(3, 4, 12, 64, 32)", style=solid]; +"2011 permute_88" -> "2014 select_59" [label="(3, 4, 12, 64, 32)", style=solid]; +"2012 select_57" -> "2015 linalg_vector_norm_38" [label="(4, 12, 64, 32)", style=solid]; +"2012 select_57" -> "2017 expand_as_38" [label="(4, 12, 64, 32)", style=solid]; +"2012 select_57" -> "2018 div_38" [label="(4, 12, 64, 32)", style=solid]; +"2013 select_58" -> "2021 linalg_vector_norm_39" [label="(4, 12, 64, 32)", style=solid]; +"2013 select_58" -> "2023 expand_as_39" [label="(4, 12, 64, 32)", style=solid]; +"2013 select_58" -> "2024 div_39" [label="(4, 12, 64, 32)", style=solid]; +"2014 select_59" -> "2050 matmul_39" [label="(4, 12, 64, 32)", style=solid]; +"2015 linalg_vector_norm_38" -> "2016 clamp_min_38" [label="(4, 12, 64, 1)", style=solid]; +"2016 clamp_min_38" -> "2017 expand_as_38" [label="(4, 12, 64, 1)", style=solid]; +"2017 expand_as_38" -> "2018 div_38" [label="(4, 12, 64, 32)", style=solid]; +"2018 div_38" -> "2019 quantize_per_tensor_default_118" [label="(4, 12, 64, 32)", style=solid]; +"2019 quantize_per_tensor_default_118" -> "2020 dequantize_per_tensor_default_118" [label="(4, 12, 64, 32)", style=solid]; +"2020 dequantize_per_tensor_default_118" -> "2028 matmul_38" [label="(4, 12, 64, 32)", style=solid]; +"2021 linalg_vector_norm_39" -> "2022 clamp_min_39" [label="(4, 12, 64, 1)", style=solid]; +"2022 clamp_min_39" -> "2023 expand_as_39" [label="(4, 12, 64, 1)", style=solid]; +"2023 expand_as_39" -> "2024 div_39" [label="(4, 12, 64, 32)", style=solid]; +"2024 div_39" -> "2025 quantize_per_tensor_default_119" [label="(4, 12, 64, 32)", style=solid]; +"2025 quantize_per_tensor_default_119" -> "2026 dequantize_per_tensor_default_119" [label="(4, 12, 64, 32)", style=solid]; +"2026 dequantize_per_tensor_default_119" -> "2027 transpose_38" [label="(4, 12, 64, 32)", style=solid]; +"2027 transpose_38" -> "2028 matmul_38" [label="(4, 12, 32, 64)", style=solid]; +"2028 matmul_38" -> "2030 mul_39" [label="(4, 12, 64, 64)", style=solid]; +"2029 _frozen_param160" -> "2030 mul_39" [label="(12, 1, 1)", style=solid]; +"2030 mul_39" -> "2031 add_66" [label="(4, 12, 64, 64)", style=solid]; +"2031 add_66" -> "2043 view_107" [label="(4, 12, 64, 64)", style=solid]; +"2032 new_zeros_9" -> "2033 view_106" [label="(16, 16)", style=solid]; +"2033 view_106" -> "2034 permute_89" [label="(2, 8, 2, 8)", style=solid]; +"2034 permute_89" -> "2035 reshape_87" [label="(2, 2, 8, 8)", style=solid]; +"2035 reshape_87" -> "2036 unsqueeze_56" [label="(4, 64)", style=solid]; +"2035 reshape_87" -> "2037 unsqueeze_57" [label="(4, 64)", style=solid]; +"2036 unsqueeze_56" -> "2038 sub_9" [label="(4, 1, 64)", style=solid]; +"2037 unsqueeze_57" -> "2038 sub_9" [label="(4, 64, 1)", style=solid]; +"2038 sub_9" -> "2039 ne_9" [label="(4, 64, 64)", style=solid]; +"2038 sub_9" -> "2040 masked_fill_18" [label="(4, 64, 64)", style=solid]; +"2038 sub_9" -> "2041 eq_9" [label="(4, 64, 64)", style=solid]; +"2039 ne_9" -> "2040 masked_fill_18" [label="(4, 64, 64)", style=solid]; +"2040 masked_fill_18" -> "2042 masked_fill_19" [label="(4, 64, 64)", style=solid]; +"2041 eq_9" -> "2042 masked_fill_19" [label="(4, 64, 64)", style=solid]; +"2042 masked_fill_19" -> "2044 unsqueeze_58" [label="(4, 64, 64)", style=solid]; +"2043 view_107" -> "2046 add_67" [label="(1, 4, 12, 64, 64)", style=solid]; +"2044 unsqueeze_58" -> "2045 unsqueeze_59" [label="(4, 1, 64, 64)", style=solid]; +"2045 unsqueeze_59" -> "2046 add_67" [label="(1, 4, 1, 64, 64)", style=solid]; +"2046 add_67" -> "2047 view_108" [label="(1, 4, 12, 64, 64)", style=solid]; +"2047 view_108" -> "2048 softmax_19" [label="(4, 12, 64, 64)", style=solid]; +"2048 softmax_19" -> "2049 dropout_76" [label="(4, 12, 64, 64)", style=solid]; +"2049 dropout_76" -> "2050 matmul_39" [label="(4, 12, 64, 64)", style=solid]; +"2050 matmul_39" -> "2051 transpose_39" [label="(4, 12, 64, 32)", style=solid]; +"2051 transpose_39" -> "2052 reshape_88" [label="(4, 64, 12, 32)", style=solid]; +"2052 reshape_88" -> "2053 reshape_88_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"2053 reshape_88_0_0_nncf_smooth_quant_0" -> "2054 quantize_per_tensor_default_120" [label="(4, 64, 384)", style=solid]; +"2054 quantize_per_tensor_default_120" -> "2055 dequantize_per_tensor_default_120" [label="(4, 64, 384)", style=solid]; +"2055 dequantize_per_tensor_default_120" -> "2062 linear_119" [label="(4, 64, 384)", style=solid]; +"2056 features_5_15_attn_proj_bias_0_0" -> "2062 linear_119" [label="(384,)", style=solid]; +"2057 scale_updated_constant80" -> "2059 mul_tensor_80" [label="(384, 1)", style=solid]; +"2058 compressed_weight_updated_constant80" -> "2059 mul_tensor_80" [label="(384, 384)", style=solid]; +"2059 mul_tensor_80" -> "2061 sub_tensor_80" [label="(384, 384)", style=solid]; +"2060 zero_point_updated_constant80" -> "2061 sub_tensor_80" [label="(384, 1)", style=solid]; +"2061 sub_tensor_80" -> "2062 linear_119" [label="(384, 384)", style=solid]; +"2062 linear_119" -> "2063 dropout_77" [label="(4, 64, 384)", style=solid]; +"2063 dropout_77" -> "2064 view_109" [label="(4, 64, 384)", style=solid]; +"2064 view_109" -> "2065 permute_90" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"2065 permute_90" -> "2066 reshape_89" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"2066 reshape_89" -> "2067 roll_19" [label="(1, 16, 16, 384)", style=solid]; +"2067 roll_19" -> "2068 slice_297" [label="(1, 16, 16, 384)", style=solid]; +"2068 slice_297" -> "2069 slice_298" [label="(1, 16, 16, 384)", style=solid]; +"2069 slice_298" -> "2070 slice_299" [label="(1, 14, 16, 384)", style=solid]; +"2070 slice_299" -> "2071 slice_300" [label="(1, 14, 14, 384)", style=solid]; +"2071 slice_300" -> "2072 contiguous_37" [label="(1, 14, 14, 384)", style=solid]; +"2072 contiguous_37" -> "2073 layer_norm_41" [label="(1, 14, 14, 384)", style=solid]; +"2073 layer_norm_41" -> "2074 add_68" [label="(1, 14, 14, 384)", style=solid]; +"2074 add_68" -> "2075 add_68_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 384)", style=solid]; +"2074 add_68" -> "2099 add_69" [label="(1, 14, 14, 384)", style=solid]; +"2075 add_68_0_0_nncf_smooth_quant_0" -> "2076 quantize_per_tensor_default_121" [label="(1, 14, 14, 384)", style=solid]; +"2076 quantize_per_tensor_default_121" -> "2077 dequantize_per_tensor_default_121" [label="(1, 14, 14, 384)", style=solid]; +"2077 dequantize_per_tensor_default_121" -> "2084 linear_120" [label="(1, 14, 14, 384)", style=solid]; +"2078 features_5_15_mlp_0_bias_0_0" -> "2084 linear_120" [label="(1536,)", style=solid]; +"2079 scale_updated_constant81" -> "2081 mul_tensor_81" [label="(1536, 1)", style=solid]; +"2080 compressed_weight_updated_constant81" -> "2081 mul_tensor_81" [label="(1536, 384)", style=solid]; +"2081 mul_tensor_81" -> "2083 sub_tensor_81" [label="(1536, 384)", style=solid]; +"2082 zero_point_updated_constant81" -> "2083 sub_tensor_81" [label="(1536, 1)", style=solid]; +"2083 sub_tensor_81" -> "2084 linear_120" [label="(1536, 384)", style=solid]; +"2084 linear_120" -> "2085 gelu_19" [label="(1, 14, 14, 1536)", style=solid]; +"2085 gelu_19" -> "2086 dropout_78" [label="(1, 14, 14, 1536)", style=solid]; +"2086 dropout_78" -> "2087 dropout_78_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 1536)", style=solid]; +"2087 dropout_78_0_0_nncf_smooth_quant_0" -> "2088 quantize_per_tensor_default_122" [label="(1, 14, 14, 1536)", style=solid]; +"2088 quantize_per_tensor_default_122" -> "2089 dequantize_per_tensor_default_122" [label="(1, 14, 14, 1536)", style=solid]; +"2089 dequantize_per_tensor_default_122" -> "2096 linear_121" [label="(1, 14, 14, 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style=solid]; +"2435 div_47" -> "2436 quantize_per_tensor_default_144" [label="(1, 24, 64, 32)", style=solid]; +"2436 quantize_per_tensor_default_144" -> "2437 dequantize_per_tensor_default_144" [label="(1, 24, 64, 32)", style=solid]; +"2437 dequantize_per_tensor_default_144" -> "2438 transpose_46" [label="(1, 24, 64, 32)", style=solid]; +"2438 transpose_46" -> "2439 matmul_46" [label="(1, 24, 32, 64)", style=solid]; +"2439 matmul_46" -> "2441 mul_47" [label="(1, 24, 64, 64)", style=solid]; +"2440 _frozen_param194" -> "2441 mul_47" [label="(24, 1, 1)", style=solid]; +"2441 mul_47" -> "2442 add_80" [label="(1, 24, 64, 64)", style=solid]; +"2442 add_80" -> "2443 softmax_23" [label="(1, 24, 64, 64)", style=solid]; +"2443 softmax_23" -> "2444 dropout_92" [label="(1, 24, 64, 64)", style=solid]; +"2444 dropout_92" -> "2445 matmul_47" [label="(1, 24, 64, 64)", style=solid]; +"2445 matmul_47" -> "2446 transpose_47" [label="(1, 24, 64, 32)", style=solid]; +"2446 transpose_47" -> "2447 reshape_105" [label="(1, 64, 24, 32)", style=solid]; +"2447 reshape_105" -> "2448 reshape_105_0_0_nncf_smooth_quant_0" [label="(1, 64, 768)", style=solid]; +"2448 reshape_105_0_0_nncf_smooth_quant_0" -> "2449 quantize_per_tensor_default_145" [label="(1, 64, 768)", style=solid]; +"2449 quantize_per_tensor_default_145" -> "2450 dequantize_per_tensor_default_145" [label="(1, 64, 768)", style=solid]; +"2450 dequantize_per_tensor_default_145" -> "2457 linear_144" [label="(1, 64, 768)", style=solid]; +"2451 features_7_1_attn_proj_bias_0_0" -> "2457 linear_144" [label="(768,)", style=solid]; +"2452 scale_updated_constant97" -> "2454 mul_tensor_97" [label="(768, 1)", style=solid]; +"2453 compressed_weight_updated_constant97" -> "2454 mul_tensor_97" [label="(768, 768)", style=solid]; +"2454 mul_tensor_97" -> "2456 sub_tensor_97" [label="(768, 768)", style=solid]; +"2455 zero_point_updated_constant97" -> "2456 sub_tensor_97" [label="(768, 1)", style=solid]; +"2456 sub_tensor_97" -> "2457 linear_144" [label="(768, 768)", style=solid]; +"2457 linear_144" -> "2458 dropout_93" [label="(1, 64, 768)", style=solid]; +"2458 dropout_93" -> "2459 view_128" [label="(1, 64, 768)", style=solid]; +"2459 view_128" -> "2460 permute_107" [label="(1, 1, 1, 8, 8, 768)", style=solid]; +"2460 permute_107" -> "2461 reshape_106" [label="(1, 1, 8, 1, 8, 768)", style=solid]; +"2461 reshape_106" -> "2462 slice_347" [label="(1, 8, 8, 768)", style=solid]; +"2462 slice_347" -> "2463 slice_348" [label="(1, 8, 8, 768)", style=solid]; +"2463 slice_348" -> "2464 slice_349" [label="(1, 7, 8, 768)", style=solid]; +"2464 slice_349" -> "2465 slice_350" [label="(1, 7, 7, 768)", style=solid]; +"2465 slice_350" -> "2466 contiguous_45" [label="(1, 7, 7, 768)", style=solid]; +"2466 contiguous_45" -> "2467 layer_norm_50" [label="(1, 7, 7, 768)", style=solid]; +"2467 layer_norm_50" -> "2468 add_81" [label="(1, 7, 7, 768)", style=solid]; +"2468 add_81" -> "2469 add_81_0_0_nncf_smooth_quant_0" [label="(1, 7, 7, 768)", style=solid]; +"2468 add_81" -> "2493 add_82" [label="(1, 7, 7, 768)", style=solid]; +"2469 add_81_0_0_nncf_smooth_quant_0" -> "2470 quantize_per_tensor_default_146" [label="(1, 7, 7, 768)", style=solid]; +"2470 quantize_per_tensor_default_146" -> "2471 dequantize_per_tensor_default_146" [label="(1, 7, 7, 768)", style=solid]; +"2471 dequantize_per_tensor_default_146" -> "2478 linear_145" [label="(1, 7, 7, 768)", style=solid]; +"2472 features_7_1_mlp_0_bias_0_0" -> "2478 linear_145" [label="(3072,)", style=solid]; +"2473 scale_updated_constant98" -> "2475 mul_tensor_98" [label="(3072, 1)", style=solid]; +"2474 compressed_weight_updated_constant98" -> "2475 mul_tensor_98" [label="(3072, 768)", style=solid]; +"2475 mul_tensor_98" -> "2477 sub_tensor_98" [label="(3072, 768)", style=solid]; +"2476 zero_point_updated_constant98" -> "2477 sub_tensor_98" [label="(3072, 1)", style=solid]; +"2477 sub_tensor_98" -> "2478 linear_145" [label="(3072, 768)", style=solid]; +"2478 linear_145" -> "2479 gelu_23" [label="(1, 7, 7, 3072)", style=solid]; +"2479 gelu_23" -> "2480 dropout_94" [label="(1, 7, 7, 3072)", style=solid]; +"2480 dropout_94" -> "2481 dropout_94_0_0_nncf_smooth_quant_0" [label="(1, 7, 7, 3072)", style=solid]; +"2481 dropout_94_0_0_nncf_smooth_quant_0" -> "2482 quantize_per_tensor_default_147" [label="(1, 7, 7, 3072)", style=solid]; +"2482 quantize_per_tensor_default_147" -> "2483 dequantize_per_tensor_default_147" [label="(1, 7, 7, 3072)", style=solid]; +"2483 dequantize_per_tensor_default_147" -> "2490 linear_146" [label="(1, 7, 7, 3072)", style=solid]; +"2484 features_7_1_mlp_3_bias_0_0" -> "2490 linear_146" [label="(768,)", style=solid]; +"2485 scale_updated_constant99" -> "2487 mul_tensor_99" [label="(768, 1)", style=solid]; +"2486 compressed_weight_updated_constant99" -> "2487 mul_tensor_99" [label="(768, 3072)", style=solid]; +"2487 mul_tensor_99" -> "2489 sub_tensor_99" [label="(768, 3072)", style=solid]; +"2488 zero_point_updated_constant99" -> "2489 sub_tensor_99" [label="(768, 1)", style=solid]; +"2489 sub_tensor_99" -> "2490 linear_146" [label="(768, 3072)", style=solid]; +"2490 linear_146" -> "2491 dropout_95" [label="(1, 7, 7, 768)", style=solid]; +"2491 dropout_95" -> "2492 layer_norm_51" [label="(1, 7, 7, 768)", style=solid]; +"2492 layer_norm_51" -> "2493 add_82" [label="(1, 7, 7, 768)", style=solid]; +"2493 add_82" -> "2494 layer_norm_52" [label="(1, 7, 7, 768)", style=solid]; +"2494 layer_norm_52" -> "2495 permute_108" [label="(1, 7, 7, 768)", style=solid]; +"2495 permute_108" -> "2496 adaptive_avg_pool2d" [label="(1, 768, 7, 7)", style=solid]; +"2496 adaptive_avg_pool2d" -> "2497 flatten" [label="(1, 768, 1, 1)", style=solid]; +"2497 flatten" -> "2498 flatten_0_0_nncf_smooth_quant_0" [label="(1, 768)", style=solid]; +"2498 flatten_0_0_nncf_smooth_quant_0" -> "2499 quantize_per_tensor_default_148" [label="(1, 768)", style=solid]; +"2499 quantize_per_tensor_default_148" -> "2500 dequantize_per_tensor_default_148" [label="(1, 768)", style=solid]; +"2500 dequantize_per_tensor_default_148" -> "2507 linear_147" [label="(1, 768)", style=solid]; +"2501 head_bias_0_0" -> "2507 linear_147" [label="(1000,)", style=solid]; +"2502 scale_updated_constant100" -> "2504 mul_tensor_100" [label="(1000, 1)", style=solid]; +"2503 compressed_weight_updated_constant100" -> "2504 mul_tensor_100" [label="(1000, 768)", style=solid]; +"2504 mul_tensor_100" -> "2506 sub_tensor_100" [label="(1000, 768)", style=solid]; +"2505 zero_point_updated_constant100" -> "2506 sub_tensor_100" [label="(1000, 1)", style=solid]; +"2506 sub_tensor_100" -> "2507 linear_147" [label="(1000, 768)", style=solid]; +"2507 linear_147" -> "2508 output" [label="(1, 1000)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/post_quantization_compressed/synthetic_transformer.dot b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/synthetic_transformer.dot new file mode 100644 index 00000000000..44026591fb1 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/synthetic_transformer.dot @@ -0,0 +1,49 @@ +strict digraph { +"0 wte_weight" [id=0, type=get_attr]; +"1 input_ids" [id=1, type=input]; +"2 embedding" [id=2, type=embedding]; +"3 embedding_0_0_nncf_smooth_quant_0" [id=3, type=call_module]; +"4 quantize_per_tensor_default" [id=4, type=quantize_per_tensor]; +"5 dequantize_per_tensor_default" [id=5, type=dequantize_per_tensor]; +"6 linear_bias_0_0" [id=6, type=get_attr]; +"7 scale_updated_constant0" [id=7, type=get_attr]; +"8 compressed_weight_updated_constant0" [id=8, type=get_attr]; +"9 mul_tensor" [id=9, type=mul]; +"10 zero_point_updated_constant0" [id=10, type=get_attr]; +"11 sub_tensor" [id=11, type=sub]; +"12 linear" [id=12, type=linear]; +"13 add_tensor_0_0_nncf_smooth_quant_0" [id=13, type=call_module]; +"14 quantize_per_tensor_default_1" [id=14, type=quantize_per_tensor]; +"15 dequantize_per_tensor_default_1" [id=15, type=dequantize_per_tensor]; +"16 lm_head_bias_0_0" [id=16, type=get_attr]; +"17 scale_updated_constant1" [id=17, type=get_attr]; +"18 compressed_weight_updated_constant1" [id=18, type=get_attr]; +"19 mul_tensor_1" [id=19, type=mul]; +"20 zero_point_updated_constant1" [id=20, type=get_attr]; +"21 sub_tensor_1" [id=21, type=sub]; +"22 linear_1" [id=22, type=linear]; +"23 output" [id=23, type=output]; +"0 wte_weight" -> "2 embedding" [label="(10, 5)", style=solid]; +"1 input_ids" -> "2 embedding" [label="(5,)", style=solid]; +"2 embedding" -> "3 embedding_0_0_nncf_smooth_quant_0" [label="(5, 5)", style=solid]; +"3 embedding_0_0_nncf_smooth_quant_0" -> "4 quantize_per_tensor_default" [label="(5, 5)", style=solid]; +"4 quantize_per_tensor_default" -> "5 dequantize_per_tensor_default" [label="(5, 5)", style=solid]; +"5 dequantize_per_tensor_default" -> "12 linear" [label="(5, 5)", style=solid]; +"6 linear_bias_0_0" -> "12 linear" [label="(5,)", style=solid]; +"7 scale_updated_constant0" -> "9 mul_tensor" [label="(5, 1)", style=solid]; +"8 compressed_weight_updated_constant0" -> "9 mul_tensor" [label="(5, 5)", style=solid]; +"9 mul_tensor" -> "11 sub_tensor" [label="(5, 5)", style=solid]; +"10 zero_point_updated_constant0" -> "11 sub_tensor" [label="(5, 1)", style=solid]; +"11 sub_tensor" -> "12 linear" [label="(5, 5)", style=solid]; +"12 linear" -> "13 add_tensor_0_0_nncf_smooth_quant_0" [label="(5, 5)", style=solid]; +"13 add_tensor_0_0_nncf_smooth_quant_0" -> "14 quantize_per_tensor_default_1" [label="(5, 5)", style=solid]; +"14 quantize_per_tensor_default_1" -> "15 dequantize_per_tensor_default_1" [label="(5, 5)", style=solid]; +"15 dequantize_per_tensor_default_1" -> "22 linear_1" [label="(5, 5)", style=solid]; +"16 lm_head_bias_0_0" -> "22 linear_1" [label="(10,)", style=solid]; +"17 scale_updated_constant1" -> "19 mul_tensor_1" [label="(10, 1)", style=solid]; +"18 compressed_weight_updated_constant1" -> "19 mul_tensor_1" [label="(10, 5)", style=solid]; +"19 mul_tensor_1" -> "21 sub_tensor_1" [label="(10, 5)", style=solid]; +"20 zero_point_updated_constant1" -> "21 sub_tensor_1" [label="(10, 1)", style=solid]; +"21 sub_tensor_1" -> "22 linear_1" [label="(10, 5)", style=solid]; +"22 linear_1" -> "23 output" [label="(5, 10)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/post_quantization_compressed/unet.dot b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/unet.dot new file mode 100644 index 00000000000..a183dc3d499 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/unet.dot @@ -0,0 +1,515 @@ +strict digraph { +"0 x" [id=0, type=input]; +"1 quantize_per_tensor_default_8" [id=1, type=quantize_per_tensor]; +"2 dequantize_per_tensor_default_12" [id=2, type=dequantize_per_tensor]; +"3 down_path_0_block_0_bias_0_0" [id=3, type=get_attr]; +"4 scale_updated_constant0" [id=4, type=get_attr]; +"5 compressed_weight_updated_constant0" [id=5, type=get_attr]; +"6 mul_tensor" [id=6, type=mul]; +"7 zero_point_updated_constant0" [id=7, type=get_attr]; +"8 sub_tensor" [id=8, type=sub]; +"9 conv2d" [id=9, type=conv2d]; +"10 relu" [id=10, type=relu]; +"11 quantize_per_tensor_default_9" [id=11, type=quantize_per_tensor]; +"12 dequantize_per_tensor_default_13" [id=12, type=dequantize_per_tensor]; +"13 down_path_0_block_3_bias_0_0" [id=13, type=get_attr]; +"14 scale_updated_constant1" [id=14, type=get_attr]; +"15 compressed_weight_updated_constant1" [id=15, type=get_attr]; +"16 mul_tensor_1" [id=16, type=mul]; +"17 zero_point_updated_constant1" [id=17, type=get_attr]; +"18 sub_tensor_1" [id=18, type=sub]; +"19 conv2d_1" [id=19, type=conv2d]; +"20 relu_1" [id=20, type=relu]; +"21 quantize_per_tensor_default" [id=21, type=quantize_per_tensor]; +"22 dequantize_per_tensor_default_1" [id=22, type=dequantize_per_tensor]; +"23 dequantize_per_tensor_default" [id=23, type=dequantize_per_tensor]; +"24 max_pool2d" [id=24, type=max_pool2d]; +"25 down_path_1_block_0_bias_0_0" [id=25, type=get_attr]; +"26 scale_updated_constant2" [id=26, type=get_attr]; +"27 compressed_weight_updated_constant2" [id=27, type=get_attr]; +"28 mul_tensor_2" [id=28, type=mul]; +"29 zero_point_updated_constant2" [id=29, type=get_attr]; +"30 sub_tensor_2" [id=30, type=sub]; +"31 conv2d_2" [id=31, type=conv2d]; +"32 relu_2" [id=32, type=relu]; +"33 quantize_per_tensor_default_10" [id=33, type=quantize_per_tensor]; +"34 dequantize_per_tensor_default_14" [id=34, type=dequantize_per_tensor]; +"35 down_path_1_block_3_bias_0_0" [id=35, type=get_attr]; +"36 scale_updated_constant3" [id=36, type=get_attr]; +"37 compressed_weight_updated_constant3" [id=37, type=get_attr]; +"38 mul_tensor_3" [id=38, type=mul]; +"39 zero_point_updated_constant3" [id=39, type=get_attr]; +"40 sub_tensor_3" [id=40, type=sub]; +"41 conv2d_3" [id=41, type=conv2d]; +"42 relu_3" [id=42, type=relu]; +"43 quantize_per_tensor_default_3" [id=43, type=quantize_per_tensor]; +"44 dequantize_per_tensor_default_5" [id=44, type=dequantize_per_tensor]; +"45 dequantize_per_tensor_default_4" [id=45, type=dequantize_per_tensor]; +"46 max_pool2d_1" [id=46, type=max_pool2d]; +"47 down_path_2_block_0_bias_0_0" [id=47, type=get_attr]; +"48 scale_updated_constant4" [id=48, type=get_attr]; +"49 compressed_weight_updated_constant4" [id=49, type=get_attr]; +"50 mul_tensor_4" [id=50, type=mul]; +"51 zero_point_updated_constant4" [id=51, type=get_attr]; +"52 sub_tensor_4" [id=52, type=sub]; +"53 conv2d_4" [id=53, type=conv2d]; +"54 relu_4" [id=54, type=relu]; +"55 quantize_per_tensor_default_11" [id=55, type=quantize_per_tensor]; +"56 dequantize_per_tensor_default_15" [id=56, type=dequantize_per_tensor]; +"57 down_path_2_block_3_bias_0_0" [id=57, type=get_attr]; +"58 scale_updated_constant5" [id=58, type=get_attr]; +"59 compressed_weight_updated_constant5" [id=59, type=get_attr]; +"60 mul_tensor_5" [id=60, type=mul]; +"61 zero_point_updated_constant5" [id=61, type=get_attr]; +"62 sub_tensor_5" [id=62, type=sub]; +"63 conv2d_5" [id=63, type=conv2d]; +"64 relu_5" [id=64, type=relu]; +"65 quantize_per_tensor_default_4" [id=65, type=quantize_per_tensor]; +"66 dequantize_per_tensor_default_7" [id=66, type=dequantize_per_tensor]; +"67 dequantize_per_tensor_default_6" [id=67, type=dequantize_per_tensor]; +"68 max_pool2d_2" [id=68, type=max_pool2d]; +"69 down_path_3_block_0_bias_0_0" [id=69, type=get_attr]; +"70 scale_updated_constant6" [id=70, type=get_attr]; +"71 compressed_weight_updated_constant6" [id=71, type=get_attr]; +"72 mul_tensor_6" [id=72, type=mul]; +"73 zero_point_updated_constant6" [id=73, type=get_attr]; +"74 sub_tensor_6" [id=74, type=sub]; +"75 conv2d_6" [id=75, type=conv2d]; +"76 relu_6" [id=76, type=relu]; +"77 quantize_per_tensor_default_12" [id=77, type=quantize_per_tensor]; +"78 dequantize_per_tensor_default_16" [id=78, type=dequantize_per_tensor]; +"79 down_path_3_block_3_bias_0_0" [id=79, type=get_attr]; +"80 scale_updated_constant7" [id=80, type=get_attr]; +"81 compressed_weight_updated_constant7" [id=81, type=get_attr]; +"82 mul_tensor_7" [id=82, type=mul]; +"83 zero_point_updated_constant7" [id=83, type=get_attr]; +"84 sub_tensor_7" [id=84, type=sub]; +"85 conv2d_7" [id=85, type=conv2d]; +"86 relu_7" [id=86, type=relu]; +"87 quantize_per_tensor_default_7" [id=87, type=quantize_per_tensor]; +"88 dequantize_per_tensor_default_11" [id=88, type=dequantize_per_tensor]; +"89 dequantize_per_tensor_default_10" [id=89, type=dequantize_per_tensor]; +"90 max_pool2d_3" [id=90, type=max_pool2d]; +"91 down_path_4_block_0_bias_0_0" [id=91, type=get_attr]; +"92 scale_updated_constant8" [id=92, type=get_attr]; +"93 compressed_weight_updated_constant8" [id=93, type=get_attr]; +"94 mul_tensor_8" [id=94, type=mul]; +"95 zero_point_updated_constant8" [id=95, type=get_attr]; +"96 sub_tensor_8" [id=96, type=sub]; +"97 conv2d_8" [id=97, type=conv2d]; +"98 relu_8" [id=98, type=relu]; +"99 quantize_per_tensor_default_13" [id=99, type=quantize_per_tensor]; +"100 dequantize_per_tensor_default_17" [id=100, type=dequantize_per_tensor]; +"101 down_path_4_block_3_bias_0_0" [id=101, type=get_attr]; +"102 scale_updated_constant9" [id=102, type=get_attr]; +"103 compressed_weight_updated_constant9" [id=103, type=get_attr]; +"104 mul_tensor_9" [id=104, type=mul]; +"105 zero_point_updated_constant9" [id=105, type=get_attr]; +"106 sub_tensor_9" [id=106, type=sub]; +"107 conv2d_9" [id=107, type=conv2d]; +"108 relu_9" [id=108, type=relu]; +"109 quantize_per_tensor_default_14" [id=109, type=quantize_per_tensor]; +"110 dequantize_per_tensor_default_18" [id=110, type=dequantize_per_tensor]; +"111 up_path_0_up_bias_0_0" [id=111, type=get_attr]; +"112 scale_updated_constant10" [id=112, type=get_attr]; +"113 compressed_weight_updated_constant10" [id=113, type=get_attr]; +"114 mul_tensor_10" [id=114, type=mul]; +"115 zero_point_updated_constant10" [id=115, type=get_attr]; +"116 sub_tensor_10" [id=116, type=sub]; +"117 conv_transpose2d" [id=117, type=conv_transpose2d]; +"118 quantize_per_tensor_default_6" [id=118, type=quantize_per_tensor]; +"119 dequantize_per_tensor_default_9" [id=119, type=dequantize_per_tensor]; +"120 slice_1" [id=120, type=slice]; +"121 slice_2" [id=121, type=slice]; +"122 slice_3" [id=122, type=slice]; +"123 slice_4" [id=123, type=slice]; +"124 cat" [id=124, type=cat]; +"125 up_path_0_conv_block_block_0_bias_0_0" [id=125, type=get_attr]; +"126 scale_updated_constant11" [id=126, type=get_attr]; +"127 compressed_weight_updated_constant11" [id=127, type=get_attr]; +"128 mul_tensor_11" [id=128, type=mul]; +"129 zero_point_updated_constant11" [id=129, type=get_attr]; +"130 sub_tensor_11" [id=130, type=sub]; +"131 conv2d_10" [id=131, type=conv2d]; +"132 relu_10" [id=132, type=relu]; +"133 quantize_per_tensor_default_15" [id=133, type=quantize_per_tensor]; +"134 dequantize_per_tensor_default_19" [id=134, type=dequantize_per_tensor]; +"135 up_path_0_conv_block_block_3_bias_0_0" [id=135, type=get_attr]; +"136 scale_updated_constant12" [id=136, type=get_attr]; +"137 compressed_weight_updated_constant12" [id=137, type=get_attr]; +"138 mul_tensor_12" [id=138, type=mul]; +"139 zero_point_updated_constant12" [id=139, type=get_attr]; +"140 sub_tensor_12" [id=140, type=sub]; +"141 conv2d_11" [id=141, type=conv2d]; +"142 relu_11" [id=142, type=relu]; +"143 quantize_per_tensor_default_16" [id=143, type=quantize_per_tensor]; +"144 dequantize_per_tensor_default_20" [id=144, type=dequantize_per_tensor]; +"145 up_path_1_up_bias_0_0" [id=145, type=get_attr]; +"146 scale_updated_constant13" [id=146, type=get_attr]; +"147 compressed_weight_updated_constant13" [id=147, type=get_attr]; +"148 mul_tensor_13" [id=148, type=mul]; +"149 zero_point_updated_constant13" [id=149, type=get_attr]; +"150 sub_tensor_13" [id=150, type=sub]; +"151 conv_transpose2d_1" [id=151, type=conv_transpose2d]; +"152 quantize_per_tensor_default_5" [id=152, type=quantize_per_tensor]; +"153 dequantize_per_tensor_default_8" [id=153, type=dequantize_per_tensor]; +"154 slice_5" [id=154, type=slice]; +"155 slice_6" [id=155, type=slice]; +"156 slice_7" [id=156, type=slice]; +"157 slice_8" [id=157, type=slice]; +"158 cat_1" [id=158, type=cat]; +"159 up_path_1_conv_block_block_0_bias_0_0" [id=159, type=get_attr]; +"160 scale_updated_constant14" [id=160, type=get_attr]; +"161 compressed_weight_updated_constant14" [id=161, type=get_attr]; +"162 mul_tensor_14" [id=162, type=mul]; +"163 zero_point_updated_constant14" [id=163, type=get_attr]; +"164 sub_tensor_14" [id=164, type=sub]; +"165 conv2d_12" [id=165, type=conv2d]; +"166 relu_12" [id=166, type=relu]; +"167 quantize_per_tensor_default_17" [id=167, type=quantize_per_tensor]; +"168 dequantize_per_tensor_default_21" [id=168, type=dequantize_per_tensor]; +"169 up_path_1_conv_block_block_3_bias_0_0" [id=169, type=get_attr]; +"170 scale_updated_constant15" [id=170, type=get_attr]; +"171 compressed_weight_updated_constant15" [id=171, type=get_attr]; +"172 mul_tensor_15" [id=172, type=mul]; +"173 zero_point_updated_constant15" [id=173, type=get_attr]; +"174 sub_tensor_15" [id=174, type=sub]; +"175 conv2d_13" [id=175, type=conv2d]; +"176 relu_13" [id=176, type=relu]; +"177 quantize_per_tensor_default_18" [id=177, type=quantize_per_tensor]; +"178 dequantize_per_tensor_default_22" [id=178, type=dequantize_per_tensor]; +"179 up_path_2_up_bias_0_0" [id=179, type=get_attr]; +"180 scale_updated_constant16" [id=180, type=get_attr]; +"181 compressed_weight_updated_constant16" [id=181, type=get_attr]; +"182 mul_tensor_16" [id=182, type=mul]; +"183 zero_point_updated_constant16" [id=183, type=get_attr]; +"184 sub_tensor_16" [id=184, type=sub]; +"185 conv_transpose2d_2" [id=185, type=conv_transpose2d]; +"186 quantize_per_tensor_default_2" [id=186, type=quantize_per_tensor]; +"187 dequantize_per_tensor_default_3" [id=187, type=dequantize_per_tensor]; +"188 slice_9" [id=188, type=slice]; +"189 slice_10" [id=189, type=slice]; +"190 slice_11" [id=190, type=slice]; 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[id=209, type=conv2d]; +"210 relu_15" [id=210, type=relu]; +"211 quantize_per_tensor_default_20" [id=211, type=quantize_per_tensor]; +"212 dequantize_per_tensor_default_24" [id=212, type=dequantize_per_tensor]; +"213 up_path_3_up_bias_0_0" [id=213, type=get_attr]; +"214 scale_updated_constant19" [id=214, type=get_attr]; +"215 compressed_weight_updated_constant19" [id=215, type=get_attr]; +"216 mul_tensor_19" [id=216, type=mul]; +"217 zero_point_updated_constant19" [id=217, type=get_attr]; +"218 sub_tensor_19" [id=218, type=sub]; +"219 conv_transpose2d_3" [id=219, type=conv_transpose2d]; +"220 quantize_per_tensor_default_1" [id=220, type=quantize_per_tensor]; +"221 dequantize_per_tensor_default_2" [id=221, type=dequantize_per_tensor]; +"222 slice_13" [id=222, type=slice]; +"223 slice_14" [id=223, type=slice]; +"224 slice_15" [id=224, type=slice]; +"225 slice_16" [id=225, type=slice]; +"226 cat_3" [id=226, type=cat]; +"227 up_path_3_conv_block_block_0_bias_0_0" [id=227, type=get_attr]; +"228 scale_updated_constant20" [id=228, type=get_attr]; +"229 compressed_weight_updated_constant20" [id=229, type=get_attr]; +"230 mul_tensor_20" [id=230, type=mul]; +"231 zero_point_updated_constant20" [id=231, type=get_attr]; +"232 sub_tensor_20" [id=232, type=sub]; +"233 conv2d_16" [id=233, type=conv2d]; +"234 relu_16" [id=234, type=relu]; +"235 quantize_per_tensor_default_21" [id=235, type=quantize_per_tensor]; +"236 dequantize_per_tensor_default_25" [id=236, type=dequantize_per_tensor]; +"237 up_path_3_conv_block_block_3_bias_0_0" [id=237, type=get_attr]; +"238 scale_updated_constant21" [id=238, type=get_attr]; +"239 compressed_weight_updated_constant21" [id=239, type=get_attr]; +"240 mul_tensor_21" [id=240, type=mul]; +"241 zero_point_updated_constant21" [id=241, type=get_attr]; +"242 sub_tensor_21" [id=242, type=sub]; +"243 conv2d_17" [id=243, type=conv2d]; +"244 relu_17" [id=244, type=relu]; +"245 quantize_per_tensor_default_22" [id=245, type=quantize_per_tensor]; +"246 dequantize_per_tensor_default_26" [id=246, type=dequantize_per_tensor]; +"247 last_bias_0_0" [id=247, type=get_attr]; +"248 scale_updated_constant22" [id=248, type=get_attr]; +"249 compressed_weight_updated_constant22" [id=249, type=get_attr]; +"250 mul_tensor_22" [id=250, type=mul]; +"251 zero_point_updated_constant22" [id=251, type=get_attr]; +"252 sub_tensor_22" [id=252, type=sub]; +"253 conv2d_18" [id=253, type=conv2d]; +"254 output" [id=254, type=output]; +"0 x" -> "1 quantize_per_tensor_default_8" [label="(1, 3, 224, 224)", style=solid]; +"1 quantize_per_tensor_default_8" -> "2 dequantize_per_tensor_default_12" [label="(1, 3, 224, 224)", style=solid]; +"2 dequantize_per_tensor_default_12" -> "9 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"3 down_path_0_block_0_bias_0_0" -> "9 conv2d" [label="(64,)", style=solid]; +"4 scale_updated_constant0" -> "6 mul_tensor" [label="(64, 1, 1, 1)", style=solid]; +"5 compressed_weight_updated_constant0" -> "6 mul_tensor" [label="(64, 3, 3, 3)", style=solid]; +"6 mul_tensor" -> "8 sub_tensor" [label="(64, 3, 3, 3)", style=solid]; +"7 zero_point_updated_constant0" -> "8 sub_tensor" [label="(64, 1, 1, 1)", style=solid]; +"8 sub_tensor" -> "9 conv2d" [label="(64, 3, 3, 3)", style=solid]; +"9 conv2d" -> "10 relu" [label="(1, 64, 222, 222)", style=solid]; +"10 relu" -> "11 quantize_per_tensor_default_9" [label="(1, 64, 222, 222)", style=solid]; +"11 quantize_per_tensor_default_9" -> "12 dequantize_per_tensor_default_13" [label="(1, 64, 222, 222)", style=solid]; +"12 dequantize_per_tensor_default_13" -> "19 conv2d_1" [label="(1, 64, 222, 222)", style=solid]; +"13 down_path_0_block_3_bias_0_0" -> "19 conv2d_1" [label="(64,)", style=solid]; +"14 scale_updated_constant1" -> "16 mul_tensor_1" [label="(64, 1, 1, 1)", style=solid]; +"15 compressed_weight_updated_constant1" -> "16 mul_tensor_1" [label="(64, 64, 3, 3)", style=solid]; +"16 mul_tensor_1" -> "18 sub_tensor_1" [label="(64, 64, 3, 3)", style=solid]; +"17 zero_point_updated_constant1" -> "18 sub_tensor_1" [label="(64, 1, 1, 1)", style=solid]; +"18 sub_tensor_1" -> "19 conv2d_1" [label="(64, 64, 3, 3)", style=solid]; +"19 conv2d_1" -> "20 relu_1" [label="(1, 64, 220, 220)", style=solid]; +"20 relu_1" -> "21 quantize_per_tensor_default" [label="(1, 64, 220, 220)", style=solid]; +"21 quantize_per_tensor_default" -> "22 dequantize_per_tensor_default_1" [label="(1, 64, 220, 220)", style=solid]; +"21 quantize_per_tensor_default" -> "23 dequantize_per_tensor_default" [label="(1, 64, 220, 220)", style=solid]; +"22 dequantize_per_tensor_default_1" -> "222 slice_13" [label="(1, 64, 220, 220)", style=solid]; +"23 dequantize_per_tensor_default" -> "24 max_pool2d" [label="(1, 64, 220, 220)", style=solid]; +"24 max_pool2d" -> "31 conv2d_2" [label="(1, 64, 110, 110)", style=solid]; +"25 down_path_1_block_0_bias_0_0" -> "31 conv2d_2" [label="(128,)", style=solid]; +"26 scale_updated_constant2" -> "28 mul_tensor_2" [label="(128, 1, 1, 1)", style=solid]; +"27 compressed_weight_updated_constant2" -> "28 mul_tensor_2" [label="(128, 64, 3, 3)", style=solid]; +"28 mul_tensor_2" -> "30 sub_tensor_2" [label="(128, 64, 3, 3)", style=solid]; +"29 zero_point_updated_constant2" -> "30 sub_tensor_2" [label="(128, 1, 1, 1)", style=solid]; +"30 sub_tensor_2" -> "31 conv2d_2" [label="(128, 64, 3, 3)", style=solid]; +"31 conv2d_2" -> "32 relu_2" [label="(1, 128, 108, 108)", style=solid]; +"32 relu_2" -> "33 quantize_per_tensor_default_10" [label="(1, 128, 108, 108)", style=solid]; +"33 quantize_per_tensor_default_10" -> "34 dequantize_per_tensor_default_14" [label="(1, 128, 108, 108)", style=solid]; +"34 dequantize_per_tensor_default_14" -> "41 conv2d_3" [label="(1, 128, 108, 108)", style=solid]; +"35 down_path_1_block_3_bias_0_0" -> "41 conv2d_3" [label="(128,)", style=solid]; +"36 scale_updated_constant3" -> "38 mul_tensor_3" [label="(128, 1, 1, 1)", style=solid]; +"37 compressed_weight_updated_constant3" -> "38 mul_tensor_3" [label="(128, 128, 3, 3)", style=solid]; +"38 mul_tensor_3" -> "40 sub_tensor_3" [label="(128, 128, 3, 3)", style=solid]; +"39 zero_point_updated_constant3" -> "40 sub_tensor_3" [label="(128, 1, 1, 1)", style=solid]; +"40 sub_tensor_3" -> "41 conv2d_3" [label="(128, 128, 3, 3)", style=solid]; +"41 conv2d_3" -> "42 relu_3" [label="(1, 128, 106, 106)", style=solid]; +"42 relu_3" -> "43 quantize_per_tensor_default_3" [label="(1, 128, 106, 106)", style=solid]; +"43 quantize_per_tensor_default_3" -> "44 dequantize_per_tensor_default_5" [label="(1, 128, 106, 106)", style=solid]; +"43 quantize_per_tensor_default_3" -> "45 dequantize_per_tensor_default_4" [label="(1, 128, 106, 106)", style=solid]; +"44 dequantize_per_tensor_default_5" -> "188 slice_9" [label="(1, 128, 106, 106)", style=solid]; +"45 dequantize_per_tensor_default_4" -> "46 max_pool2d_1" [label="(1, 128, 106, 106)", style=solid]; +"46 max_pool2d_1" -> "53 conv2d_4" [label="(1, 128, 53, 53)", style=solid]; +"47 down_path_2_block_0_bias_0_0" -> "53 conv2d_4" [label="(256,)", style=solid]; +"48 scale_updated_constant4" -> "50 mul_tensor_4" [label="(256, 1, 1, 1)", style=solid]; +"49 compressed_weight_updated_constant4" -> "50 mul_tensor_4" [label="(256, 128, 3, 3)", style=solid]; +"50 mul_tensor_4" -> "52 sub_tensor_4" [label="(256, 128, 3, 3)", style=solid]; +"51 zero_point_updated_constant4" -> "52 sub_tensor_4" [label="(256, 1, 1, 1)", style=solid]; +"52 sub_tensor_4" -> "53 conv2d_4" [label="(256, 128, 3, 3)", style=solid]; +"53 conv2d_4" -> "54 relu_4" [label="(1, 256, 51, 51)", style=solid]; +"54 relu_4" -> "55 quantize_per_tensor_default_11" [label="(1, 256, 51, 51)", style=solid]; +"55 quantize_per_tensor_default_11" -> "56 dequantize_per_tensor_default_15" [label="(1, 256, 51, 51)", style=solid]; +"56 dequantize_per_tensor_default_15" -> "63 conv2d_5" [label="(1, 256, 51, 51)", style=solid]; +"57 down_path_2_block_3_bias_0_0" -> "63 conv2d_5" [label="(256,)", style=solid]; +"58 scale_updated_constant5" -> "60 mul_tensor_5" [label="(256, 1, 1, 1)", style=solid]; +"59 compressed_weight_updated_constant5" -> "60 mul_tensor_5" [label="(256, 256, 3, 3)", style=solid]; +"60 mul_tensor_5" -> "62 sub_tensor_5" [label="(256, 256, 3, 3)", style=solid]; +"61 zero_point_updated_constant5" -> "62 sub_tensor_5" [label="(256, 1, 1, 1)", style=solid]; +"62 sub_tensor_5" -> "63 conv2d_5" [label="(256, 256, 3, 3)", style=solid]; +"63 conv2d_5" -> "64 relu_5" [label="(1, 256, 49, 49)", style=solid]; +"64 relu_5" -> "65 quantize_per_tensor_default_4" [label="(1, 256, 49, 49)", style=solid]; +"65 quantize_per_tensor_default_4" -> "66 dequantize_per_tensor_default_7" [label="(1, 256, 49, 49)", style=solid]; +"65 quantize_per_tensor_default_4" -> "67 dequantize_per_tensor_default_6" [label="(1, 256, 49, 49)", style=solid]; +"66 dequantize_per_tensor_default_7" -> "154 slice_5" [label="(1, 256, 49, 49)", style=solid]; +"67 dequantize_per_tensor_default_6" -> "68 max_pool2d_2" [label="(1, 256, 49, 49)", style=solid]; +"68 max_pool2d_2" -> "75 conv2d_6" [label="(1, 256, 24, 24)", style=solid]; +"69 down_path_3_block_0_bias_0_0" -> "75 conv2d_6" [label="(512,)", style=solid]; +"70 scale_updated_constant6" -> "72 mul_tensor_6" [label="(512, 1, 1, 1)", style=solid]; +"71 compressed_weight_updated_constant6" -> "72 mul_tensor_6" [label="(512, 256, 3, 3)", style=solid]; +"72 mul_tensor_6" -> "74 sub_tensor_6" [label="(512, 256, 3, 3)", style=solid]; +"73 zero_point_updated_constant6" -> "74 sub_tensor_6" [label="(512, 1, 1, 1)", style=solid]; +"74 sub_tensor_6" -> "75 conv2d_6" [label="(512, 256, 3, 3)", style=solid]; +"75 conv2d_6" -> "76 relu_6" [label="(1, 512, 22, 22)", style=solid]; +"76 relu_6" -> "77 quantize_per_tensor_default_12" [label="(1, 512, 22, 22)", style=solid]; +"77 quantize_per_tensor_default_12" -> "78 dequantize_per_tensor_default_16" [label="(1, 512, 22, 22)", style=solid]; +"78 dequantize_per_tensor_default_16" -> "85 conv2d_7" [label="(1, 512, 22, 22)", style=solid]; +"79 down_path_3_block_3_bias_0_0" -> "85 conv2d_7" [label="(512,)", style=solid]; +"80 scale_updated_constant7" -> "82 mul_tensor_7" [label="(512, 1, 1, 1)", style=solid]; +"81 compressed_weight_updated_constant7" -> "82 mul_tensor_7" [label="(512, 512, 3, 3)", style=solid]; +"82 mul_tensor_7" -> "84 sub_tensor_7" [label="(512, 512, 3, 3)", style=solid]; +"83 zero_point_updated_constant7" -> "84 sub_tensor_7" [label="(512, 1, 1, 1)", style=solid]; +"84 sub_tensor_7" -> "85 conv2d_7" [label="(512, 512, 3, 3)", style=solid]; +"85 conv2d_7" -> "86 relu_7" [label="(1, 512, 20, 20)", style=solid]; +"86 relu_7" -> "87 quantize_per_tensor_default_7" [label="(1, 512, 20, 20)", style=solid]; +"87 quantize_per_tensor_default_7" -> "88 dequantize_per_tensor_default_11" [label="(1, 512, 20, 20)", style=solid]; +"87 quantize_per_tensor_default_7" -> "89 dequantize_per_tensor_default_10" [label="(1, 512, 20, 20)", style=solid]; +"88 dequantize_per_tensor_default_11" -> "120 slice_1" [label="(1, 512, 20, 20)", style=solid]; +"89 dequantize_per_tensor_default_10" -> "90 max_pool2d_3" [label="(1, 512, 20, 20)", style=solid]; +"90 max_pool2d_3" -> "97 conv2d_8" [label="(1, 512, 10, 10)", style=solid]; +"91 down_path_4_block_0_bias_0_0" -> "97 conv2d_8" [label="(1024,)", style=solid]; +"92 scale_updated_constant8" -> "94 mul_tensor_8" [label="(1024, 1, 1, 1)", style=solid]; +"93 compressed_weight_updated_constant8" -> "94 mul_tensor_8" [label="(1024, 512, 3, 3)", style=solid]; +"94 mul_tensor_8" -> "96 sub_tensor_8" [label="(1024, 512, 3, 3)", style=solid]; +"95 zero_point_updated_constant8" -> "96 sub_tensor_8" [label="(1024, 1, 1, 1)", style=solid]; +"96 sub_tensor_8" -> "97 conv2d_8" [label="(1024, 512, 3, 3)", style=solid]; +"97 conv2d_8" -> "98 relu_8" [label="(1, 1024, 8, 8)", style=solid]; +"98 relu_8" -> "99 quantize_per_tensor_default_13" [label="(1, 1024, 8, 8)", style=solid]; +"99 quantize_per_tensor_default_13" -> "100 dequantize_per_tensor_default_17" [label="(1, 1024, 8, 8)", style=solid]; +"100 dequantize_per_tensor_default_17" -> "107 conv2d_9" [label="(1, 1024, 8, 8)", style=solid]; +"101 down_path_4_block_3_bias_0_0" -> "107 conv2d_9" [label="(1024,)", style=solid]; +"102 scale_updated_constant9" -> "104 mul_tensor_9" [label="(1024, 1, 1, 1)", style=solid]; +"103 compressed_weight_updated_constant9" -> "104 mul_tensor_9" [label="(1024, 1024, 3, 3)", style=solid]; +"104 mul_tensor_9" -> "106 sub_tensor_9" [label="(1024, 1024, 3, 3)", style=solid]; +"105 zero_point_updated_constant9" -> "106 sub_tensor_9" [label="(1024, 1, 1, 1)", style=solid]; +"106 sub_tensor_9" -> "107 conv2d_9" [label="(1024, 1024, 3, 3)", style=solid]; +"107 conv2d_9" -> "108 relu_9" [label="(1, 1024, 6, 6)", style=solid]; +"108 relu_9" -> "109 quantize_per_tensor_default_14" [label="(1, 1024, 6, 6)", style=solid]; +"109 quantize_per_tensor_default_14" -> "110 dequantize_per_tensor_default_18" [label="(1, 1024, 6, 6)", style=solid]; +"110 dequantize_per_tensor_default_18" -> "117 conv_transpose2d" [label="(1, 1024, 6, 6)", style=solid]; +"111 up_path_0_up_bias_0_0" -> "117 conv_transpose2d" [label="(512,)", style=solid]; +"112 scale_updated_constant10" -> "114 mul_tensor_10" [label="(1024, 1, 1, 1)", style=solid]; +"113 compressed_weight_updated_constant10" -> "114 mul_tensor_10" [label="(1024, 512, 2, 2)", style=solid]; +"114 mul_tensor_10" -> "116 sub_tensor_10" [label="(1024, 512, 2, 2)", style=solid]; +"115 zero_point_updated_constant10" -> "116 sub_tensor_10" [label="(1024, 1, 1, 1)", style=solid]; +"116 sub_tensor_10" -> "117 conv_transpose2d" [label="(1024, 512, 2, 2)", style=solid]; +"117 conv_transpose2d" -> "118 quantize_per_tensor_default_6" [label="(1, 512, 12, 12)", style=solid]; +"118 quantize_per_tensor_default_6" -> "119 dequantize_per_tensor_default_9" [label="(1, 512, 12, 12)", style=solid]; +"119 dequantize_per_tensor_default_9" -> "124 cat" [label="(1, 512, 12, 12)", style=solid]; +"120 slice_1" -> "121 slice_2" [label="(1, 512, 20, 20)", style=solid]; +"121 slice_2" -> "122 slice_3" [label="(1, 512, 20, 20)", style=solid]; +"122 slice_3" -> "123 slice_4" [label="(1, 512, 12, 20)", style=solid]; +"123 slice_4" -> "124 cat" [label="(1, 512, 12, 12)", style=solid]; +"124 cat" -> "131 conv2d_10" [label="(1, 1024, 12, 12)", style=solid]; +"125 up_path_0_conv_block_block_0_bias_0_0" -> "131 conv2d_10" [label="(512,)", style=solid]; +"126 scale_updated_constant11" -> "128 mul_tensor_11" [label="(512, 1, 1, 1)", style=solid]; +"127 compressed_weight_updated_constant11" -> "128 mul_tensor_11" [label="(512, 1024, 3, 3)", style=solid]; +"128 mul_tensor_11" -> "130 sub_tensor_11" [label="(512, 1024, 3, 3)", style=solid]; +"129 zero_point_updated_constant11" -> "130 sub_tensor_11" [label="(512, 1, 1, 1)", style=solid]; +"130 sub_tensor_11" -> "131 conv2d_10" [label="(512, 1024, 3, 3)", style=solid]; +"131 conv2d_10" -> "132 relu_10" [label="(1, 512, 10, 10)", style=solid]; +"132 relu_10" -> "133 quantize_per_tensor_default_15" [label="(1, 512, 10, 10)", style=solid]; +"133 quantize_per_tensor_default_15" -> "134 dequantize_per_tensor_default_19" [label="(1, 512, 10, 10)", style=solid]; +"134 dequantize_per_tensor_default_19" -> "141 conv2d_11" [label="(1, 512, 10, 10)", style=solid]; +"135 up_path_0_conv_block_block_3_bias_0_0" -> "141 conv2d_11" [label="(512,)", style=solid]; +"136 scale_updated_constant12" -> "138 mul_tensor_12" [label="(512, 1, 1, 1)", style=solid]; +"137 compressed_weight_updated_constant12" -> "138 mul_tensor_12" [label="(512, 512, 3, 3)", style=solid]; +"138 mul_tensor_12" -> "140 sub_tensor_12" [label="(512, 512, 3, 3)", style=solid]; +"139 zero_point_updated_constant12" -> "140 sub_tensor_12" [label="(512, 1, 1, 1)", style=solid]; +"140 sub_tensor_12" -> "141 conv2d_11" [label="(512, 512, 3, 3)", style=solid]; +"141 conv2d_11" -> "142 relu_11" [label="(1, 512, 8, 8)", style=solid]; +"142 relu_11" -> "143 quantize_per_tensor_default_16" [label="(1, 512, 8, 8)", style=solid]; +"143 quantize_per_tensor_default_16" -> "144 dequantize_per_tensor_default_20" [label="(1, 512, 8, 8)", style=solid]; +"144 dequantize_per_tensor_default_20" -> "151 conv_transpose2d_1" [label="(1, 512, 8, 8)", style=solid]; +"145 up_path_1_up_bias_0_0" -> "151 conv_transpose2d_1" [label="(256,)", style=solid]; +"146 scale_updated_constant13" -> "148 mul_tensor_13" [label="(512, 1, 1, 1)", style=solid]; +"147 compressed_weight_updated_constant13" -> "148 mul_tensor_13" [label="(512, 256, 2, 2)", style=solid]; +"148 mul_tensor_13" -> "150 sub_tensor_13" [label="(512, 256, 2, 2)", style=solid]; +"149 zero_point_updated_constant13" -> "150 sub_tensor_13" [label="(512, 1, 1, 1)", style=solid]; +"150 sub_tensor_13" -> "151 conv_transpose2d_1" [label="(512, 256, 2, 2)", style=solid]; +"151 conv_transpose2d_1" -> "152 quantize_per_tensor_default_5" [label="(1, 256, 16, 16)", style=solid]; +"152 quantize_per_tensor_default_5" -> "153 dequantize_per_tensor_default_8" [label="(1, 256, 16, 16)", style=solid]; +"153 dequantize_per_tensor_default_8" -> "158 cat_1" [label="(1, 256, 16, 16)", style=solid]; +"154 slice_5" -> "155 slice_6" [label="(1, 256, 49, 49)", style=solid]; +"155 slice_6" -> "156 slice_7" [label="(1, 256, 49, 49)", style=solid]; +"156 slice_7" -> "157 slice_8" [label="(1, 256, 16, 49)", style=solid]; +"157 slice_8" -> "158 cat_1" [label="(1, 256, 16, 16)", style=solid]; +"158 cat_1" -> "165 conv2d_12" [label="(1, 512, 16, 16)", style=solid]; +"159 up_path_1_conv_block_block_0_bias_0_0" -> "165 conv2d_12" [label="(256,)", style=solid]; +"160 scale_updated_constant14" -> "162 mul_tensor_14" [label="(256, 1, 1, 1)", style=solid]; +"161 compressed_weight_updated_constant14" -> "162 mul_tensor_14" [label="(256, 512, 3, 3)", style=solid]; +"162 mul_tensor_14" -> "164 sub_tensor_14" [label="(256, 512, 3, 3)", style=solid]; +"163 zero_point_updated_constant14" -> "164 sub_tensor_14" [label="(256, 1, 1, 1)", style=solid]; +"164 sub_tensor_14" -> "165 conv2d_12" [label="(256, 512, 3, 3)", style=solid]; +"165 conv2d_12" -> "166 relu_12" [label="(1, 256, 14, 14)", style=solid]; +"166 relu_12" -> "167 quantize_per_tensor_default_17" [label="(1, 256, 14, 14)", style=solid]; +"167 quantize_per_tensor_default_17" -> "168 dequantize_per_tensor_default_21" [label="(1, 256, 14, 14)", style=solid]; +"168 dequantize_per_tensor_default_21" -> "175 conv2d_13" [label="(1, 256, 14, 14)", style=solid]; +"169 up_path_1_conv_block_block_3_bias_0_0" -> "175 conv2d_13" [label="(256,)", style=solid]; +"170 scale_updated_constant15" -> "172 mul_tensor_15" [label="(256, 1, 1, 1)", style=solid]; +"171 compressed_weight_updated_constant15" -> "172 mul_tensor_15" [label="(256, 256, 3, 3)", style=solid]; +"172 mul_tensor_15" -> "174 sub_tensor_15" [label="(256, 256, 3, 3)", style=solid]; +"173 zero_point_updated_constant15" -> "174 sub_tensor_15" [label="(256, 1, 1, 1)", style=solid]; +"174 sub_tensor_15" -> "175 conv2d_13" [label="(256, 256, 3, 3)", style=solid]; +"175 conv2d_13" -> "176 relu_13" [label="(1, 256, 12, 12)", style=solid]; +"176 relu_13" -> "177 quantize_per_tensor_default_18" [label="(1, 256, 12, 12)", style=solid]; +"177 quantize_per_tensor_default_18" -> "178 dequantize_per_tensor_default_22" [label="(1, 256, 12, 12)", style=solid]; +"178 dequantize_per_tensor_default_22" -> "185 conv_transpose2d_2" [label="(1, 256, 12, 12)", style=solid]; +"179 up_path_2_up_bias_0_0" -> "185 conv_transpose2d_2" [label="(128,)", style=solid]; +"180 scale_updated_constant16" -> "182 mul_tensor_16" [label="(256, 1, 1, 1)", style=solid]; +"181 compressed_weight_updated_constant16" -> "182 mul_tensor_16" [label="(256, 128, 2, 2)", style=solid]; +"182 mul_tensor_16" -> "184 sub_tensor_16" [label="(256, 128, 2, 2)", style=solid]; +"183 zero_point_updated_constant16" -> "184 sub_tensor_16" [label="(256, 1, 1, 1)", style=solid]; +"184 sub_tensor_16" -> "185 conv_transpose2d_2" [label="(256, 128, 2, 2)", style=solid]; +"185 conv_transpose2d_2" -> "186 quantize_per_tensor_default_2" [label="(1, 128, 24, 24)", style=solid]; +"186 quantize_per_tensor_default_2" -> "187 dequantize_per_tensor_default_3" [label="(1, 128, 24, 24)", style=solid]; +"187 dequantize_per_tensor_default_3" -> "192 cat_2" [label="(1, 128, 24, 24)", style=solid]; +"188 slice_9" -> "189 slice_10" [label="(1, 128, 106, 106)", style=solid]; +"189 slice_10" -> "190 slice_11" [label="(1, 128, 106, 106)", style=solid]; +"190 slice_11" -> "191 slice_12" [label="(1, 128, 24, 106)", style=solid]; +"191 slice_12" -> "192 cat_2" [label="(1, 128, 24, 24)", style=solid]; +"192 cat_2" -> "199 conv2d_14" [label="(1, 256, 24, 24)", style=solid]; +"193 up_path_2_conv_block_block_0_bias_0_0" -> "199 conv2d_14" [label="(128,)", style=solid]; +"194 scale_updated_constant17" -> "196 mul_tensor_17" [label="(128, 1, 1, 1)", style=solid]; +"195 compressed_weight_updated_constant17" -> "196 mul_tensor_17" [label="(128, 256, 3, 3)", style=solid]; +"196 mul_tensor_17" -> "198 sub_tensor_17" [label="(128, 256, 3, 3)", style=solid]; +"197 zero_point_updated_constant17" -> "198 sub_tensor_17" [label="(128, 1, 1, 1)", style=solid]; +"198 sub_tensor_17" -> "199 conv2d_14" [label="(128, 256, 3, 3)", style=solid]; +"199 conv2d_14" -> "200 relu_14" [label="(1, 128, 22, 22)", style=solid]; +"200 relu_14" -> "201 quantize_per_tensor_default_19" [label="(1, 128, 22, 22)", style=solid]; +"201 quantize_per_tensor_default_19" -> "202 dequantize_per_tensor_default_23" [label="(1, 128, 22, 22)", style=solid]; +"202 dequantize_per_tensor_default_23" -> "209 conv2d_15" [label="(1, 128, 22, 22)", style=solid]; +"203 up_path_2_conv_block_block_3_bias_0_0" -> "209 conv2d_15" [label="(128,)", style=solid]; +"204 scale_updated_constant18" -> "206 mul_tensor_18" [label="(128, 1, 1, 1)", style=solid]; +"205 compressed_weight_updated_constant18" -> "206 mul_tensor_18" [label="(128, 128, 3, 3)", style=solid]; +"206 mul_tensor_18" -> "208 sub_tensor_18" [label="(128, 128, 3, 3)", style=solid]; +"207 zero_point_updated_constant18" -> "208 sub_tensor_18" [label="(128, 1, 1, 1)", style=solid]; +"208 sub_tensor_18" -> "209 conv2d_15" [label="(128, 128, 3, 3)", style=solid]; +"209 conv2d_15" -> "210 relu_15" [label="(1, 128, 20, 20)", style=solid]; +"210 relu_15" -> "211 quantize_per_tensor_default_20" [label="(1, 128, 20, 20)", style=solid]; +"211 quantize_per_tensor_default_20" -> "212 dequantize_per_tensor_default_24" [label="(1, 128, 20, 20)", style=solid]; +"212 dequantize_per_tensor_default_24" -> "219 conv_transpose2d_3" [label="(1, 128, 20, 20)", style=solid]; +"213 up_path_3_up_bias_0_0" -> "219 conv_transpose2d_3" [label="(64,)", style=solid]; +"214 scale_updated_constant19" -> "216 mul_tensor_19" [label="(128, 1, 1, 1)", style=solid]; +"215 compressed_weight_updated_constant19" -> "216 mul_tensor_19" [label="(128, 64, 2, 2)", style=solid]; +"216 mul_tensor_19" -> "218 sub_tensor_19" [label="(128, 64, 2, 2)", style=solid]; +"217 zero_point_updated_constant19" -> "218 sub_tensor_19" [label="(128, 1, 1, 1)", style=solid]; +"218 sub_tensor_19" -> "219 conv_transpose2d_3" [label="(128, 64, 2, 2)", style=solid]; +"219 conv_transpose2d_3" -> "220 quantize_per_tensor_default_1" [label="(1, 64, 40, 40)", style=solid]; +"220 quantize_per_tensor_default_1" -> "221 dequantize_per_tensor_default_2" [label="(1, 64, 40, 40)", style=solid]; +"221 dequantize_per_tensor_default_2" -> "226 cat_3" [label="(1, 64, 40, 40)", style=solid]; +"222 slice_13" -> "223 slice_14" [label="(1, 64, 220, 220)", style=solid]; +"223 slice_14" -> "224 slice_15" [label="(1, 64, 220, 220)", style=solid]; +"224 slice_15" -> "225 slice_16" [label="(1, 64, 40, 220)", style=solid]; +"225 slice_16" -> "226 cat_3" [label="(1, 64, 40, 40)", style=solid]; +"226 cat_3" -> "233 conv2d_16" [label="(1, 128, 40, 40)", style=solid]; +"227 up_path_3_conv_block_block_0_bias_0_0" -> "233 conv2d_16" [label="(64,)", style=solid]; +"228 scale_updated_constant20" -> "230 mul_tensor_20" [label="(64, 1, 1, 1)", style=solid]; +"229 compressed_weight_updated_constant20" -> "230 mul_tensor_20" [label="(64, 128, 3, 3)", style=solid]; +"230 mul_tensor_20" -> "232 sub_tensor_20" [label="(64, 128, 3, 3)", style=solid]; +"231 zero_point_updated_constant20" -> "232 sub_tensor_20" [label="(64, 1, 1, 1)", style=solid]; +"232 sub_tensor_20" -> "233 conv2d_16" [label="(64, 128, 3, 3)", style=solid]; +"233 conv2d_16" -> "234 relu_16" [label="(1, 64, 38, 38)", style=solid]; +"234 relu_16" -> "235 quantize_per_tensor_default_21" [label="(1, 64, 38, 38)", style=solid]; +"235 quantize_per_tensor_default_21" -> "236 dequantize_per_tensor_default_25" [label="(1, 64, 38, 38)", style=solid]; +"236 dequantize_per_tensor_default_25" -> "243 conv2d_17" [label="(1, 64, 38, 38)", style=solid]; +"237 up_path_3_conv_block_block_3_bias_0_0" -> "243 conv2d_17" [label="(64,)", style=solid]; +"238 scale_updated_constant21" -> "240 mul_tensor_21" [label="(64, 1, 1, 1)", style=solid]; +"239 compressed_weight_updated_constant21" -> "240 mul_tensor_21" [label="(64, 64, 3, 3)", style=solid]; +"240 mul_tensor_21" -> "242 sub_tensor_21" [label="(64, 64, 3, 3)", style=solid]; +"241 zero_point_updated_constant21" -> "242 sub_tensor_21" [label="(64, 1, 1, 1)", style=solid]; +"242 sub_tensor_21" -> "243 conv2d_17" [label="(64, 64, 3, 3)", style=solid]; +"243 conv2d_17" -> "244 relu_17" [label="(1, 64, 36, 36)", style=solid]; +"244 relu_17" -> "245 quantize_per_tensor_default_22" [label="(1, 64, 36, 36)", style=solid]; +"245 quantize_per_tensor_default_22" -> "246 dequantize_per_tensor_default_26" [label="(1, 64, 36, 36)", style=solid]; +"246 dequantize_per_tensor_default_26" -> "253 conv2d_18" [label="(1, 64, 36, 36)", style=solid]; +"247 last_bias_0_0" -> "253 conv2d_18" [label="(12,)", style=solid]; +"248 scale_updated_constant22" -> "250 mul_tensor_22" [label="(12, 1, 1, 1)", style=solid]; +"249 compressed_weight_updated_constant22" -> "250 mul_tensor_22" [label="(12, 64, 1, 1)", style=solid]; +"250 mul_tensor_22" -> "252 sub_tensor_22" [label="(12, 64, 1, 1)", style=solid]; +"251 zero_point_updated_constant22" -> "252 sub_tensor_22" [label="(12, 1, 1, 1)", style=solid]; +"252 sub_tensor_22" -> "253 conv2d_18" [label="(12, 64, 1, 1)", style=solid]; +"253 conv2d_18" -> "254 output" [label="(1, 12, 36, 36)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/post_quantization_compressed/vit_b_16.dot b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/vit_b_16.dot new file mode 100644 index 00000000000..88e4a8c30d1 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/post_quantization_compressed/vit_b_16.dot @@ -0,0 +1,2011 @@ +strict digraph { +"0 encoder_pos_embedding" [id=0, type=get_attr]; +"1 encoder_layers_encoder_layer_0_ln_1_weight" [id=1, type=get_attr]; +"2 encoder_layers_encoder_layer_0_ln_1_bias" [id=2, type=get_attr]; +"3 encoder_layers_encoder_layer_0_ln_2_weight" [id=3, type=get_attr]; +"4 encoder_layers_encoder_layer_0_ln_2_bias" [id=4, type=get_attr]; +"5 encoder_layers_encoder_layer_1_ln_1_weight" [id=5, type=get_attr]; +"6 encoder_layers_encoder_layer_1_ln_1_bias" [id=6, type=get_attr]; +"7 encoder_layers_encoder_layer_1_ln_2_weight" [id=7, type=get_attr]; +"8 encoder_layers_encoder_layer_1_ln_2_bias" [id=8, type=get_attr]; +"9 encoder_layers_encoder_layer_2_ln_1_weight" [id=9, type=get_attr]; +"10 encoder_layers_encoder_layer_2_ln_1_bias" [id=10, type=get_attr]; +"11 encoder_layers_encoder_layer_2_ln_2_weight" [id=11, type=get_attr]; +"12 encoder_layers_encoder_layer_2_ln_2_bias" [id=12, type=get_attr]; +"13 encoder_layers_encoder_layer_3_ln_1_weight" [id=13, type=get_attr]; +"14 encoder_layers_encoder_layer_3_ln_1_bias" [id=14, type=get_attr]; +"15 encoder_layers_encoder_layer_3_ln_2_weight" [id=15, type=get_attr]; +"16 encoder_layers_encoder_layer_3_ln_2_bias" [id=16, type=get_attr]; +"17 encoder_layers_encoder_layer_4_ln_1_weight" [id=17, type=get_attr]; +"18 encoder_layers_encoder_layer_4_ln_1_bias" [id=18, type=get_attr]; +"19 encoder_layers_encoder_layer_4_ln_2_weight" [id=19, type=get_attr]; +"20 encoder_layers_encoder_layer_4_ln_2_bias" [id=20, type=get_attr]; +"21 encoder_layers_encoder_layer_5_ln_1_weight" [id=21, type=get_attr]; +"22 encoder_layers_encoder_layer_5_ln_1_bias" [id=22, type=get_attr]; +"23 encoder_layers_encoder_layer_5_ln_2_weight" [id=23, type=get_attr]; +"24 encoder_layers_encoder_layer_5_ln_2_bias" [id=24, type=get_attr]; +"25 encoder_layers_encoder_layer_6_ln_1_weight" [id=25, type=get_attr]; +"26 encoder_layers_encoder_layer_6_ln_1_bias" [id=26, type=get_attr]; +"27 encoder_layers_encoder_layer_6_ln_2_weight" [id=27, type=get_attr]; +"28 encoder_layers_encoder_layer_6_ln_2_bias" [id=28, type=get_attr]; +"29 encoder_layers_encoder_layer_7_ln_1_weight" [id=29, type=get_attr]; +"30 encoder_layers_encoder_layer_7_ln_1_bias" [id=30, type=get_attr]; +"31 encoder_layers_encoder_layer_7_ln_2_weight" [id=31, type=get_attr]; +"32 encoder_layers_encoder_layer_7_ln_2_bias" [id=32, type=get_attr]; +"33 encoder_layers_encoder_layer_8_ln_1_weight" [id=33, type=get_attr]; +"34 encoder_layers_encoder_layer_8_ln_1_bias" [id=34, type=get_attr]; +"35 encoder_layers_encoder_layer_8_ln_2_weight" [id=35, type=get_attr]; +"36 encoder_layers_encoder_layer_8_ln_2_bias" [id=36, type=get_attr]; +"37 encoder_layers_encoder_layer_9_ln_1_weight" [id=37, type=get_attr]; +"38 encoder_layers_encoder_layer_9_ln_1_bias" [id=38, type=get_attr]; +"39 encoder_layers_encoder_layer_9_ln_2_weight" [id=39, type=get_attr]; +"40 encoder_layers_encoder_layer_9_ln_2_bias" [id=40, type=get_attr]; +"41 encoder_layers_encoder_layer_10_ln_1_weight" [id=41, type=get_attr]; +"42 encoder_layers_encoder_layer_10_ln_1_bias" [id=42, type=get_attr]; +"43 encoder_layers_encoder_layer_10_ln_2_weight" [id=43, type=get_attr]; +"44 encoder_layers_encoder_layer_10_ln_2_bias" [id=44, type=get_attr]; +"45 encoder_layers_encoder_layer_11_ln_1_weight" [id=45, type=get_attr]; +"46 encoder_layers_encoder_layer_11_ln_1_bias" [id=46, type=get_attr]; +"47 encoder_layers_encoder_layer_11_ln_2_weight" [id=47, type=get_attr]; +"48 encoder_layers_encoder_layer_11_ln_2_bias" [id=48, type=get_attr]; +"49 encoder_ln_weight" [id=49, type=get_attr]; +"50 encoder_ln_bias" [id=50, type=get_attr]; +"51 x" [id=51, type=input]; +"52 quantize_per_tensor_default" [id=52, type=quantize_per_tensor]; +"53 dequantize_per_tensor_default" [id=53, type=dequantize_per_tensor]; +"54 conv_proj_bias_0_0" [id=54, type=get_attr]; +"55 scale_updated_constant0" [id=55, type=get_attr]; +"56 compressed_weight_updated_constant0" [id=56, type=get_attr]; +"57 mul_tensor" [id=57, type=mul]; +"58 zero_point_updated_constant0" [id=58, type=get_attr]; +"59 sub_tensor" [id=59, type=sub]; +"60 conv2d" [id=60, type=conv2d]; +"61 reshape" [id=61, type=reshape]; +"62 permute" [id=62, type=permute]; +"63 _frozen_param0" [id=63, 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-> "367 layer_norm_8" [label="(768,)", style=solid]; +"19 encoder_layers_encoder_layer_4_ln_2_weight" -> "417 layer_norm_9" [label="(768,)", style=solid]; +"20 encoder_layers_encoder_layer_4_ln_2_bias" -> "417 layer_norm_9" [label="(768,)", style=solid]; +"21 encoder_layers_encoder_layer_5_ln_1_weight" -> "442 layer_norm_10" [label="(768,)", style=solid]; +"22 encoder_layers_encoder_layer_5_ln_1_bias" -> "442 layer_norm_10" [label="(768,)", style=solid]; +"23 encoder_layers_encoder_layer_5_ln_2_weight" -> "492 layer_norm_11" [label="(768,)", style=solid]; +"24 encoder_layers_encoder_layer_5_ln_2_bias" -> "492 layer_norm_11" [label="(768,)", style=solid]; +"25 encoder_layers_encoder_layer_6_ln_1_weight" -> "517 layer_norm_12" [label="(768,)", style=solid]; +"26 encoder_layers_encoder_layer_6_ln_1_bias" -> "517 layer_norm_12" [label="(768,)", style=solid]; +"27 encoder_layers_encoder_layer_6_ln_2_weight" -> "567 layer_norm_13" [label="(768,)", style=solid]; +"28 encoder_layers_encoder_layer_6_ln_2_bias" -> "567 layer_norm_13" [label="(768,)", style=solid]; +"29 encoder_layers_encoder_layer_7_ln_1_weight" -> "592 layer_norm_14" [label="(768,)", style=solid]; +"30 encoder_layers_encoder_layer_7_ln_1_bias" -> "592 layer_norm_14" [label="(768,)", style=solid]; +"31 encoder_layers_encoder_layer_7_ln_2_weight" -> "642 layer_norm_15" [label="(768,)", style=solid]; +"32 encoder_layers_encoder_layer_7_ln_2_bias" -> "642 layer_norm_15" [label="(768,)", style=solid]; +"33 encoder_layers_encoder_layer_8_ln_1_weight" -> "667 layer_norm_16" [label="(768,)", style=solid]; +"34 encoder_layers_encoder_layer_8_ln_1_bias" -> "667 layer_norm_16" [label="(768,)", style=solid]; +"35 encoder_layers_encoder_layer_8_ln_2_weight" -> "717 layer_norm_17" [label="(768,)", style=solid]; +"36 encoder_layers_encoder_layer_8_ln_2_bias" -> "717 layer_norm_17" [label="(768,)", style=solid]; +"37 encoder_layers_encoder_layer_9_ln_1_weight" -> "742 layer_norm_18" [label="(768,)", style=solid]; +"38 encoder_layers_encoder_layer_9_ln_1_bias" -> "742 layer_norm_18" [label="(768,)", style=solid]; +"39 encoder_layers_encoder_layer_9_ln_2_weight" -> "792 layer_norm_19" [label="(768,)", style=solid]; +"40 encoder_layers_encoder_layer_9_ln_2_bias" -> "792 layer_norm_19" [label="(768,)", style=solid]; +"41 encoder_layers_encoder_layer_10_ln_1_weight" -> "817 layer_norm_20" [label="(768,)", style=solid]; +"42 encoder_layers_encoder_layer_10_ln_1_bias" -> "817 layer_norm_20" [label="(768,)", style=solid]; +"43 encoder_layers_encoder_layer_10_ln_2_weight" -> "867 layer_norm_21" [label="(768,)", style=solid]; +"44 encoder_layers_encoder_layer_10_ln_2_bias" -> "867 layer_norm_21" [label="(768,)", style=solid]; +"45 encoder_layers_encoder_layer_11_ln_1_weight" -> "892 layer_norm_22" [label="(768,)", style=solid]; +"46 encoder_layers_encoder_layer_11_ln_1_bias" -> "892 layer_norm_22" [label="(768,)", style=solid]; +"47 encoder_layers_encoder_layer_11_ln_2_weight" -> "942 layer_norm_23" [label="(768,)", style=solid]; +"48 encoder_layers_encoder_layer_11_ln_2_bias" -> "942 layer_norm_23" [label="(768,)", style=solid]; +"49 encoder_ln_weight" -> "967 layer_norm_24" [label="(768,)", style=solid]; +"50 encoder_ln_bias" -> "967 layer_norm_24" [label="(768,)", style=solid]; +"51 x" -> "52 quantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"52 quantize_per_tensor_default" -> "53 dequantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"53 dequantize_per_tensor_default" -> "60 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"54 conv_proj_bias_0_0" -> "60 conv2d" [label="(768,)", style=solid]; +"55 scale_updated_constant0" -> "57 mul_tensor" [label="(768, 1, 1, 1)", style=solid]; +"56 compressed_weight_updated_constant0" -> "57 mul_tensor" [label="(768, 3, 16, 16)", style=solid]; +"57 mul_tensor" -> "59 sub_tensor" [label="(768, 3, 16, 16)", style=solid]; +"58 zero_point_updated_constant0" -> "59 sub_tensor" [label="(768, 1, 1, 1)", style=solid]; +"59 sub_tensor" -> "60 conv2d" [label="(768, 3, 16, 16)", style=solid]; +"60 conv2d" -> "61 reshape" [label="(1, 768, 14, 14)", style=solid]; +"61 reshape" -> "62 permute" [label="(1, 768, 196)", style=solid]; +"62 permute" -> "64 cat" [label="(1, 196, 768)", style=solid]; +"63 _frozen_param0" -> "64 cat" [label="(1, 1, 768)", style=solid]; +"64 cat" -> "65 add" [label="(1, 197, 768)", style=solid]; +"65 add" -> "66 dropout" [label="(1, 197, 768)", style=solid]; +"66 dropout" -> "67 layer_norm" [label="(1, 197, 768)", style=solid]; +"66 dropout" -> "116 add_1" [label="(1, 197, 768)", style=solid]; +"67 layer_norm" -> "68 transpose" [label="(1, 197, 768)", style=solid]; +"68 transpose" -> "69 transpose_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"69 transpose_0_0_nncf_smooth_quant_0" -> "70 quantize_per_tensor_default_1" [label="(197, 1, 768)", style=solid]; +"70 quantize_per_tensor_default_1" -> "71 dequantize_per_tensor_default_1" [label="(197, 1, 768)", style=solid]; +"71 dequantize_per_tensor_default_1" -> "78 linear" [label="(197, 1, 768)", style=solid]; +"72 encoder_layers_encoder_layer_0_self_attention_in_proj_bias_0_0" -> "78 linear" [label="(2304,)", style=solid]; +"73 scale_updated_constant1" -> "75 mul_tensor_1" [label="(2304, 1)", style=solid]; +"74 compressed_weight_updated_constant1" -> "75 mul_tensor_1" [label="(2304, 768)", style=solid]; +"75 mul_tensor_1" -> "77 sub_tensor_1" [label="(2304, 768)", style=solid]; +"76 zero_point_updated_constant1" -> "77 sub_tensor_1" [label="(2304, 1)", style=solid]; +"77 sub_tensor_1" -> "78 linear" [label="(2304, 768)", style=solid]; +"78 linear" -> "79 unflatten" [label="(197, 1, 2304)", style=solid]; +"79 unflatten" -> "80 unsqueeze" [label="(197, 1, 3, 768)", style=solid]; +"80 unsqueeze" -> "81 transpose_1" [label="(1, 197, 1, 3, 768)", style=solid]; +"81 transpose_1" -> "82 squeeze" [label="(3, 197, 1, 1, 768)", style=solid]; +"82 squeeze" -> "83 contiguous" [label="(3, 197, 1, 768)", style=solid]; +"83 contiguous" -> "84 quantize_per_tensor_default_2" [label="(3, 197, 1, 768)", style=solid]; +"83 contiguous" -> "87 quantize_per_tensor_default_3" [label="(3, 197, 1, 768)", style=solid]; +"83 contiguous" -> "90 select_2" [label="(3, 197, 1, 768)", style=solid]; +"84 quantize_per_tensor_default_2" -> "85 dequantize_per_tensor_default_2" [label="(3, 197, 1, 768)", style=solid]; +"85 dequantize_per_tensor_default_2" -> "86 select" [label="(3, 197, 1, 768)", style=solid]; +"86 select" -> "91 view" [label="(197, 1, 768)", style=solid]; +"87 quantize_per_tensor_default_3" -> "88 dequantize_per_tensor_default_3" [label="(3, 197, 1, 768)", style=solid]; +"88 dequantize_per_tensor_default_3" -> "89 select_1" [label="(3, 197, 1, 768)", style=solid]; +"89 select_1" -> "93 view_1" [label="(197, 1, 768)", style=solid]; +"90 select_2" -> "95 view_2" [label="(197, 1, 768)", style=solid]; +"91 view" -> "92 transpose_2" [label="(197, 12, 64)", style=solid]; +"92 transpose_2" -> "97 view_3" [label="(12, 197, 64)", style=solid]; +"93 view_1" -> "94 transpose_3" [label="(197, 12, 64)", style=solid]; +"94 transpose_3" -> "98 view_4" [label="(12, 197, 64)", style=solid]; +"95 view_2" -> "96 transpose_4" [label="(197, 12, 64)", style=solid]; +"96 transpose_4" -> "99 view_5" [label="(12, 197, 64)", style=solid]; +"97 view_3" -> "100 scaled_dot_product_attention" [label="(1, 12, 197, 64)", style=solid]; +"98 view_4" -> "100 scaled_dot_product_attention" [label="(1, 12, 197, 64)", style=solid]; +"99 view_5" -> "100 scaled_dot_product_attention" [label="(1, 12, 197, 64)", style=solid]; +"100 scaled_dot_product_attention" -> "101 permute_1" [label="(1, 12, 197, 64)", style=solid]; +"101 permute_1" -> "102 view_6" [label="(197, 1, 12, 64)", style=solid]; +"102 view_6" -> "103 view_6_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"103 view_6_0_0_nncf_smooth_quant_0" -> "104 quantize_per_tensor_default_4" [label="(197, 768)", style=solid]; +"104 quantize_per_tensor_default_4" -> "105 dequantize_per_tensor_default_4" [label="(197, 768)", style=solid]; +"105 dequantize_per_tensor_default_4" -> "112 linear_1" [label="(197, 768)", style=solid]; +"106 encoder_layers_encoder_layer_0_self_attention_out_proj_bias_0_0" -> "112 linear_1" [label="(768,)", style=solid]; +"107 scale_updated_constant2" -> "109 mul_tensor_2" [label="(768, 1)", style=solid]; +"108 compressed_weight_updated_constant2" -> "109 mul_tensor_2" [label="(768, 768)", style=solid]; +"109 mul_tensor_2" -> "111 sub_tensor_2" [label="(768, 768)", style=solid]; +"110 zero_point_updated_constant2" -> "111 sub_tensor_2" [label="(768, 1)", style=solid]; +"111 sub_tensor_2" -> "112 linear_1" [label="(768, 768)", style=solid]; +"112 linear_1" -> "113 view_7" [label="(197, 768)", style=solid]; +"113 view_7" -> "114 transpose_5" [label="(197, 1, 768)", style=solid]; +"114 transpose_5" -> "115 dropout_1" [label="(1, 197, 768)", style=solid]; +"115 dropout_1" -> "116 add_1" [label="(1, 197, 768)", style=solid]; +"116 add_1" -> "117 layer_norm_1" [label="(1, 197, 768)", style=solid]; +"116 add_1" -> "141 add_2" [label="(1, 197, 768)", style=solid]; +"117 layer_norm_1" -> "118 layer_norm_1_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"118 layer_norm_1_0_0_nncf_smooth_quant_0" -> "119 quantize_per_tensor_default_5" [label="(1, 197, 768)", style=solid]; +"119 quantize_per_tensor_default_5" -> "120 dequantize_per_tensor_default_5" [label="(1, 197, 768)", style=solid]; +"120 dequantize_per_tensor_default_5" -> "127 linear_2" [label="(1, 197, 768)", style=solid]; +"121 encoder_layers_encoder_layer_0_mlp_0_bias_0_0" -> "127 linear_2" [label="(3072,)", style=solid]; +"122 scale_updated_constant3" -> "124 mul_tensor_3" [label="(3072, 1)", style=solid]; +"123 compressed_weight_updated_constant3" -> "124 mul_tensor_3" [label="(3072, 768)", style=solid]; +"124 mul_tensor_3" -> "126 sub_tensor_3" [label="(3072, 768)", style=solid]; +"125 zero_point_updated_constant3" -> "126 sub_tensor_3" [label="(3072, 1)", style=solid]; +"126 sub_tensor_3" -> "127 linear_2" [label="(3072, 768)", style=solid]; +"127 linear_2" -> "128 gelu" [label="(1, 197, 3072)", style=solid]; +"128 gelu" -> "129 dropout_2" [label="(1, 197, 3072)", style=solid]; +"129 dropout_2" -> "130 dropout_2_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"130 dropout_2_0_0_nncf_smooth_quant_0" -> "131 quantize_per_tensor_default_6" [label="(1, 197, 3072)", style=solid]; +"131 quantize_per_tensor_default_6" -> "132 dequantize_per_tensor_default_6" [label="(1, 197, 3072)", style=solid]; +"132 dequantize_per_tensor_default_6" -> "139 linear_3" [label="(1, 197, 3072)", style=solid]; +"133 encoder_layers_encoder_layer_0_mlp_3_bias_0_0" -> "139 linear_3" [label="(768,)", style=solid]; +"134 scale_updated_constant4" -> "136 mul_tensor_4" [label="(768, 1)", style=solid]; +"135 compressed_weight_updated_constant4" -> "136 mul_tensor_4" [label="(768, 3072)", style=solid]; +"136 mul_tensor_4" -> "138 sub_tensor_4" [label="(768, 3072)", style=solid]; +"137 zero_point_updated_constant4" -> "138 sub_tensor_4" [label="(768, 1)", style=solid]; +"138 sub_tensor_4" -> "139 linear_3" [label="(768, 3072)", style=solid]; +"139 linear_3" -> "140 dropout_3" [label="(1, 197, 768)", style=solid]; +"140 dropout_3" -> "141 add_2" [label="(1, 197, 768)", style=solid]; +"141 add_2" -> "142 layer_norm_2" [label="(1, 197, 768)", style=solid]; +"141 add_2" -> "191 add_3" [label="(1, 197, 768)", style=solid]; +"142 layer_norm_2" -> "143 transpose_6" [label="(1, 197, 768)", style=solid]; +"143 transpose_6" -> "144 transpose_6_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"144 transpose_6_0_0_nncf_smooth_quant_0" -> "145 quantize_per_tensor_default_7" [label="(197, 1, 768)", style=solid]; +"145 quantize_per_tensor_default_7" -> "146 dequantize_per_tensor_default_7" [label="(197, 1, 768)", style=solid]; +"146 dequantize_per_tensor_default_7" -> "153 linear_4" [label="(197, 1, 768)", style=solid]; +"147 encoder_layers_encoder_layer_1_self_attention_in_proj_bias_0_0" -> "153 linear_4" [label="(2304,)", style=solid]; +"148 scale_updated_constant5" -> "150 mul_tensor_5" [label="(2304, 1)", style=solid]; +"149 compressed_weight_updated_constant5" -> "150 mul_tensor_5" [label="(2304, 768)", style=solid]; +"150 mul_tensor_5" -> "152 sub_tensor_5" [label="(2304, 768)", style=solid]; +"151 zero_point_updated_constant5" -> "152 sub_tensor_5" [label="(2304, 1)", style=solid]; +"152 sub_tensor_5" -> "153 linear_4" [label="(2304, 768)", style=solid]; +"153 linear_4" -> "154 unflatten_1" [label="(197, 1, 2304)", style=solid]; +"154 unflatten_1" -> "155 unsqueeze_1" [label="(197, 1, 3, 768)", style=solid]; +"155 unsqueeze_1" -> "156 transpose_7" [label="(1, 197, 1, 3, 768)", style=solid]; +"156 transpose_7" -> "157 squeeze_1" [label="(3, 197, 1, 1, 768)", style=solid]; +"157 squeeze_1" -> "158 contiguous_1" [label="(3, 197, 1, 768)", style=solid]; +"158 contiguous_1" -> "159 quantize_per_tensor_default_8" [label="(3, 197, 1, 768)", style=solid]; +"158 contiguous_1" -> "162 quantize_per_tensor_default_9" [label="(3, 197, 1, 768)", style=solid]; +"158 contiguous_1" -> "165 select_5" [label="(3, 197, 1, 768)", style=solid]; +"159 quantize_per_tensor_default_8" -> "160 dequantize_per_tensor_default_8" [label="(3, 197, 1, 768)", style=solid]; +"160 dequantize_per_tensor_default_8" -> "161 select_3" [label="(3, 197, 1, 768)", style=solid]; +"161 select_3" -> "166 view_8" [label="(197, 1, 768)", style=solid]; +"162 quantize_per_tensor_default_9" -> "163 dequantize_per_tensor_default_9" [label="(3, 197, 1, 768)", style=solid]; +"163 dequantize_per_tensor_default_9" -> "164 select_4" [label="(3, 197, 1, 768)", style=solid]; +"164 select_4" -> "168 view_9" [label="(197, 1, 768)", style=solid]; +"165 select_5" -> "170 view_10" [label="(197, 1, 768)", style=solid]; +"166 view_8" -> "167 transpose_8" [label="(197, 12, 64)", style=solid]; +"167 transpose_8" -> "172 view_11" [label="(12, 197, 64)", style=solid]; +"168 view_9" -> "169 transpose_9" [label="(197, 12, 64)", style=solid]; +"169 transpose_9" -> "173 view_12" [label="(12, 197, 64)", style=solid]; +"170 view_10" -> "171 transpose_10" [label="(197, 12, 64)", style=solid]; +"171 transpose_10" -> "174 view_13" [label="(12, 197, 64)", style=solid]; +"172 view_11" -> "175 scaled_dot_product_attention_1" [label="(1, 12, 197, 64)", style=solid]; +"173 view_12" -> "175 scaled_dot_product_attention_1" [label="(1, 12, 197, 64)", style=solid]; +"174 view_13" -> "175 scaled_dot_product_attention_1" [label="(1, 12, 197, 64)", style=solid]; +"175 scaled_dot_product_attention_1" -> "176 permute_2" [label="(1, 12, 197, 64)", style=solid]; +"176 permute_2" -> "177 view_14" [label="(197, 1, 12, 64)", style=solid]; +"177 view_14" -> "178 view_14_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"178 view_14_0_0_nncf_smooth_quant_0" -> "179 quantize_per_tensor_default_10" [label="(197, 768)", style=solid]; +"179 quantize_per_tensor_default_10" -> "180 dequantize_per_tensor_default_10" [label="(197, 768)", style=solid]; +"180 dequantize_per_tensor_default_10" -> "187 linear_5" [label="(197, 768)", style=solid]; +"181 encoder_layers_encoder_layer_1_self_attention_out_proj_bias_0_0" -> "187 linear_5" [label="(768,)", style=solid]; +"182 scale_updated_constant6" -> "184 mul_tensor_6" [label="(768, 1)", style=solid]; +"183 compressed_weight_updated_constant6" -> "184 mul_tensor_6" [label="(768, 768)", style=solid]; +"184 mul_tensor_6" -> "186 sub_tensor_6" [label="(768, 768)", style=solid]; +"185 zero_point_updated_constant6" -> "186 sub_tensor_6" [label="(768, 1)", style=solid]; +"186 sub_tensor_6" -> "187 linear_5" [label="(768, 768)", style=solid]; +"187 linear_5" -> "188 view_15" [label="(197, 768)", style=solid]; +"188 view_15" -> "189 transpose_11" [label="(197, 1, 768)", style=solid]; +"189 transpose_11" -> "190 dropout_4" [label="(1, 197, 768)", style=solid]; +"190 dropout_4" -> "191 add_3" [label="(1, 197, 768)", style=solid]; +"191 add_3" -> "192 layer_norm_3" [label="(1, 197, 768)", style=solid]; +"191 add_3" -> "216 add_4" [label="(1, 197, 768)", style=solid]; +"192 layer_norm_3" -> "193 layer_norm_3_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"193 layer_norm_3_0_0_nncf_smooth_quant_0" -> "194 quantize_per_tensor_default_11" [label="(1, 197, 768)", style=solid]; +"194 quantize_per_tensor_default_11" -> "195 dequantize_per_tensor_default_11" [label="(1, 197, 768)", style=solid]; +"195 dequantize_per_tensor_default_11" -> "202 linear_6" [label="(1, 197, 768)", style=solid]; +"196 encoder_layers_encoder_layer_1_mlp_0_bias_0_0" -> "202 linear_6" [label="(3072,)", style=solid]; +"197 scale_updated_constant7" -> "199 mul_tensor_7" [label="(3072, 1)", style=solid]; +"198 compressed_weight_updated_constant7" -> "199 mul_tensor_7" [label="(3072, 768)", style=solid]; +"199 mul_tensor_7" -> "201 sub_tensor_7" [label="(3072, 768)", style=solid]; +"200 zero_point_updated_constant7" -> "201 sub_tensor_7" [label="(3072, 1)", style=solid]; +"201 sub_tensor_7" -> "202 linear_6" [label="(3072, 768)", style=solid]; +"202 linear_6" -> "203 gelu_1" [label="(1, 197, 3072)", style=solid]; +"203 gelu_1" -> "204 dropout_5" [label="(1, 197, 3072)", style=solid]; +"204 dropout_5" -> "205 dropout_5_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"205 dropout_5_0_0_nncf_smooth_quant_0" -> "206 quantize_per_tensor_default_12" [label="(1, 197, 3072)", style=solid]; +"206 quantize_per_tensor_default_12" -> "207 dequantize_per_tensor_default_12" [label="(1, 197, 3072)", style=solid]; +"207 dequantize_per_tensor_default_12" -> "214 linear_7" [label="(1, 197, 3072)", style=solid]; +"208 encoder_layers_encoder_layer_1_mlp_3_bias_0_0" -> "214 linear_7" [label="(768,)", style=solid]; +"209 scale_updated_constant8" -> "211 mul_tensor_8" [label="(768, 1)", style=solid]; +"210 compressed_weight_updated_constant8" -> "211 mul_tensor_8" [label="(768, 3072)", style=solid]; +"211 mul_tensor_8" -> "213 sub_tensor_8" [label="(768, 3072)", style=solid]; +"212 zero_point_updated_constant8" -> "213 sub_tensor_8" [label="(768, 1)", style=solid]; +"213 sub_tensor_8" -> "214 linear_7" [label="(768, 3072)", style=solid]; +"214 linear_7" -> "215 dropout_6" [label="(1, 197, 768)", style=solid]; +"215 dropout_6" -> "216 add_4" [label="(1, 197, 768)", style=solid]; +"216 add_4" -> "217 layer_norm_4" [label="(1, 197, 768)", style=solid]; +"216 add_4" -> "266 add_5" [label="(1, 197, 768)", style=solid]; +"217 layer_norm_4" -> "218 transpose_12" [label="(1, 197, 768)", style=solid]; +"218 transpose_12" -> "219 transpose_12_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"219 transpose_12_0_0_nncf_smooth_quant_0" -> "220 quantize_per_tensor_default_13" [label="(197, 1, 768)", style=solid]; +"220 quantize_per_tensor_default_13" -> "221 dequantize_per_tensor_default_13" [label="(197, 1, 768)", style=solid]; +"221 dequantize_per_tensor_default_13" -> "228 linear_8" [label="(197, 1, 768)", style=solid]; +"222 encoder_layers_encoder_layer_2_self_attention_in_proj_bias_0_0" -> "228 linear_8" [label="(2304,)", style=solid]; +"223 scale_updated_constant9" -> "225 mul_tensor_9" [label="(2304, 1)", style=solid]; +"224 compressed_weight_updated_constant9" -> "225 mul_tensor_9" [label="(2304, 768)", style=solid]; +"225 mul_tensor_9" -> "227 sub_tensor_9" [label="(2304, 768)", style=solid]; +"226 zero_point_updated_constant9" -> "227 sub_tensor_9" [label="(2304, 1)", style=solid]; +"227 sub_tensor_9" -> "228 linear_8" [label="(2304, 768)", style=solid]; +"228 linear_8" -> "229 unflatten_2" [label="(197, 1, 2304)", style=solid]; +"229 unflatten_2" -> "230 unsqueeze_2" [label="(197, 1, 3, 768)", style=solid]; +"230 unsqueeze_2" -> "231 transpose_13" [label="(1, 197, 1, 3, 768)", style=solid]; +"231 transpose_13" -> "232 squeeze_2" [label="(3, 197, 1, 1, 768)", style=solid]; +"232 squeeze_2" -> "233 contiguous_2" [label="(3, 197, 1, 768)", style=solid]; +"233 contiguous_2" -> "234 quantize_per_tensor_default_14" [label="(3, 197, 1, 768)", style=solid]; +"233 contiguous_2" -> "237 quantize_per_tensor_default_15" [label="(3, 197, 1, 768)", style=solid]; +"233 contiguous_2" -> "240 select_8" [label="(3, 197, 1, 768)", style=solid]; +"234 quantize_per_tensor_default_14" -> "235 dequantize_per_tensor_default_14" [label="(3, 197, 1, 768)", style=solid]; +"235 dequantize_per_tensor_default_14" -> "236 select_6" [label="(3, 197, 1, 768)", style=solid]; +"236 select_6" -> "241 view_16" [label="(197, 1, 768)", style=solid]; +"237 quantize_per_tensor_default_15" -> "238 dequantize_per_tensor_default_15" [label="(3, 197, 1, 768)", style=solid]; +"238 dequantize_per_tensor_default_15" -> "239 select_7" [label="(3, 197, 1, 768)", style=solid]; +"239 select_7" -> "243 view_17" [label="(197, 1, 768)", style=solid]; +"240 select_8" -> "245 view_18" [label="(197, 1, 768)", style=solid]; +"241 view_16" -> "242 transpose_14" [label="(197, 12, 64)", style=solid]; +"242 transpose_14" -> "247 view_19" [label="(12, 197, 64)", style=solid]; +"243 view_17" -> "244 transpose_15" [label="(197, 12, 64)", style=solid]; +"244 transpose_15" -> "248 view_20" [label="(12, 197, 64)", style=solid]; +"245 view_18" -> "246 transpose_16" [label="(197, 12, 64)", style=solid]; +"246 transpose_16" -> "249 view_21" [label="(12, 197, 64)", style=solid]; +"247 view_19" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"248 view_20" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"249 view_21" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"250 scaled_dot_product_attention_2" -> "251 permute_3" [label="(1, 12, 197, 64)", style=solid]; +"251 permute_3" -> "252 view_22" [label="(197, 1, 12, 64)", style=solid]; +"252 view_22" -> "253 view_22_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"253 view_22_0_0_nncf_smooth_quant_0" -> "254 quantize_per_tensor_default_16" [label="(197, 768)", style=solid]; +"254 quantize_per_tensor_default_16" -> "255 dequantize_per_tensor_default_16" [label="(197, 768)", style=solid]; +"255 dequantize_per_tensor_default_16" -> "262 linear_9" [label="(197, 768)", style=solid]; +"256 encoder_layers_encoder_layer_2_self_attention_out_proj_bias_0_0" -> "262 linear_9" [label="(768,)", style=solid]; +"257 scale_updated_constant10" -> "259 mul_tensor_10" [label="(768, 1)", style=solid]; +"258 compressed_weight_updated_constant10" -> "259 mul_tensor_10" [label="(768, 768)", style=solid]; +"259 mul_tensor_10" -> "261 sub_tensor_10" [label="(768, 768)", style=solid]; +"260 zero_point_updated_constant10" -> "261 sub_tensor_10" [label="(768, 1)", style=solid]; +"261 sub_tensor_10" -> "262 linear_9" [label="(768, 768)", style=solid]; +"262 linear_9" -> "263 view_23" [label="(197, 768)", style=solid]; +"263 view_23" -> "264 transpose_17" [label="(197, 1, 768)", style=solid]; +"264 transpose_17" -> "265 dropout_7" [label="(1, 197, 768)", style=solid]; +"265 dropout_7" -> "266 add_5" [label="(1, 197, 768)", style=solid]; +"266 add_5" -> "267 layer_norm_5" [label="(1, 197, 768)", style=solid]; +"266 add_5" -> "291 add_6" [label="(1, 197, 768)", style=solid]; +"267 layer_norm_5" -> "268 layer_norm_5_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"268 layer_norm_5_0_0_nncf_smooth_quant_0" -> "269 quantize_per_tensor_default_17" [label="(1, 197, 768)", style=solid]; +"269 quantize_per_tensor_default_17" -> "270 dequantize_per_tensor_default_17" [label="(1, 197, 768)", style=solid]; +"270 dequantize_per_tensor_default_17" -> "277 linear_10" [label="(1, 197, 768)", style=solid]; +"271 encoder_layers_encoder_layer_2_mlp_0_bias_0_0" -> "277 linear_10" [label="(3072,)", style=solid]; +"272 scale_updated_constant11" -> "274 mul_tensor_11" [label="(3072, 1)", style=solid]; +"273 compressed_weight_updated_constant11" -> "274 mul_tensor_11" [label="(3072, 768)", style=solid]; +"274 mul_tensor_11" -> "276 sub_tensor_11" [label="(3072, 768)", style=solid]; +"275 zero_point_updated_constant11" -> "276 sub_tensor_11" [label="(3072, 1)", style=solid]; +"276 sub_tensor_11" -> "277 linear_10" [label="(3072, 768)", style=solid]; +"277 linear_10" -> "278 gelu_2" [label="(1, 197, 3072)", style=solid]; +"278 gelu_2" -> "279 dropout_8" [label="(1, 197, 3072)", style=solid]; +"279 dropout_8" -> "280 dropout_8_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"280 dropout_8_0_0_nncf_smooth_quant_0" -> "281 quantize_per_tensor_default_18" [label="(1, 197, 3072)", style=solid]; +"281 quantize_per_tensor_default_18" -> "282 dequantize_per_tensor_default_18" [label="(1, 197, 3072)", style=solid]; +"282 dequantize_per_tensor_default_18" -> "289 linear_11" [label="(1, 197, 3072)", style=solid]; +"283 encoder_layers_encoder_layer_2_mlp_3_bias_0_0" -> "289 linear_11" [label="(768,)", style=solid]; +"284 scale_updated_constant12" -> "286 mul_tensor_12" [label="(768, 1)", style=solid]; +"285 compressed_weight_updated_constant12" -> "286 mul_tensor_12" [label="(768, 3072)", style=solid]; +"286 mul_tensor_12" -> "288 sub_tensor_12" [label="(768, 3072)", style=solid]; +"287 zero_point_updated_constant12" -> "288 sub_tensor_12" [label="(768, 1)", style=solid]; +"288 sub_tensor_12" -> "289 linear_11" [label="(768, 3072)", style=solid]; +"289 linear_11" -> "290 dropout_9" [label="(1, 197, 768)", style=solid]; +"290 dropout_9" -> "291 add_6" [label="(1, 197, 768)", style=solid]; +"291 add_6" -> "292 layer_norm_6" [label="(1, 197, 768)", style=solid]; +"291 add_6" -> "341 add_7" [label="(1, 197, 768)", style=solid]; +"292 layer_norm_6" -> "293 transpose_18" [label="(1, 197, 768)", style=solid]; +"293 transpose_18" -> "294 transpose_18_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"294 transpose_18_0_0_nncf_smooth_quant_0" -> "295 quantize_per_tensor_default_19" [label="(197, 1, 768)", style=solid]; +"295 quantize_per_tensor_default_19" -> "296 dequantize_per_tensor_default_19" [label="(197, 1, 768)", style=solid]; +"296 dequantize_per_tensor_default_19" -> "303 linear_12" [label="(197, 1, 768)", style=solid]; +"297 encoder_layers_encoder_layer_3_self_attention_in_proj_bias_0_0" -> "303 linear_12" [label="(2304,)", style=solid]; +"298 scale_updated_constant13" -> "300 mul_tensor_13" [label="(2304, 1)", style=solid]; +"299 compressed_weight_updated_constant13" -> "300 mul_tensor_13" [label="(2304, 768)", style=solid]; +"300 mul_tensor_13" -> "302 sub_tensor_13" [label="(2304, 768)", style=solid]; +"301 zero_point_updated_constant13" -> "302 sub_tensor_13" [label="(2304, 1)", style=solid]; +"302 sub_tensor_13" -> "303 linear_12" [label="(2304, 768)", style=solid]; +"303 linear_12" -> "304 unflatten_3" [label="(197, 1, 2304)", style=solid]; +"304 unflatten_3" -> "305 unsqueeze_3" [label="(197, 1, 3, 768)", style=solid]; +"305 unsqueeze_3" -> "306 transpose_19" [label="(1, 197, 1, 3, 768)", style=solid]; +"306 transpose_19" -> "307 squeeze_3" [label="(3, 197, 1, 1, 768)", style=solid]; +"307 squeeze_3" -> "308 contiguous_3" [label="(3, 197, 1, 768)", style=solid]; +"308 contiguous_3" -> "309 quantize_per_tensor_default_20" [label="(3, 197, 1, 768)", style=solid]; +"308 contiguous_3" -> "312 quantize_per_tensor_default_21" [label="(3, 197, 1, 768)", style=solid]; +"308 contiguous_3" -> "315 select_11" [label="(3, 197, 1, 768)", style=solid]; +"309 quantize_per_tensor_default_20" -> "310 dequantize_per_tensor_default_20" [label="(3, 197, 1, 768)", style=solid]; +"310 dequantize_per_tensor_default_20" -> "311 select_9" [label="(3, 197, 1, 768)", style=solid]; +"311 select_9" -> "316 view_24" [label="(197, 1, 768)", style=solid]; +"312 quantize_per_tensor_default_21" -> "313 dequantize_per_tensor_default_21" [label="(3, 197, 1, 768)", style=solid]; +"313 dequantize_per_tensor_default_21" -> "314 select_10" [label="(3, 197, 1, 768)", style=solid]; +"314 select_10" -> "318 view_25" [label="(197, 1, 768)", style=solid]; +"315 select_11" -> "320 view_26" [label="(197, 1, 768)", style=solid]; +"316 view_24" -> "317 transpose_20" [label="(197, 12, 64)", style=solid]; +"317 transpose_20" -> "322 view_27" [label="(12, 197, 64)", style=solid]; +"318 view_25" -> "319 transpose_21" [label="(197, 12, 64)", style=solid]; +"319 transpose_21" -> "323 view_28" [label="(12, 197, 64)", style=solid]; +"320 view_26" -> "321 transpose_22" [label="(197, 12, 64)", style=solid]; +"321 transpose_22" -> "324 view_29" [label="(12, 197, 64)", style=solid]; +"322 view_27" -> "325 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"323 view_28" -> "325 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"324 view_29" -> "325 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"325 scaled_dot_product_attention_3" -> "326 permute_4" [label="(1, 12, 197, 64)", style=solid]; +"326 permute_4" -> "327 view_30" [label="(197, 1, 12, 64)", style=solid]; +"327 view_30" -> "328 view_30_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"328 view_30_0_0_nncf_smooth_quant_0" -> "329 quantize_per_tensor_default_22" [label="(197, 768)", style=solid]; +"329 quantize_per_tensor_default_22" -> "330 dequantize_per_tensor_default_22" [label="(197, 768)", style=solid]; +"330 dequantize_per_tensor_default_22" -> "337 linear_13" [label="(197, 768)", style=solid]; +"331 encoder_layers_encoder_layer_3_self_attention_out_proj_bias_0_0" -> "337 linear_13" [label="(768,)", style=solid]; +"332 scale_updated_constant14" -> "334 mul_tensor_14" [label="(768, 1)", style=solid]; +"333 compressed_weight_updated_constant14" -> "334 mul_tensor_14" [label="(768, 768)", style=solid]; +"334 mul_tensor_14" -> "336 sub_tensor_14" [label="(768, 768)", style=solid]; +"335 zero_point_updated_constant14" -> "336 sub_tensor_14" [label="(768, 1)", style=solid]; +"336 sub_tensor_14" -> "337 linear_13" [label="(768, 768)", style=solid]; +"337 linear_13" -> "338 view_31" [label="(197, 768)", style=solid]; +"338 view_31" -> "339 transpose_23" [label="(197, 1, 768)", style=solid]; +"339 transpose_23" -> "340 dropout_10" [label="(1, 197, 768)", style=solid]; +"340 dropout_10" -> "341 add_7" [label="(1, 197, 768)", style=solid]; +"341 add_7" -> "342 layer_norm_7" [label="(1, 197, 768)", style=solid]; +"341 add_7" -> "366 add_8" [label="(1, 197, 768)", style=solid]; +"342 layer_norm_7" -> "343 layer_norm_7_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"343 layer_norm_7_0_0_nncf_smooth_quant_0" -> "344 quantize_per_tensor_default_23" [label="(1, 197, 768)", style=solid]; +"344 quantize_per_tensor_default_23" -> "345 dequantize_per_tensor_default_23" [label="(1, 197, 768)", style=solid]; +"345 dequantize_per_tensor_default_23" -> 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197, 64)", style=solid]; +"398 view_36" -> "400 scaled_dot_product_attention_4" [label="(1, 12, 197, 64)", style=solid]; +"399 view_37" -> "400 scaled_dot_product_attention_4" [label="(1, 12, 197, 64)", style=solid]; +"400 scaled_dot_product_attention_4" -> "401 permute_5" [label="(1, 12, 197, 64)", style=solid]; +"401 permute_5" -> "402 view_38" [label="(197, 1, 12, 64)", style=solid]; +"402 view_38" -> "403 view_38_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"403 view_38_0_0_nncf_smooth_quant_0" -> "404 quantize_per_tensor_default_28" [label="(197, 768)", style=solid]; +"404 quantize_per_tensor_default_28" -> "405 dequantize_per_tensor_default_28" [label="(197, 768)", style=solid]; +"405 dequantize_per_tensor_default_28" -> "412 linear_17" [label="(197, 768)", style=solid]; +"406 encoder_layers_encoder_layer_4_self_attention_out_proj_bias_0_0" -> "412 linear_17" [label="(768,)", style=solid]; +"407 scale_updated_constant18" -> "409 mul_tensor_18" [label="(768, 1)", 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768)", style=solid]; +"419 quantize_per_tensor_default_29" -> "420 dequantize_per_tensor_default_29" [label="(1, 197, 768)", style=solid]; +"420 dequantize_per_tensor_default_29" -> "427 linear_18" [label="(1, 197, 768)", style=solid]; +"421 encoder_layers_encoder_layer_4_mlp_0_bias_0_0" -> "427 linear_18" [label="(3072,)", style=solid]; +"422 scale_updated_constant19" -> "424 mul_tensor_19" [label="(3072, 1)", style=solid]; +"423 compressed_weight_updated_constant19" -> "424 mul_tensor_19" [label="(3072, 768)", style=solid]; +"424 mul_tensor_19" -> "426 sub_tensor_19" [label="(3072, 768)", style=solid]; +"425 zero_point_updated_constant19" -> "426 sub_tensor_19" [label="(3072, 1)", style=solid]; +"426 sub_tensor_19" -> "427 linear_18" [label="(3072, 768)", style=solid]; +"427 linear_18" -> "428 gelu_4" [label="(1, 197, 3072)", style=solid]; +"428 gelu_4" -> "429 dropout_14" [label="(1, 197, 3072)", style=solid]; +"429 dropout_14" -> "430 dropout_14_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"430 dropout_14_0_0_nncf_smooth_quant_0" -> "431 quantize_per_tensor_default_30" [label="(1, 197, 3072)", style=solid]; +"431 quantize_per_tensor_default_30" -> "432 dequantize_per_tensor_default_30" [label="(1, 197, 3072)", style=solid]; +"432 dequantize_per_tensor_default_30" -> "439 linear_19" [label="(1, 197, 3072)", style=solid]; +"433 encoder_layers_encoder_layer_4_mlp_3_bias_0_0" -> "439 linear_19" [label="(768,)", style=solid]; +"434 scale_updated_constant20" -> "436 mul_tensor_20" [label="(768, 1)", style=solid]; +"435 compressed_weight_updated_constant20" -> "436 mul_tensor_20" [label="(768, 3072)", style=solid]; +"436 mul_tensor_20" -> "438 sub_tensor_20" [label="(768, 3072)", style=solid]; +"437 zero_point_updated_constant20" -> "438 sub_tensor_20" [label="(768, 1)", style=solid]; +"438 sub_tensor_20" -> "439 linear_19" [label="(768, 3072)", style=solid]; +"439 linear_19" -> "440 dropout_15" [label="(1, 197, 768)", style=solid]; +"440 dropout_15" -> "441 add_10" [label="(1, 197, 768)", style=solid]; +"441 add_10" -> "442 layer_norm_10" [label="(1, 197, 768)", style=solid]; +"441 add_10" -> "491 add_11" [label="(1, 197, 768)", style=solid]; +"442 layer_norm_10" -> "443 transpose_30" [label="(1, 197, 768)", style=solid]; +"443 transpose_30" -> "444 transpose_30_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"444 transpose_30_0_0_nncf_smooth_quant_0" -> "445 quantize_per_tensor_default_31" [label="(197, 1, 768)", style=solid]; +"445 quantize_per_tensor_default_31" -> "446 dequantize_per_tensor_default_31" [label="(197, 1, 768)", style=solid]; +"446 dequantize_per_tensor_default_31" -> "453 linear_20" [label="(197, 1, 768)", style=solid]; +"447 encoder_layers_encoder_layer_5_self_attention_in_proj_bias_0_0" -> "453 linear_20" [label="(2304,)", style=solid]; +"448 scale_updated_constant21" -> "450 mul_tensor_21" [label="(2304, 1)", style=solid]; +"449 compressed_weight_updated_constant21" -> "450 mul_tensor_21" [label="(2304, 768)", style=solid]; +"450 mul_tensor_21" -> "452 sub_tensor_21" [label="(2304, 768)", style=solid]; +"451 zero_point_updated_constant21" -> "452 sub_tensor_21" [label="(2304, 1)", style=solid]; +"452 sub_tensor_21" -> "453 linear_20" [label="(2304, 768)", style=solid]; +"453 linear_20" -> "454 unflatten_5" [label="(197, 1, 2304)", style=solid]; +"454 unflatten_5" -> "455 unsqueeze_5" [label="(197, 1, 3, 768)", style=solid]; +"455 unsqueeze_5" -> "456 transpose_31" [label="(1, 197, 1, 3, 768)", style=solid]; +"456 transpose_31" -> "457 squeeze_5" [label="(3, 197, 1, 1, 768)", style=solid]; +"457 squeeze_5" -> "458 contiguous_5" [label="(3, 197, 1, 768)", style=solid]; +"458 contiguous_5" -> "459 quantize_per_tensor_default_32" [label="(3, 197, 1, 768)", style=solid]; +"458 contiguous_5" -> "462 quantize_per_tensor_default_33" [label="(3, 197, 1, 768)", style=solid]; +"458 contiguous_5" -> "465 select_17" [label="(3, 197, 1, 768)", style=solid]; +"459 quantize_per_tensor_default_32" -> "460 dequantize_per_tensor_default_32" [label="(3, 197, 1, 768)", style=solid]; +"460 dequantize_per_tensor_default_32" -> "461 select_15" [label="(3, 197, 1, 768)", style=solid]; +"461 select_15" -> "466 view_40" [label="(197, 1, 768)", style=solid]; +"462 quantize_per_tensor_default_33" -> "463 dequantize_per_tensor_default_33" [label="(3, 197, 1, 768)", style=solid]; +"463 dequantize_per_tensor_default_33" -> "464 select_16" [label="(3, 197, 1, 768)", style=solid]; +"464 select_16" -> "468 view_41" [label="(197, 1, 768)", style=solid]; +"465 select_17" -> "470 view_42" [label="(197, 1, 768)", style=solid]; +"466 view_40" -> "467 transpose_32" [label="(197, 12, 64)", style=solid]; +"467 transpose_32" -> "472 view_43" [label="(12, 197, 64)", style=solid]; +"468 view_41" -> "469 transpose_33" [label="(197, 12, 64)", style=solid]; +"469 transpose_33" -> "473 view_44" [label="(12, 197, 64)", style=solid]; +"470 view_42" -> "471 transpose_34" [label="(197, 12, 64)", style=solid]; +"471 transpose_34" -> "474 view_45" [label="(12, 197, 64)", style=solid]; +"472 view_43" -> "475 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"473 view_44" -> "475 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"474 view_45" -> "475 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"475 scaled_dot_product_attention_5" -> "476 permute_6" [label="(1, 12, 197, 64)", style=solid]; +"476 permute_6" -> "477 view_46" [label="(197, 1, 12, 64)", style=solid]; +"477 view_46" -> "478 view_46_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"478 view_46_0_0_nncf_smooth_quant_0" -> "479 quantize_per_tensor_default_34" [label="(197, 768)", style=solid]; +"479 quantize_per_tensor_default_34" -> "480 dequantize_per_tensor_default_34" [label="(197, 768)", style=solid]; +"480 dequantize_per_tensor_default_34" -> "487 linear_21" [label="(197, 768)", style=solid]; +"481 encoder_layers_encoder_layer_5_self_attention_out_proj_bias_0_0" -> "487 linear_21" [label="(768,)", style=solid]; +"482 scale_updated_constant22" -> "484 mul_tensor_22" [label="(768, 1)", style=solid]; +"483 compressed_weight_updated_constant22" -> "484 mul_tensor_22" [label="(768, 768)", style=solid]; +"484 mul_tensor_22" -> "486 sub_tensor_22" [label="(768, 768)", style=solid]; +"485 zero_point_updated_constant22" -> "486 sub_tensor_22" [label="(768, 1)", style=solid]; +"486 sub_tensor_22" -> "487 linear_21" [label="(768, 768)", style=solid]; +"487 linear_21" -> "488 view_47" [label="(197, 768)", style=solid]; +"488 view_47" -> "489 transpose_35" [label="(197, 1, 768)", style=solid]; +"489 transpose_35" -> "490 dropout_16" [label="(1, 197, 768)", style=solid]; +"490 dropout_16" -> "491 add_11" [label="(1, 197, 768)", style=solid]; +"491 add_11" -> "492 layer_norm_11" [label="(1, 197, 768)", style=solid]; +"491 add_11" -> "516 add_12" [label="(1, 197, 768)", style=solid]; +"492 layer_norm_11" -> "493 layer_norm_11_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"493 layer_norm_11_0_0_nncf_smooth_quant_0" -> "494 quantize_per_tensor_default_35" [label="(1, 197, 768)", style=solid]; +"494 quantize_per_tensor_default_35" -> "495 dequantize_per_tensor_default_35" [label="(1, 197, 768)", style=solid]; +"495 dequantize_per_tensor_default_35" -> "502 linear_22" [label="(1, 197, 768)", style=solid]; +"496 encoder_layers_encoder_layer_5_mlp_0_bias_0_0" -> "502 linear_22" [label="(3072,)", style=solid]; +"497 scale_updated_constant23" -> "499 mul_tensor_23" [label="(3072, 1)", style=solid]; +"498 compressed_weight_updated_constant23" -> "499 mul_tensor_23" [label="(3072, 768)", style=solid]; +"499 mul_tensor_23" -> "501 sub_tensor_23" [label="(3072, 768)", style=solid]; +"500 zero_point_updated_constant23" -> "501 sub_tensor_23" [label="(3072, 1)", style=solid]; +"501 sub_tensor_23" -> "502 linear_22" [label="(3072, 768)", style=solid]; +"502 linear_22" -> "503 gelu_5" [label="(1, 197, 3072)", style=solid]; +"503 gelu_5" -> "504 dropout_17" [label="(1, 197, 3072)", style=solid]; +"504 dropout_17" -> "505 dropout_17_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"505 dropout_17_0_0_nncf_smooth_quant_0" -> "506 quantize_per_tensor_default_36" [label="(1, 197, 3072)", style=solid]; +"506 quantize_per_tensor_default_36" -> "507 dequantize_per_tensor_default_36" [label="(1, 197, 3072)", style=solid]; +"507 dequantize_per_tensor_default_36" -> "514 linear_23" [label="(1, 197, 3072)", style=solid]; +"508 encoder_layers_encoder_layer_5_mlp_3_bias_0_0" -> "514 linear_23" [label="(768,)", style=solid]; +"509 scale_updated_constant24" -> "511 mul_tensor_24" [label="(768, 1)", style=solid]; +"510 compressed_weight_updated_constant24" -> "511 mul_tensor_24" [label="(768, 3072)", style=solid]; +"511 mul_tensor_24" -> "513 sub_tensor_24" [label="(768, 3072)", style=solid]; +"512 zero_point_updated_constant24" -> "513 sub_tensor_24" [label="(768, 1)", style=solid]; +"513 sub_tensor_24" -> "514 linear_23" [label="(768, 3072)", style=solid]; +"514 linear_23" -> "515 dropout_18" [label="(1, 197, 768)", style=solid]; +"515 dropout_18" -> "516 add_12" [label="(1, 197, 768)", style=solid]; +"516 add_12" -> "517 layer_norm_12" [label="(1, 197, 768)", style=solid]; +"516 add_12" -> "566 add_13" [label="(1, 197, 768)", style=solid]; +"517 layer_norm_12" -> "518 transpose_36" [label="(1, 197, 768)", style=solid]; +"518 transpose_36" -> "519 transpose_36_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"519 transpose_36_0_0_nncf_smooth_quant_0" -> "520 quantize_per_tensor_default_37" [label="(197, 1, 768)", style=solid]; +"520 quantize_per_tensor_default_37" -> "521 dequantize_per_tensor_default_37" [label="(197, 1, 768)", style=solid]; +"521 dequantize_per_tensor_default_37" -> "528 linear_24" [label="(197, 1, 768)", style=solid]; +"522 encoder_layers_encoder_layer_6_self_attention_in_proj_bias_0_0" -> "528 linear_24" [label="(2304,)", style=solid]; +"523 scale_updated_constant25" -> "525 mul_tensor_25" [label="(2304, 1)", style=solid]; +"524 compressed_weight_updated_constant25" -> "525 mul_tensor_25" [label="(2304, 768)", style=solid]; +"525 mul_tensor_25" -> "527 sub_tensor_25" [label="(2304, 768)", style=solid]; +"526 zero_point_updated_constant25" -> "527 sub_tensor_25" [label="(2304, 1)", style=solid]; +"527 sub_tensor_25" -> "528 linear_24" [label="(2304, 768)", style=solid]; +"528 linear_24" -> "529 unflatten_6" [label="(197, 1, 2304)", style=solid]; +"529 unflatten_6" -> "530 unsqueeze_6" [label="(197, 1, 3, 768)", style=solid]; +"530 unsqueeze_6" -> "531 transpose_37" [label="(1, 197, 1, 3, 768)", style=solid]; +"531 transpose_37" -> "532 squeeze_6" [label="(3, 197, 1, 1, 768)", style=solid]; +"532 squeeze_6" -> "533 contiguous_6" [label="(3, 197, 1, 768)", style=solid]; +"533 contiguous_6" -> "534 quantize_per_tensor_default_38" [label="(3, 197, 1, 768)", style=solid]; +"533 contiguous_6" -> "537 quantize_per_tensor_default_39" [label="(3, 197, 1, 768)", style=solid]; +"533 contiguous_6" -> "540 select_20" [label="(3, 197, 1, 768)", style=solid]; +"534 quantize_per_tensor_default_38" -> "535 dequantize_per_tensor_default_38" [label="(3, 197, 1, 768)", style=solid]; +"535 dequantize_per_tensor_default_38" -> "536 select_18" [label="(3, 197, 1, 768)", style=solid]; +"536 select_18" -> "541 view_48" [label="(197, 1, 768)", style=solid]; +"537 quantize_per_tensor_default_39" -> "538 dequantize_per_tensor_default_39" [label="(3, 197, 1, 768)", style=solid]; +"538 dequantize_per_tensor_default_39" -> "539 select_19" [label="(3, 197, 1, 768)", style=solid]; +"539 select_19" -> "543 view_49" [label="(197, 1, 768)", style=solid]; +"540 select_20" -> "545 view_50" [label="(197, 1, 768)", style=solid]; +"541 view_48" -> "542 transpose_38" [label="(197, 12, 64)", style=solid]; +"542 transpose_38" -> "547 view_51" [label="(12, 197, 64)", style=solid]; +"543 view_49" -> "544 transpose_39" [label="(197, 12, 64)", style=solid]; +"544 transpose_39" -> "548 view_52" [label="(12, 197, 64)", style=solid]; +"545 view_50" -> "546 transpose_40" [label="(197, 12, 64)", style=solid]; +"546 transpose_40" -> "549 view_53" [label="(12, 197, 64)", style=solid]; +"547 view_51" -> "550 scaled_dot_product_attention_6" [label="(1, 12, 197, 64)", style=solid]; +"548 view_52" -> "550 scaled_dot_product_attention_6" [label="(1, 12, 197, 64)", style=solid]; +"549 view_53" -> "550 scaled_dot_product_attention_6" [label="(1, 12, 197, 64)", style=solid]; +"550 scaled_dot_product_attention_6" -> "551 permute_7" [label="(1, 12, 197, 64)", style=solid]; +"551 permute_7" -> "552 view_54" [label="(197, 1, 12, 64)", style=solid]; +"552 view_54" -> "553 view_54_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"553 view_54_0_0_nncf_smooth_quant_0" -> "554 quantize_per_tensor_default_40" [label="(197, 768)", style=solid]; +"554 quantize_per_tensor_default_40" -> "555 dequantize_per_tensor_default_40" [label="(197, 768)", style=solid]; +"555 dequantize_per_tensor_default_40" -> "562 linear_25" [label="(197, 768)", style=solid]; +"556 encoder_layers_encoder_layer_6_self_attention_out_proj_bias_0_0" -> "562 linear_25" [label="(768,)", style=solid]; +"557 scale_updated_constant26" -> "559 mul_tensor_26" [label="(768, 1)", style=solid]; +"558 compressed_weight_updated_constant26" -> "559 mul_tensor_26" [label="(768, 768)", style=solid]; +"559 mul_tensor_26" -> "561 sub_tensor_26" [label="(768, 768)", style=solid]; +"560 zero_point_updated_constant26" -> "561 sub_tensor_26" [label="(768, 1)", style=solid]; +"561 sub_tensor_26" -> "562 linear_25" [label="(768, 768)", style=solid]; +"562 linear_25" -> "563 view_55" [label="(197, 768)", style=solid]; +"563 view_55" -> "564 transpose_41" [label="(197, 1, 768)", style=solid]; +"564 transpose_41" -> "565 dropout_19" [label="(1, 197, 768)", style=solid]; +"565 dropout_19" -> "566 add_13" [label="(1, 197, 768)", style=solid]; +"566 add_13" -> "567 layer_norm_13" [label="(1, 197, 768)", style=solid]; +"566 add_13" -> "591 add_14" [label="(1, 197, 768)", style=solid]; +"567 layer_norm_13" -> "568 layer_norm_13_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"568 layer_norm_13_0_0_nncf_smooth_quant_0" -> "569 quantize_per_tensor_default_41" [label="(1, 197, 768)", style=solid]; +"569 quantize_per_tensor_default_41" -> "570 dequantize_per_tensor_default_41" [label="(1, 197, 768)", style=solid]; +"570 dequantize_per_tensor_default_41" -> "577 linear_26" [label="(1, 197, 768)", style=solid]; +"571 encoder_layers_encoder_layer_6_mlp_0_bias_0_0" -> "577 linear_26" [label="(3072,)", style=solid]; +"572 scale_updated_constant27" -> "574 mul_tensor_27" [label="(3072, 1)", style=solid]; +"573 compressed_weight_updated_constant27" -> "574 mul_tensor_27" [label="(3072, 768)", style=solid]; +"574 mul_tensor_27" -> "576 sub_tensor_27" [label="(3072, 768)", style=solid]; +"575 zero_point_updated_constant27" -> "576 sub_tensor_27" [label="(3072, 1)", style=solid]; +"576 sub_tensor_27" -> "577 linear_26" [label="(3072, 768)", style=solid]; +"577 linear_26" -> "578 gelu_6" [label="(1, 197, 3072)", style=solid]; +"578 gelu_6" -> "579 dropout_20" [label="(1, 197, 3072)", style=solid]; +"579 dropout_20" -> "580 dropout_20_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"580 dropout_20_0_0_nncf_smooth_quant_0" -> "581 quantize_per_tensor_default_42" [label="(1, 197, 3072)", style=solid]; +"581 quantize_per_tensor_default_42" -> "582 dequantize_per_tensor_default_42" [label="(1, 197, 3072)", style=solid]; +"582 dequantize_per_tensor_default_42" -> "589 linear_27" [label="(1, 197, 3072)", style=solid]; +"583 encoder_layers_encoder_layer_6_mlp_3_bias_0_0" -> "589 linear_27" [label="(768,)", style=solid]; +"584 scale_updated_constant28" -> "586 mul_tensor_28" [label="(768, 1)", style=solid]; +"585 compressed_weight_updated_constant28" -> "586 mul_tensor_28" [label="(768, 3072)", style=solid]; +"586 mul_tensor_28" -> "588 sub_tensor_28" [label="(768, 3072)", style=solid]; +"587 zero_point_updated_constant28" -> "588 sub_tensor_28" [label="(768, 1)", style=solid]; +"588 sub_tensor_28" -> "589 linear_27" [label="(768, 3072)", style=solid]; +"589 linear_27" -> "590 dropout_21" [label="(1, 197, 768)", style=solid]; +"590 dropout_21" -> "591 add_14" [label="(1, 197, 768)", style=solid]; +"591 add_14" -> "592 layer_norm_14" [label="(1, 197, 768)", style=solid]; +"591 add_14" -> "641 add_15" [label="(1, 197, 768)", style=solid]; +"592 layer_norm_14" -> "593 transpose_42" [label="(1, 197, 768)", style=solid]; +"593 transpose_42" -> "594 transpose_42_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"594 transpose_42_0_0_nncf_smooth_quant_0" -> "595 quantize_per_tensor_default_43" [label="(197, 1, 768)", style=solid]; +"595 quantize_per_tensor_default_43" -> "596 dequantize_per_tensor_default_43" [label="(197, 1, 768)", style=solid]; +"596 dequantize_per_tensor_default_43" -> "603 linear_28" [label="(197, 1, 768)", style=solid]; +"597 encoder_layers_encoder_layer_7_self_attention_in_proj_bias_0_0" -> "603 linear_28" [label="(2304,)", style=solid]; +"598 scale_updated_constant29" -> "600 mul_tensor_29" [label="(2304, 1)", style=solid]; +"599 compressed_weight_updated_constant29" -> "600 mul_tensor_29" [label="(2304, 768)", style=solid]; +"600 mul_tensor_29" -> "602 sub_tensor_29" [label="(2304, 768)", style=solid]; +"601 zero_point_updated_constant29" -> "602 sub_tensor_29" [label="(2304, 1)", style=solid]; +"602 sub_tensor_29" -> "603 linear_28" [label="(2304, 768)", style=solid]; +"603 linear_28" -> "604 unflatten_7" [label="(197, 1, 2304)", style=solid]; +"604 unflatten_7" -> "605 unsqueeze_7" [label="(197, 1, 3, 768)", style=solid]; +"605 unsqueeze_7" -> "606 transpose_43" [label="(1, 197, 1, 3, 768)", style=solid]; +"606 transpose_43" -> "607 squeeze_7" [label="(3, 197, 1, 1, 768)", style=solid]; +"607 squeeze_7" -> "608 contiguous_7" [label="(3, 197, 1, 768)", style=solid]; +"608 contiguous_7" -> "609 quantize_per_tensor_default_44" [label="(3, 197, 1, 768)", style=solid]; +"608 contiguous_7" -> "612 quantize_per_tensor_default_45" [label="(3, 197, 1, 768)", style=solid]; +"608 contiguous_7" -> "615 select_23" [label="(3, 197, 1, 768)", style=solid]; +"609 quantize_per_tensor_default_44" -> "610 dequantize_per_tensor_default_44" [label="(3, 197, 1, 768)", style=solid]; +"610 dequantize_per_tensor_default_44" -> "611 select_21" [label="(3, 197, 1, 768)", style=solid]; +"611 select_21" -> "616 view_56" [label="(197, 1, 768)", style=solid]; +"612 quantize_per_tensor_default_45" -> "613 dequantize_per_tensor_default_45" [label="(3, 197, 1, 768)", style=solid]; +"613 dequantize_per_tensor_default_45" -> "614 select_22" [label="(3, 197, 1, 768)", style=solid]; +"614 select_22" -> "618 view_57" [label="(197, 1, 768)", style=solid]; +"615 select_23" -> "620 view_58" [label="(197, 1, 768)", style=solid]; +"616 view_56" -> "617 transpose_44" [label="(197, 12, 64)", style=solid]; +"617 transpose_44" -> "622 view_59" [label="(12, 197, 64)", style=solid]; +"618 view_57" -> "619 transpose_45" [label="(197, 12, 64)", style=solid]; +"619 transpose_45" -> "623 view_60" [label="(12, 197, 64)", style=solid]; +"620 view_58" -> "621 transpose_46" [label="(197, 12, 64)", style=solid]; +"621 transpose_46" -> "624 view_61" [label="(12, 197, 64)", style=solid]; +"622 view_59" -> "625 scaled_dot_product_attention_7" [label="(1, 12, 197, 64)", style=solid]; +"623 view_60" -> "625 scaled_dot_product_attention_7" [label="(1, 12, 197, 64)", style=solid]; +"624 view_61" -> "625 scaled_dot_product_attention_7" [label="(1, 12, 197, 64)", style=solid]; +"625 scaled_dot_product_attention_7" -> "626 permute_8" [label="(1, 12, 197, 64)", style=solid]; +"626 permute_8" -> "627 view_62" [label="(197, 1, 12, 64)", style=solid]; +"627 view_62" -> "628 view_62_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"628 view_62_0_0_nncf_smooth_quant_0" -> "629 quantize_per_tensor_default_46" [label="(197, 768)", style=solid]; +"629 quantize_per_tensor_default_46" -> "630 dequantize_per_tensor_default_46" [label="(197, 768)", style=solid]; +"630 dequantize_per_tensor_default_46" -> "637 linear_29" [label="(197, 768)", style=solid]; +"631 encoder_layers_encoder_layer_7_self_attention_out_proj_bias_0_0" -> "637 linear_29" [label="(768,)", style=solid]; +"632 scale_updated_constant30" -> "634 mul_tensor_30" [label="(768, 1)", style=solid]; +"633 compressed_weight_updated_constant30" -> "634 mul_tensor_30" [label="(768, 768)", style=solid]; +"634 mul_tensor_30" -> "636 sub_tensor_30" [label="(768, 768)", style=solid]; +"635 zero_point_updated_constant30" -> "636 sub_tensor_30" [label="(768, 1)", style=solid]; +"636 sub_tensor_30" -> "637 linear_29" [label="(768, 768)", style=solid]; +"637 linear_29" -> "638 view_63" [label="(197, 768)", style=solid]; +"638 view_63" -> "639 transpose_47" [label="(197, 1, 768)", style=solid]; +"639 transpose_47" -> "640 dropout_22" [label="(1, 197, 768)", style=solid]; +"640 dropout_22" -> "641 add_15" [label="(1, 197, 768)", style=solid]; +"641 add_15" -> "642 layer_norm_15" [label="(1, 197, 768)", style=solid]; +"641 add_15" -> "666 add_16" [label="(1, 197, 768)", style=solid]; +"642 layer_norm_15" -> "643 layer_norm_15_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"643 layer_norm_15_0_0_nncf_smooth_quant_0" -> "644 quantize_per_tensor_default_47" [label="(1, 197, 768)", style=solid]; +"644 quantize_per_tensor_default_47" -> "645 dequantize_per_tensor_default_47" [label="(1, 197, 768)", style=solid]; +"645 dequantize_per_tensor_default_47" -> "652 linear_30" [label="(1, 197, 768)", style=solid]; +"646 encoder_layers_encoder_layer_7_mlp_0_bias_0_0" -> "652 linear_30" [label="(3072,)", style=solid]; +"647 scale_updated_constant31" -> "649 mul_tensor_31" [label="(3072, 1)", style=solid]; +"648 compressed_weight_updated_constant31" -> "649 mul_tensor_31" [label="(3072, 768)", style=solid]; +"649 mul_tensor_31" -> "651 sub_tensor_31" [label="(3072, 768)", style=solid]; +"650 zero_point_updated_constant31" -> "651 sub_tensor_31" [label="(3072, 1)", style=solid]; +"651 sub_tensor_31" -> "652 linear_30" [label="(3072, 768)", style=solid]; +"652 linear_30" -> "653 gelu_7" [label="(1, 197, 3072)", style=solid]; +"653 gelu_7" -> "654 dropout_23" [label="(1, 197, 3072)", style=solid]; +"654 dropout_23" -> "655 dropout_23_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"655 dropout_23_0_0_nncf_smooth_quant_0" -> "656 quantize_per_tensor_default_48" [label="(1, 197, 3072)", style=solid]; +"656 quantize_per_tensor_default_48" -> "657 dequantize_per_tensor_default_48" [label="(1, 197, 3072)", style=solid]; +"657 dequantize_per_tensor_default_48" -> "664 linear_31" [label="(1, 197, 3072)", style=solid]; +"658 encoder_layers_encoder_layer_7_mlp_3_bias_0_0" -> "664 linear_31" [label="(768,)", style=solid]; +"659 scale_updated_constant32" -> "661 mul_tensor_32" [label="(768, 1)", style=solid]; +"660 compressed_weight_updated_constant32" -> "661 mul_tensor_32" [label="(768, 3072)", style=solid]; +"661 mul_tensor_32" -> "663 sub_tensor_32" [label="(768, 3072)", style=solid]; +"662 zero_point_updated_constant32" -> "663 sub_tensor_32" [label="(768, 1)", style=solid]; +"663 sub_tensor_32" -> "664 linear_31" [label="(768, 3072)", style=solid]; +"664 linear_31" -> "665 dropout_24" [label="(1, 197, 768)", style=solid]; +"665 dropout_24" -> "666 add_16" [label="(1, 197, 768)", style=solid]; +"666 add_16" -> "667 layer_norm_16" [label="(1, 197, 768)", style=solid]; +"666 add_16" -> "716 add_17" [label="(1, 197, 768)", style=solid]; +"667 layer_norm_16" -> "668 transpose_48" [label="(1, 197, 768)", style=solid]; +"668 transpose_48" -> "669 transpose_48_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"669 transpose_48_0_0_nncf_smooth_quant_0" -> "670 quantize_per_tensor_default_49" [label="(197, 1, 768)", style=solid]; +"670 quantize_per_tensor_default_49" -> "671 dequantize_per_tensor_default_49" [label="(197, 1, 768)", style=solid]; +"671 dequantize_per_tensor_default_49" -> "678 linear_32" [label="(197, 1, 768)", style=solid]; +"672 encoder_layers_encoder_layer_8_self_attention_in_proj_bias_0_0" -> "678 linear_32" [label="(2304,)", style=solid]; +"673 scale_updated_constant33" -> "675 mul_tensor_33" [label="(2304, 1)", style=solid]; +"674 compressed_weight_updated_constant33" -> "675 mul_tensor_33" [label="(2304, 768)", style=solid]; +"675 mul_tensor_33" -> "677 sub_tensor_33" [label="(2304, 768)", style=solid]; +"676 zero_point_updated_constant33" -> "677 sub_tensor_33" [label="(2304, 1)", style=solid]; +"677 sub_tensor_33" -> "678 linear_32" [label="(2304, 768)", style=solid]; +"678 linear_32" -> "679 unflatten_8" [label="(197, 1, 2304)", style=solid]; +"679 unflatten_8" -> "680 unsqueeze_8" [label="(197, 1, 3, 768)", style=solid]; +"680 unsqueeze_8" -> "681 transpose_49" [label="(1, 197, 1, 3, 768)", style=solid]; +"681 transpose_49" -> "682 squeeze_8" [label="(3, 197, 1, 1, 768)", style=solid]; +"682 squeeze_8" -> "683 contiguous_8" [label="(3, 197, 1, 768)", style=solid]; +"683 contiguous_8" -> "684 quantize_per_tensor_default_50" [label="(3, 197, 1, 768)", style=solid]; +"683 contiguous_8" -> "687 quantize_per_tensor_default_51" [label="(3, 197, 1, 768)", style=solid]; +"683 contiguous_8" -> "690 select_26" [label="(3, 197, 1, 768)", style=solid]; +"684 quantize_per_tensor_default_50" -> "685 dequantize_per_tensor_default_50" [label="(3, 197, 1, 768)", style=solid]; +"685 dequantize_per_tensor_default_50" -> "686 select_24" [label="(3, 197, 1, 768)", style=solid]; +"686 select_24" -> "691 view_64" [label="(197, 1, 768)", style=solid]; +"687 quantize_per_tensor_default_51" -> "688 dequantize_per_tensor_default_51" [label="(3, 197, 1, 768)", style=solid]; +"688 dequantize_per_tensor_default_51" -> "689 select_25" [label="(3, 197, 1, 768)", style=solid]; +"689 select_25" -> "693 view_65" [label="(197, 1, 768)", style=solid]; +"690 select_26" -> "695 view_66" [label="(197, 1, 768)", style=solid]; +"691 view_64" -> "692 transpose_50" [label="(197, 12, 64)", style=solid]; +"692 transpose_50" -> "697 view_67" [label="(12, 197, 64)", style=solid]; +"693 view_65" -> "694 transpose_51" [label="(197, 12, 64)", style=solid]; +"694 transpose_51" -> "698 view_68" [label="(12, 197, 64)", style=solid]; +"695 view_66" -> "696 transpose_52" [label="(197, 12, 64)", style=solid]; +"696 transpose_52" -> "699 view_69" [label="(12, 197, 64)", style=solid]; +"697 view_67" -> "700 scaled_dot_product_attention_8" [label="(1, 12, 197, 64)", style=solid]; +"698 view_68" -> "700 scaled_dot_product_attention_8" [label="(1, 12, 197, 64)", style=solid]; +"699 view_69" -> "700 scaled_dot_product_attention_8" [label="(1, 12, 197, 64)", style=solid]; +"700 scaled_dot_product_attention_8" -> "701 permute_9" [label="(1, 12, 197, 64)", style=solid]; +"701 permute_9" -> "702 view_70" [label="(197, 1, 12, 64)", style=solid]; +"702 view_70" -> "703 view_70_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"703 view_70_0_0_nncf_smooth_quant_0" -> "704 quantize_per_tensor_default_52" [label="(197, 768)", style=solid]; +"704 quantize_per_tensor_default_52" -> "705 dequantize_per_tensor_default_52" [label="(197, 768)", style=solid]; +"705 dequantize_per_tensor_default_52" -> "712 linear_33" [label="(197, 768)", style=solid]; +"706 encoder_layers_encoder_layer_8_self_attention_out_proj_bias_0_0" -> "712 linear_33" [label="(768,)", style=solid]; +"707 scale_updated_constant34" -> "709 mul_tensor_34" [label="(768, 1)", style=solid]; +"708 compressed_weight_updated_constant34" -> "709 mul_tensor_34" [label="(768, 768)", style=solid]; +"709 mul_tensor_34" -> "711 sub_tensor_34" [label="(768, 768)", style=solid]; +"710 zero_point_updated_constant34" -> "711 sub_tensor_34" [label="(768, 1)", style=solid]; +"711 sub_tensor_34" -> "712 linear_33" [label="(768, 768)", style=solid]; +"712 linear_33" -> "713 view_71" [label="(197, 768)", style=solid]; +"713 view_71" -> "714 transpose_53" [label="(197, 1, 768)", style=solid]; +"714 transpose_53" -> "715 dropout_25" [label="(1, 197, 768)", style=solid]; +"715 dropout_25" -> "716 add_17" [label="(1, 197, 768)", style=solid]; +"716 add_17" -> "717 layer_norm_17" [label="(1, 197, 768)", style=solid]; +"716 add_17" -> "741 add_18" [label="(1, 197, 768)", style=solid]; +"717 layer_norm_17" -> "718 layer_norm_17_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"718 layer_norm_17_0_0_nncf_smooth_quant_0" -> "719 quantize_per_tensor_default_53" [label="(1, 197, 768)", style=solid]; +"719 quantize_per_tensor_default_53" -> "720 dequantize_per_tensor_default_53" [label="(1, 197, 768)", style=solid]; +"720 dequantize_per_tensor_default_53" -> "727 linear_34" [label="(1, 197, 768)", style=solid]; +"721 encoder_layers_encoder_layer_8_mlp_0_bias_0_0" -> "727 linear_34" [label="(3072,)", style=solid]; +"722 scale_updated_constant35" -> "724 mul_tensor_35" [label="(3072, 1)", style=solid]; +"723 compressed_weight_updated_constant35" -> "724 mul_tensor_35" [label="(3072, 768)", style=solid]; +"724 mul_tensor_35" -> "726 sub_tensor_35" [label="(3072, 768)", style=solid]; +"725 zero_point_updated_constant35" -> "726 sub_tensor_35" [label="(3072, 1)", style=solid]; +"726 sub_tensor_35" -> "727 linear_34" [label="(3072, 768)", style=solid]; +"727 linear_34" -> "728 gelu_8" [label="(1, 197, 3072)", style=solid]; +"728 gelu_8" -> "729 dropout_26" [label="(1, 197, 3072)", style=solid]; +"729 dropout_26" -> "730 dropout_26_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"730 dropout_26_0_0_nncf_smooth_quant_0" -> "731 quantize_per_tensor_default_54" [label="(1, 197, 3072)", style=solid]; +"731 quantize_per_tensor_default_54" -> "732 dequantize_per_tensor_default_54" [label="(1, 197, 3072)", style=solid]; +"732 dequantize_per_tensor_default_54" -> "739 linear_35" [label="(1, 197, 3072)", style=solid]; +"733 encoder_layers_encoder_layer_8_mlp_3_bias_0_0" -> "739 linear_35" [label="(768,)", style=solid]; +"734 scale_updated_constant36" -> "736 mul_tensor_36" [label="(768, 1)", style=solid]; +"735 compressed_weight_updated_constant36" -> "736 mul_tensor_36" [label="(768, 3072)", style=solid]; +"736 mul_tensor_36" -> "738 sub_tensor_36" [label="(768, 3072)", style=solid]; +"737 zero_point_updated_constant36" -> "738 sub_tensor_36" [label="(768, 1)", style=solid]; +"738 sub_tensor_36" -> "739 linear_35" [label="(768, 3072)", style=solid]; +"739 linear_35" -> "740 dropout_27" [label="(1, 197, 768)", style=solid]; +"740 dropout_27" -> "741 add_18" [label="(1, 197, 768)", style=solid]; +"741 add_18" -> "742 layer_norm_18" [label="(1, 197, 768)", style=solid]; +"741 add_18" -> "791 add_19" [label="(1, 197, 768)", style=solid]; +"742 layer_norm_18" -> "743 transpose_54" [label="(1, 197, 768)", style=solid]; +"743 transpose_54" -> "744 transpose_54_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"744 transpose_54_0_0_nncf_smooth_quant_0" -> "745 quantize_per_tensor_default_55" [label="(197, 1, 768)", style=solid]; +"745 quantize_per_tensor_default_55" -> "746 dequantize_per_tensor_default_55" [label="(197, 1, 768)", style=solid]; +"746 dequantize_per_tensor_default_55" -> "753 linear_36" [label="(197, 1, 768)", style=solid]; +"747 encoder_layers_encoder_layer_9_self_attention_in_proj_bias_0_0" -> "753 linear_36" [label="(2304,)", style=solid]; +"748 scale_updated_constant37" -> "750 mul_tensor_37" [label="(2304, 1)", style=solid]; +"749 compressed_weight_updated_constant37" -> "750 mul_tensor_37" [label="(2304, 768)", style=solid]; +"750 mul_tensor_37" -> "752 sub_tensor_37" [label="(2304, 768)", style=solid]; +"751 zero_point_updated_constant37" -> "752 sub_tensor_37" [label="(2304, 1)", style=solid]; +"752 sub_tensor_37" -> "753 linear_36" [label="(2304, 768)", style=solid]; +"753 linear_36" -> "754 unflatten_9" [label="(197, 1, 2304)", style=solid]; +"754 unflatten_9" -> "755 unsqueeze_9" [label="(197, 1, 3, 768)", style=solid]; +"755 unsqueeze_9" -> "756 transpose_55" [label="(1, 197, 1, 3, 768)", style=solid]; +"756 transpose_55" -> "757 squeeze_9" [label="(3, 197, 1, 1, 768)", style=solid]; +"757 squeeze_9" -> "758 contiguous_9" [label="(3, 197, 1, 768)", style=solid]; +"758 contiguous_9" -> "759 quantize_per_tensor_default_56" [label="(3, 197, 1, 768)", style=solid]; +"758 contiguous_9" -> "762 quantize_per_tensor_default_57" [label="(3, 197, 1, 768)", style=solid]; +"758 contiguous_9" -> "765 select_29" [label="(3, 197, 1, 768)", style=solid]; +"759 quantize_per_tensor_default_56" -> "760 dequantize_per_tensor_default_56" [label="(3, 197, 1, 768)", style=solid]; +"760 dequantize_per_tensor_default_56" -> "761 select_27" [label="(3, 197, 1, 768)", style=solid]; +"761 select_27" -> "766 view_72" [label="(197, 1, 768)", style=solid]; +"762 quantize_per_tensor_default_57" -> "763 dequantize_per_tensor_default_57" [label="(3, 197, 1, 768)", style=solid]; +"763 dequantize_per_tensor_default_57" -> "764 select_28" [label="(3, 197, 1, 768)", style=solid]; +"764 select_28" -> "768 view_73" [label="(197, 1, 768)", style=solid]; +"765 select_29" -> "770 view_74" [label="(197, 1, 768)", style=solid]; +"766 view_72" -> "767 transpose_56" [label="(197, 12, 64)", style=solid]; +"767 transpose_56" -> "772 view_75" [label="(12, 197, 64)", style=solid]; +"768 view_73" -> "769 transpose_57" [label="(197, 12, 64)", style=solid]; +"769 transpose_57" -> "773 view_76" [label="(12, 197, 64)", style=solid]; +"770 view_74" -> "771 transpose_58" [label="(197, 12, 64)", style=solid]; +"771 transpose_58" -> "774 view_77" [label="(12, 197, 64)", style=solid]; +"772 view_75" -> "775 scaled_dot_product_attention_9" [label="(1, 12, 197, 64)", style=solid]; +"773 view_76" -> "775 scaled_dot_product_attention_9" [label="(1, 12, 197, 64)", style=solid]; +"774 view_77" -> "775 scaled_dot_product_attention_9" [label="(1, 12, 197, 64)", style=solid]; +"775 scaled_dot_product_attention_9" -> "776 permute_10" [label="(1, 12, 197, 64)", style=solid]; +"776 permute_10" -> "777 view_78" [label="(197, 1, 12, 64)", style=solid]; +"777 view_78" -> "778 view_78_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"778 view_78_0_0_nncf_smooth_quant_0" -> "779 quantize_per_tensor_default_58" [label="(197, 768)", style=solid]; +"779 quantize_per_tensor_default_58" -> "780 dequantize_per_tensor_default_58" [label="(197, 768)", style=solid]; +"780 dequantize_per_tensor_default_58" -> "787 linear_37" [label="(197, 768)", style=solid]; +"781 encoder_layers_encoder_layer_9_self_attention_out_proj_bias_0_0" -> "787 linear_37" [label="(768,)", style=solid]; +"782 scale_updated_constant38" -> "784 mul_tensor_38" [label="(768, 1)", style=solid]; +"783 compressed_weight_updated_constant38" -> "784 mul_tensor_38" [label="(768, 768)", style=solid]; +"784 mul_tensor_38" -> "786 sub_tensor_38" [label="(768, 768)", style=solid]; +"785 zero_point_updated_constant38" -> "786 sub_tensor_38" [label="(768, 1)", style=solid]; +"786 sub_tensor_38" -> "787 linear_37" [label="(768, 768)", style=solid]; +"787 linear_37" -> "788 view_79" [label="(197, 768)", style=solid]; +"788 view_79" -> "789 transpose_59" [label="(197, 1, 768)", style=solid]; +"789 transpose_59" -> "790 dropout_28" [label="(1, 197, 768)", style=solid]; +"790 dropout_28" -> "791 add_19" [label="(1, 197, 768)", style=solid]; +"791 add_19" -> "792 layer_norm_19" [label="(1, 197, 768)", style=solid]; +"791 add_19" -> "816 add_20" [label="(1, 197, 768)", style=solid]; +"792 layer_norm_19" -> "793 layer_norm_19_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"793 layer_norm_19_0_0_nncf_smooth_quant_0" -> "794 quantize_per_tensor_default_59" [label="(1, 197, 768)", style=solid]; +"794 quantize_per_tensor_default_59" -> "795 dequantize_per_tensor_default_59" [label="(1, 197, 768)", style=solid]; +"795 dequantize_per_tensor_default_59" -> "802 linear_38" [label="(1, 197, 768)", style=solid]; +"796 encoder_layers_encoder_layer_9_mlp_0_bias_0_0" -> "802 linear_38" [label="(3072,)", style=solid]; +"797 scale_updated_constant39" -> "799 mul_tensor_39" [label="(3072, 1)", style=solid]; +"798 compressed_weight_updated_constant39" -> "799 mul_tensor_39" [label="(3072, 768)", style=solid]; +"799 mul_tensor_39" -> "801 sub_tensor_39" [label="(3072, 768)", style=solid]; +"800 zero_point_updated_constant39" -> "801 sub_tensor_39" [label="(3072, 1)", style=solid]; +"801 sub_tensor_39" -> "802 linear_38" [label="(3072, 768)", style=solid]; +"802 linear_38" -> "803 gelu_9" [label="(1, 197, 3072)", style=solid]; +"803 gelu_9" -> "804 dropout_29" [label="(1, 197, 3072)", style=solid]; +"804 dropout_29" -> "805 dropout_29_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"805 dropout_29_0_0_nncf_smooth_quant_0" -> "806 quantize_per_tensor_default_60" [label="(1, 197, 3072)", style=solid]; +"806 quantize_per_tensor_default_60" -> "807 dequantize_per_tensor_default_60" [label="(1, 197, 3072)", style=solid]; +"807 dequantize_per_tensor_default_60" -> "814 linear_39" [label="(1, 197, 3072)", style=solid]; +"808 encoder_layers_encoder_layer_9_mlp_3_bias_0_0" -> "814 linear_39" [label="(768,)", style=solid]; +"809 scale_updated_constant40" -> "811 mul_tensor_40" [label="(768, 1)", style=solid]; +"810 compressed_weight_updated_constant40" -> "811 mul_tensor_40" [label="(768, 3072)", style=solid]; +"811 mul_tensor_40" -> "813 sub_tensor_40" [label="(768, 3072)", style=solid]; +"812 zero_point_updated_constant40" -> "813 sub_tensor_40" [label="(768, 1)", style=solid]; +"813 sub_tensor_40" -> "814 linear_39" [label="(768, 3072)", style=solid]; +"814 linear_39" -> "815 dropout_30" [label="(1, 197, 768)", style=solid]; +"815 dropout_30" -> "816 add_20" [label="(1, 197, 768)", style=solid]; +"816 add_20" -> "817 layer_norm_20" [label="(1, 197, 768)", style=solid]; +"816 add_20" -> "866 add_21" [label="(1, 197, 768)", style=solid]; +"817 layer_norm_20" -> "818 transpose_60" [label="(1, 197, 768)", style=solid]; +"818 transpose_60" -> "819 transpose_60_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"819 transpose_60_0_0_nncf_smooth_quant_0" -> "820 quantize_per_tensor_default_61" [label="(197, 1, 768)", style=solid]; +"820 quantize_per_tensor_default_61" -> "821 dequantize_per_tensor_default_61" [label="(197, 1, 768)", style=solid]; +"821 dequantize_per_tensor_default_61" -> "828 linear_40" [label="(197, 1, 768)", style=solid]; +"822 encoder_layers_encoder_layer_10_self_attention_in_proj_bias_0_0" -> "828 linear_40" [label="(2304,)", style=solid]; +"823 scale_updated_constant41" -> "825 mul_tensor_41" [label="(2304, 1)", style=solid]; +"824 compressed_weight_updated_constant41" -> "825 mul_tensor_41" [label="(2304, 768)", style=solid]; +"825 mul_tensor_41" -> "827 sub_tensor_41" [label="(2304, 768)", style=solid]; +"826 zero_point_updated_constant41" -> "827 sub_tensor_41" [label="(2304, 1)", style=solid]; +"827 sub_tensor_41" -> "828 linear_40" [label="(2304, 768)", style=solid]; +"828 linear_40" -> "829 unflatten_10" [label="(197, 1, 2304)", style=solid]; +"829 unflatten_10" -> "830 unsqueeze_10" [label="(197, 1, 3, 768)", style=solid]; +"830 unsqueeze_10" -> "831 transpose_61" [label="(1, 197, 1, 3, 768)", style=solid]; +"831 transpose_61" -> "832 squeeze_10" [label="(3, 197, 1, 1, 768)", style=solid]; +"832 squeeze_10" -> "833 contiguous_10" [label="(3, 197, 1, 768)", style=solid]; +"833 contiguous_10" -> "834 quantize_per_tensor_default_62" [label="(3, 197, 1, 768)", style=solid]; +"833 contiguous_10" -> "837 quantize_per_tensor_default_63" [label="(3, 197, 1, 768)", style=solid]; +"833 contiguous_10" -> "840 select_32" [label="(3, 197, 1, 768)", style=solid]; +"834 quantize_per_tensor_default_62" -> "835 dequantize_per_tensor_default_62" [label="(3, 197, 1, 768)", style=solid]; +"835 dequantize_per_tensor_default_62" -> "836 select_30" [label="(3, 197, 1, 768)", style=solid]; +"836 select_30" -> "841 view_80" [label="(197, 1, 768)", style=solid]; +"837 quantize_per_tensor_default_63" -> "838 dequantize_per_tensor_default_63" [label="(3, 197, 1, 768)", style=solid]; +"838 dequantize_per_tensor_default_63" -> "839 select_31" [label="(3, 197, 1, 768)", style=solid]; +"839 select_31" -> "843 view_81" [label="(197, 1, 768)", style=solid]; +"840 select_32" -> "845 view_82" [label="(197, 1, 768)", style=solid]; +"841 view_80" -> "842 transpose_62" [label="(197, 12, 64)", style=solid]; +"842 transpose_62" -> "847 view_83" [label="(12, 197, 64)", style=solid]; +"843 view_81" -> "844 transpose_63" [label="(197, 12, 64)", style=solid]; +"844 transpose_63" -> "848 view_84" [label="(12, 197, 64)", style=solid]; +"845 view_82" -> "846 transpose_64" [label="(197, 12, 64)", style=solid]; +"846 transpose_64" -> "849 view_85" [label="(12, 197, 64)", style=solid]; +"847 view_83" -> "850 scaled_dot_product_attention_10" [label="(1, 12, 197, 64)", style=solid]; +"848 view_84" -> "850 scaled_dot_product_attention_10" [label="(1, 12, 197, 64)", style=solid]; +"849 view_85" -> "850 scaled_dot_product_attention_10" [label="(1, 12, 197, 64)", style=solid]; +"850 scaled_dot_product_attention_10" -> "851 permute_11" [label="(1, 12, 197, 64)", style=solid]; +"851 permute_11" -> "852 view_86" [label="(197, 1, 12, 64)", style=solid]; +"852 view_86" -> "853 view_86_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"853 view_86_0_0_nncf_smooth_quant_0" -> "854 quantize_per_tensor_default_64" [label="(197, 768)", style=solid]; +"854 quantize_per_tensor_default_64" -> "855 dequantize_per_tensor_default_64" [label="(197, 768)", style=solid]; +"855 dequantize_per_tensor_default_64" -> "862 linear_41" [label="(197, 768)", style=solid]; +"856 encoder_layers_encoder_layer_10_self_attention_out_proj_bias_0_0" -> "862 linear_41" [label="(768,)", style=solid]; +"857 scale_updated_constant42" -> "859 mul_tensor_42" [label="(768, 1)", style=solid]; +"858 compressed_weight_updated_constant42" -> "859 mul_tensor_42" [label="(768, 768)", style=solid]; +"859 mul_tensor_42" -> "861 sub_tensor_42" [label="(768, 768)", style=solid]; +"860 zero_point_updated_constant42" -> "861 sub_tensor_42" [label="(768, 1)", style=solid]; +"861 sub_tensor_42" -> "862 linear_41" [label="(768, 768)", style=solid]; +"862 linear_41" -> "863 view_87" [label="(197, 768)", style=solid]; +"863 view_87" -> "864 transpose_65" [label="(197, 1, 768)", style=solid]; +"864 transpose_65" -> "865 dropout_31" [label="(1, 197, 768)", style=solid]; +"865 dropout_31" -> "866 add_21" [label="(1, 197, 768)", style=solid]; +"866 add_21" -> "867 layer_norm_21" [label="(1, 197, 768)", style=solid]; +"866 add_21" -> "891 add_22" [label="(1, 197, 768)", style=solid]; +"867 layer_norm_21" -> "868 layer_norm_21_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"868 layer_norm_21_0_0_nncf_smooth_quant_0" -> "869 quantize_per_tensor_default_65" [label="(1, 197, 768)", style=solid]; +"869 quantize_per_tensor_default_65" -> "870 dequantize_per_tensor_default_65" [label="(1, 197, 768)", style=solid]; +"870 dequantize_per_tensor_default_65" -> "877 linear_42" [label="(1, 197, 768)", style=solid]; +"871 encoder_layers_encoder_layer_10_mlp_0_bias_0_0" -> "877 linear_42" [label="(3072,)", style=solid]; +"872 scale_updated_constant43" -> "874 mul_tensor_43" [label="(3072, 1)", style=solid]; +"873 compressed_weight_updated_constant43" -> "874 mul_tensor_43" [label="(3072, 768)", style=solid]; +"874 mul_tensor_43" -> "876 sub_tensor_43" [label="(3072, 768)", style=solid]; +"875 zero_point_updated_constant43" -> "876 sub_tensor_43" [label="(3072, 1)", style=solid]; +"876 sub_tensor_43" -> "877 linear_42" [label="(3072, 768)", style=solid]; +"877 linear_42" -> "878 gelu_10" [label="(1, 197, 3072)", style=solid]; +"878 gelu_10" -> "879 dropout_32" [label="(1, 197, 3072)", style=solid]; +"879 dropout_32" -> "880 dropout_32_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"880 dropout_32_0_0_nncf_smooth_quant_0" -> "881 quantize_per_tensor_default_66" [label="(1, 197, 3072)", style=solid]; +"881 quantize_per_tensor_default_66" -> "882 dequantize_per_tensor_default_66" [label="(1, 197, 3072)", style=solid]; +"882 dequantize_per_tensor_default_66" -> "889 linear_43" [label="(1, 197, 3072)", style=solid]; +"883 encoder_layers_encoder_layer_10_mlp_3_bias_0_0" -> "889 linear_43" [label="(768,)", style=solid]; +"884 scale_updated_constant44" -> "886 mul_tensor_44" [label="(768, 1)", style=solid]; +"885 compressed_weight_updated_constant44" -> "886 mul_tensor_44" [label="(768, 3072)", style=solid]; +"886 mul_tensor_44" -> "888 sub_tensor_44" [label="(768, 3072)", style=solid]; +"887 zero_point_updated_constant44" -> "888 sub_tensor_44" [label="(768, 1)", style=solid]; +"888 sub_tensor_44" -> "889 linear_43" [label="(768, 3072)", style=solid]; +"889 linear_43" -> "890 dropout_33" [label="(1, 197, 768)", style=solid]; +"890 dropout_33" -> "891 add_22" [label="(1, 197, 768)", style=solid]; +"891 add_22" -> "892 layer_norm_22" [label="(1, 197, 768)", style=solid]; +"891 add_22" -> "941 add_23" [label="(1, 197, 768)", style=solid]; +"892 layer_norm_22" -> "893 transpose_66" [label="(1, 197, 768)", style=solid]; +"893 transpose_66" -> "894 transpose_66_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"894 transpose_66_0_0_nncf_smooth_quant_0" -> "895 quantize_per_tensor_default_67" [label="(197, 1, 768)", style=solid]; +"895 quantize_per_tensor_default_67" -> "896 dequantize_per_tensor_default_67" [label="(197, 1, 768)", style=solid]; +"896 dequantize_per_tensor_default_67" -> "903 linear_44" [label="(197, 1, 768)", style=solid]; +"897 encoder_layers_encoder_layer_11_self_attention_in_proj_bias_0_0" -> "903 linear_44" [label="(2304,)", style=solid]; +"898 scale_updated_constant45" -> "900 mul_tensor_45" [label="(2304, 1)", style=solid]; +"899 compressed_weight_updated_constant45" -> "900 mul_tensor_45" [label="(2304, 768)", style=solid]; +"900 mul_tensor_45" -> "902 sub_tensor_45" [label="(2304, 768)", style=solid]; +"901 zero_point_updated_constant45" -> "902 sub_tensor_45" [label="(2304, 1)", style=solid]; +"902 sub_tensor_45" -> "903 linear_44" [label="(2304, 768)", style=solid]; +"903 linear_44" -> "904 unflatten_11" [label="(197, 1, 2304)", style=solid]; +"904 unflatten_11" -> "905 unsqueeze_11" [label="(197, 1, 3, 768)", style=solid]; +"905 unsqueeze_11" -> "906 transpose_67" [label="(1, 197, 1, 3, 768)", style=solid]; +"906 transpose_67" -> "907 squeeze_11" [label="(3, 197, 1, 1, 768)", style=solid]; +"907 squeeze_11" -> "908 contiguous_11" [label="(3, 197, 1, 768)", style=solid]; +"908 contiguous_11" -> "909 quantize_per_tensor_default_68" [label="(3, 197, 1, 768)", style=solid]; +"908 contiguous_11" -> "912 quantize_per_tensor_default_69" [label="(3, 197, 1, 768)", style=solid]; +"908 contiguous_11" -> "915 select_35" [label="(3, 197, 1, 768)", style=solid]; +"909 quantize_per_tensor_default_68" -> "910 dequantize_per_tensor_default_68" [label="(3, 197, 1, 768)", style=solid]; +"910 dequantize_per_tensor_default_68" -> "911 select_33" [label="(3, 197, 1, 768)", style=solid]; +"911 select_33" -> "916 view_88" [label="(197, 1, 768)", style=solid]; +"912 quantize_per_tensor_default_69" -> "913 dequantize_per_tensor_default_69" [label="(3, 197, 1, 768)", style=solid]; +"913 dequantize_per_tensor_default_69" -> "914 select_34" [label="(3, 197, 1, 768)", style=solid]; +"914 select_34" -> "918 view_89" [label="(197, 1, 768)", style=solid]; +"915 select_35" -> "920 view_90" [label="(197, 1, 768)", style=solid]; +"916 view_88" -> "917 transpose_68" [label="(197, 12, 64)", style=solid]; +"917 transpose_68" -> "922 view_91" [label="(12, 197, 64)", style=solid]; +"918 view_89" -> "919 transpose_69" [label="(197, 12, 64)", style=solid]; +"919 transpose_69" -> "923 view_92" [label="(12, 197, 64)", style=solid]; +"920 view_90" -> "921 transpose_70" [label="(197, 12, 64)", style=solid]; +"921 transpose_70" -> "924 view_93" [label="(12, 197, 64)", style=solid]; +"922 view_91" -> "925 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"923 view_92" -> "925 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"924 view_93" -> "925 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"925 scaled_dot_product_attention_11" -> "926 permute_12" [label="(1, 12, 197, 64)", style=solid]; +"926 permute_12" -> "927 view_94" [label="(197, 1, 12, 64)", style=solid]; +"927 view_94" -> "928 view_94_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"928 view_94_0_0_nncf_smooth_quant_0" -> "929 quantize_per_tensor_default_70" [label="(197, 768)", style=solid]; +"929 quantize_per_tensor_default_70" -> "930 dequantize_per_tensor_default_70" [label="(197, 768)", style=solid]; +"930 dequantize_per_tensor_default_70" -> "937 linear_45" [label="(197, 768)", style=solid]; +"931 encoder_layers_encoder_layer_11_self_attention_out_proj_bias_0_0" -> "937 linear_45" [label="(768,)", style=solid]; +"932 scale_updated_constant46" -> "934 mul_tensor_46" [label="(768, 1)", style=solid]; +"933 compressed_weight_updated_constant46" -> "934 mul_tensor_46" [label="(768, 768)", style=solid]; +"934 mul_tensor_46" -> "936 sub_tensor_46" [label="(768, 768)", style=solid]; +"935 zero_point_updated_constant46" -> "936 sub_tensor_46" [label="(768, 1)", style=solid]; +"936 sub_tensor_46" -> "937 linear_45" [label="(768, 768)", style=solid]; +"937 linear_45" -> "938 view_95" [label="(197, 768)", style=solid]; +"938 view_95" -> "939 transpose_71" [label="(197, 1, 768)", style=solid]; +"939 transpose_71" -> "940 dropout_34" [label="(1, 197, 768)", style=solid]; +"940 dropout_34" -> "941 add_23" [label="(1, 197, 768)", style=solid]; +"941 add_23" -> "942 layer_norm_23" [label="(1, 197, 768)", style=solid]; +"941 add_23" -> "966 add_24" [label="(1, 197, 768)", style=solid]; +"942 layer_norm_23" -> "943 layer_norm_23_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"943 layer_norm_23_0_0_nncf_smooth_quant_0" -> "944 quantize_per_tensor_default_71" [label="(1, 197, 768)", style=solid]; +"944 quantize_per_tensor_default_71" -> "945 dequantize_per_tensor_default_71" [label="(1, 197, 768)", style=solid]; +"945 dequantize_per_tensor_default_71" -> "952 linear_46" [label="(1, 197, 768)", style=solid]; +"946 encoder_layers_encoder_layer_11_mlp_0_bias_0_0" -> "952 linear_46" [label="(3072,)", style=solid]; +"947 scale_updated_constant47" -> "949 mul_tensor_47" [label="(3072, 1)", style=solid]; +"948 compressed_weight_updated_constant47" -> "949 mul_tensor_47" [label="(3072, 768)", style=solid]; +"949 mul_tensor_47" -> "951 sub_tensor_47" [label="(3072, 768)", style=solid]; +"950 zero_point_updated_constant47" -> "951 sub_tensor_47" [label="(3072, 1)", style=solid]; +"951 sub_tensor_47" -> "952 linear_46" [label="(3072, 768)", style=solid]; +"952 linear_46" -> "953 gelu_11" [label="(1, 197, 3072)", style=solid]; +"953 gelu_11" -> "954 dropout_35" [label="(1, 197, 3072)", style=solid]; +"954 dropout_35" -> "955 dropout_35_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"955 dropout_35_0_0_nncf_smooth_quant_0" -> "956 quantize_per_tensor_default_72" [label="(1, 197, 3072)", style=solid]; +"956 quantize_per_tensor_default_72" -> "957 dequantize_per_tensor_default_72" [label="(1, 197, 3072)", style=solid]; +"957 dequantize_per_tensor_default_72" -> "964 linear_47" [label="(1, 197, 3072)", style=solid]; +"958 encoder_layers_encoder_layer_11_mlp_3_bias_0_0" -> "964 linear_47" [label="(768,)", style=solid]; +"959 scale_updated_constant48" -> "961 mul_tensor_48" [label="(768, 1)", style=solid]; +"960 compressed_weight_updated_constant48" -> "961 mul_tensor_48" [label="(768, 3072)", style=solid]; +"961 mul_tensor_48" -> "963 sub_tensor_48" [label="(768, 3072)", style=solid]; +"962 zero_point_updated_constant48" -> "963 sub_tensor_48" [label="(768, 1)", style=solid]; +"963 sub_tensor_48" -> "964 linear_47" [label="(768, 3072)", style=solid]; +"964 linear_47" -> "965 dropout_36" [label="(1, 197, 768)", style=solid]; +"965 dropout_36" -> "966 add_24" [label="(1, 197, 768)", style=solid]; +"966 add_24" -> "967 layer_norm_24" [label="(1, 197, 768)", style=solid]; +"967 layer_norm_24" -> "968 slice_1" [label="(1, 197, 768)", style=solid]; +"968 slice_1" -> "969 select_36" [label="(1, 197, 768)", style=solid]; +"969 select_36" -> "970 select_36_0_0_nncf_smooth_quant_0" [label="(1, 768)", style=solid]; +"970 select_36_0_0_nncf_smooth_quant_0" -> "971 quantize_per_tensor_default_73" [label="(1, 768)", style=solid]; +"971 quantize_per_tensor_default_73" -> "972 dequantize_per_tensor_default_73" [label="(1, 768)", style=solid]; +"972 dequantize_per_tensor_default_73" -> "979 linear_48" [label="(1, 768)", style=solid]; +"973 heads_head_bias_0_0" -> "979 linear_48" [label="(1000,)", style=solid]; +"974 scale_updated_constant49" -> "976 mul_tensor_49" [label="(1000, 1)", style=solid]; +"975 compressed_weight_updated_constant49" -> "976 mul_tensor_49" [label="(1000, 768)", style=solid]; +"976 mul_tensor_49" -> "978 sub_tensor_49" [label="(1000, 768)", style=solid]; +"977 zero_point_updated_constant49" -> "978 sub_tensor_49" [label="(1000, 1)", style=solid]; +"978 sub_tensor_49" -> "979 linear_48" [label="(1000, 768)", style=solid]; +"979 linear_48" -> "980 output" [label="(1, 1000)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/quantized/mobilenet_v3_small.dot b/tests/torch/data/reference_graphs/fx/quantized/mobilenet_v3_small.dot index 4417e47306b..bbba2af8408 100644 --- a/tests/torch/data/reference_graphs/fx/quantized/mobilenet_v3_small.dot +++ b/tests/torch/data/reference_graphs/fx/quantized/mobilenet_v3_small.dot @@ -1,488 +1,488 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; -"1 quantize_per_tensor_default" [id=1, type=quantize_per_tensor]; -"2 dequantize_per_tensor_default" [id=2, type=dequantize_per_tensor]; -"3 _param_constant0" [id=3, type=get_attr]; -"4 conv2d_scale_0" [id=4, type=get_attr]; -"5 conv2d_zero_point_0" [id=5, type=get_attr]; -"6 quantize_per_channel_default" [id=6, type=quantize_per_channel]; -"7 dequantize_per_channel_default" [id=7, type=dequantize_per_channel]; -"8 features_0_0_weight_bias_0_0" [id=8, type=get_attr]; -"9 conv2d" [id=9, type=conv2d]; -"10 hardswish__scale_0" [id=10, type=get_attr]; -"11 hardswish__zero_point_0" [id=11, type=get_attr]; -"12 hardswish_" [id=12, type=hardswish_]; -"13 quantize_per_channel_default_1" [id=13, type=quantize_per_channel]; -"14 dequantize_per_channel_default_1" [id=14, type=dequantize_per_channel]; -"15 _param_constant3" [id=15, type=get_attr]; -"16 conv2d_1_scale_0" [id=16, type=get_attr]; -"17 conv2d_1_zero_point_0" [id=17, type=get_attr]; -"18 quantize_per_channel_default_2" [id=18, type=quantize_per_channel]; -"19 dequantize_per_channel_default_2" [id=19, type=dequantize_per_channel]; -"20 features_1_block_0_0_weight_bias_0_0" [id=20, type=get_attr]; -"21 conv2d_1" [id=21, type=conv2d]; -"22 relu_" [id=22, type=relu_]; -"23 quantize_per_tensor_default_1" [id=23, type=quantize_per_tensor]; -"24 dequantize_per_tensor_default_2" [id=24, type=dequantize_per_tensor]; -"25 dequantize_per_tensor_default_1" [id=25, type=dequantize_per_tensor]; -"26 adaptive_avg_pool2d" [id=26, type=adaptive_avg_pool2d]; -"27 _param_constant6" [id=27, type=get_attr]; -"28 _param_constant7_0_0" [id=28, type=get_attr]; -"29 conv2d_2" [id=29, type=conv2d]; -"30 relu" [id=30, type=relu]; -"31 _param_constant8" [id=31, type=get_attr]; -"32 _param_constant9_0_0" [id=32, type=get_attr]; -"33 conv2d_3" [id=33, type=conv2d]; -"34 hardsigmoid" [id=34, type=hardsigmoid]; -"35 quantize_per_tensor_default_2" [id=35, type=quantize_per_tensor]; -"36 dequantize_per_tensor_default_3" [id=36, type=dequantize_per_tensor]; -"37 mul" [id=37, type=mul]; -"38 quantize_per_tensor_default_3" [id=38, type=quantize_per_tensor]; -"39 dequantize_per_tensor_default_4" [id=39, type=dequantize_per_tensor]; -"40 _param_constant10" [id=40, type=get_attr]; -"41 conv2d_4_scale_0" [id=41, type=get_attr]; -"42 conv2d_4_zero_point_0" [id=42, type=get_attr]; -"43 quantize_per_channel_default_3" [id=43, type=quantize_per_channel]; -"44 dequantize_per_channel_default_3" [id=44, type=dequantize_per_channel]; -"45 features_1_block_2_0_weight_bias_0_0" [id=45, type=get_attr]; -"46 conv2d_4" [id=46, type=conv2d]; -"47 quantize_per_tensor_default_4" [id=47, type=quantize_per_tensor]; -"48 dequantize_per_tensor_default_5" [id=48, type=dequantize_per_tensor]; -"49 _param_constant13" [id=49, type=get_attr]; -"50 conv2d_5_scale_0" [id=50, type=get_attr]; -"51 conv2d_5_zero_point_0" [id=51, type=get_attr]; -"52 quantize_per_channel_default_4" [id=52, type=quantize_per_channel]; -"53 dequantize_per_channel_default_4" [id=53, type=dequantize_per_channel]; -"54 features_2_block_0_0_weight_bias_0_0" [id=54, type=get_attr]; -"55 conv2d_5" [id=55, type=conv2d]; -"56 relu__1_scale_0" [id=56, type=get_attr]; -"57 relu__1_zero_point_0" [id=57, type=get_attr]; -"58 relu__1" [id=58, type=relu_]; -"59 quantize_per_channel_default_5" [id=59, type=quantize_per_channel]; -"60 dequantize_per_channel_default_5" [id=60, type=dequantize_per_channel]; -"61 _param_constant16" [id=61, type=get_attr]; -"62 conv2d_6_scale_0" [id=62, type=get_attr]; -"63 conv2d_6_zero_point_0" [id=63, type=get_attr]; -"64 quantize_per_channel_default_6" [id=64, type=quantize_per_channel]; -"65 dequantize_per_channel_default_6" [id=65, type=dequantize_per_channel]; -"66 features_2_block_1_0_weight_bias_0_0" [id=66, type=get_attr]; -"67 conv2d_6" [id=67, type=conv2d]; -"68 relu__2" [id=68, type=relu_]; -"69 quantize_per_tensor_default_5" [id=69, type=quantize_per_tensor]; -"70 dequantize_per_tensor_default_6" [id=70, type=dequantize_per_tensor]; -"71 _param_constant19" [id=71, type=get_attr]; -"72 conv2d_7_scale_0" [id=72, type=get_attr]; -"73 conv2d_7_zero_point_0" [id=73, type=get_attr]; -"74 quantize_per_channel_default_7" [id=74, type=quantize_per_channel]; -"75 dequantize_per_channel_default_7" [id=75, type=dequantize_per_channel]; -"76 features_2_block_2_0_weight_bias_0_0" [id=76, type=get_attr]; -"77 conv2d_7" [id=77, type=conv2d]; -"78 quantize_per_tensor_default_6" [id=78, type=quantize_per_tensor]; -"79 dequantize_per_tensor_default_8" [id=79, type=dequantize_per_tensor]; -"80 dequantize_per_tensor_default_7" [id=80, type=dequantize_per_tensor]; -"81 _param_constant22" [id=81, type=get_attr]; -"82 conv2d_8_scale_0" [id=82, type=get_attr]; -"83 conv2d_8_zero_point_0" [id=83, type=get_attr]; -"84 quantize_per_channel_default_8" [id=84, type=quantize_per_channel]; -"85 dequantize_per_channel_default_8" [id=85, type=dequantize_per_channel]; -"86 features_3_block_0_0_weight_bias_0_0" [id=86, type=get_attr]; -"87 conv2d_8" [id=87, type=conv2d]; -"88 relu__3_scale_0" [id=88, type=get_attr]; -"89 relu__3_zero_point_0" [id=89, type=get_attr]; -"90 relu__3" [id=90, type=relu_]; -"91 quantize_per_channel_default_9" [id=91, type=quantize_per_channel]; -"92 dequantize_per_channel_default_9" [id=92, type=dequantize_per_channel]; -"93 _param_constant25" [id=93, type=get_attr]; -"94 conv2d_9_scale_0" [id=94, type=get_attr]; -"95 conv2d_9_zero_point_0" [id=95, type=get_attr]; -"96 quantize_per_channel_default_10" [id=96, type=quantize_per_channel]; -"97 dequantize_per_channel_default_10" [id=97, type=dequantize_per_channel]; -"98 features_3_block_1_0_weight_bias_0_0" [id=98, type=get_attr]; -"99 conv2d_9" [id=99, type=conv2d]; -"100 relu__4" [id=100, type=relu_]; -"101 quantize_per_tensor_default_7" [id=101, type=quantize_per_tensor]; -"102 dequantize_per_tensor_default_9" [id=102, type=dequantize_per_tensor]; -"103 _param_constant28" [id=103, type=get_attr]; -"104 conv2d_10_scale_0" [id=104, type=get_attr]; -"105 conv2d_10_zero_point_0" [id=105, type=get_attr]; -"106 quantize_per_channel_default_11" [id=106, type=quantize_per_channel]; -"107 dequantize_per_channel_default_11" [id=107, type=dequantize_per_channel]; -"108 features_3_block_2_0_weight_bias_0_0" [id=108, type=get_attr]; -"109 conv2d_10" [id=109, type=conv2d]; -"110 quantize_per_tensor_default_8" [id=110, type=quantize_per_tensor]; -"111 dequantize_per_tensor_default_10" [id=111, type=dequantize_per_tensor]; -"112 add_" [id=112, type=add_]; -"113 quantize_per_tensor_default_9" [id=113, type=quantize_per_tensor]; -"114 dequantize_per_tensor_default_11" [id=114, type=dequantize_per_tensor]; -"115 _param_constant31" [id=115, type=get_attr]; -"116 conv2d_11_scale_0" [id=116, type=get_attr]; -"117 conv2d_11_zero_point_0" [id=117, type=get_attr]; -"118 quantize_per_channel_default_12" [id=118, type=quantize_per_channel]; -"119 dequantize_per_channel_default_12" [id=119, type=dequantize_per_channel]; -"120 features_4_block_0_0_weight_bias_0_0" [id=120, type=get_attr]; -"121 conv2d_11" [id=121, type=conv2d]; -"122 hardswish__1_scale_0" [id=122, type=get_attr]; -"123 hardswish__1_zero_point_0" [id=123, type=get_attr]; -"124 hardswish__1" [id=124, type=hardswish_]; -"125 quantize_per_channel_default_13" [id=125, type=quantize_per_channel]; -"126 dequantize_per_channel_default_13" [id=126, type=dequantize_per_channel]; -"127 _param_constant34" [id=127, type=get_attr]; -"128 conv2d_12_scale_0" [id=128, type=get_attr]; -"129 conv2d_12_zero_point_0" [id=129, type=get_attr]; -"130 quantize_per_channel_default_14" [id=130, type=quantize_per_channel]; -"131 dequantize_per_channel_default_14" [id=131, type=dequantize_per_channel]; -"132 features_4_block_1_0_weight_bias_0_0" [id=132, type=get_attr]; -"133 conv2d_12" [id=133, type=conv2d]; -"134 hardswish__2" [id=134, type=hardswish_]; -"135 quantize_per_tensor_default_10" [id=135, type=quantize_per_tensor]; -"136 dequantize_per_tensor_default_13" [id=136, type=dequantize_per_tensor]; -"137 dequantize_per_tensor_default_12" [id=137, type=dequantize_per_tensor]; -"138 adaptive_avg_pool2d_1" [id=138, type=adaptive_avg_pool2d]; -"139 _param_constant37" [id=139, type=get_attr]; -"140 _param_constant38_0_0" [id=140, type=get_attr]; -"141 conv2d_13" [id=141, type=conv2d]; -"142 relu_1" [id=142, type=relu]; -"143 _param_constant39" [id=143, type=get_attr]; -"144 _param_constant40_0_0" [id=144, type=get_attr]; -"145 conv2d_14" [id=145, type=conv2d]; -"146 hardsigmoid_1" [id=146, type=hardsigmoid]; -"147 quantize_per_tensor_default_11" [id=147, type=quantize_per_tensor]; -"148 dequantize_per_tensor_default_14" [id=148, type=dequantize_per_tensor]; -"149 mul_1" [id=149, type=mul]; -"150 quantize_per_tensor_default_12" [id=150, type=quantize_per_tensor]; -"151 dequantize_per_tensor_default_15" [id=151, type=dequantize_per_tensor]; -"152 _param_constant41" [id=152, type=get_attr]; -"153 conv2d_15_scale_0" [id=153, type=get_attr]; -"154 conv2d_15_zero_point_0" [id=154, type=get_attr]; -"155 quantize_per_channel_default_15" [id=155, type=quantize_per_channel]; -"156 dequantize_per_channel_default_15" [id=156, type=dequantize_per_channel]; -"157 features_4_block_3_0_weight_bias_0_0" [id=157, type=get_attr]; -"158 conv2d_15" [id=158, type=conv2d]; -"159 quantize_per_tensor_default_13" [id=159, type=quantize_per_tensor]; -"160 dequantize_per_tensor_default_17" [id=160, type=dequantize_per_tensor]; -"161 dequantize_per_tensor_default_16" [id=161, type=dequantize_per_tensor]; -"162 _param_constant44" [id=162, type=get_attr]; -"163 conv2d_16_scale_0" [id=163, type=get_attr]; -"164 conv2d_16_zero_point_0" [id=164, type=get_attr]; -"165 quantize_per_channel_default_16" [id=165, type=quantize_per_channel]; -"166 dequantize_per_channel_default_16" [id=166, type=dequantize_per_channel]; -"167 features_5_block_0_0_weight_bias_0_0" [id=167, type=get_attr]; -"168 conv2d_16" [id=168, type=conv2d]; -"169 hardswish__3_scale_0" [id=169, type=get_attr]; -"170 hardswish__3_zero_point_0" [id=170, type=get_attr]; -"171 hardswish__3" [id=171, type=hardswish_]; -"172 quantize_per_channel_default_17" [id=172, type=quantize_per_channel]; -"173 dequantize_per_channel_default_17" [id=173, type=dequantize_per_channel]; -"174 _param_constant47" [id=174, type=get_attr]; -"175 conv2d_17_scale_0" [id=175, type=get_attr]; -"176 conv2d_17_zero_point_0" [id=176, type=get_attr]; -"177 quantize_per_channel_default_18" [id=177, type=quantize_per_channel]; -"178 dequantize_per_channel_default_18" [id=178, type=dequantize_per_channel]; -"179 features_5_block_1_0_weight_bias_0_0" [id=179, type=get_attr]; -"180 conv2d_17" [id=180, type=conv2d]; -"181 hardswish__4" [id=181, type=hardswish_]; -"182 quantize_per_tensor_default_14" [id=182, type=quantize_per_tensor]; -"183 dequantize_per_tensor_default_19" [id=183, type=dequantize_per_tensor]; -"184 dequantize_per_tensor_default_18" [id=184, type=dequantize_per_tensor]; -"185 adaptive_avg_pool2d_2" [id=185, type=adaptive_avg_pool2d]; -"186 _param_constant50" [id=186, type=get_attr]; -"187 _param_constant51_0_0" [id=187, type=get_attr]; -"188 conv2d_18" [id=188, type=conv2d]; -"189 relu_2" [id=189, type=relu]; -"190 _param_constant52" [id=190, type=get_attr]; -"191 _param_constant53_0_0" [id=191, type=get_attr]; -"192 conv2d_19" [id=192, type=conv2d]; -"193 hardsigmoid_2" [id=193, type=hardsigmoid]; -"194 quantize_per_tensor_default_15" [id=194, type=quantize_per_tensor]; -"195 dequantize_per_tensor_default_20" [id=195, type=dequantize_per_tensor]; -"196 mul_2" [id=196, type=mul]; -"197 quantize_per_tensor_default_16" [id=197, type=quantize_per_tensor]; -"198 dequantize_per_tensor_default_21" [id=198, type=dequantize_per_tensor]; -"199 _param_constant54" [id=199, type=get_attr]; -"200 conv2d_20_scale_0" [id=200, type=get_attr]; -"201 conv2d_20_zero_point_0" [id=201, type=get_attr]; -"202 quantize_per_channel_default_19" [id=202, type=quantize_per_channel]; -"203 dequantize_per_channel_default_19" [id=203, type=dequantize_per_channel]; -"204 features_5_block_3_0_weight_bias_0_0" [id=204, type=get_attr]; -"205 conv2d_20" [id=205, type=conv2d]; -"206 quantize_per_tensor_default_17" [id=206, type=quantize_per_tensor]; -"207 dequantize_per_tensor_default_22" [id=207, type=dequantize_per_tensor]; -"208 add__1" [id=208, type=add_]; -"209 quantize_per_tensor_default_18" [id=209, type=quantize_per_tensor]; -"210 dequantize_per_tensor_default_24" [id=210, type=dequantize_per_tensor]; -"211 dequantize_per_tensor_default_23" [id=211, type=dequantize_per_tensor]; -"212 _param_constant57" [id=212, type=get_attr]; -"213 conv2d_21_scale_0" [id=213, type=get_attr]; -"214 conv2d_21_zero_point_0" [id=214, type=get_attr]; -"215 quantize_per_channel_default_20" [id=215, type=quantize_per_channel]; -"216 dequantize_per_channel_default_20" [id=216, type=dequantize_per_channel]; -"217 features_6_block_0_0_weight_bias_0_0" [id=217, type=get_attr]; -"218 conv2d_21" [id=218, type=conv2d]; -"219 hardswish__5_scale_0" [id=219, type=get_attr]; -"220 hardswish__5_zero_point_0" [id=220, type=get_attr]; -"221 hardswish__5" [id=221, type=hardswish_]; -"222 quantize_per_channel_default_21" [id=222, type=quantize_per_channel]; -"223 dequantize_per_channel_default_21" [id=223, type=dequantize_per_channel]; -"224 _param_constant60" [id=224, type=get_attr]; -"225 conv2d_22_scale_0" [id=225, type=get_attr]; -"226 conv2d_22_zero_point_0" [id=226, type=get_attr]; -"227 quantize_per_channel_default_22" [id=227, type=quantize_per_channel]; -"228 dequantize_per_channel_default_22" [id=228, type=dequantize_per_channel]; -"229 features_6_block_1_0_weight_bias_0_0" [id=229, type=get_attr]; -"230 conv2d_22" [id=230, type=conv2d]; -"231 hardswish__6" [id=231, type=hardswish_]; -"232 quantize_per_tensor_default_19" [id=232, type=quantize_per_tensor]; -"233 dequantize_per_tensor_default_26" [id=233, type=dequantize_per_tensor]; -"234 dequantize_per_tensor_default_25" [id=234, type=dequantize_per_tensor]; -"235 adaptive_avg_pool2d_3" [id=235, type=adaptive_avg_pool2d]; -"236 _param_constant63" [id=236, type=get_attr]; -"237 _param_constant64_0_0" [id=237, type=get_attr]; -"238 conv2d_23" [id=238, type=conv2d]; -"239 relu_3" [id=239, type=relu]; -"240 _param_constant65" [id=240, type=get_attr]; -"241 _param_constant66_0_0" [id=241, type=get_attr]; -"242 conv2d_24" [id=242, type=conv2d]; -"243 hardsigmoid_3" [id=243, type=hardsigmoid]; -"244 quantize_per_tensor_default_20" [id=244, type=quantize_per_tensor]; -"245 dequantize_per_tensor_default_27" [id=245, type=dequantize_per_tensor]; -"246 mul_3" [id=246, type=mul]; -"247 quantize_per_tensor_default_21" [id=247, type=quantize_per_tensor]; -"248 dequantize_per_tensor_default_28" [id=248, type=dequantize_per_tensor]; -"249 _param_constant67" [id=249, type=get_attr]; -"250 conv2d_25_scale_0" [id=250, type=get_attr]; -"251 conv2d_25_zero_point_0" [id=251, type=get_attr]; -"252 quantize_per_channel_default_23" [id=252, type=quantize_per_channel]; -"253 dequantize_per_channel_default_23" [id=253, type=dequantize_per_channel]; -"254 features_6_block_3_0_weight_bias_0_0" [id=254, type=get_attr]; -"255 conv2d_25" [id=255, type=conv2d]; -"256 quantize_per_tensor_default_22" [id=256, type=quantize_per_tensor]; -"257 dequantize_per_tensor_default_29" [id=257, type=dequantize_per_tensor]; -"258 add__2" [id=258, type=add_]; -"259 quantize_per_tensor_default_23" [id=259, type=quantize_per_tensor]; -"260 dequantize_per_tensor_default_30" [id=260, type=dequantize_per_tensor]; -"261 _param_constant70" [id=261, type=get_attr]; -"262 conv2d_26_scale_0" [id=262, type=get_attr]; -"263 conv2d_26_zero_point_0" [id=263, type=get_attr]; -"264 quantize_per_channel_default_24" [id=264, type=quantize_per_channel]; -"265 dequantize_per_channel_default_24" [id=265, type=dequantize_per_channel]; -"266 features_7_block_0_0_weight_bias_0_0" [id=266, type=get_attr]; -"267 conv2d_26" [id=267, type=conv2d]; -"268 hardswish__7_scale_0" [id=268, type=get_attr]; -"269 hardswish__7_zero_point_0" [id=269, type=get_attr]; -"270 hardswish__7" [id=270, type=hardswish_]; -"271 quantize_per_channel_default_25" [id=271, type=quantize_per_channel]; -"272 dequantize_per_channel_default_25" [id=272, type=dequantize_per_channel]; -"273 _param_constant73" [id=273, type=get_attr]; -"274 conv2d_27_scale_0" [id=274, type=get_attr]; -"275 conv2d_27_zero_point_0" [id=275, type=get_attr]; -"276 quantize_per_channel_default_26" [id=276, type=quantize_per_channel]; -"277 dequantize_per_channel_default_26" [id=277, type=dequantize_per_channel]; -"278 features_7_block_1_0_weight_bias_0_0" [id=278, type=get_attr]; -"279 conv2d_27" [id=279, type=conv2d]; -"280 hardswish__8" [id=280, type=hardswish_]; -"281 quantize_per_tensor_default_24" [id=281, type=quantize_per_tensor]; -"282 dequantize_per_tensor_default_32" [id=282, type=dequantize_per_tensor]; -"283 dequantize_per_tensor_default_31" [id=283, type=dequantize_per_tensor]; -"284 adaptive_avg_pool2d_4" [id=284, type=adaptive_avg_pool2d]; -"285 _param_constant76" [id=285, type=get_attr]; -"286 _param_constant77_0_0" [id=286, type=get_attr]; -"287 conv2d_28" [id=287, type=conv2d]; -"288 relu_4" [id=288, type=relu]; -"289 _param_constant78" [id=289, type=get_attr]; -"290 _param_constant79_0_0" [id=290, type=get_attr]; -"291 conv2d_29" [id=291, type=conv2d]; -"292 hardsigmoid_4" [id=292, type=hardsigmoid]; -"293 quantize_per_tensor_default_25" [id=293, type=quantize_per_tensor]; -"294 dequantize_per_tensor_default_33" [id=294, type=dequantize_per_tensor]; -"295 mul_4" [id=295, type=mul]; -"296 quantize_per_tensor_default_26" [id=296, type=quantize_per_tensor]; -"297 dequantize_per_tensor_default_34" [id=297, type=dequantize_per_tensor]; -"298 _param_constant80" [id=298, type=get_attr]; -"299 conv2d_30_scale_0" [id=299, type=get_attr]; -"300 conv2d_30_zero_point_0" [id=300, type=get_attr]; -"301 quantize_per_channel_default_27" [id=301, type=quantize_per_channel]; -"302 dequantize_per_channel_default_27" [id=302, type=dequantize_per_channel]; -"303 features_7_block_3_0_weight_bias_0_0" [id=303, type=get_attr]; -"304 conv2d_30" [id=304, type=conv2d]; -"305 quantize_per_tensor_default_27" [id=305, type=quantize_per_tensor]; -"306 dequantize_per_tensor_default_36" [id=306, type=dequantize_per_tensor]; -"307 dequantize_per_tensor_default_35" [id=307, type=dequantize_per_tensor]; -"308 _param_constant83" [id=308, type=get_attr]; -"309 conv2d_31_scale_0" [id=309, type=get_attr]; -"310 conv2d_31_zero_point_0" [id=310, type=get_attr]; -"311 quantize_per_channel_default_28" [id=311, type=quantize_per_channel]; -"312 dequantize_per_channel_default_28" [id=312, type=dequantize_per_channel]; -"313 features_8_block_0_0_weight_bias_0_0" [id=313, type=get_attr]; -"314 conv2d_31" [id=314, type=conv2d]; -"315 hardswish__9_scale_0" [id=315, type=get_attr]; -"316 hardswish__9_zero_point_0" [id=316, type=get_attr]; -"317 hardswish__9" [id=317, type=hardswish_]; -"318 quantize_per_channel_default_29" [id=318, type=quantize_per_channel]; -"319 dequantize_per_channel_default_29" [id=319, type=dequantize_per_channel]; -"320 _param_constant86" [id=320, type=get_attr]; -"321 conv2d_32_scale_0" [id=321, type=get_attr]; -"322 conv2d_32_zero_point_0" [id=322, type=get_attr]; -"323 quantize_per_channel_default_30" [id=323, type=quantize_per_channel]; -"324 dequantize_per_channel_default_30" [id=324, type=dequantize_per_channel]; -"325 features_8_block_1_0_weight_bias_0_0" [id=325, type=get_attr]; -"326 conv2d_32" [id=326, type=conv2d]; -"327 hardswish__10" [id=327, type=hardswish_]; -"328 quantize_per_tensor_default_28" [id=328, type=quantize_per_tensor]; -"329 dequantize_per_tensor_default_38" [id=329, type=dequantize_per_tensor]; -"330 dequantize_per_tensor_default_37" [id=330, type=dequantize_per_tensor]; -"331 adaptive_avg_pool2d_5" [id=331, type=adaptive_avg_pool2d]; -"332 _param_constant89" [id=332, type=get_attr]; -"333 _param_constant90_0_0" [id=333, type=get_attr]; -"334 conv2d_33" [id=334, type=conv2d]; -"335 relu_5" [id=335, type=relu]; -"336 _param_constant91" [id=336, type=get_attr]; -"337 _param_constant92_0_0" [id=337, type=get_attr]; -"338 conv2d_34" [id=338, type=conv2d]; -"339 hardsigmoid_5" [id=339, type=hardsigmoid]; -"340 quantize_per_tensor_default_29" [id=340, type=quantize_per_tensor]; -"341 dequantize_per_tensor_default_39" [id=341, type=dequantize_per_tensor]; -"342 mul_5" [id=342, type=mul]; -"343 quantize_per_tensor_default_30" [id=343, type=quantize_per_tensor]; -"344 dequantize_per_tensor_default_40" [id=344, type=dequantize_per_tensor]; -"345 _param_constant93" [id=345, 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type=quantize_per_tensor]; -"378 dequantize_per_tensor_default_44" [id=378, type=dequantize_per_tensor]; -"379 dequantize_per_tensor_default_43" [id=379, type=dequantize_per_tensor]; -"380 adaptive_avg_pool2d_6" [id=380, type=adaptive_avg_pool2d]; -"381 _param_constant102" [id=381, type=get_attr]; -"382 _param_constant103_0_0" [id=382, type=get_attr]; -"383 conv2d_38" [id=383, type=conv2d]; -"384 relu_6" [id=384, type=relu]; -"385 _param_constant104" [id=385, type=get_attr]; -"386 _param_constant105_0_0" [id=386, type=get_attr]; -"387 conv2d_39" [id=387, type=conv2d]; -"388 hardsigmoid_6" [id=388, type=hardsigmoid]; -"389 quantize_per_tensor_default_34" [id=389, type=quantize_per_tensor]; -"390 dequantize_per_tensor_default_45" [id=390, type=dequantize_per_tensor]; -"391 mul_6" [id=391, type=mul]; -"392 quantize_per_tensor_default_35" [id=392, type=quantize_per_tensor]; -"393 dequantize_per_tensor_default_46" [id=393, type=dequantize_per_tensor]; -"394 _param_constant106" [id=394, type=get_attr]; -"395 conv2d_40_scale_0" [id=395, type=get_attr]; -"396 conv2d_40_zero_point_0" [id=396, type=get_attr]; -"397 quantize_per_channel_default_35" [id=397, type=quantize_per_channel]; -"398 dequantize_per_channel_default_35" [id=398, type=dequantize_per_channel]; -"399 features_9_block_3_0_weight_bias_0_0" [id=399, type=get_attr]; -"400 conv2d_40" [id=400, type=conv2d]; -"401 quantize_per_tensor_default_36" [id=401, type=quantize_per_tensor]; -"402 dequantize_per_tensor_default_48" [id=402, type=dequantize_per_tensor]; -"403 dequantize_per_tensor_default_47" [id=403, type=dequantize_per_tensor]; -"404 _param_constant109" [id=404, type=get_attr]; -"405 conv2d_41_scale_0" [id=405, type=get_attr]; -"406 conv2d_41_zero_point_0" [id=406, type=get_attr]; -"407 quantize_per_channel_default_36" [id=407, type=quantize_per_channel]; -"408 dequantize_per_channel_default_36" [id=408, type=dequantize_per_channel]; -"409 features_10_block_0_0_weight_bias_0_0" [id=409, type=get_attr]; 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dequantize_per_tensor_default_49" [id=426, type=dequantize_per_tensor]; -"427 adaptive_avg_pool2d_7" [id=427, type=adaptive_avg_pool2d]; -"428 _param_constant115" [id=428, type=get_attr]; -"429 _param_constant116_0_0" [id=429, type=get_attr]; -"430 conv2d_43" [id=430, type=conv2d]; -"431 relu_7" [id=431, type=relu]; -"432 _param_constant117" [id=432, type=get_attr]; -"433 _param_constant118_0_0" [id=433, type=get_attr]; -"434 conv2d_44" [id=434, type=conv2d]; -"435 hardsigmoid_7" [id=435, type=hardsigmoid]; -"436 quantize_per_tensor_default_38" [id=436, type=quantize_per_tensor]; -"437 dequantize_per_tensor_default_51" [id=437, type=dequantize_per_tensor]; -"438 mul_7" [id=438, type=mul]; -"439 quantize_per_tensor_default_39" [id=439, type=quantize_per_tensor]; -"440 dequantize_per_tensor_default_52" [id=440, type=dequantize_per_tensor]; -"441 _param_constant119" [id=441, type=get_attr]; -"442 conv2d_45_scale_0" [id=442, type=get_attr]; -"443 conv2d_45_zero_point_0" [id=443, type=get_attr]; -"444 quantize_per_channel_default_39" [id=444, type=quantize_per_channel]; -"445 dequantize_per_channel_default_39" [id=445, type=dequantize_per_channel]; -"446 features_10_block_3_0_weight_bias_0_0" [id=446, type=get_attr]; -"447 conv2d_45" [id=447, type=conv2d]; -"448 quantize_per_tensor_default_40" [id=448, type=quantize_per_tensor]; -"449 dequantize_per_tensor_default_53" [id=449, type=dequantize_per_tensor]; -"450 add__4" [id=450, type=add_]; -"451 quantize_per_tensor_default_41" [id=451, type=quantize_per_tensor]; -"452 dequantize_per_tensor_default_55" [id=452, type=dequantize_per_tensor]; -"453 dequantize_per_tensor_default_54" [id=453, type=dequantize_per_tensor]; -"454 _param_constant122" [id=454, type=get_attr]; -"455 conv2d_46_scale_0" [id=455, type=get_attr]; -"456 conv2d_46_zero_point_0" [id=456, type=get_attr]; -"457 quantize_per_channel_default_40" [id=457, type=quantize_per_channel]; -"458 dequantize_per_channel_default_40" [id=458, type=dequantize_per_channel]; -"459 features_11_block_0_0_weight_bias_0_0" [id=459, type=get_attr]; -"460 conv2d_46" [id=460, type=conv2d]; -"461 hardswish__15_scale_0" [id=461, type=get_attr]; -"462 hardswish__15_zero_point_0" [id=462, type=get_attr]; -"463 hardswish__15" [id=463, type=hardswish_]; -"464 quantize_per_channel_default_41" [id=464, type=quantize_per_channel]; -"465 dequantize_per_channel_default_41" [id=465, type=dequantize_per_channel]; -"466 _param_constant125" [id=466, type=get_attr]; -"467 conv2d_47_scale_0" [id=467, type=get_attr]; -"468 conv2d_47_zero_point_0" [id=468, type=get_attr]; -"469 quantize_per_channel_default_42" [id=469, type=quantize_per_channel]; -"470 dequantize_per_channel_default_42" [id=470, type=dequantize_per_channel]; -"471 features_11_block_1_0_weight_bias_0_0" [id=471, type=get_attr]; -"472 conv2d_47" [id=472, type=conv2d]; -"473 hardswish__16" [id=473, type=hardswish_]; -"474 quantize_per_tensor_default_42" [id=474, type=quantize_per_tensor]; -"475 dequantize_per_tensor_default_57" [id=475, type=dequantize_per_tensor]; -"476 dequantize_per_tensor_default_56" [id=476, type=dequantize_per_tensor]; -"477 adaptive_avg_pool2d_8" [id=477, type=adaptive_avg_pool2d]; -"478 _param_constant128" [id=478, type=get_attr]; -"479 _param_constant129_0_0" [id=479, type=get_attr]; -"480 conv2d_48" [id=480, type=conv2d]; -"481 relu_8" [id=481, type=relu]; -"482 _param_constant130" [id=482, type=get_attr]; -"483 _param_constant131_0_0" [id=483, type=get_attr]; +"0 features_1_block_1_fc1_weight" [id=0, type=get_attr]; +"1 features_1_block_1_fc2_weight" [id=1, type=get_attr]; +"2 features_4_block_2_fc1_weight" [id=2, type=get_attr]; +"3 features_4_block_2_fc2_weight" [id=3, type=get_attr]; +"4 features_5_block_2_fc1_weight" [id=4, type=get_attr]; +"5 features_5_block_2_fc2_weight" [id=5, type=get_attr]; +"6 features_6_block_2_fc1_weight" [id=6, type=get_attr]; +"7 features_6_block_2_fc2_weight" [id=7, type=get_attr]; +"8 features_7_block_2_fc1_weight" [id=8, type=get_attr]; +"9 features_7_block_2_fc2_weight" [id=9, type=get_attr]; +"10 features_8_block_2_fc1_weight" [id=10, type=get_attr]; +"11 features_8_block_2_fc2_weight" [id=11, type=get_attr]; +"12 features_9_block_2_fc1_weight" [id=12, type=get_attr]; +"13 features_9_block_2_fc2_weight" [id=13, type=get_attr]; +"14 features_10_block_2_fc1_weight" [id=14, type=get_attr]; +"15 features_10_block_2_fc2_weight" [id=15, type=get_attr]; +"16 features_11_block_2_fc1_weight" [id=16, type=get_attr]; +"17 features_11_block_2_fc2_weight" [id=17, type=get_attr]; +"18 x" [id=18, type=input]; +"19 quantize_per_tensor_default" [id=19, type=quantize_per_tensor]; +"20 dequantize_per_tensor_default" [id=20, type=dequantize_per_tensor]; +"21 conv2d_scale_0" [id=21, type=get_attr]; +"22 conv2d_zero_point_0" [id=22, type=get_attr]; +"23 compressed_weight_updated_constant0" [id=23, type=get_attr]; +"24 quantize_per_channel_default" [id=24, type=quantize_per_channel]; +"25 dequantize_per_channel_default" [id=25, type=dequantize_per_channel]; +"26 features_0_0_weight_bias_0_0" [id=26, type=get_attr]; +"27 conv2d" [id=27, type=conv2d]; +"28 hardswish__scale_0" [id=28, type=get_attr]; +"29 hardswish__zero_point_0" [id=29, type=get_attr]; +"30 hardswish_" [id=30, type=hardswish_]; +"31 quantize_per_channel_default_1" [id=31, type=quantize_per_channel]; +"32 dequantize_per_channel_default_1" [id=32, type=dequantize_per_channel]; +"33 conv2d_1_scale_0" [id=33, type=get_attr]; +"34 conv2d_1_zero_point_0" [id=34, type=get_attr]; +"35 compressed_weight_updated_constant1" [id=35, type=get_attr]; +"36 quantize_per_channel_default_2" [id=36, type=quantize_per_channel]; +"37 dequantize_per_channel_default_2" [id=37, type=dequantize_per_channel]; +"38 features_1_block_0_0_weight_bias_0_0" [id=38, type=get_attr]; +"39 conv2d_1" [id=39, type=conv2d]; +"40 relu_" [id=40, type=relu_]; +"41 quantize_per_tensor_default_1" [id=41, type=quantize_per_tensor]; +"42 dequantize_per_tensor_default_2" [id=42, type=dequantize_per_tensor]; +"43 dequantize_per_tensor_default_1" [id=43, type=dequantize_per_tensor]; +"44 adaptive_avg_pool2d" [id=44, type=adaptive_avg_pool2d]; +"45 features_1_block_1_fc1_bias_0_0" [id=45, type=get_attr]; +"46 conv2d_2" [id=46, type=conv2d]; +"47 relu" [id=47, type=relu]; +"48 features_1_block_1_fc2_bias_0_0" [id=48, type=get_attr]; +"49 conv2d_3" [id=49, type=conv2d]; +"50 hardsigmoid" [id=50, type=hardsigmoid]; +"51 quantize_per_tensor_default_2" [id=51, type=quantize_per_tensor]; +"52 dequantize_per_tensor_default_3" [id=52, type=dequantize_per_tensor]; +"53 mul" [id=53, type=mul]; +"54 quantize_per_tensor_default_3" [id=54, type=quantize_per_tensor]; +"55 dequantize_per_tensor_default_4" [id=55, type=dequantize_per_tensor]; +"56 conv2d_4_scale_0" [id=56, type=get_attr]; +"57 conv2d_4_zero_point_0" [id=57, type=get_attr]; +"58 compressed_weight_updated_constant2" [id=58, type=get_attr]; +"59 quantize_per_channel_default_3" [id=59, type=quantize_per_channel]; +"60 dequantize_per_channel_default_3" [id=60, type=dequantize_per_channel]; +"61 features_1_block_2_0_weight_bias_0_0" [id=61, type=get_attr]; +"62 conv2d_4" [id=62, type=conv2d]; +"63 quantize_per_tensor_default_4" [id=63, type=quantize_per_tensor]; +"64 dequantize_per_tensor_default_5" [id=64, type=dequantize_per_tensor]; +"65 conv2d_5_scale_0" [id=65, type=get_attr]; +"66 conv2d_5_zero_point_0" [id=66, type=get_attr]; +"67 compressed_weight_updated_constant3" [id=67, type=get_attr]; +"68 quantize_per_channel_default_4" [id=68, type=quantize_per_channel]; +"69 dequantize_per_channel_default_4" [id=69, type=dequantize_per_channel]; +"70 features_2_block_0_0_weight_bias_0_0" [id=70, type=get_attr]; +"71 conv2d_5" [id=71, type=conv2d]; +"72 relu__1_scale_0" [id=72, type=get_attr]; +"73 relu__1_zero_point_0" [id=73, type=get_attr]; +"74 relu__1" [id=74, type=relu_]; +"75 quantize_per_channel_default_5" [id=75, type=quantize_per_channel]; +"76 dequantize_per_channel_default_5" [id=76, type=dequantize_per_channel]; +"77 conv2d_6_scale_0" [id=77, type=get_attr]; +"78 conv2d_6_zero_point_0" [id=78, type=get_attr]; +"79 compressed_weight_updated_constant4" [id=79, type=get_attr]; +"80 quantize_per_channel_default_6" [id=80, type=quantize_per_channel]; +"81 dequantize_per_channel_default_6" [id=81, type=dequantize_per_channel]; +"82 features_2_block_1_0_weight_bias_0_0" [id=82, type=get_attr]; +"83 conv2d_6" [id=83, type=conv2d]; +"84 relu__2" [id=84, type=relu_]; +"85 quantize_per_tensor_default_5" [id=85, type=quantize_per_tensor]; +"86 dequantize_per_tensor_default_6" [id=86, type=dequantize_per_tensor]; +"87 conv2d_7_scale_0" [id=87, type=get_attr]; +"88 conv2d_7_zero_point_0" [id=88, type=get_attr]; +"89 compressed_weight_updated_constant5" [id=89, type=get_attr]; +"90 quantize_per_channel_default_7" [id=90, type=quantize_per_channel]; +"91 dequantize_per_channel_default_7" [id=91, type=dequantize_per_channel]; +"92 features_2_block_2_0_weight_bias_0_0" [id=92, type=get_attr]; +"93 conv2d_7" [id=93, type=conv2d]; +"94 quantize_per_tensor_default_6" [id=94, type=quantize_per_tensor]; +"95 dequantize_per_tensor_default_8" [id=95, type=dequantize_per_tensor]; +"96 dequantize_per_tensor_default_7" [id=96, type=dequantize_per_tensor]; +"97 conv2d_8_scale_0" [id=97, type=get_attr]; +"98 conv2d_8_zero_point_0" [id=98, type=get_attr]; +"99 compressed_weight_updated_constant6" [id=99, type=get_attr]; +"100 quantize_per_channel_default_8" [id=100, type=quantize_per_channel]; +"101 dequantize_per_channel_default_8" [id=101, type=dequantize_per_channel]; +"102 features_3_block_0_0_weight_bias_0_0" [id=102, type=get_attr]; +"103 conv2d_8" [id=103, type=conv2d]; +"104 relu__3_scale_0" [id=104, type=get_attr]; +"105 relu__3_zero_point_0" [id=105, type=get_attr]; +"106 relu__3" [id=106, type=relu_]; +"107 quantize_per_channel_default_9" [id=107, type=quantize_per_channel]; +"108 dequantize_per_channel_default_9" [id=108, type=dequantize_per_channel]; +"109 conv2d_9_scale_0" [id=109, type=get_attr]; +"110 conv2d_9_zero_point_0" [id=110, type=get_attr]; +"111 compressed_weight_updated_constant7" [id=111, type=get_attr]; +"112 quantize_per_channel_default_10" [id=112, type=quantize_per_channel]; +"113 dequantize_per_channel_default_10" [id=113, type=dequantize_per_channel]; +"114 features_3_block_1_0_weight_bias_0_0" [id=114, type=get_attr]; +"115 conv2d_9" [id=115, type=conv2d]; +"116 relu__4" [id=116, type=relu_]; +"117 quantize_per_tensor_default_7" [id=117, type=quantize_per_tensor]; +"118 dequantize_per_tensor_default_9" [id=118, type=dequantize_per_tensor]; +"119 conv2d_10_scale_0" [id=119, type=get_attr]; +"120 conv2d_10_zero_point_0" [id=120, type=get_attr]; +"121 compressed_weight_updated_constant8" [id=121, type=get_attr]; +"122 quantize_per_channel_default_11" [id=122, type=quantize_per_channel]; +"123 dequantize_per_channel_default_11" [id=123, type=dequantize_per_channel]; +"124 features_3_block_2_0_weight_bias_0_0" [id=124, type=get_attr]; +"125 conv2d_10" [id=125, type=conv2d]; +"126 quantize_per_tensor_default_8" [id=126, type=quantize_per_tensor]; +"127 dequantize_per_tensor_default_10" [id=127, type=dequantize_per_tensor]; +"128 add_" [id=128, type=add_]; +"129 quantize_per_tensor_default_9" [id=129, type=quantize_per_tensor]; +"130 dequantize_per_tensor_default_11" [id=130, type=dequantize_per_tensor]; +"131 conv2d_11_scale_0" [id=131, type=get_attr]; +"132 conv2d_11_zero_point_0" [id=132, type=get_attr]; +"133 compressed_weight_updated_constant9" [id=133, type=get_attr]; +"134 quantize_per_channel_default_12" [id=134, type=quantize_per_channel]; +"135 dequantize_per_channel_default_12" [id=135, type=dequantize_per_channel]; +"136 features_4_block_0_0_weight_bias_0_0" [id=136, type=get_attr]; +"137 conv2d_11" [id=137, type=conv2d]; +"138 hardswish__1_scale_0" [id=138, type=get_attr]; +"139 hardswish__1_zero_point_0" 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type=adaptive_avg_pool2d]; +"155 features_4_block_2_fc1_bias_0_0" [id=155, type=get_attr]; +"156 conv2d_13" [id=156, type=conv2d]; +"157 relu_1" [id=157, type=relu]; +"158 features_4_block_2_fc2_bias_0_0" [id=158, type=get_attr]; +"159 conv2d_14" [id=159, type=conv2d]; +"160 hardsigmoid_1" [id=160, type=hardsigmoid]; +"161 quantize_per_tensor_default_11" [id=161, type=quantize_per_tensor]; +"162 dequantize_per_tensor_default_14" [id=162, type=dequantize_per_tensor]; +"163 mul_1" [id=163, type=mul]; +"164 quantize_per_tensor_default_12" [id=164, type=quantize_per_tensor]; +"165 dequantize_per_tensor_default_15" [id=165, type=dequantize_per_tensor]; +"166 conv2d_15_scale_0" [id=166, type=get_attr]; +"167 conv2d_15_zero_point_0" [id=167, type=get_attr]; +"168 compressed_weight_updated_constant11" [id=168, type=get_attr]; +"169 quantize_per_channel_default_15" [id=169, type=quantize_per_channel]; +"170 dequantize_per_channel_default_15" [id=170, type=dequantize_per_channel]; +"171 features_4_block_3_0_weight_bias_0_0" [id=171, type=get_attr]; +"172 conv2d_15" [id=172, type=conv2d]; +"173 quantize_per_tensor_default_13" [id=173, type=quantize_per_tensor]; +"174 dequantize_per_tensor_default_17" [id=174, type=dequantize_per_tensor]; +"175 dequantize_per_tensor_default_16" [id=175, type=dequantize_per_tensor]; +"176 conv2d_16_scale_0" [id=176, type=get_attr]; +"177 conv2d_16_zero_point_0" [id=177, type=get_attr]; +"178 compressed_weight_updated_constant12" [id=178, type=get_attr]; +"179 quantize_per_channel_default_16" [id=179, type=quantize_per_channel]; +"180 dequantize_per_channel_default_16" [id=180, type=dequantize_per_channel]; +"181 features_5_block_0_0_weight_bias_0_0" [id=181, type=get_attr]; +"182 conv2d_16" [id=182, type=conv2d]; +"183 hardswish__3_scale_0" [id=183, type=get_attr]; +"184 hardswish__3_zero_point_0" [id=184, type=get_attr]; +"185 hardswish__3" [id=185, type=hardswish_]; +"186 quantize_per_channel_default_17" [id=186, type=quantize_per_channel]; +"187 dequantize_per_channel_default_17" [id=187, type=dequantize_per_channel]; +"188 conv2d_17_scale_0" [id=188, type=get_attr]; +"189 conv2d_17_zero_point_0" [id=189, type=get_attr]; +"190 compressed_weight_updated_constant13" [id=190, type=get_attr]; +"191 quantize_per_channel_default_18" [id=191, type=quantize_per_channel]; +"192 dequantize_per_channel_default_18" [id=192, type=dequantize_per_channel]; +"193 features_5_block_1_0_weight_bias_0_0" [id=193, type=get_attr]; +"194 conv2d_17" [id=194, type=conv2d]; +"195 hardswish__4" [id=195, type=hardswish_]; +"196 quantize_per_tensor_default_14" [id=196, type=quantize_per_tensor]; +"197 dequantize_per_tensor_default_19" [id=197, type=dequantize_per_tensor]; +"198 dequantize_per_tensor_default_18" [id=198, type=dequantize_per_tensor]; +"199 adaptive_avg_pool2d_2" [id=199, type=adaptive_avg_pool2d]; +"200 features_5_block_2_fc1_bias_0_0" [id=200, type=get_attr]; +"201 conv2d_18" [id=201, type=conv2d]; +"202 relu_2" [id=202, type=relu]; +"203 features_5_block_2_fc2_bias_0_0" [id=203, type=get_attr]; +"204 conv2d_19" [id=204, type=conv2d]; +"205 hardsigmoid_2" [id=205, type=hardsigmoid]; +"206 quantize_per_tensor_default_15" [id=206, type=quantize_per_tensor]; +"207 dequantize_per_tensor_default_20" [id=207, type=dequantize_per_tensor]; +"208 mul_2" [id=208, type=mul]; +"209 quantize_per_tensor_default_16" [id=209, type=quantize_per_tensor]; +"210 dequantize_per_tensor_default_21" [id=210, type=dequantize_per_tensor]; +"211 conv2d_20_scale_0" [id=211, type=get_attr]; +"212 conv2d_20_zero_point_0" [id=212, type=get_attr]; +"213 compressed_weight_updated_constant14" [id=213, type=get_attr]; +"214 quantize_per_channel_default_19" [id=214, type=quantize_per_channel]; +"215 dequantize_per_channel_default_19" [id=215, type=dequantize_per_channel]; +"216 features_5_block_3_0_weight_bias_0_0" [id=216, type=get_attr]; +"217 conv2d_20" [id=217, type=conv2d]; +"218 quantize_per_tensor_default_17" [id=218, type=quantize_per_tensor]; +"219 dequantize_per_tensor_default_22" [id=219, type=dequantize_per_tensor]; +"220 add__1" [id=220, type=add_]; +"221 quantize_per_tensor_default_18" [id=221, type=quantize_per_tensor]; +"222 dequantize_per_tensor_default_24" [id=222, type=dequantize_per_tensor]; +"223 dequantize_per_tensor_default_23" [id=223, type=dequantize_per_tensor]; +"224 conv2d_21_scale_0" [id=224, type=get_attr]; +"225 conv2d_21_zero_point_0" [id=225, type=get_attr]; +"226 compressed_weight_updated_constant15" [id=226, type=get_attr]; +"227 quantize_per_channel_default_20" [id=227, type=quantize_per_channel]; +"228 dequantize_per_channel_default_20" [id=228, type=dequantize_per_channel]; +"229 features_6_block_0_0_weight_bias_0_0" [id=229, type=get_attr]; +"230 conv2d_21" [id=230, type=conv2d]; +"231 hardswish__5_scale_0" [id=231, type=get_attr]; +"232 hardswish__5_zero_point_0" [id=232, type=get_attr]; +"233 hardswish__5" [id=233, type=hardswish_]; +"234 quantize_per_channel_default_21" [id=234, type=quantize_per_channel]; +"235 dequantize_per_channel_default_21" [id=235, type=dequantize_per_channel]; +"236 conv2d_22_scale_0" [id=236, type=get_attr]; +"237 conv2d_22_zero_point_0" [id=237, type=get_attr]; +"238 compressed_weight_updated_constant16" [id=238, type=get_attr]; +"239 quantize_per_channel_default_22" [id=239, type=quantize_per_channel]; +"240 dequantize_per_channel_default_22" [id=240, type=dequantize_per_channel]; +"241 features_6_block_1_0_weight_bias_0_0" [id=241, type=get_attr]; +"242 conv2d_22" [id=242, type=conv2d]; +"243 hardswish__6" [id=243, type=hardswish_]; +"244 quantize_per_tensor_default_19" [id=244, type=quantize_per_tensor]; +"245 dequantize_per_tensor_default_26" [id=245, type=dequantize_per_tensor]; +"246 dequantize_per_tensor_default_25" [id=246, type=dequantize_per_tensor]; +"247 adaptive_avg_pool2d_3" [id=247, type=adaptive_avg_pool2d]; +"248 features_6_block_2_fc1_bias_0_0" [id=248, type=get_attr]; +"249 conv2d_23" [id=249, type=conv2d]; +"250 relu_3" [id=250, type=relu]; +"251 features_6_block_2_fc2_bias_0_0" [id=251, type=get_attr]; +"252 conv2d_24" [id=252, type=conv2d]; +"253 hardsigmoid_3" [id=253, type=hardsigmoid]; +"254 quantize_per_tensor_default_20" [id=254, type=quantize_per_tensor]; +"255 dequantize_per_tensor_default_27" [id=255, type=dequantize_per_tensor]; +"256 mul_3" [id=256, type=mul]; +"257 quantize_per_tensor_default_21" [id=257, type=quantize_per_tensor]; +"258 dequantize_per_tensor_default_28" [id=258, type=dequantize_per_tensor]; +"259 conv2d_25_scale_0" [id=259, type=get_attr]; +"260 conv2d_25_zero_point_0" [id=260, type=get_attr]; +"261 compressed_weight_updated_constant17" [id=261, type=get_attr]; +"262 quantize_per_channel_default_23" [id=262, type=quantize_per_channel]; +"263 dequantize_per_channel_default_23" [id=263, type=dequantize_per_channel]; +"264 features_6_block_3_0_weight_bias_0_0" [id=264, type=get_attr]; +"265 conv2d_25" [id=265, type=conv2d]; +"266 quantize_per_tensor_default_22" [id=266, type=quantize_per_tensor]; +"267 dequantize_per_tensor_default_29" [id=267, type=dequantize_per_tensor]; +"268 add__2" [id=268, type=add_]; +"269 quantize_per_tensor_default_23" [id=269, type=quantize_per_tensor]; +"270 dequantize_per_tensor_default_30" [id=270, type=dequantize_per_tensor]; +"271 conv2d_26_scale_0" [id=271, type=get_attr]; +"272 conv2d_26_zero_point_0" [id=272, type=get_attr]; +"273 compressed_weight_updated_constant18" [id=273, type=get_attr]; +"274 quantize_per_channel_default_24" [id=274, type=quantize_per_channel]; +"275 dequantize_per_channel_default_24" [id=275, type=dequantize_per_channel]; +"276 features_7_block_0_0_weight_bias_0_0" [id=276, type=get_attr]; +"277 conv2d_26" [id=277, type=conv2d]; +"278 hardswish__7_scale_0" [id=278, type=get_attr]; +"279 hardswish__7_zero_point_0" [id=279, type=get_attr]; +"280 hardswish__7" [id=280, type=hardswish_]; +"281 quantize_per_channel_default_25" [id=281, type=quantize_per_channel]; +"282 dequantize_per_channel_default_25" [id=282, type=dequantize_per_channel]; +"283 conv2d_27_scale_0" [id=283, type=get_attr]; +"284 conv2d_27_zero_point_0" [id=284, type=get_attr]; +"285 compressed_weight_updated_constant19" [id=285, type=get_attr]; +"286 quantize_per_channel_default_26" [id=286, type=quantize_per_channel]; +"287 dequantize_per_channel_default_26" [id=287, type=dequantize_per_channel]; +"288 features_7_block_1_0_weight_bias_0_0" [id=288, type=get_attr]; +"289 conv2d_27" [id=289, type=conv2d]; +"290 hardswish__8" [id=290, type=hardswish_]; +"291 quantize_per_tensor_default_24" [id=291, type=quantize_per_tensor]; +"292 dequantize_per_tensor_default_32" [id=292, type=dequantize_per_tensor]; +"293 dequantize_per_tensor_default_31" [id=293, type=dequantize_per_tensor]; +"294 adaptive_avg_pool2d_4" [id=294, type=adaptive_avg_pool2d]; +"295 features_7_block_2_fc1_bias_0_0" [id=295, type=get_attr]; +"296 conv2d_28" [id=296, type=conv2d]; +"297 relu_4" [id=297, type=relu]; +"298 features_7_block_2_fc2_bias_0_0" [id=298, type=get_attr]; +"299 conv2d_29" [id=299, type=conv2d]; +"300 hardsigmoid_4" [id=300, type=hardsigmoid]; +"301 quantize_per_tensor_default_25" [id=301, type=quantize_per_tensor]; +"302 dequantize_per_tensor_default_33" [id=302, type=dequantize_per_tensor]; +"303 mul_4" [id=303, type=mul]; +"304 quantize_per_tensor_default_26" [id=304, type=quantize_per_tensor]; +"305 dequantize_per_tensor_default_34" [id=305, type=dequantize_per_tensor]; +"306 conv2d_30_scale_0" [id=306, type=get_attr]; +"307 conv2d_30_zero_point_0" [id=307, type=get_attr]; +"308 compressed_weight_updated_constant20" [id=308, type=get_attr]; +"309 quantize_per_channel_default_27" [id=309, type=quantize_per_channel]; +"310 dequantize_per_channel_default_27" [id=310, type=dequantize_per_channel]; +"311 features_7_block_3_0_weight_bias_0_0" [id=311, type=get_attr]; +"312 conv2d_30" [id=312, type=conv2d]; +"313 quantize_per_tensor_default_27" [id=313, type=quantize_per_tensor]; +"314 dequantize_per_tensor_default_36" [id=314, type=dequantize_per_tensor]; +"315 dequantize_per_tensor_default_35" [id=315, type=dequantize_per_tensor]; +"316 conv2d_31_scale_0" [id=316, type=get_attr]; +"317 conv2d_31_zero_point_0" [id=317, type=get_attr]; +"318 compressed_weight_updated_constant21" [id=318, type=get_attr]; +"319 quantize_per_channel_default_28" [id=319, type=quantize_per_channel]; +"320 dequantize_per_channel_default_28" [id=320, type=dequantize_per_channel]; +"321 features_8_block_0_0_weight_bias_0_0" [id=321, type=get_attr]; +"322 conv2d_31" [id=322, type=conv2d]; +"323 hardswish__9_scale_0" [id=323, type=get_attr]; +"324 hardswish__9_zero_point_0" [id=324, type=get_attr]; +"325 hardswish__9" [id=325, type=hardswish_]; +"326 quantize_per_channel_default_29" [id=326, type=quantize_per_channel]; +"327 dequantize_per_channel_default_29" [id=327, type=dequantize_per_channel]; +"328 conv2d_32_scale_0" [id=328, type=get_attr]; +"329 conv2d_32_zero_point_0" [id=329, type=get_attr]; +"330 compressed_weight_updated_constant22" [id=330, type=get_attr]; +"331 quantize_per_channel_default_30" [id=331, type=quantize_per_channel]; +"332 dequantize_per_channel_default_30" [id=332, type=dequantize_per_channel]; +"333 features_8_block_1_0_weight_bias_0_0" [id=333, type=get_attr]; +"334 conv2d_32" [id=334, type=conv2d]; +"335 hardswish__10" [id=335, type=hardswish_]; +"336 quantize_per_tensor_default_28" [id=336, type=quantize_per_tensor]; +"337 dequantize_per_tensor_default_38" [id=337, type=dequantize_per_tensor]; +"338 dequantize_per_tensor_default_37" [id=338, type=dequantize_per_tensor]; +"339 adaptive_avg_pool2d_5" [id=339, type=adaptive_avg_pool2d]; +"340 features_8_block_2_fc1_bias_0_0" [id=340, type=get_attr]; +"341 conv2d_33" [id=341, type=conv2d]; +"342 relu_5" [id=342, type=relu]; +"343 features_8_block_2_fc2_bias_0_0" [id=343, type=get_attr]; +"344 conv2d_34" [id=344, type=conv2d]; +"345 hardsigmoid_5" [id=345, type=hardsigmoid]; +"346 quantize_per_tensor_default_29" [id=346, type=quantize_per_tensor]; +"347 dequantize_per_tensor_default_39" [id=347, type=dequantize_per_tensor]; +"348 mul_5" [id=348, type=mul]; +"349 quantize_per_tensor_default_30" [id=349, type=quantize_per_tensor]; +"350 dequantize_per_tensor_default_40" [id=350, type=dequantize_per_tensor]; +"351 conv2d_35_scale_0" [id=351, type=get_attr]; +"352 conv2d_35_zero_point_0" [id=352, type=get_attr]; +"353 compressed_weight_updated_constant23" [id=353, type=get_attr]; +"354 quantize_per_channel_default_31" [id=354, type=quantize_per_channel]; +"355 dequantize_per_channel_default_31" [id=355, type=dequantize_per_channel]; +"356 features_8_block_3_0_weight_bias_0_0" [id=356, type=get_attr]; +"357 conv2d_35" [id=357, type=conv2d]; +"358 quantize_per_tensor_default_31" [id=358, type=quantize_per_tensor]; +"359 dequantize_per_tensor_default_41" [id=359, type=dequantize_per_tensor]; +"360 add__3" [id=360, type=add_]; +"361 quantize_per_tensor_default_32" [id=361, type=quantize_per_tensor]; +"362 dequantize_per_tensor_default_42" [id=362, type=dequantize_per_tensor]; +"363 conv2d_36_scale_0" [id=363, type=get_attr]; +"364 conv2d_36_zero_point_0" [id=364, type=get_attr]; +"365 compressed_weight_updated_constant24" [id=365, type=get_attr]; +"366 quantize_per_channel_default_32" [id=366, type=quantize_per_channel]; +"367 dequantize_per_channel_default_32" [id=367, type=dequantize_per_channel]; +"368 features_9_block_0_0_weight_bias_0_0" [id=368, type=get_attr]; +"369 conv2d_36" [id=369, type=conv2d]; +"370 hardswish__11_scale_0" [id=370, type=get_attr]; +"371 hardswish__11_zero_point_0" [id=371, type=get_attr]; +"372 hardswish__11" [id=372, type=hardswish_]; +"373 quantize_per_channel_default_33" [id=373, type=quantize_per_channel]; +"374 dequantize_per_channel_default_33" [id=374, type=dequantize_per_channel]; +"375 conv2d_37_scale_0" [id=375, type=get_attr]; +"376 conv2d_37_zero_point_0" [id=376, type=get_attr]; +"377 compressed_weight_updated_constant25" [id=377, type=get_attr]; +"378 quantize_per_channel_default_34" [id=378, type=quantize_per_channel]; +"379 dequantize_per_channel_default_34" [id=379, type=dequantize_per_channel]; +"380 features_9_block_1_0_weight_bias_0_0" [id=380, type=get_attr]; +"381 conv2d_37" [id=381, type=conv2d]; +"382 hardswish__12" [id=382, type=hardswish_]; +"383 quantize_per_tensor_default_33" [id=383, type=quantize_per_tensor]; +"384 dequantize_per_tensor_default_44" [id=384, type=dequantize_per_tensor]; +"385 dequantize_per_tensor_default_43" [id=385, type=dequantize_per_tensor]; +"386 adaptive_avg_pool2d_6" [id=386, type=adaptive_avg_pool2d]; +"387 features_9_block_2_fc1_bias_0_0" [id=387, type=get_attr]; +"388 conv2d_38" [id=388, type=conv2d]; +"389 relu_6" [id=389, type=relu]; +"390 features_9_block_2_fc2_bias_0_0" [id=390, type=get_attr]; +"391 conv2d_39" [id=391, type=conv2d]; +"392 hardsigmoid_6" [id=392, type=hardsigmoid]; +"393 quantize_per_tensor_default_34" [id=393, type=quantize_per_tensor]; +"394 dequantize_per_tensor_default_45" [id=394, type=dequantize_per_tensor]; +"395 mul_6" [id=395, type=mul]; +"396 quantize_per_tensor_default_35" [id=396, type=quantize_per_tensor]; +"397 dequantize_per_tensor_default_46" [id=397, type=dequantize_per_tensor]; +"398 conv2d_40_scale_0" [id=398, type=get_attr]; +"399 conv2d_40_zero_point_0" [id=399, type=get_attr]; +"400 compressed_weight_updated_constant26" [id=400, type=get_attr]; +"401 quantize_per_channel_default_35" [id=401, type=quantize_per_channel]; +"402 dequantize_per_channel_default_35" [id=402, type=dequantize_per_channel]; +"403 features_9_block_3_0_weight_bias_0_0" [id=403, type=get_attr]; +"404 conv2d_40" [id=404, type=conv2d]; +"405 quantize_per_tensor_default_36" [id=405, type=quantize_per_tensor]; +"406 dequantize_per_tensor_default_48" [id=406, type=dequantize_per_tensor]; +"407 dequantize_per_tensor_default_47" [id=407, type=dequantize_per_tensor]; +"408 conv2d_41_scale_0" [id=408, type=get_attr]; +"409 conv2d_41_zero_point_0" [id=409, type=get_attr]; +"410 compressed_weight_updated_constant27" [id=410, type=get_attr]; +"411 quantize_per_channel_default_36" [id=411, type=quantize_per_channel]; +"412 dequantize_per_channel_default_36" [id=412, type=dequantize_per_channel]; +"413 features_10_block_0_0_weight_bias_0_0" [id=413, type=get_attr]; +"414 conv2d_41" [id=414, type=conv2d]; +"415 hardswish__13_scale_0" [id=415, type=get_attr]; +"416 hardswish__13_zero_point_0" [id=416, type=get_attr]; +"417 hardswish__13" [id=417, type=hardswish_]; +"418 quantize_per_channel_default_37" [id=418, type=quantize_per_channel]; +"419 dequantize_per_channel_default_37" [id=419, type=dequantize_per_channel]; +"420 conv2d_42_scale_0" [id=420, type=get_attr]; +"421 conv2d_42_zero_point_0" [id=421, type=get_attr]; +"422 compressed_weight_updated_constant28" [id=422, type=get_attr]; +"423 quantize_per_channel_default_38" [id=423, type=quantize_per_channel]; +"424 dequantize_per_channel_default_38" [id=424, type=dequantize_per_channel]; +"425 features_10_block_1_0_weight_bias_0_0" [id=425, type=get_attr]; +"426 conv2d_42" [id=426, type=conv2d]; +"427 hardswish__14" [id=427, type=hardswish_]; +"428 quantize_per_tensor_default_37" [id=428, type=quantize_per_tensor]; +"429 dequantize_per_tensor_default_50" [id=429, type=dequantize_per_tensor]; +"430 dequantize_per_tensor_default_49" [id=430, type=dequantize_per_tensor]; +"431 adaptive_avg_pool2d_7" [id=431, type=adaptive_avg_pool2d]; +"432 features_10_block_2_fc1_bias_0_0" [id=432, type=get_attr]; +"433 conv2d_43" [id=433, type=conv2d]; +"434 relu_7" [id=434, type=relu]; +"435 features_10_block_2_fc2_bias_0_0" [id=435, type=get_attr]; +"436 conv2d_44" [id=436, type=conv2d]; +"437 hardsigmoid_7" [id=437, type=hardsigmoid]; +"438 quantize_per_tensor_default_38" [id=438, type=quantize_per_tensor]; +"439 dequantize_per_tensor_default_51" [id=439, type=dequantize_per_tensor]; +"440 mul_7" [id=440, type=mul]; +"441 quantize_per_tensor_default_39" [id=441, type=quantize_per_tensor]; +"442 dequantize_per_tensor_default_52" [id=442, type=dequantize_per_tensor]; +"443 conv2d_45_scale_0" [id=443, type=get_attr]; +"444 conv2d_45_zero_point_0" [id=444, type=get_attr]; +"445 compressed_weight_updated_constant29" [id=445, type=get_attr]; +"446 quantize_per_channel_default_39" [id=446, type=quantize_per_channel]; +"447 dequantize_per_channel_default_39" [id=447, type=dequantize_per_channel]; +"448 features_10_block_3_0_weight_bias_0_0" [id=448, type=get_attr]; +"449 conv2d_45" [id=449, type=conv2d]; +"450 quantize_per_tensor_default_40" [id=450, type=quantize_per_tensor]; +"451 dequantize_per_tensor_default_53" [id=451, type=dequantize_per_tensor]; +"452 add__4" [id=452, type=add_]; +"453 quantize_per_tensor_default_41" [id=453, type=quantize_per_tensor]; +"454 dequantize_per_tensor_default_55" [id=454, type=dequantize_per_tensor]; +"455 dequantize_per_tensor_default_54" [id=455, type=dequantize_per_tensor]; +"456 conv2d_46_scale_0" [id=456, type=get_attr]; +"457 conv2d_46_zero_point_0" [id=457, type=get_attr]; +"458 compressed_weight_updated_constant30" [id=458, type=get_attr]; +"459 quantize_per_channel_default_40" [id=459, type=quantize_per_channel]; +"460 dequantize_per_channel_default_40" [id=460, type=dequantize_per_channel]; +"461 features_11_block_0_0_weight_bias_0_0" [id=461, type=get_attr]; +"462 conv2d_46" [id=462, type=conv2d]; +"463 hardswish__15_scale_0" [id=463, type=get_attr]; +"464 hardswish__15_zero_point_0" [id=464, type=get_attr]; +"465 hardswish__15" [id=465, type=hardswish_]; +"466 quantize_per_channel_default_41" [id=466, type=quantize_per_channel]; +"467 dequantize_per_channel_default_41" [id=467, type=dequantize_per_channel]; +"468 conv2d_47_scale_0" [id=468, type=get_attr]; +"469 conv2d_47_zero_point_0" [id=469, type=get_attr]; +"470 compressed_weight_updated_constant31" [id=470, type=get_attr]; +"471 quantize_per_channel_default_42" [id=471, type=quantize_per_channel]; +"472 dequantize_per_channel_default_42" [id=472, type=dequantize_per_channel]; +"473 features_11_block_1_0_weight_bias_0_0" [id=473, type=get_attr]; +"474 conv2d_47" [id=474, type=conv2d]; +"475 hardswish__16" [id=475, type=hardswish_]; +"476 quantize_per_tensor_default_42" [id=476, type=quantize_per_tensor]; +"477 dequantize_per_tensor_default_57" [id=477, type=dequantize_per_tensor]; +"478 dequantize_per_tensor_default_56" [id=478, type=dequantize_per_tensor]; +"479 adaptive_avg_pool2d_8" [id=479, type=adaptive_avg_pool2d]; +"480 features_11_block_2_fc1_bias_0_0" [id=480, type=get_attr]; +"481 conv2d_48" [id=481, type=conv2d]; +"482 relu_8" [id=482, type=relu]; +"483 features_11_block_2_fc2_bias_0_0" [id=483, type=get_attr]; "484 conv2d_49" [id=484, type=conv2d]; "485 hardsigmoid_8" [id=485, type=hardsigmoid]; "486 quantize_per_tensor_default_43" [id=486, type=quantize_per_tensor]; @@ -490,9 +490,9 @@ strict digraph { "488 mul_8" [id=488, type=mul]; "489 quantize_per_tensor_default_44" [id=489, type=quantize_per_tensor]; "490 dequantize_per_tensor_default_59" [id=490, type=dequantize_per_tensor]; -"491 _param_constant132" [id=491, type=get_attr]; -"492 conv2d_50_scale_0" [id=492, type=get_attr]; -"493 conv2d_50_zero_point_0" [id=493, type=get_attr]; +"491 conv2d_50_scale_0" [id=491, type=get_attr]; +"492 conv2d_50_zero_point_0" [id=492, type=get_attr]; +"493 compressed_weight_updated_constant32" [id=493, type=get_attr]; "494 quantize_per_channel_default_43" [id=494, type=quantize_per_channel]; "495 dequantize_per_channel_default_43" [id=495, type=dequantize_per_channel]; "496 features_11_block_3_0_weight_bias_0_0" [id=496, type=get_attr]; @@ -502,9 +502,9 @@ strict digraph { "500 add__5" [id=500, type=add_]; "501 quantize_per_tensor_default_46" [id=501, type=quantize_per_tensor]; "502 dequantize_per_tensor_default_61" [id=502, type=dequantize_per_tensor]; -"503 _param_constant135" [id=503, type=get_attr]; -"504 conv2d_51_scale_0" [id=504, type=get_attr]; -"505 conv2d_51_zero_point_0" [id=505, type=get_attr]; +"503 conv2d_51_scale_0" [id=503, type=get_attr]; +"504 conv2d_51_zero_point_0" [id=504, type=get_attr]; +"505 compressed_weight_updated_constant33" [id=505, type=get_attr]; "506 quantize_per_channel_default_44" [id=506, type=quantize_per_channel]; "507 dequantize_per_channel_default_44" [id=507, type=dequantize_per_channel]; "508 features_12_0_weight_bias_0_0" [id=508, type=get_attr]; @@ -516,667 +516,667 @@ strict digraph { "514 quantize_per_tensor_default_48" [id=514, type=quantize_per_tensor]; "515 dequantize_per_tensor_default_63" [id=515, type=dequantize_per_tensor]; "516 flatten" [id=516, type=flatten]; -"517 _param_constant138" [id=517, type=get_attr]; -"518 linear_scale_0" [id=518, type=get_attr]; -"519 linear_zero_point_0" [id=519, type=get_attr]; +"517 linear_scale_0" [id=517, type=get_attr]; +"518 linear_zero_point_0" [id=518, type=get_attr]; +"519 compressed_weight_updated_constant34" [id=519, type=get_attr]; "520 quantize_per_channel_default_45" [id=520, type=quantize_per_channel]; "521 dequantize_per_channel_default_45" [id=521, type=dequantize_per_channel]; -"522 _param_constant139_0_0" [id=522, type=get_attr]; +"522 classifier_0_bias_0_0" [id=522, type=get_attr]; "523 linear" [id=523, type=linear]; "524 hardswish__18" [id=524, type=hardswish_]; "525 quantize_per_tensor_default_49" [id=525, type=quantize_per_tensor]; "526 dequantize_per_tensor_default_64" [id=526, type=dequantize_per_tensor]; "527 dropout_" [id=527, type=dropout_]; -"528 _param_constant140" [id=528, type=get_attr]; -"529 linear_1_scale_0" [id=529, type=get_attr]; -"530 linear_1_zero_point_0" [id=530, type=get_attr]; +"528 linear_1_scale_0" [id=528, type=get_attr]; +"529 linear_1_zero_point_0" [id=529, type=get_attr]; +"530 compressed_weight_updated_constant35" [id=530, type=get_attr]; "531 quantize_per_channel_default_46" [id=531, type=quantize_per_channel]; "532 dequantize_per_channel_default_46" [id=532, type=dequantize_per_channel]; -"533 _param_constant141_0_0" [id=533, type=get_attr]; +"533 classifier_3_bias_0_0" [id=533, type=get_attr]; "534 linear_1" [id=534, type=linear]; "535 output" [id=535, type=output]; -"0 arg0_1" -> "1 quantize_per_tensor_default"; -"1 quantize_per_tensor_default" -> "2 dequantize_per_tensor_default"; -"2 dequantize_per_tensor_default" -> "9 conv2d"; -"3 _param_constant0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "7 dequantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "6 quantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "7 dequantize_per_channel_default"; -"6 quantize_per_channel_default" -> "7 dequantize_per_channel_default"; -"7 dequantize_per_channel_default" -> "9 conv2d"; -"8 features_0_0_weight_bias_0_0" -> "9 conv2d"; -"9 conv2d" -> "12 hardswish_"; -"10 hardswish__scale_0" -> "13 quantize_per_channel_default_1"; -"10 hardswish__scale_0" -> "14 dequantize_per_channel_default_1"; -"11 hardswish__zero_point_0" -> "13 quantize_per_channel_default_1"; -"11 hardswish__zero_point_0" -> "14 dequantize_per_channel_default_1"; -"12 hardswish_" -> "13 quantize_per_channel_default_1"; -"13 quantize_per_channel_default_1" -> "14 dequantize_per_channel_default_1"; -"14 dequantize_per_channel_default_1" -> "21 conv2d_1"; -"15 _param_constant3" -> "18 quantize_per_channel_default_2"; -"16 conv2d_1_scale_0" -> "18 quantize_per_channel_default_2"; -"16 conv2d_1_scale_0" -> "19 dequantize_per_channel_default_2"; -"17 conv2d_1_zero_point_0" -> "18 quantize_per_channel_default_2"; -"17 conv2d_1_zero_point_0" -> "19 dequantize_per_channel_default_2"; -"18 quantize_per_channel_default_2" -> "19 dequantize_per_channel_default_2"; -"19 dequantize_per_channel_default_2" -> "21 conv2d_1"; -"20 features_1_block_0_0_weight_bias_0_0" -> "21 conv2d_1"; -"21 conv2d_1" -> "22 relu_"; -"22 relu_" -> "23 quantize_per_tensor_default_1"; -"23 quantize_per_tensor_default_1" -> "24 dequantize_per_tensor_default_2"; -"23 quantize_per_tensor_default_1" -> "25 dequantize_per_tensor_default_1"; -"24 dequantize_per_tensor_default_2" -> "37 mul"; -"25 dequantize_per_tensor_default_1" -> "26 adaptive_avg_pool2d"; -"26 adaptive_avg_pool2d" -> "29 conv2d_2"; -"27 _param_constant6" -> "29 conv2d_2"; -"28 _param_constant7_0_0" -> "29 conv2d_2"; -"29 conv2d_2" -> "30 relu"; -"30 relu" -> "33 conv2d_3"; -"31 _param_constant8" -> "33 conv2d_3"; -"32 _param_constant9_0_0" -> "33 conv2d_3"; -"33 conv2d_3" -> "34 hardsigmoid"; -"34 hardsigmoid" -> "35 quantize_per_tensor_default_2"; -"35 quantize_per_tensor_default_2" -> "36 dequantize_per_tensor_default_3"; -"36 dequantize_per_tensor_default_3" -> "37 mul"; -"37 mul" -> "38 quantize_per_tensor_default_3"; -"38 quantize_per_tensor_default_3" -> "39 dequantize_per_tensor_default_4"; -"39 dequantize_per_tensor_default_4" -> "46 conv2d_4"; -"40 _param_constant10" -> "43 quantize_per_channel_default_3"; -"41 conv2d_4_scale_0" -> "43 quantize_per_channel_default_3"; -"41 conv2d_4_scale_0" -> "44 dequantize_per_channel_default_3"; -"42 conv2d_4_zero_point_0" -> "43 quantize_per_channel_default_3"; -"42 conv2d_4_zero_point_0" -> "44 dequantize_per_channel_default_3"; -"43 quantize_per_channel_default_3" -> "44 dequantize_per_channel_default_3"; -"44 dequantize_per_channel_default_3" -> "46 conv2d_4"; -"45 features_1_block_2_0_weight_bias_0_0" -> "46 conv2d_4"; -"46 conv2d_4" -> "47 quantize_per_tensor_default_4"; -"47 quantize_per_tensor_default_4" -> "48 dequantize_per_tensor_default_5"; -"48 dequantize_per_tensor_default_5" -> "55 conv2d_5"; -"49 _param_constant13" -> "52 quantize_per_channel_default_4"; -"50 conv2d_5_scale_0" -> "52 quantize_per_channel_default_4"; -"50 conv2d_5_scale_0" -> "53 dequantize_per_channel_default_4"; -"51 conv2d_5_zero_point_0" -> "52 quantize_per_channel_default_4"; -"51 conv2d_5_zero_point_0" -> "53 dequantize_per_channel_default_4"; -"52 quantize_per_channel_default_4" -> "53 dequantize_per_channel_default_4"; -"53 dequantize_per_channel_default_4" -> "55 conv2d_5"; -"54 features_2_block_0_0_weight_bias_0_0" -> "55 conv2d_5"; -"55 conv2d_5" -> "58 relu__1"; -"56 relu__1_scale_0" -> "59 quantize_per_channel_default_5"; -"56 relu__1_scale_0" -> "60 dequantize_per_channel_default_5"; -"57 relu__1_zero_point_0" -> "59 quantize_per_channel_default_5"; -"57 relu__1_zero_point_0" -> "60 dequantize_per_channel_default_5"; -"58 relu__1" -> "59 quantize_per_channel_default_5"; -"59 quantize_per_channel_default_5" -> "60 dequantize_per_channel_default_5"; -"60 dequantize_per_channel_default_5" -> "67 conv2d_6"; -"61 _param_constant16" -> "64 quantize_per_channel_default_6"; -"62 conv2d_6_scale_0" -> "64 quantize_per_channel_default_6"; -"62 conv2d_6_scale_0" -> "65 dequantize_per_channel_default_6"; -"63 conv2d_6_zero_point_0" -> "64 quantize_per_channel_default_6"; -"63 conv2d_6_zero_point_0" -> "65 dequantize_per_channel_default_6"; -"64 quantize_per_channel_default_6" -> "65 dequantize_per_channel_default_6"; -"65 dequantize_per_channel_default_6" -> "67 conv2d_6"; -"66 features_2_block_1_0_weight_bias_0_0" -> "67 conv2d_6"; -"67 conv2d_6" -> "68 relu__2"; -"68 relu__2" -> "69 quantize_per_tensor_default_5"; -"69 quantize_per_tensor_default_5" -> "70 dequantize_per_tensor_default_6"; -"70 dequantize_per_tensor_default_6" -> "77 conv2d_7"; -"71 _param_constant19" -> "74 quantize_per_channel_default_7"; -"72 conv2d_7_scale_0" -> "74 quantize_per_channel_default_7"; -"72 conv2d_7_scale_0" -> "75 dequantize_per_channel_default_7"; -"73 conv2d_7_zero_point_0" -> "74 quantize_per_channel_default_7"; -"73 conv2d_7_zero_point_0" -> "75 dequantize_per_channel_default_7"; -"74 quantize_per_channel_default_7" -> "75 dequantize_per_channel_default_7"; -"75 dequantize_per_channel_default_7" -> "77 conv2d_7"; -"76 features_2_block_2_0_weight_bias_0_0" -> "77 conv2d_7"; -"77 conv2d_7" -> "78 quantize_per_tensor_default_6"; -"78 quantize_per_tensor_default_6" -> "79 dequantize_per_tensor_default_8"; -"78 quantize_per_tensor_default_6" -> "80 dequantize_per_tensor_default_7"; -"79 dequantize_per_tensor_default_8" -> "112 add_"; -"80 dequantize_per_tensor_default_7" -> "87 conv2d_8"; -"81 _param_constant22" -> "84 quantize_per_channel_default_8"; -"82 conv2d_8_scale_0" -> "84 quantize_per_channel_default_8"; -"82 conv2d_8_scale_0" -> "85 dequantize_per_channel_default_8"; -"83 conv2d_8_zero_point_0" -> "84 quantize_per_channel_default_8"; -"83 conv2d_8_zero_point_0" -> "85 dequantize_per_channel_default_8"; -"84 quantize_per_channel_default_8" -> "85 dequantize_per_channel_default_8"; -"85 dequantize_per_channel_default_8" -> "87 conv2d_8"; -"86 features_3_block_0_0_weight_bias_0_0" -> "87 conv2d_8"; -"87 conv2d_8" -> "90 relu__3"; -"88 relu__3_scale_0" -> "91 quantize_per_channel_default_9"; -"88 relu__3_scale_0" -> "92 dequantize_per_channel_default_9"; -"89 relu__3_zero_point_0" -> "91 quantize_per_channel_default_9"; -"89 relu__3_zero_point_0" -> "92 dequantize_per_channel_default_9"; -"90 relu__3" -> "91 quantize_per_channel_default_9"; -"91 quantize_per_channel_default_9" -> "92 dequantize_per_channel_default_9"; -"92 dequantize_per_channel_default_9" -> "99 conv2d_9"; -"93 _param_constant25" -> "96 quantize_per_channel_default_10"; -"94 conv2d_9_scale_0" -> "96 quantize_per_channel_default_10"; -"94 conv2d_9_scale_0" -> "97 dequantize_per_channel_default_10"; -"95 conv2d_9_zero_point_0" -> "96 quantize_per_channel_default_10"; -"95 conv2d_9_zero_point_0" -> "97 dequantize_per_channel_default_10"; -"96 quantize_per_channel_default_10" -> "97 dequantize_per_channel_default_10"; -"97 dequantize_per_channel_default_10" -> "99 conv2d_9"; -"98 features_3_block_1_0_weight_bias_0_0" -> "99 conv2d_9"; -"99 conv2d_9" -> "100 relu__4"; -"100 relu__4" -> "101 quantize_per_tensor_default_7"; -"101 quantize_per_tensor_default_7" -> "102 dequantize_per_tensor_default_9"; -"102 dequantize_per_tensor_default_9" -> "109 conv2d_10"; -"103 _param_constant28" -> "106 quantize_per_channel_default_11"; -"104 conv2d_10_scale_0" -> "106 quantize_per_channel_default_11"; -"104 conv2d_10_scale_0" -> "107 dequantize_per_channel_default_11"; -"105 conv2d_10_zero_point_0" -> "106 quantize_per_channel_default_11"; -"105 conv2d_10_zero_point_0" -> "107 dequantize_per_channel_default_11"; -"106 quantize_per_channel_default_11" -> "107 dequantize_per_channel_default_11"; -"107 dequantize_per_channel_default_11" -> "109 conv2d_10"; -"108 features_3_block_2_0_weight_bias_0_0" -> "109 conv2d_10"; -"109 conv2d_10" -> "110 quantize_per_tensor_default_8"; -"110 quantize_per_tensor_default_8" -> "111 dequantize_per_tensor_default_10"; -"111 dequantize_per_tensor_default_10" -> "112 add_"; -"112 add_" -> "113 quantize_per_tensor_default_9"; -"113 quantize_per_tensor_default_9" -> "114 dequantize_per_tensor_default_11"; -"114 dequantize_per_tensor_default_11" -> "121 conv2d_11"; -"115 _param_constant31" -> "118 quantize_per_channel_default_12"; -"116 conv2d_11_scale_0" -> "118 quantize_per_channel_default_12"; -"116 conv2d_11_scale_0" -> "119 dequantize_per_channel_default_12"; -"117 conv2d_11_zero_point_0" -> "118 quantize_per_channel_default_12"; -"117 conv2d_11_zero_point_0" -> "119 dequantize_per_channel_default_12"; -"118 quantize_per_channel_default_12" -> "119 dequantize_per_channel_default_12"; -"119 dequantize_per_channel_default_12" -> "121 conv2d_11"; -"120 features_4_block_0_0_weight_bias_0_0" -> "121 conv2d_11"; -"121 conv2d_11" -> "124 hardswish__1"; -"122 hardswish__1_scale_0" -> "125 quantize_per_channel_default_13"; -"122 hardswish__1_scale_0" -> "126 dequantize_per_channel_default_13"; -"123 hardswish__1_zero_point_0" -> "125 quantize_per_channel_default_13"; -"123 hardswish__1_zero_point_0" -> "126 dequantize_per_channel_default_13"; -"124 hardswish__1" -> "125 quantize_per_channel_default_13"; -"125 quantize_per_channel_default_13" -> "126 dequantize_per_channel_default_13"; -"126 dequantize_per_channel_default_13" -> "133 conv2d_12"; -"127 _param_constant34" -> "130 quantize_per_channel_default_14"; -"128 conv2d_12_scale_0" -> "130 quantize_per_channel_default_14"; -"128 conv2d_12_scale_0" -> "131 dequantize_per_channel_default_14"; -"129 conv2d_12_zero_point_0" -> "130 quantize_per_channel_default_14"; -"129 conv2d_12_zero_point_0" -> "131 dequantize_per_channel_default_14"; -"130 quantize_per_channel_default_14" -> "131 dequantize_per_channel_default_14"; -"131 dequantize_per_channel_default_14" -> "133 conv2d_12"; -"132 features_4_block_1_0_weight_bias_0_0" -> "133 conv2d_12"; -"133 conv2d_12" -> "134 hardswish__2"; -"134 hardswish__2" -> "135 quantize_per_tensor_default_10"; -"135 quantize_per_tensor_default_10" -> "136 dequantize_per_tensor_default_13"; -"135 quantize_per_tensor_default_10" -> "137 dequantize_per_tensor_default_12"; -"136 dequantize_per_tensor_default_13" -> "149 mul_1"; -"137 dequantize_per_tensor_default_12" -> "138 adaptive_avg_pool2d_1"; -"138 adaptive_avg_pool2d_1" -> "141 conv2d_13"; -"139 _param_constant37" -> "141 conv2d_13"; -"140 _param_constant38_0_0" -> "141 conv2d_13"; -"141 conv2d_13" -> "142 relu_1"; -"142 relu_1" -> "145 conv2d_14"; -"143 _param_constant39" -> "145 conv2d_14"; -"144 _param_constant40_0_0" -> "145 conv2d_14"; -"145 conv2d_14" -> "146 hardsigmoid_1"; -"146 hardsigmoid_1" -> "147 quantize_per_tensor_default_11"; -"147 quantize_per_tensor_default_11" -> "148 dequantize_per_tensor_default_14"; -"148 dequantize_per_tensor_default_14" -> "149 mul_1"; -"149 mul_1" -> "150 quantize_per_tensor_default_12"; -"150 quantize_per_tensor_default_12" -> "151 dequantize_per_tensor_default_15"; -"151 dequantize_per_tensor_default_15" -> "158 conv2d_15"; -"152 _param_constant41" -> "155 quantize_per_channel_default_15"; -"153 conv2d_15_scale_0" -> "155 quantize_per_channel_default_15"; -"153 conv2d_15_scale_0" -> "156 dequantize_per_channel_default_15"; -"154 conv2d_15_zero_point_0" -> "155 quantize_per_channel_default_15"; -"154 conv2d_15_zero_point_0" -> "156 dequantize_per_channel_default_15"; -"155 quantize_per_channel_default_15" -> "156 dequantize_per_channel_default_15"; -"156 dequantize_per_channel_default_15" -> "158 conv2d_15"; -"157 features_4_block_3_0_weight_bias_0_0" -> "158 conv2d_15"; -"158 conv2d_15" -> "159 quantize_per_tensor_default_13"; -"159 quantize_per_tensor_default_13" -> "160 dequantize_per_tensor_default_17"; -"159 quantize_per_tensor_default_13" -> "161 dequantize_per_tensor_default_16"; -"160 dequantize_per_tensor_default_17" -> "208 add__1"; -"161 dequantize_per_tensor_default_16" -> "168 conv2d_16"; -"162 _param_constant44" -> "165 quantize_per_channel_default_16"; -"163 conv2d_16_scale_0" -> "165 quantize_per_channel_default_16"; -"163 conv2d_16_scale_0" -> "166 dequantize_per_channel_default_16"; -"164 conv2d_16_zero_point_0" -> "165 quantize_per_channel_default_16"; -"164 conv2d_16_zero_point_0" -> "166 dequantize_per_channel_default_16"; -"165 quantize_per_channel_default_16" -> "166 dequantize_per_channel_default_16"; -"166 dequantize_per_channel_default_16" -> "168 conv2d_16"; -"167 features_5_block_0_0_weight_bias_0_0" -> "168 conv2d_16"; -"168 conv2d_16" -> "171 hardswish__3"; -"169 hardswish__3_scale_0" -> "172 quantize_per_channel_default_17"; -"169 hardswish__3_scale_0" -> "173 dequantize_per_channel_default_17"; -"170 hardswish__3_zero_point_0" -> "172 quantize_per_channel_default_17"; -"170 hardswish__3_zero_point_0" -> "173 dequantize_per_channel_default_17"; -"171 hardswish__3" -> "172 quantize_per_channel_default_17"; -"172 quantize_per_channel_default_17" -> "173 dequantize_per_channel_default_17"; -"173 dequantize_per_channel_default_17" -> "180 conv2d_17"; -"174 _param_constant47" -> "177 quantize_per_channel_default_18"; -"175 conv2d_17_scale_0" -> "177 quantize_per_channel_default_18"; -"175 conv2d_17_scale_0" -> "178 dequantize_per_channel_default_18"; -"176 conv2d_17_zero_point_0" -> "177 quantize_per_channel_default_18"; -"176 conv2d_17_zero_point_0" -> "178 dequantize_per_channel_default_18"; -"177 quantize_per_channel_default_18" -> "178 dequantize_per_channel_default_18"; -"178 dequantize_per_channel_default_18" -> "180 conv2d_17"; -"179 features_5_block_1_0_weight_bias_0_0" -> "180 conv2d_17"; -"180 conv2d_17" -> "181 hardswish__4"; -"181 hardswish__4" -> "182 quantize_per_tensor_default_14"; -"182 quantize_per_tensor_default_14" -> "183 dequantize_per_tensor_default_19"; -"182 quantize_per_tensor_default_14" -> "184 dequantize_per_tensor_default_18"; -"183 dequantize_per_tensor_default_19" -> "196 mul_2"; -"184 dequantize_per_tensor_default_18" -> "185 adaptive_avg_pool2d_2"; -"185 adaptive_avg_pool2d_2" -> "188 conv2d_18"; -"186 _param_constant50" -> "188 conv2d_18"; -"187 _param_constant51_0_0" -> "188 conv2d_18"; -"188 conv2d_18" -> "189 relu_2"; -"189 relu_2" -> "192 conv2d_19"; -"190 _param_constant52" -> "192 conv2d_19"; -"191 _param_constant53_0_0" -> "192 conv2d_19"; -"192 conv2d_19" -> "193 hardsigmoid_2"; -"193 hardsigmoid_2" -> "194 quantize_per_tensor_default_15"; -"194 quantize_per_tensor_default_15" -> "195 dequantize_per_tensor_default_20"; -"195 dequantize_per_tensor_default_20" -> "196 mul_2"; -"196 mul_2" -> "197 quantize_per_tensor_default_16"; -"197 quantize_per_tensor_default_16" -> "198 dequantize_per_tensor_default_21"; -"198 dequantize_per_tensor_default_21" -> "205 conv2d_20"; -"199 _param_constant54" -> "202 quantize_per_channel_default_19"; -"200 conv2d_20_scale_0" -> "202 quantize_per_channel_default_19"; -"200 conv2d_20_scale_0" -> "203 dequantize_per_channel_default_19"; -"201 conv2d_20_zero_point_0" -> "202 quantize_per_channel_default_19"; -"201 conv2d_20_zero_point_0" -> "203 dequantize_per_channel_default_19"; -"202 quantize_per_channel_default_19" -> "203 dequantize_per_channel_default_19"; -"203 dequantize_per_channel_default_19" -> "205 conv2d_20"; -"204 features_5_block_3_0_weight_bias_0_0" -> "205 conv2d_20"; -"205 conv2d_20" -> "206 quantize_per_tensor_default_17"; -"206 quantize_per_tensor_default_17" -> "207 dequantize_per_tensor_default_22"; -"207 dequantize_per_tensor_default_22" -> "208 add__1"; -"208 add__1" -> "209 quantize_per_tensor_default_18"; -"209 quantize_per_tensor_default_18" -> "210 dequantize_per_tensor_default_24"; -"209 quantize_per_tensor_default_18" -> "211 dequantize_per_tensor_default_23"; -"210 dequantize_per_tensor_default_24" -> "258 add__2"; -"211 dequantize_per_tensor_default_23" -> "218 conv2d_21"; -"212 _param_constant57" -> "215 quantize_per_channel_default_20"; -"213 conv2d_21_scale_0" -> "215 quantize_per_channel_default_20"; -"213 conv2d_21_scale_0" -> "216 dequantize_per_channel_default_20"; -"214 conv2d_21_zero_point_0" -> "215 quantize_per_channel_default_20"; -"214 conv2d_21_zero_point_0" -> "216 dequantize_per_channel_default_20"; -"215 quantize_per_channel_default_20" -> "216 dequantize_per_channel_default_20"; -"216 dequantize_per_channel_default_20" -> "218 conv2d_21"; -"217 features_6_block_0_0_weight_bias_0_0" -> "218 conv2d_21"; -"218 conv2d_21" -> "221 hardswish__5"; -"219 hardswish__5_scale_0" -> "222 quantize_per_channel_default_21"; -"219 hardswish__5_scale_0" -> "223 dequantize_per_channel_default_21"; -"220 hardswish__5_zero_point_0" -> "222 quantize_per_channel_default_21"; -"220 hardswish__5_zero_point_0" -> "223 dequantize_per_channel_default_21"; -"221 hardswish__5" -> "222 quantize_per_channel_default_21"; -"222 quantize_per_channel_default_21" -> "223 dequantize_per_channel_default_21"; -"223 dequantize_per_channel_default_21" -> "230 conv2d_22"; -"224 _param_constant60" -> "227 quantize_per_channel_default_22"; -"225 conv2d_22_scale_0" -> "227 quantize_per_channel_default_22"; -"225 conv2d_22_scale_0" -> "228 dequantize_per_channel_default_22"; -"226 conv2d_22_zero_point_0" -> "227 quantize_per_channel_default_22"; -"226 conv2d_22_zero_point_0" -> "228 dequantize_per_channel_default_22"; -"227 quantize_per_channel_default_22" -> "228 dequantize_per_channel_default_22"; -"228 dequantize_per_channel_default_22" -> "230 conv2d_22"; -"229 features_6_block_1_0_weight_bias_0_0" -> "230 conv2d_22"; -"230 conv2d_22" -> "231 hardswish__6"; -"231 hardswish__6" -> "232 quantize_per_tensor_default_19"; -"232 quantize_per_tensor_default_19" -> "233 dequantize_per_tensor_default_26"; -"232 quantize_per_tensor_default_19" -> "234 dequantize_per_tensor_default_25"; -"233 dequantize_per_tensor_default_26" -> "246 mul_3"; -"234 dequantize_per_tensor_default_25" -> "235 adaptive_avg_pool2d_3"; -"235 adaptive_avg_pool2d_3" -> "238 conv2d_23"; -"236 _param_constant63" -> "238 conv2d_23"; -"237 _param_constant64_0_0" -> "238 conv2d_23"; -"238 conv2d_23" -> "239 relu_3"; -"239 relu_3" -> "242 conv2d_24"; -"240 _param_constant65" -> "242 conv2d_24"; -"241 _param_constant66_0_0" -> "242 conv2d_24"; -"242 conv2d_24" -> "243 hardsigmoid_3"; -"243 hardsigmoid_3" -> "244 quantize_per_tensor_default_20"; -"244 quantize_per_tensor_default_20" -> "245 dequantize_per_tensor_default_27"; -"245 dequantize_per_tensor_default_27" -> "246 mul_3"; -"246 mul_3" -> "247 quantize_per_tensor_default_21"; -"247 quantize_per_tensor_default_21" -> "248 dequantize_per_tensor_default_28"; -"248 dequantize_per_tensor_default_28" -> "255 conv2d_25"; -"249 _param_constant67" -> "252 quantize_per_channel_default_23"; -"250 conv2d_25_scale_0" -> "252 quantize_per_channel_default_23"; -"250 conv2d_25_scale_0" -> "253 dequantize_per_channel_default_23"; -"251 conv2d_25_zero_point_0" -> "252 quantize_per_channel_default_23"; -"251 conv2d_25_zero_point_0" -> "253 dequantize_per_channel_default_23"; -"252 quantize_per_channel_default_23" -> "253 dequantize_per_channel_default_23"; -"253 dequantize_per_channel_default_23" -> "255 conv2d_25"; -"254 features_6_block_3_0_weight_bias_0_0" -> "255 conv2d_25"; -"255 conv2d_25" -> "256 quantize_per_tensor_default_22"; -"256 quantize_per_tensor_default_22" -> "257 dequantize_per_tensor_default_29"; -"257 dequantize_per_tensor_default_29" -> "258 add__2"; -"258 add__2" -> "259 quantize_per_tensor_default_23"; -"259 quantize_per_tensor_default_23" -> "260 dequantize_per_tensor_default_30"; -"260 dequantize_per_tensor_default_30" -> "267 conv2d_26"; -"261 _param_constant70" -> "264 quantize_per_channel_default_24"; -"262 conv2d_26_scale_0" -> "264 quantize_per_channel_default_24"; -"262 conv2d_26_scale_0" -> "265 dequantize_per_channel_default_24"; -"263 conv2d_26_zero_point_0" -> "264 quantize_per_channel_default_24"; -"263 conv2d_26_zero_point_0" -> "265 dequantize_per_channel_default_24"; -"264 quantize_per_channel_default_24" -> "265 dequantize_per_channel_default_24"; -"265 dequantize_per_channel_default_24" -> "267 conv2d_26"; -"266 features_7_block_0_0_weight_bias_0_0" -> "267 conv2d_26"; -"267 conv2d_26" -> "270 hardswish__7"; -"268 hardswish__7_scale_0" -> "271 quantize_per_channel_default_25"; -"268 hardswish__7_scale_0" -> "272 dequantize_per_channel_default_25"; -"269 hardswish__7_zero_point_0" -> "271 quantize_per_channel_default_25"; -"269 hardswish__7_zero_point_0" -> "272 dequantize_per_channel_default_25"; -"270 hardswish__7" -> "271 quantize_per_channel_default_25"; -"271 quantize_per_channel_default_25" -> "272 dequantize_per_channel_default_25"; -"272 dequantize_per_channel_default_25" -> "279 conv2d_27"; -"273 _param_constant73" -> "276 quantize_per_channel_default_26"; -"274 conv2d_27_scale_0" -> "276 quantize_per_channel_default_26"; -"274 conv2d_27_scale_0" -> "277 dequantize_per_channel_default_26"; -"275 conv2d_27_zero_point_0" -> "276 quantize_per_channel_default_26"; -"275 conv2d_27_zero_point_0" -> "277 dequantize_per_channel_default_26"; -"276 quantize_per_channel_default_26" -> "277 dequantize_per_channel_default_26"; -"277 dequantize_per_channel_default_26" -> "279 conv2d_27"; -"278 features_7_block_1_0_weight_bias_0_0" -> "279 conv2d_27"; -"279 conv2d_27" -> "280 hardswish__8"; -"280 hardswish__8" -> "281 quantize_per_tensor_default_24"; -"281 quantize_per_tensor_default_24" -> "282 dequantize_per_tensor_default_32"; -"281 quantize_per_tensor_default_24" -> "283 dequantize_per_tensor_default_31"; -"282 dequantize_per_tensor_default_32" -> "295 mul_4"; -"283 dequantize_per_tensor_default_31" -> "284 adaptive_avg_pool2d_4"; -"284 adaptive_avg_pool2d_4" -> "287 conv2d_28"; -"285 _param_constant76" -> "287 conv2d_28"; -"286 _param_constant77_0_0" -> "287 conv2d_28"; -"287 conv2d_28" -> "288 relu_4"; -"288 relu_4" -> "291 conv2d_29"; -"289 _param_constant78" -> "291 conv2d_29"; -"290 _param_constant79_0_0" -> "291 conv2d_29"; -"291 conv2d_29" -> "292 hardsigmoid_4"; -"292 hardsigmoid_4" -> "293 quantize_per_tensor_default_25"; -"293 quantize_per_tensor_default_25" -> "294 dequantize_per_tensor_default_33"; -"294 dequantize_per_tensor_default_33" -> "295 mul_4"; -"295 mul_4" -> "296 quantize_per_tensor_default_26"; -"296 quantize_per_tensor_default_26" -> "297 dequantize_per_tensor_default_34"; -"297 dequantize_per_tensor_default_34" -> "304 conv2d_30"; -"298 _param_constant80" -> "301 quantize_per_channel_default_27"; -"299 conv2d_30_scale_0" -> "301 quantize_per_channel_default_27"; -"299 conv2d_30_scale_0" -> "302 dequantize_per_channel_default_27"; -"300 conv2d_30_zero_point_0" -> "301 quantize_per_channel_default_27"; -"300 conv2d_30_zero_point_0" -> "302 dequantize_per_channel_default_27"; -"301 quantize_per_channel_default_27" -> "302 dequantize_per_channel_default_27"; -"302 dequantize_per_channel_default_27" -> "304 conv2d_30"; -"303 features_7_block_3_0_weight_bias_0_0" -> "304 conv2d_30"; -"304 conv2d_30" -> "305 quantize_per_tensor_default_27"; -"305 quantize_per_tensor_default_27" -> "306 dequantize_per_tensor_default_36"; -"305 quantize_per_tensor_default_27" -> "307 dequantize_per_tensor_default_35"; -"306 dequantize_per_tensor_default_36" -> "354 add__3"; -"307 dequantize_per_tensor_default_35" -> "314 conv2d_31"; -"308 _param_constant83" -> "311 quantize_per_channel_default_28"; -"309 conv2d_31_scale_0" -> "311 quantize_per_channel_default_28"; -"309 conv2d_31_scale_0" -> "312 dequantize_per_channel_default_28"; -"310 conv2d_31_zero_point_0" -> "311 quantize_per_channel_default_28"; -"310 conv2d_31_zero_point_0" -> "312 dequantize_per_channel_default_28"; -"311 quantize_per_channel_default_28" -> "312 dequantize_per_channel_default_28"; -"312 dequantize_per_channel_default_28" -> "314 conv2d_31"; -"313 features_8_block_0_0_weight_bias_0_0" -> "314 conv2d_31"; -"314 conv2d_31" -> "317 hardswish__9"; -"315 hardswish__9_scale_0" -> "318 quantize_per_channel_default_29"; -"315 hardswish__9_scale_0" -> "319 dequantize_per_channel_default_29"; -"316 hardswish__9_zero_point_0" -> "318 quantize_per_channel_default_29"; -"316 hardswish__9_zero_point_0" -> "319 dequantize_per_channel_default_29"; -"317 hardswish__9" -> "318 quantize_per_channel_default_29"; -"318 quantize_per_channel_default_29" -> "319 dequantize_per_channel_default_29"; -"319 dequantize_per_channel_default_29" -> "326 conv2d_32"; -"320 _param_constant86" -> "323 quantize_per_channel_default_30"; -"321 conv2d_32_scale_0" -> "323 quantize_per_channel_default_30"; -"321 conv2d_32_scale_0" -> "324 dequantize_per_channel_default_30"; -"322 conv2d_32_zero_point_0" -> "323 quantize_per_channel_default_30"; -"322 conv2d_32_zero_point_0" -> "324 dequantize_per_channel_default_30"; -"323 quantize_per_channel_default_30" -> "324 dequantize_per_channel_default_30"; -"324 dequantize_per_channel_default_30" -> "326 conv2d_32"; -"325 features_8_block_1_0_weight_bias_0_0" -> "326 conv2d_32"; -"326 conv2d_32" -> "327 hardswish__10"; -"327 hardswish__10" -> "328 quantize_per_tensor_default_28"; -"328 quantize_per_tensor_default_28" -> "329 dequantize_per_tensor_default_38"; -"328 quantize_per_tensor_default_28" -> "330 dequantize_per_tensor_default_37"; -"329 dequantize_per_tensor_default_38" -> "342 mul_5"; -"330 dequantize_per_tensor_default_37" -> "331 adaptive_avg_pool2d_5"; -"331 adaptive_avg_pool2d_5" -> "334 conv2d_33"; -"332 _param_constant89" -> "334 conv2d_33"; -"333 _param_constant90_0_0" -> "334 conv2d_33"; -"334 conv2d_33" -> "335 relu_5"; -"335 relu_5" -> "338 conv2d_34"; -"336 _param_constant91" -> "338 conv2d_34"; -"337 _param_constant92_0_0" -> "338 conv2d_34"; -"338 conv2d_34" -> "339 hardsigmoid_5"; -"339 hardsigmoid_5" -> "340 quantize_per_tensor_default_29"; -"340 quantize_per_tensor_default_29" -> "341 dequantize_per_tensor_default_39"; -"341 dequantize_per_tensor_default_39" -> "342 mul_5"; -"342 mul_5" -> "343 quantize_per_tensor_default_30"; -"343 quantize_per_tensor_default_30" -> "344 dequantize_per_tensor_default_40"; -"344 dequantize_per_tensor_default_40" -> "351 conv2d_35"; -"345 _param_constant93" -> "348 quantize_per_channel_default_31"; -"346 conv2d_35_scale_0" -> "348 quantize_per_channel_default_31"; -"346 conv2d_35_scale_0" -> "349 dequantize_per_channel_default_31"; -"347 conv2d_35_zero_point_0" -> "348 quantize_per_channel_default_31"; -"347 conv2d_35_zero_point_0" -> "349 dequantize_per_channel_default_31"; -"348 quantize_per_channel_default_31" -> "349 dequantize_per_channel_default_31"; -"349 dequantize_per_channel_default_31" -> "351 conv2d_35"; -"350 features_8_block_3_0_weight_bias_0_0" -> "351 conv2d_35"; -"351 conv2d_35" -> "352 quantize_per_tensor_default_31"; -"352 quantize_per_tensor_default_31" -> "353 dequantize_per_tensor_default_41"; -"353 dequantize_per_tensor_default_41" -> "354 add__3"; -"354 add__3" -> "355 quantize_per_tensor_default_32"; -"355 quantize_per_tensor_default_32" -> "356 dequantize_per_tensor_default_42"; -"356 dequantize_per_tensor_default_42" -> "363 conv2d_36"; -"357 _param_constant96" -> "360 quantize_per_channel_default_32"; -"358 conv2d_36_scale_0" -> "360 quantize_per_channel_default_32"; -"358 conv2d_36_scale_0" -> "361 dequantize_per_channel_default_32"; -"359 conv2d_36_zero_point_0" -> "360 quantize_per_channel_default_32"; -"359 conv2d_36_zero_point_0" -> "361 dequantize_per_channel_default_32"; -"360 quantize_per_channel_default_32" -> "361 dequantize_per_channel_default_32"; -"361 dequantize_per_channel_default_32" -> "363 conv2d_36"; -"362 features_9_block_0_0_weight_bias_0_0" -> "363 conv2d_36"; -"363 conv2d_36" -> "366 hardswish__11"; -"364 hardswish__11_scale_0" -> "367 quantize_per_channel_default_33"; -"364 hardswish__11_scale_0" -> "368 dequantize_per_channel_default_33"; -"365 hardswish__11_zero_point_0" -> "367 quantize_per_channel_default_33"; -"365 hardswish__11_zero_point_0" -> "368 dequantize_per_channel_default_33"; -"366 hardswish__11" -> "367 quantize_per_channel_default_33"; -"367 quantize_per_channel_default_33" -> "368 dequantize_per_channel_default_33"; -"368 dequantize_per_channel_default_33" -> "375 conv2d_37"; -"369 _param_constant99" -> "372 quantize_per_channel_default_34"; -"370 conv2d_37_scale_0" -> "372 quantize_per_channel_default_34"; -"370 conv2d_37_scale_0" -> "373 dequantize_per_channel_default_34"; -"371 conv2d_37_zero_point_0" -> "372 quantize_per_channel_default_34"; -"371 conv2d_37_zero_point_0" -> "373 dequantize_per_channel_default_34"; -"372 quantize_per_channel_default_34" -> "373 dequantize_per_channel_default_34"; -"373 dequantize_per_channel_default_34" -> "375 conv2d_37"; -"374 features_9_block_1_0_weight_bias_0_0" -> "375 conv2d_37"; -"375 conv2d_37" -> "376 hardswish__12"; -"376 hardswish__12" -> "377 quantize_per_tensor_default_33"; -"377 quantize_per_tensor_default_33" -> "378 dequantize_per_tensor_default_44"; -"377 quantize_per_tensor_default_33" -> "379 dequantize_per_tensor_default_43"; -"378 dequantize_per_tensor_default_44" -> "391 mul_6"; -"379 dequantize_per_tensor_default_43" -> "380 adaptive_avg_pool2d_6"; -"380 adaptive_avg_pool2d_6" -> "383 conv2d_38"; -"381 _param_constant102" -> "383 conv2d_38"; -"382 _param_constant103_0_0" -> "383 conv2d_38"; -"383 conv2d_38" -> "384 relu_6"; -"384 relu_6" -> "387 conv2d_39"; -"385 _param_constant104" -> "387 conv2d_39"; -"386 _param_constant105_0_0" -> "387 conv2d_39"; -"387 conv2d_39" -> "388 hardsigmoid_6"; -"388 hardsigmoid_6" -> "389 quantize_per_tensor_default_34"; -"389 quantize_per_tensor_default_34" -> "390 dequantize_per_tensor_default_45"; -"390 dequantize_per_tensor_default_45" -> "391 mul_6"; -"391 mul_6" -> "392 quantize_per_tensor_default_35"; -"392 quantize_per_tensor_default_35" -> "393 dequantize_per_tensor_default_46"; -"393 dequantize_per_tensor_default_46" -> "400 conv2d_40"; -"394 _param_constant106" -> "397 quantize_per_channel_default_35"; -"395 conv2d_40_scale_0" -> "397 quantize_per_channel_default_35"; -"395 conv2d_40_scale_0" -> "398 dequantize_per_channel_default_35"; -"396 conv2d_40_zero_point_0" -> "397 quantize_per_channel_default_35"; -"396 conv2d_40_zero_point_0" -> "398 dequantize_per_channel_default_35"; -"397 quantize_per_channel_default_35" -> "398 dequantize_per_channel_default_35"; -"398 dequantize_per_channel_default_35" -> "400 conv2d_40"; -"399 features_9_block_3_0_weight_bias_0_0" -> "400 conv2d_40"; -"400 conv2d_40" -> "401 quantize_per_tensor_default_36"; -"401 quantize_per_tensor_default_36" -> "402 dequantize_per_tensor_default_48"; -"401 quantize_per_tensor_default_36" -> "403 dequantize_per_tensor_default_47"; -"402 dequantize_per_tensor_default_48" -> "450 add__4"; -"403 dequantize_per_tensor_default_47" -> "410 conv2d_41"; -"404 _param_constant109" -> "407 quantize_per_channel_default_36"; -"405 conv2d_41_scale_0" -> "407 quantize_per_channel_default_36"; -"405 conv2d_41_scale_0" -> "408 dequantize_per_channel_default_36"; -"406 conv2d_41_zero_point_0" -> "407 quantize_per_channel_default_36"; -"406 conv2d_41_zero_point_0" -> "408 dequantize_per_channel_default_36"; -"407 quantize_per_channel_default_36" -> "408 dequantize_per_channel_default_36"; -"408 dequantize_per_channel_default_36" -> "410 conv2d_41"; -"409 features_10_block_0_0_weight_bias_0_0" -> "410 conv2d_41"; -"410 conv2d_41" -> "413 hardswish__13"; -"411 hardswish__13_scale_0" -> "414 quantize_per_channel_default_37"; -"411 hardswish__13_scale_0" -> "415 dequantize_per_channel_default_37"; -"412 hardswish__13_zero_point_0" -> "414 quantize_per_channel_default_37"; -"412 hardswish__13_zero_point_0" -> "415 dequantize_per_channel_default_37"; -"413 hardswish__13" -> "414 quantize_per_channel_default_37"; -"414 quantize_per_channel_default_37" -> "415 dequantize_per_channel_default_37"; -"415 dequantize_per_channel_default_37" -> "422 conv2d_42"; -"416 _param_constant112" -> "419 quantize_per_channel_default_38"; -"417 conv2d_42_scale_0" -> "419 quantize_per_channel_default_38"; -"417 conv2d_42_scale_0" -> "420 dequantize_per_channel_default_38"; -"418 conv2d_42_zero_point_0" -> "419 quantize_per_channel_default_38"; -"418 conv2d_42_zero_point_0" -> "420 dequantize_per_channel_default_38"; -"419 quantize_per_channel_default_38" -> "420 dequantize_per_channel_default_38"; -"420 dequantize_per_channel_default_38" -> "422 conv2d_42"; -"421 features_10_block_1_0_weight_bias_0_0" -> "422 conv2d_42"; -"422 conv2d_42" -> "423 hardswish__14"; -"423 hardswish__14" -> "424 quantize_per_tensor_default_37"; -"424 quantize_per_tensor_default_37" -> "425 dequantize_per_tensor_default_50"; -"424 quantize_per_tensor_default_37" -> "426 dequantize_per_tensor_default_49"; -"425 dequantize_per_tensor_default_50" -> "438 mul_7"; -"426 dequantize_per_tensor_default_49" -> "427 adaptive_avg_pool2d_7"; -"427 adaptive_avg_pool2d_7" -> "430 conv2d_43"; -"428 _param_constant115" -> "430 conv2d_43"; -"429 _param_constant116_0_0" -> "430 conv2d_43"; -"430 conv2d_43" -> "431 relu_7"; -"431 relu_7" -> "434 conv2d_44"; -"432 _param_constant117" -> "434 conv2d_44"; -"433 _param_constant118_0_0" -> "434 conv2d_44"; -"434 conv2d_44" -> "435 hardsigmoid_7"; -"435 hardsigmoid_7" -> "436 quantize_per_tensor_default_38"; -"436 quantize_per_tensor_default_38" -> "437 dequantize_per_tensor_default_51"; -"437 dequantize_per_tensor_default_51" -> "438 mul_7"; -"438 mul_7" -> "439 quantize_per_tensor_default_39"; -"439 quantize_per_tensor_default_39" -> "440 dequantize_per_tensor_default_52"; -"440 dequantize_per_tensor_default_52" -> "447 conv2d_45"; -"441 _param_constant119" -> "444 quantize_per_channel_default_39"; -"442 conv2d_45_scale_0" -> "444 quantize_per_channel_default_39"; -"442 conv2d_45_scale_0" -> "445 dequantize_per_channel_default_39"; -"443 conv2d_45_zero_point_0" -> "444 quantize_per_channel_default_39"; -"443 conv2d_45_zero_point_0" -> "445 dequantize_per_channel_default_39"; -"444 quantize_per_channel_default_39" -> "445 dequantize_per_channel_default_39"; -"445 dequantize_per_channel_default_39" -> "447 conv2d_45"; -"446 features_10_block_3_0_weight_bias_0_0" -> "447 conv2d_45"; -"447 conv2d_45" -> "448 quantize_per_tensor_default_40"; -"448 quantize_per_tensor_default_40" -> "449 dequantize_per_tensor_default_53"; -"449 dequantize_per_tensor_default_53" -> "450 add__4"; -"450 add__4" -> "451 quantize_per_tensor_default_41"; -"451 quantize_per_tensor_default_41" -> "452 dequantize_per_tensor_default_55"; -"451 quantize_per_tensor_default_41" -> "453 dequantize_per_tensor_default_54"; -"452 dequantize_per_tensor_default_55" -> "500 add__5"; -"453 dequantize_per_tensor_default_54" -> "460 conv2d_46"; -"454 _param_constant122" -> "457 quantize_per_channel_default_40"; -"455 conv2d_46_scale_0" -> "457 quantize_per_channel_default_40"; -"455 conv2d_46_scale_0" -> "458 dequantize_per_channel_default_40"; -"456 conv2d_46_zero_point_0" -> "457 quantize_per_channel_default_40"; -"456 conv2d_46_zero_point_0" -> "458 dequantize_per_channel_default_40"; -"457 quantize_per_channel_default_40" -> "458 dequantize_per_channel_default_40"; -"458 dequantize_per_channel_default_40" -> "460 conv2d_46"; -"459 features_11_block_0_0_weight_bias_0_0" -> "460 conv2d_46"; -"460 conv2d_46" -> "463 hardswish__15"; -"461 hardswish__15_scale_0" -> "464 quantize_per_channel_default_41"; -"461 hardswish__15_scale_0" -> "465 dequantize_per_channel_default_41"; -"462 hardswish__15_zero_point_0" -> "464 quantize_per_channel_default_41"; -"462 hardswish__15_zero_point_0" -> "465 dequantize_per_channel_default_41"; -"463 hardswish__15" -> "464 quantize_per_channel_default_41"; -"464 quantize_per_channel_default_41" -> "465 dequantize_per_channel_default_41"; -"465 dequantize_per_channel_default_41" -> "472 conv2d_47"; -"466 _param_constant125" -> "469 quantize_per_channel_default_42"; -"467 conv2d_47_scale_0" -> "469 quantize_per_channel_default_42"; -"467 conv2d_47_scale_0" -> "470 dequantize_per_channel_default_42"; -"468 conv2d_47_zero_point_0" -> "469 quantize_per_channel_default_42"; -"468 conv2d_47_zero_point_0" -> "470 dequantize_per_channel_default_42"; -"469 quantize_per_channel_default_42" -> "470 dequantize_per_channel_default_42"; -"470 dequantize_per_channel_default_42" -> "472 conv2d_47"; -"471 features_11_block_1_0_weight_bias_0_0" -> "472 conv2d_47"; -"472 conv2d_47" -> "473 hardswish__16"; -"473 hardswish__16" -> "474 quantize_per_tensor_default_42"; -"474 quantize_per_tensor_default_42" -> "475 dequantize_per_tensor_default_57"; -"474 quantize_per_tensor_default_42" -> "476 dequantize_per_tensor_default_56"; -"475 dequantize_per_tensor_default_57" -> "488 mul_8"; -"476 dequantize_per_tensor_default_56" -> "477 adaptive_avg_pool2d_8"; -"477 adaptive_avg_pool2d_8" -> "480 conv2d_48"; -"478 _param_constant128" -> "480 conv2d_48"; -"479 _param_constant129_0_0" -> "480 conv2d_48"; -"480 conv2d_48" -> "481 relu_8"; -"481 relu_8" -> "484 conv2d_49"; -"482 _param_constant130" -> "484 conv2d_49"; -"483 _param_constant131_0_0" -> "484 conv2d_49"; -"484 conv2d_49" -> "485 hardsigmoid_8"; -"485 hardsigmoid_8" -> "486 quantize_per_tensor_default_43"; -"486 quantize_per_tensor_default_43" -> "487 dequantize_per_tensor_default_58"; -"487 dequantize_per_tensor_default_58" -> "488 mul_8"; -"488 mul_8" -> "489 quantize_per_tensor_default_44"; -"489 quantize_per_tensor_default_44" -> "490 dequantize_per_tensor_default_59"; -"490 dequantize_per_tensor_default_59" -> "497 conv2d_50"; -"491 _param_constant132" -> "494 quantize_per_channel_default_43"; -"492 conv2d_50_scale_0" -> "494 quantize_per_channel_default_43"; -"492 conv2d_50_scale_0" -> "495 dequantize_per_channel_default_43"; -"493 conv2d_50_zero_point_0" -> "494 quantize_per_channel_default_43"; -"493 conv2d_50_zero_point_0" -> "495 dequantize_per_channel_default_43"; -"494 quantize_per_channel_default_43" -> "495 dequantize_per_channel_default_43"; -"495 dequantize_per_channel_default_43" -> "497 conv2d_50"; -"496 features_11_block_3_0_weight_bias_0_0" -> "497 conv2d_50"; -"497 conv2d_50" -> "498 quantize_per_tensor_default_45"; -"498 quantize_per_tensor_default_45" -> "499 dequantize_per_tensor_default_60"; -"499 dequantize_per_tensor_default_60" -> "500 add__5"; -"500 add__5" -> "501 quantize_per_tensor_default_46"; -"501 quantize_per_tensor_default_46" -> "502 dequantize_per_tensor_default_61"; -"502 dequantize_per_tensor_default_61" -> "509 conv2d_51"; -"503 _param_constant135" -> "506 quantize_per_channel_default_44"; -"504 conv2d_51_scale_0" -> "506 quantize_per_channel_default_44"; -"504 conv2d_51_scale_0" -> "507 dequantize_per_channel_default_44"; -"505 conv2d_51_zero_point_0" -> "506 quantize_per_channel_default_44"; -"505 conv2d_51_zero_point_0" -> "507 dequantize_per_channel_default_44"; -"506 quantize_per_channel_default_44" -> "507 dequantize_per_channel_default_44"; -"507 dequantize_per_channel_default_44" -> "509 conv2d_51"; -"508 features_12_0_weight_bias_0_0" -> "509 conv2d_51"; -"509 conv2d_51" -> "510 hardswish__17"; -"510 hardswish__17" -> "511 quantize_per_tensor_default_47"; -"511 quantize_per_tensor_default_47" -> "512 dequantize_per_tensor_default_62"; -"512 dequantize_per_tensor_default_62" -> "513 adaptive_avg_pool2d_9"; -"513 adaptive_avg_pool2d_9" -> "514 quantize_per_tensor_default_48"; -"514 quantize_per_tensor_default_48" -> "515 dequantize_per_tensor_default_63"; -"515 dequantize_per_tensor_default_63" -> "516 flatten"; -"516 flatten" -> "523 linear"; -"517 _param_constant138" -> "520 quantize_per_channel_default_45"; -"518 linear_scale_0" -> "520 quantize_per_channel_default_45"; -"518 linear_scale_0" -> "521 dequantize_per_channel_default_45"; -"519 linear_zero_point_0" -> "520 quantize_per_channel_default_45"; -"519 linear_zero_point_0" -> "521 dequantize_per_channel_default_45"; -"520 quantize_per_channel_default_45" -> "521 dequantize_per_channel_default_45"; -"521 dequantize_per_channel_default_45" -> "523 linear"; -"522 _param_constant139_0_0" -> "523 linear"; -"523 linear" -> "524 hardswish__18"; -"524 hardswish__18" -> "525 quantize_per_tensor_default_49"; -"525 quantize_per_tensor_default_49" -> "526 dequantize_per_tensor_default_64"; -"526 dequantize_per_tensor_default_64" -> "527 dropout_"; -"527 dropout_" -> "534 linear_1"; -"528 _param_constant140" -> "531 quantize_per_channel_default_46"; -"529 linear_1_scale_0" -> "531 quantize_per_channel_default_46"; -"529 linear_1_scale_0" -> "532 dequantize_per_channel_default_46"; -"530 linear_1_zero_point_0" -> "531 quantize_per_channel_default_46"; -"530 linear_1_zero_point_0" -> "532 dequantize_per_channel_default_46"; -"531 quantize_per_channel_default_46" -> "532 dequantize_per_channel_default_46"; -"532 dequantize_per_channel_default_46" -> "534 linear_1"; -"533 _param_constant141_0_0" -> "534 linear_1"; -"534 linear_1" -> "535 output"; +"0 features_1_block_1_fc1_weight" -> "46 conv2d_2" [label="(8, 16, 1, 1)", style=solid]; +"1 features_1_block_1_fc2_weight" -> "49 conv2d_3" [label="(16, 8, 1, 1)", style=solid]; +"2 features_4_block_2_fc1_weight" -> "156 conv2d_13" [label="(24, 96, 1, 1)", style=solid]; +"3 features_4_block_2_fc2_weight" -> "159 conv2d_14" [label="(96, 24, 1, 1)", style=solid]; +"4 features_5_block_2_fc1_weight" -> "201 conv2d_18" [label="(64, 240, 1, 1)", style=solid]; +"5 features_5_block_2_fc2_weight" -> "204 conv2d_19" [label="(240, 64, 1, 1)", style=solid]; +"6 features_6_block_2_fc1_weight" -> "249 conv2d_23" [label="(64, 240, 1, 1)", style=solid]; +"7 features_6_block_2_fc2_weight" -> "252 conv2d_24" [label="(240, 64, 1, 1)", style=solid]; +"8 features_7_block_2_fc1_weight" -> "296 conv2d_28" [label="(32, 120, 1, 1)", style=solid]; +"9 features_7_block_2_fc2_weight" -> "299 conv2d_29" [label="(120, 32, 1, 1)", style=solid]; +"10 features_8_block_2_fc1_weight" -> "341 conv2d_33" [label="(40, 144, 1, 1)", style=solid]; +"11 features_8_block_2_fc2_weight" -> "344 conv2d_34" [label="(144, 40, 1, 1)", style=solid]; +"12 features_9_block_2_fc1_weight" -> "388 conv2d_38" [label="(72, 288, 1, 1)", style=solid]; +"13 features_9_block_2_fc2_weight" -> "391 conv2d_39" [label="(288, 72, 1, 1)", style=solid]; +"14 features_10_block_2_fc1_weight" -> "433 conv2d_43" [label="(144, 576, 1, 1)", style=solid]; +"15 features_10_block_2_fc2_weight" -> "436 conv2d_44" [label="(576, 144, 1, 1)", style=solid]; +"16 features_11_block_2_fc1_weight" -> "481 conv2d_48" [label="(144, 576, 1, 1)", style=solid]; +"17 features_11_block_2_fc2_weight" -> "484 conv2d_49" [label="(576, 144, 1, 1)", style=solid]; +"18 x" -> "19 quantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"19 quantize_per_tensor_default" -> "20 dequantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"20 dequantize_per_tensor_default" -> "27 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"21 conv2d_scale_0" -> "24 quantize_per_channel_default" [label="(16,)", style=solid]; +"21 conv2d_scale_0" -> "25 dequantize_per_channel_default" [label="(16,)", style=solid]; +"22 conv2d_zero_point_0" -> "24 quantize_per_channel_default" [label="(16,)", style=solid]; +"22 conv2d_zero_point_0" -> "25 dequantize_per_channel_default" [label="(16,)", style=solid]; +"23 compressed_weight_updated_constant0" -> "24 quantize_per_channel_default" [label="(16, 3, 3, 3)", style=solid]; +"24 quantize_per_channel_default" -> "25 dequantize_per_channel_default" [label="(16, 3, 3, 3)", style=solid]; +"25 dequantize_per_channel_default" -> "27 conv2d" [label="(16, 3, 3, 3)", style=solid]; +"26 features_0_0_weight_bias_0_0" -> "27 conv2d" [label="(16,)", style=solid]; +"27 conv2d" -> "30 hardswish_" [label="(1, 16, 112, 112)", style=solid]; +"28 hardswish__scale_0" -> "31 quantize_per_channel_default_1" [label="(16,)", style=solid]; +"28 hardswish__scale_0" -> "32 dequantize_per_channel_default_1" [label="(16,)", style=solid]; +"29 hardswish__zero_point_0" -> "31 quantize_per_channel_default_1" [label="(16,)", style=solid]; +"29 hardswish__zero_point_0" -> "32 dequantize_per_channel_default_1" [label="(16,)", style=solid]; +"30 hardswish_" -> "31 quantize_per_channel_default_1" [label="(1, 16, 112, 112)", style=solid]; +"31 quantize_per_channel_default_1" -> "32 dequantize_per_channel_default_1" [label="(1, 16, 112, 112)", style=solid]; +"32 dequantize_per_channel_default_1" -> "39 conv2d_1" [label="(1, 16, 112, 112)", style=solid]; +"33 conv2d_1_scale_0" -> "36 quantize_per_channel_default_2" [label="(16,)", style=solid]; +"33 conv2d_1_scale_0" -> "37 dequantize_per_channel_default_2" [label="(16,)", style=solid]; +"34 conv2d_1_zero_point_0" -> "36 quantize_per_channel_default_2" [label="(16,)", style=solid]; +"34 conv2d_1_zero_point_0" -> "37 dequantize_per_channel_default_2" [label="(16,)", style=solid]; +"35 compressed_weight_updated_constant1" -> "36 quantize_per_channel_default_2" [label="(16, 1, 3, 3)", style=solid]; +"36 quantize_per_channel_default_2" -> "37 dequantize_per_channel_default_2" [label="(16, 1, 3, 3)", style=solid]; +"37 dequantize_per_channel_default_2" -> "39 conv2d_1" [label="(16, 1, 3, 3)", style=solid]; +"38 features_1_block_0_0_weight_bias_0_0" -> "39 conv2d_1" [label="(16,)", style=solid]; +"39 conv2d_1" -> "40 relu_" [label="(1, 16, 56, 56)", style=solid]; +"40 relu_" -> "41 quantize_per_tensor_default_1" [label="(1, 16, 56, 56)", style=solid]; +"41 quantize_per_tensor_default_1" -> "42 dequantize_per_tensor_default_2" [label="(1, 16, 56, 56)", style=solid]; +"41 quantize_per_tensor_default_1" -> "43 dequantize_per_tensor_default_1" [label="(1, 16, 56, 56)", style=solid]; +"42 dequantize_per_tensor_default_2" -> "53 mul" [label="(1, 16, 56, 56)", style=solid]; +"43 dequantize_per_tensor_default_1" -> "44 adaptive_avg_pool2d" [label="(1, 16, 56, 56)", style=solid]; +"44 adaptive_avg_pool2d" -> "46 conv2d_2" [label="(1, 16, 1, 1)", style=solid]; +"45 features_1_block_1_fc1_bias_0_0" -> "46 conv2d_2" [label="(8,)", style=solid]; +"46 conv2d_2" -> "47 relu" [label="(1, 8, 1, 1)", style=solid]; +"47 relu" -> "49 conv2d_3" [label="(1, 8, 1, 1)", style=solid]; +"48 features_1_block_1_fc2_bias_0_0" -> "49 conv2d_3" [label="(16,)", style=solid]; +"49 conv2d_3" -> "50 hardsigmoid" [label="(1, 16, 1, 1)", style=solid]; +"50 hardsigmoid" -> "51 quantize_per_tensor_default_2" [label="(1, 16, 1, 1)", style=solid]; +"51 quantize_per_tensor_default_2" -> "52 dequantize_per_tensor_default_3" [label="(1, 16, 1, 1)", style=solid]; +"52 dequantize_per_tensor_default_3" -> "53 mul" [label="(1, 16, 1, 1)", style=solid]; +"53 mul" -> "54 quantize_per_tensor_default_3" [label="(1, 16, 56, 56)", style=solid]; +"54 quantize_per_tensor_default_3" -> "55 dequantize_per_tensor_default_4" [label="(1, 16, 56, 56)", style=solid]; +"55 dequantize_per_tensor_default_4" -> "62 conv2d_4" [label="(1, 16, 56, 56)", style=solid]; +"56 conv2d_4_scale_0" -> "59 quantize_per_channel_default_3" [label="(16,)", style=solid]; +"56 conv2d_4_scale_0" -> "60 dequantize_per_channel_default_3" [label="(16,)", style=solid]; +"57 conv2d_4_zero_point_0" -> "59 quantize_per_channel_default_3" [label="(16,)", style=solid]; +"57 conv2d_4_zero_point_0" -> "60 dequantize_per_channel_default_3" [label="(16,)", style=solid]; +"58 compressed_weight_updated_constant2" -> "59 quantize_per_channel_default_3" [label="(16, 16, 1, 1)", style=solid]; +"59 quantize_per_channel_default_3" -> "60 dequantize_per_channel_default_3" [label="(16, 16, 1, 1)", style=solid]; +"60 dequantize_per_channel_default_3" -> "62 conv2d_4" [label="(16, 16, 1, 1)", style=solid]; +"61 features_1_block_2_0_weight_bias_0_0" -> "62 conv2d_4" [label="(16,)", style=solid]; +"62 conv2d_4" -> "63 quantize_per_tensor_default_4" [label="(1, 16, 56, 56)", style=solid]; +"63 quantize_per_tensor_default_4" -> "64 dequantize_per_tensor_default_5" [label="(1, 16, 56, 56)", style=solid]; +"64 dequantize_per_tensor_default_5" -> "71 conv2d_5" [label="(1, 16, 56, 56)", style=solid]; +"65 conv2d_5_scale_0" -> "68 quantize_per_channel_default_4" [label="(72,)", style=solid]; +"65 conv2d_5_scale_0" -> "69 dequantize_per_channel_default_4" [label="(72,)", style=solid]; +"66 conv2d_5_zero_point_0" -> "68 quantize_per_channel_default_4" [label="(72,)", style=solid]; +"66 conv2d_5_zero_point_0" -> "69 dequantize_per_channel_default_4" [label="(72,)", style=solid]; +"67 compressed_weight_updated_constant3" -> "68 quantize_per_channel_default_4" [label="(72, 16, 1, 1)", style=solid]; +"68 quantize_per_channel_default_4" -> "69 dequantize_per_channel_default_4" [label="(72, 16, 1, 1)", style=solid]; +"69 dequantize_per_channel_default_4" -> "71 conv2d_5" [label="(72, 16, 1, 1)", style=solid]; +"70 features_2_block_0_0_weight_bias_0_0" -> "71 conv2d_5" [label="(72,)", style=solid]; +"71 conv2d_5" -> "74 relu__1" [label="(1, 72, 56, 56)", style=solid]; +"72 relu__1_scale_0" -> "75 quantize_per_channel_default_5" [label="(72,)", style=solid]; +"72 relu__1_scale_0" -> "76 dequantize_per_channel_default_5" [label="(72,)", style=solid]; +"73 relu__1_zero_point_0" -> "75 quantize_per_channel_default_5" [label="(72,)", style=solid]; +"73 relu__1_zero_point_0" -> "76 dequantize_per_channel_default_5" [label="(72,)", style=solid]; +"74 relu__1" -> "75 quantize_per_channel_default_5" [label="(1, 72, 56, 56)", style=solid]; +"75 quantize_per_channel_default_5" -> "76 dequantize_per_channel_default_5" [label="(1, 72, 56, 56)", style=solid]; +"76 dequantize_per_channel_default_5" -> "83 conv2d_6" [label="(1, 72, 56, 56)", style=solid]; +"77 conv2d_6_scale_0" -> "80 quantize_per_channel_default_6" [label="(72,)", style=solid]; +"77 conv2d_6_scale_0" -> "81 dequantize_per_channel_default_6" [label="(72,)", style=solid]; +"78 conv2d_6_zero_point_0" -> "80 quantize_per_channel_default_6" [label="(72,)", style=solid]; +"78 conv2d_6_zero_point_0" -> "81 dequantize_per_channel_default_6" [label="(72,)", style=solid]; +"79 compressed_weight_updated_constant4" -> "80 quantize_per_channel_default_6" [label="(72, 1, 3, 3)", style=solid]; +"80 quantize_per_channel_default_6" -> "81 dequantize_per_channel_default_6" [label="(72, 1, 3, 3)", style=solid]; +"81 dequantize_per_channel_default_6" -> "83 conv2d_6" [label="(72, 1, 3, 3)", style=solid]; +"82 features_2_block_1_0_weight_bias_0_0" -> "83 conv2d_6" [label="(72,)", style=solid]; +"83 conv2d_6" -> "84 relu__2" [label="(1, 72, 28, 28)", style=solid]; +"84 relu__2" -> "85 quantize_per_tensor_default_5" [label="(1, 72, 28, 28)", style=solid]; +"85 quantize_per_tensor_default_5" -> "86 dequantize_per_tensor_default_6" [label="(1, 72, 28, 28)", style=solid]; +"86 dequantize_per_tensor_default_6" -> "93 conv2d_7" [label="(1, 72, 28, 28)", style=solid]; +"87 conv2d_7_scale_0" -> "90 quantize_per_channel_default_7" [label="(24,)", style=solid]; +"87 conv2d_7_scale_0" -> "91 dequantize_per_channel_default_7" [label="(24,)", style=solid]; +"88 conv2d_7_zero_point_0" -> "90 quantize_per_channel_default_7" [label="(24,)", style=solid]; +"88 conv2d_7_zero_point_0" -> "91 dequantize_per_channel_default_7" [label="(24,)", style=solid]; +"89 compressed_weight_updated_constant5" -> "90 quantize_per_channel_default_7" [label="(24, 72, 1, 1)", style=solid]; +"90 quantize_per_channel_default_7" -> "91 dequantize_per_channel_default_7" [label="(24, 72, 1, 1)", style=solid]; +"91 dequantize_per_channel_default_7" -> "93 conv2d_7" [label="(24, 72, 1, 1)", style=solid]; +"92 features_2_block_2_0_weight_bias_0_0" -> "93 conv2d_7" [label="(24,)", style=solid]; +"93 conv2d_7" -> "94 quantize_per_tensor_default_6" [label="(1, 24, 28, 28)", style=solid]; +"94 quantize_per_tensor_default_6" -> "95 dequantize_per_tensor_default_8" [label="(1, 24, 28, 28)", style=solid]; +"94 quantize_per_tensor_default_6" -> "96 dequantize_per_tensor_default_7" [label="(1, 24, 28, 28)", style=solid]; +"95 dequantize_per_tensor_default_8" -> "128 add_" [label="(1, 24, 28, 28)", style=solid]; +"96 dequantize_per_tensor_default_7" -> "103 conv2d_8" [label="(1, 24, 28, 28)", style=solid]; +"97 conv2d_8_scale_0" -> "100 quantize_per_channel_default_8" [label="(88,)", style=solid]; +"97 conv2d_8_scale_0" -> "101 dequantize_per_channel_default_8" [label="(88,)", style=solid]; +"98 conv2d_8_zero_point_0" -> "100 quantize_per_channel_default_8" [label="(88,)", style=solid]; +"98 conv2d_8_zero_point_0" -> "101 dequantize_per_channel_default_8" [label="(88,)", style=solid]; +"99 compressed_weight_updated_constant6" -> "100 quantize_per_channel_default_8" [label="(88, 24, 1, 1)", style=solid]; +"100 quantize_per_channel_default_8" -> "101 dequantize_per_channel_default_8" [label="(88, 24, 1, 1)", style=solid]; +"101 dequantize_per_channel_default_8" -> "103 conv2d_8" [label="(88, 24, 1, 1)", style=solid]; +"102 features_3_block_0_0_weight_bias_0_0" -> "103 conv2d_8" [label="(88,)", style=solid]; +"103 conv2d_8" -> "106 relu__3" [label="(1, 88, 28, 28)", style=solid]; +"104 relu__3_scale_0" -> "107 quantize_per_channel_default_9" [label="(88,)", style=solid]; +"104 relu__3_scale_0" -> "108 dequantize_per_channel_default_9" [label="(88,)", style=solid]; +"105 relu__3_zero_point_0" -> "107 quantize_per_channel_default_9" [label="(88,)", style=solid]; +"105 relu__3_zero_point_0" -> "108 dequantize_per_channel_default_9" [label="(88,)", style=solid]; +"106 relu__3" -> "107 quantize_per_channel_default_9" [label="(1, 88, 28, 28)", style=solid]; +"107 quantize_per_channel_default_9" -> "108 dequantize_per_channel_default_9" [label="(1, 88, 28, 28)", style=solid]; +"108 dequantize_per_channel_default_9" -> "115 conv2d_9" [label="(1, 88, 28, 28)", style=solid]; +"109 conv2d_9_scale_0" -> "112 quantize_per_channel_default_10" [label="(88,)", style=solid]; +"109 conv2d_9_scale_0" -> "113 dequantize_per_channel_default_10" [label="(88,)", style=solid]; +"110 conv2d_9_zero_point_0" -> "112 quantize_per_channel_default_10" [label="(88,)", style=solid]; +"110 conv2d_9_zero_point_0" -> "113 dequantize_per_channel_default_10" [label="(88,)", style=solid]; +"111 compressed_weight_updated_constant7" -> "112 quantize_per_channel_default_10" [label="(88, 1, 3, 3)", style=solid]; +"112 quantize_per_channel_default_10" -> "113 dequantize_per_channel_default_10" [label="(88, 1, 3, 3)", style=solid]; +"113 dequantize_per_channel_default_10" -> "115 conv2d_9" [label="(88, 1, 3, 3)", style=solid]; +"114 features_3_block_1_0_weight_bias_0_0" -> "115 conv2d_9" [label="(88,)", style=solid]; +"115 conv2d_9" -> "116 relu__4" [label="(1, 88, 28, 28)", style=solid]; +"116 relu__4" -> "117 quantize_per_tensor_default_7" [label="(1, 88, 28, 28)", style=solid]; +"117 quantize_per_tensor_default_7" -> "118 dequantize_per_tensor_default_9" [label="(1, 88, 28, 28)", style=solid]; +"118 dequantize_per_tensor_default_9" -> "125 conv2d_10" [label="(1, 88, 28, 28)", style=solid]; +"119 conv2d_10_scale_0" -> "122 quantize_per_channel_default_11" [label="(24,)", style=solid]; +"119 conv2d_10_scale_0" -> "123 dequantize_per_channel_default_11" [label="(24,)", style=solid]; +"120 conv2d_10_zero_point_0" -> "122 quantize_per_channel_default_11" [label="(24,)", style=solid]; +"120 conv2d_10_zero_point_0" -> "123 dequantize_per_channel_default_11" [label="(24,)", style=solid]; +"121 compressed_weight_updated_constant8" -> "122 quantize_per_channel_default_11" [label="(24, 88, 1, 1)", style=solid]; +"122 quantize_per_channel_default_11" -> "123 dequantize_per_channel_default_11" [label="(24, 88, 1, 1)", style=solid]; +"123 dequantize_per_channel_default_11" -> "125 conv2d_10" [label="(24, 88, 1, 1)", style=solid]; +"124 features_3_block_2_0_weight_bias_0_0" -> "125 conv2d_10" [label="(24,)", style=solid]; +"125 conv2d_10" -> "126 quantize_per_tensor_default_8" [label="(1, 24, 28, 28)", style=solid]; +"126 quantize_per_tensor_default_8" -> "127 dequantize_per_tensor_default_10" [label="(1, 24, 28, 28)", style=solid]; +"127 dequantize_per_tensor_default_10" -> "128 add_" [label="(1, 24, 28, 28)", style=solid]; +"128 add_" -> "129 quantize_per_tensor_default_9" [label="(1, 24, 28, 28)", style=solid]; +"129 quantize_per_tensor_default_9" -> "130 dequantize_per_tensor_default_11" [label="(1, 24, 28, 28)", style=solid]; +"130 dequantize_per_tensor_default_11" -> "137 conv2d_11" [label="(1, 24, 28, 28)", style=solid]; +"131 conv2d_11_scale_0" -> "134 quantize_per_channel_default_12" [label="(96,)", style=solid]; +"131 conv2d_11_scale_0" -> "135 dequantize_per_channel_default_12" [label="(96,)", style=solid]; +"132 conv2d_11_zero_point_0" -> "134 quantize_per_channel_default_12" [label="(96,)", style=solid]; +"132 conv2d_11_zero_point_0" -> "135 dequantize_per_channel_default_12" [label="(96,)", style=solid]; +"133 compressed_weight_updated_constant9" -> "134 quantize_per_channel_default_12" [label="(96, 24, 1, 1)", style=solid]; +"134 quantize_per_channel_default_12" -> "135 dequantize_per_channel_default_12" [label="(96, 24, 1, 1)", style=solid]; +"135 dequantize_per_channel_default_12" -> "137 conv2d_11" [label="(96, 24, 1, 1)", style=solid]; +"136 features_4_block_0_0_weight_bias_0_0" -> "137 conv2d_11" [label="(96,)", style=solid]; +"137 conv2d_11" -> "140 hardswish__1" [label="(1, 96, 28, 28)", style=solid]; +"138 hardswish__1_scale_0" -> "141 quantize_per_channel_default_13" [label="(96,)", style=solid]; +"138 hardswish__1_scale_0" -> "142 dequantize_per_channel_default_13" [label="(96,)", style=solid]; +"139 hardswish__1_zero_point_0" -> "141 quantize_per_channel_default_13" [label="(96,)", style=solid]; +"139 hardswish__1_zero_point_0" -> "142 dequantize_per_channel_default_13" [label="(96,)", style=solid]; +"140 hardswish__1" -> "141 quantize_per_channel_default_13" [label="(1, 96, 28, 28)", style=solid]; +"141 quantize_per_channel_default_13" -> "142 dequantize_per_channel_default_13" [label="(1, 96, 28, 28)", style=solid]; +"142 dequantize_per_channel_default_13" -> "149 conv2d_12" [label="(1, 96, 28, 28)", style=solid]; +"143 conv2d_12_scale_0" -> "146 quantize_per_channel_default_14" [label="(96,)", style=solid]; +"143 conv2d_12_scale_0" -> "147 dequantize_per_channel_default_14" [label="(96,)", style=solid]; +"144 conv2d_12_zero_point_0" -> "146 quantize_per_channel_default_14" [label="(96,)", style=solid]; +"144 conv2d_12_zero_point_0" -> "147 dequantize_per_channel_default_14" [label="(96,)", style=solid]; +"145 compressed_weight_updated_constant10" -> "146 quantize_per_channel_default_14" [label="(96, 1, 5, 5)", style=solid]; +"146 quantize_per_channel_default_14" -> "147 dequantize_per_channel_default_14" [label="(96, 1, 5, 5)", style=solid]; +"147 dequantize_per_channel_default_14" -> "149 conv2d_12" [label="(96, 1, 5, 5)", style=solid]; +"148 features_4_block_1_0_weight_bias_0_0" -> "149 conv2d_12" [label="(96,)", style=solid]; +"149 conv2d_12" -> "150 hardswish__2" [label="(1, 96, 14, 14)", style=solid]; +"150 hardswish__2" -> "151 quantize_per_tensor_default_10" [label="(1, 96, 14, 14)", style=solid]; +"151 quantize_per_tensor_default_10" -> "152 dequantize_per_tensor_default_13" [label="(1, 96, 14, 14)", style=solid]; +"151 quantize_per_tensor_default_10" -> "153 dequantize_per_tensor_default_12" [label="(1, 96, 14, 14)", style=solid]; +"152 dequantize_per_tensor_default_13" -> "163 mul_1" [label="(1, 96, 14, 14)", style=solid]; +"153 dequantize_per_tensor_default_12" -> "154 adaptive_avg_pool2d_1" [label="(1, 96, 14, 14)", style=solid]; +"154 adaptive_avg_pool2d_1" -> "156 conv2d_13" [label="(1, 96, 1, 1)", style=solid]; +"155 features_4_block_2_fc1_bias_0_0" -> "156 conv2d_13" [label="(24,)", style=solid]; +"156 conv2d_13" -> "157 relu_1" [label="(1, 24, 1, 1)", style=solid]; +"157 relu_1" -> "159 conv2d_14" [label="(1, 24, 1, 1)", style=solid]; +"158 features_4_block_2_fc2_bias_0_0" -> "159 conv2d_14" [label="(96,)", style=solid]; +"159 conv2d_14" -> "160 hardsigmoid_1" [label="(1, 96, 1, 1)", style=solid]; +"160 hardsigmoid_1" -> "161 quantize_per_tensor_default_11" [label="(1, 96, 1, 1)", style=solid]; +"161 quantize_per_tensor_default_11" -> "162 dequantize_per_tensor_default_14" [label="(1, 96, 1, 1)", style=solid]; +"162 dequantize_per_tensor_default_14" -> "163 mul_1" [label="(1, 96, 1, 1)", style=solid]; +"163 mul_1" -> "164 quantize_per_tensor_default_12" [label="(1, 96, 14, 14)", style=solid]; +"164 quantize_per_tensor_default_12" -> "165 dequantize_per_tensor_default_15" [label="(1, 96, 14, 14)", style=solid]; +"165 dequantize_per_tensor_default_15" -> "172 conv2d_15" [label="(1, 96, 14, 14)", style=solid]; +"166 conv2d_15_scale_0" -> "169 quantize_per_channel_default_15" [label="(40,)", style=solid]; +"166 conv2d_15_scale_0" -> "170 dequantize_per_channel_default_15" [label="(40,)", style=solid]; +"167 conv2d_15_zero_point_0" -> "169 quantize_per_channel_default_15" [label="(40,)", style=solid]; +"167 conv2d_15_zero_point_0" -> "170 dequantize_per_channel_default_15" [label="(40,)", style=solid]; +"168 compressed_weight_updated_constant11" -> "169 quantize_per_channel_default_15" [label="(40, 96, 1, 1)", style=solid]; +"169 quantize_per_channel_default_15" -> "170 dequantize_per_channel_default_15" [label="(40, 96, 1, 1)", style=solid]; +"170 dequantize_per_channel_default_15" -> "172 conv2d_15" [label="(40, 96, 1, 1)", style=solid]; +"171 features_4_block_3_0_weight_bias_0_0" -> "172 conv2d_15" [label="(40,)", style=solid]; +"172 conv2d_15" -> "173 quantize_per_tensor_default_13" [label="(1, 40, 14, 14)", style=solid]; +"173 quantize_per_tensor_default_13" -> "174 dequantize_per_tensor_default_17" [label="(1, 40, 14, 14)", style=solid]; +"173 quantize_per_tensor_default_13" -> "175 dequantize_per_tensor_default_16" [label="(1, 40, 14, 14)", style=solid]; +"174 dequantize_per_tensor_default_17" -> "220 add__1" [label="(1, 40, 14, 14)", style=solid]; +"175 dequantize_per_tensor_default_16" -> "182 conv2d_16" [label="(1, 40, 14, 14)", style=solid]; +"176 conv2d_16_scale_0" -> "179 quantize_per_channel_default_16" [label="(240,)", style=solid]; +"176 conv2d_16_scale_0" -> "180 dequantize_per_channel_default_16" [label="(240,)", style=solid]; +"177 conv2d_16_zero_point_0" -> "179 quantize_per_channel_default_16" [label="(240,)", style=solid]; +"177 conv2d_16_zero_point_0" -> "180 dequantize_per_channel_default_16" [label="(240,)", style=solid]; +"178 compressed_weight_updated_constant12" -> "179 quantize_per_channel_default_16" [label="(240, 40, 1, 1)", style=solid]; +"179 quantize_per_channel_default_16" -> "180 dequantize_per_channel_default_16" [label="(240, 40, 1, 1)", style=solid]; +"180 dequantize_per_channel_default_16" -> "182 conv2d_16" [label="(240, 40, 1, 1)", style=solid]; +"181 features_5_block_0_0_weight_bias_0_0" -> "182 conv2d_16" [label="(240,)", style=solid]; +"182 conv2d_16" -> "185 hardswish__3" [label="(1, 240, 14, 14)", style=solid]; +"183 hardswish__3_scale_0" -> "186 quantize_per_channel_default_17" [label="(240,)", style=solid]; +"183 hardswish__3_scale_0" -> "187 dequantize_per_channel_default_17" [label="(240,)", style=solid]; +"184 hardswish__3_zero_point_0" -> "186 quantize_per_channel_default_17" [label="(240,)", style=solid]; +"184 hardswish__3_zero_point_0" -> "187 dequantize_per_channel_default_17" [label="(240,)", style=solid]; +"185 hardswish__3" -> "186 quantize_per_channel_default_17" [label="(1, 240, 14, 14)", style=solid]; +"186 quantize_per_channel_default_17" -> "187 dequantize_per_channel_default_17" [label="(1, 240, 14, 14)", style=solid]; +"187 dequantize_per_channel_default_17" -> "194 conv2d_17" [label="(1, 240, 14, 14)", style=solid]; +"188 conv2d_17_scale_0" -> "191 quantize_per_channel_default_18" [label="(240,)", style=solid]; +"188 conv2d_17_scale_0" -> "192 dequantize_per_channel_default_18" [label="(240,)", style=solid]; +"189 conv2d_17_zero_point_0" -> "191 quantize_per_channel_default_18" [label="(240,)", style=solid]; +"189 conv2d_17_zero_point_0" -> "192 dequantize_per_channel_default_18" [label="(240,)", style=solid]; +"190 compressed_weight_updated_constant13" -> "191 quantize_per_channel_default_18" [label="(240, 1, 5, 5)", style=solid]; +"191 quantize_per_channel_default_18" -> "192 dequantize_per_channel_default_18" [label="(240, 1, 5, 5)", style=solid]; +"192 dequantize_per_channel_default_18" -> "194 conv2d_17" [label="(240, 1, 5, 5)", style=solid]; +"193 features_5_block_1_0_weight_bias_0_0" -> "194 conv2d_17" [label="(240,)", style=solid]; +"194 conv2d_17" -> "195 hardswish__4" [label="(1, 240, 14, 14)", style=solid]; +"195 hardswish__4" -> "196 quantize_per_tensor_default_14" [label="(1, 240, 14, 14)", style=solid]; +"196 quantize_per_tensor_default_14" -> "197 dequantize_per_tensor_default_19" [label="(1, 240, 14, 14)", style=solid]; +"196 quantize_per_tensor_default_14" -> "198 dequantize_per_tensor_default_18" [label="(1, 240, 14, 14)", style=solid]; +"197 dequantize_per_tensor_default_19" -> "208 mul_2" [label="(1, 240, 14, 14)", style=solid]; +"198 dequantize_per_tensor_default_18" -> "199 adaptive_avg_pool2d_2" [label="(1, 240, 14, 14)", style=solid]; +"199 adaptive_avg_pool2d_2" -> "201 conv2d_18" [label="(1, 240, 1, 1)", style=solid]; +"200 features_5_block_2_fc1_bias_0_0" -> "201 conv2d_18" [label="(64,)", style=solid]; +"201 conv2d_18" -> "202 relu_2" [label="(1, 64, 1, 1)", style=solid]; +"202 relu_2" -> "204 conv2d_19" [label="(1, 64, 1, 1)", style=solid]; +"203 features_5_block_2_fc2_bias_0_0" -> "204 conv2d_19" [label="(240,)", style=solid]; +"204 conv2d_19" -> "205 hardsigmoid_2" [label="(1, 240, 1, 1)", style=solid]; +"205 hardsigmoid_2" -> "206 quantize_per_tensor_default_15" [label="(1, 240, 1, 1)", style=solid]; +"206 quantize_per_tensor_default_15" -> "207 dequantize_per_tensor_default_20" [label="(1, 240, 1, 1)", style=solid]; +"207 dequantize_per_tensor_default_20" -> "208 mul_2" [label="(1, 240, 1, 1)", style=solid]; +"208 mul_2" -> "209 quantize_per_tensor_default_16" [label="(1, 240, 14, 14)", style=solid]; +"209 quantize_per_tensor_default_16" -> "210 dequantize_per_tensor_default_21" [label="(1, 240, 14, 14)", style=solid]; +"210 dequantize_per_tensor_default_21" -> "217 conv2d_20" [label="(1, 240, 14, 14)", style=solid]; +"211 conv2d_20_scale_0" -> "214 quantize_per_channel_default_19" [label="(40,)", style=solid]; +"211 conv2d_20_scale_0" -> "215 dequantize_per_channel_default_19" [label="(40,)", style=solid]; +"212 conv2d_20_zero_point_0" -> "214 quantize_per_channel_default_19" [label="(40,)", style=solid]; +"212 conv2d_20_zero_point_0" -> "215 dequantize_per_channel_default_19" [label="(40,)", style=solid]; +"213 compressed_weight_updated_constant14" -> "214 quantize_per_channel_default_19" [label="(40, 240, 1, 1)", style=solid]; +"214 quantize_per_channel_default_19" -> "215 dequantize_per_channel_default_19" [label="(40, 240, 1, 1)", style=solid]; +"215 dequantize_per_channel_default_19" -> "217 conv2d_20" [label="(40, 240, 1, 1)", style=solid]; +"216 features_5_block_3_0_weight_bias_0_0" -> "217 conv2d_20" [label="(40,)", style=solid]; +"217 conv2d_20" -> "218 quantize_per_tensor_default_17" [label="(1, 40, 14, 14)", style=solid]; +"218 quantize_per_tensor_default_17" -> "219 dequantize_per_tensor_default_22" [label="(1, 40, 14, 14)", style=solid]; +"219 dequantize_per_tensor_default_22" -> "220 add__1" [label="(1, 40, 14, 14)", style=solid]; +"220 add__1" -> "221 quantize_per_tensor_default_18" [label="(1, 40, 14, 14)", style=solid]; +"221 quantize_per_tensor_default_18" -> "222 dequantize_per_tensor_default_24" [label="(1, 40, 14, 14)", style=solid]; +"221 quantize_per_tensor_default_18" -> "223 dequantize_per_tensor_default_23" [label="(1, 40, 14, 14)", style=solid]; +"222 dequantize_per_tensor_default_24" -> "268 add__2" [label="(1, 40, 14, 14)", style=solid]; +"223 dequantize_per_tensor_default_23" -> "230 conv2d_21" [label="(1, 40, 14, 14)", style=solid]; +"224 conv2d_21_scale_0" -> "227 quantize_per_channel_default_20" [label="(240,)", style=solid]; +"224 conv2d_21_scale_0" -> "228 dequantize_per_channel_default_20" [label="(240,)", style=solid]; +"225 conv2d_21_zero_point_0" -> "227 quantize_per_channel_default_20" [label="(240,)", style=solid]; +"225 conv2d_21_zero_point_0" -> "228 dequantize_per_channel_default_20" [label="(240,)", style=solid]; +"226 compressed_weight_updated_constant15" -> "227 quantize_per_channel_default_20" [label="(240, 40, 1, 1)", style=solid]; +"227 quantize_per_channel_default_20" -> "228 dequantize_per_channel_default_20" [label="(240, 40, 1, 1)", style=solid]; +"228 dequantize_per_channel_default_20" -> "230 conv2d_21" [label="(240, 40, 1, 1)", style=solid]; +"229 features_6_block_0_0_weight_bias_0_0" -> "230 conv2d_21" [label="(240,)", style=solid]; +"230 conv2d_21" -> "233 hardswish__5" [label="(1, 240, 14, 14)", style=solid]; +"231 hardswish__5_scale_0" -> "234 quantize_per_channel_default_21" [label="(240,)", style=solid]; +"231 hardswish__5_scale_0" -> "235 dequantize_per_channel_default_21" [label="(240,)", style=solid]; +"232 hardswish__5_zero_point_0" -> "234 quantize_per_channel_default_21" [label="(240,)", style=solid]; +"232 hardswish__5_zero_point_0" -> "235 dequantize_per_channel_default_21" [label="(240,)", style=solid]; +"233 hardswish__5" -> "234 quantize_per_channel_default_21" [label="(1, 240, 14, 14)", style=solid]; +"234 quantize_per_channel_default_21" -> "235 dequantize_per_channel_default_21" [label="(1, 240, 14, 14)", style=solid]; +"235 dequantize_per_channel_default_21" -> "242 conv2d_22" [label="(1, 240, 14, 14)", style=solid]; +"236 conv2d_22_scale_0" -> "239 quantize_per_channel_default_22" [label="(240,)", style=solid]; +"236 conv2d_22_scale_0" -> "240 dequantize_per_channel_default_22" [label="(240,)", style=solid]; +"237 conv2d_22_zero_point_0" -> "239 quantize_per_channel_default_22" [label="(240,)", style=solid]; +"237 conv2d_22_zero_point_0" -> "240 dequantize_per_channel_default_22" [label="(240,)", style=solid]; +"238 compressed_weight_updated_constant16" -> "239 quantize_per_channel_default_22" [label="(240, 1, 5, 5)", style=solid]; +"239 quantize_per_channel_default_22" -> "240 dequantize_per_channel_default_22" [label="(240, 1, 5, 5)", style=solid]; +"240 dequantize_per_channel_default_22" -> "242 conv2d_22" [label="(240, 1, 5, 5)", style=solid]; +"241 features_6_block_1_0_weight_bias_0_0" -> "242 conv2d_22" [label="(240,)", style=solid]; +"242 conv2d_22" -> "243 hardswish__6" [label="(1, 240, 14, 14)", style=solid]; +"243 hardswish__6" -> "244 quantize_per_tensor_default_19" [label="(1, 240, 14, 14)", style=solid]; +"244 quantize_per_tensor_default_19" -> "245 dequantize_per_tensor_default_26" [label="(1, 240, 14, 14)", style=solid]; +"244 quantize_per_tensor_default_19" -> "246 dequantize_per_tensor_default_25" [label="(1, 240, 14, 14)", style=solid]; +"245 dequantize_per_tensor_default_26" -> "256 mul_3" [label="(1, 240, 14, 14)", style=solid]; +"246 dequantize_per_tensor_default_25" -> "247 adaptive_avg_pool2d_3" [label="(1, 240, 14, 14)", style=solid]; +"247 adaptive_avg_pool2d_3" -> "249 conv2d_23" [label="(1, 240, 1, 1)", style=solid]; +"248 features_6_block_2_fc1_bias_0_0" -> "249 conv2d_23" [label="(64,)", style=solid]; +"249 conv2d_23" -> "250 relu_3" [label="(1, 64, 1, 1)", style=solid]; +"250 relu_3" -> "252 conv2d_24" [label="(1, 64, 1, 1)", style=solid]; +"251 features_6_block_2_fc2_bias_0_0" -> "252 conv2d_24" [label="(240,)", style=solid]; +"252 conv2d_24" -> "253 hardsigmoid_3" [label="(1, 240, 1, 1)", style=solid]; +"253 hardsigmoid_3" -> "254 quantize_per_tensor_default_20" [label="(1, 240, 1, 1)", style=solid]; +"254 quantize_per_tensor_default_20" -> "255 dequantize_per_tensor_default_27" [label="(1, 240, 1, 1)", style=solid]; +"255 dequantize_per_tensor_default_27" -> "256 mul_3" [label="(1, 240, 1, 1)", style=solid]; +"256 mul_3" -> "257 quantize_per_tensor_default_21" [label="(1, 240, 14, 14)", style=solid]; +"257 quantize_per_tensor_default_21" -> "258 dequantize_per_tensor_default_28" [label="(1, 240, 14, 14)", style=solid]; +"258 dequantize_per_tensor_default_28" -> "265 conv2d_25" [label="(1, 240, 14, 14)", style=solid]; +"259 conv2d_25_scale_0" -> "262 quantize_per_channel_default_23" [label="(40,)", style=solid]; +"259 conv2d_25_scale_0" -> "263 dequantize_per_channel_default_23" [label="(40,)", style=solid]; +"260 conv2d_25_zero_point_0" -> "262 quantize_per_channel_default_23" [label="(40,)", style=solid]; +"260 conv2d_25_zero_point_0" -> "263 dequantize_per_channel_default_23" [label="(40,)", style=solid]; +"261 compressed_weight_updated_constant17" -> "262 quantize_per_channel_default_23" [label="(40, 240, 1, 1)", style=solid]; +"262 quantize_per_channel_default_23" -> "263 dequantize_per_channel_default_23" [label="(40, 240, 1, 1)", style=solid]; +"263 dequantize_per_channel_default_23" -> "265 conv2d_25" [label="(40, 240, 1, 1)", style=solid]; +"264 features_6_block_3_0_weight_bias_0_0" -> "265 conv2d_25" [label="(40,)", style=solid]; +"265 conv2d_25" -> "266 quantize_per_tensor_default_22" [label="(1, 40, 14, 14)", style=solid]; +"266 quantize_per_tensor_default_22" -> "267 dequantize_per_tensor_default_29" [label="(1, 40, 14, 14)", style=solid]; +"267 dequantize_per_tensor_default_29" -> "268 add__2" [label="(1, 40, 14, 14)", style=solid]; +"268 add__2" -> "269 quantize_per_tensor_default_23" [label="(1, 40, 14, 14)", style=solid]; +"269 quantize_per_tensor_default_23" -> "270 dequantize_per_tensor_default_30" [label="(1, 40, 14, 14)", style=solid]; +"270 dequantize_per_tensor_default_30" -> "277 conv2d_26" [label="(1, 40, 14, 14)", style=solid]; +"271 conv2d_26_scale_0" -> "274 quantize_per_channel_default_24" [label="(120,)", style=solid]; +"271 conv2d_26_scale_0" -> "275 dequantize_per_channel_default_24" [label="(120,)", style=solid]; +"272 conv2d_26_zero_point_0" -> "274 quantize_per_channel_default_24" [label="(120,)", style=solid]; +"272 conv2d_26_zero_point_0" -> "275 dequantize_per_channel_default_24" [label="(120,)", style=solid]; +"273 compressed_weight_updated_constant18" -> "274 quantize_per_channel_default_24" [label="(120, 40, 1, 1)", style=solid]; +"274 quantize_per_channel_default_24" -> "275 dequantize_per_channel_default_24" [label="(120, 40, 1, 1)", style=solid]; +"275 dequantize_per_channel_default_24" -> "277 conv2d_26" [label="(120, 40, 1, 1)", style=solid]; +"276 features_7_block_0_0_weight_bias_0_0" -> "277 conv2d_26" [label="(120,)", style=solid]; +"277 conv2d_26" -> "280 hardswish__7" [label="(1, 120, 14, 14)", style=solid]; +"278 hardswish__7_scale_0" -> "281 quantize_per_channel_default_25" [label="(120,)", style=solid]; +"278 hardswish__7_scale_0" -> "282 dequantize_per_channel_default_25" [label="(120,)", style=solid]; +"279 hardswish__7_zero_point_0" -> "281 quantize_per_channel_default_25" [label="(120,)", style=solid]; +"279 hardswish__7_zero_point_0" -> "282 dequantize_per_channel_default_25" [label="(120,)", style=solid]; +"280 hardswish__7" -> "281 quantize_per_channel_default_25" [label="(1, 120, 14, 14)", style=solid]; +"281 quantize_per_channel_default_25" -> "282 dequantize_per_channel_default_25" [label="(1, 120, 14, 14)", style=solid]; +"282 dequantize_per_channel_default_25" -> "289 conv2d_27" [label="(1, 120, 14, 14)", style=solid]; +"283 conv2d_27_scale_0" -> "286 quantize_per_channel_default_26" [label="(120,)", style=solid]; +"283 conv2d_27_scale_0" -> "287 dequantize_per_channel_default_26" [label="(120,)", style=solid]; +"284 conv2d_27_zero_point_0" -> "286 quantize_per_channel_default_26" [label="(120,)", style=solid]; +"284 conv2d_27_zero_point_0" -> "287 dequantize_per_channel_default_26" [label="(120,)", style=solid]; +"285 compressed_weight_updated_constant19" -> "286 quantize_per_channel_default_26" [label="(120, 1, 5, 5)", style=solid]; +"286 quantize_per_channel_default_26" -> "287 dequantize_per_channel_default_26" [label="(120, 1, 5, 5)", style=solid]; +"287 dequantize_per_channel_default_26" -> "289 conv2d_27" [label="(120, 1, 5, 5)", style=solid]; +"288 features_7_block_1_0_weight_bias_0_0" -> "289 conv2d_27" [label="(120,)", style=solid]; +"289 conv2d_27" -> "290 hardswish__8" [label="(1, 120, 14, 14)", style=solid]; +"290 hardswish__8" -> "291 quantize_per_tensor_default_24" [label="(1, 120, 14, 14)", style=solid]; +"291 quantize_per_tensor_default_24" -> "292 dequantize_per_tensor_default_32" [label="(1, 120, 14, 14)", style=solid]; +"291 quantize_per_tensor_default_24" -> "293 dequantize_per_tensor_default_31" [label="(1, 120, 14, 14)", style=solid]; +"292 dequantize_per_tensor_default_32" -> "303 mul_4" [label="(1, 120, 14, 14)", style=solid]; +"293 dequantize_per_tensor_default_31" -> "294 adaptive_avg_pool2d_4" [label="(1, 120, 14, 14)", style=solid]; +"294 adaptive_avg_pool2d_4" -> "296 conv2d_28" [label="(1, 120, 1, 1)", style=solid]; +"295 features_7_block_2_fc1_bias_0_0" -> "296 conv2d_28" [label="(32,)", style=solid]; +"296 conv2d_28" -> "297 relu_4" [label="(1, 32, 1, 1)", style=solid]; +"297 relu_4" -> "299 conv2d_29" [label="(1, 32, 1, 1)", style=solid]; +"298 features_7_block_2_fc2_bias_0_0" -> "299 conv2d_29" [label="(120,)", style=solid]; +"299 conv2d_29" -> "300 hardsigmoid_4" [label="(1, 120, 1, 1)", style=solid]; +"300 hardsigmoid_4" -> "301 quantize_per_tensor_default_25" [label="(1, 120, 1, 1)", style=solid]; +"301 quantize_per_tensor_default_25" -> "302 dequantize_per_tensor_default_33" [label="(1, 120, 1, 1)", style=solid]; +"302 dequantize_per_tensor_default_33" -> "303 mul_4" [label="(1, 120, 1, 1)", style=solid]; +"303 mul_4" -> "304 quantize_per_tensor_default_26" [label="(1, 120, 14, 14)", style=solid]; +"304 quantize_per_tensor_default_26" -> "305 dequantize_per_tensor_default_34" [label="(1, 120, 14, 14)", style=solid]; +"305 dequantize_per_tensor_default_34" -> "312 conv2d_30" [label="(1, 120, 14, 14)", style=solid]; +"306 conv2d_30_scale_0" -> "309 quantize_per_channel_default_27" [label="(48,)", style=solid]; +"306 conv2d_30_scale_0" -> "310 dequantize_per_channel_default_27" [label="(48,)", style=solid]; +"307 conv2d_30_zero_point_0" -> "309 quantize_per_channel_default_27" [label="(48,)", style=solid]; +"307 conv2d_30_zero_point_0" -> "310 dequantize_per_channel_default_27" [label="(48,)", style=solid]; +"308 compressed_weight_updated_constant20" -> "309 quantize_per_channel_default_27" [label="(48, 120, 1, 1)", style=solid]; +"309 quantize_per_channel_default_27" -> "310 dequantize_per_channel_default_27" [label="(48, 120, 1, 1)", style=solid]; +"310 dequantize_per_channel_default_27" -> "312 conv2d_30" [label="(48, 120, 1, 1)", style=solid]; +"311 features_7_block_3_0_weight_bias_0_0" -> "312 conv2d_30" [label="(48,)", style=solid]; +"312 conv2d_30" -> "313 quantize_per_tensor_default_27" [label="(1, 48, 14, 14)", style=solid]; +"313 quantize_per_tensor_default_27" -> "314 dequantize_per_tensor_default_36" [label="(1, 48, 14, 14)", style=solid]; +"313 quantize_per_tensor_default_27" -> "315 dequantize_per_tensor_default_35" [label="(1, 48, 14, 14)", style=solid]; +"314 dequantize_per_tensor_default_36" -> "360 add__3" [label="(1, 48, 14, 14)", style=solid]; +"315 dequantize_per_tensor_default_35" -> "322 conv2d_31" [label="(1, 48, 14, 14)", style=solid]; +"316 conv2d_31_scale_0" -> "319 quantize_per_channel_default_28" [label="(144,)", style=solid]; +"316 conv2d_31_scale_0" -> "320 dequantize_per_channel_default_28" [label="(144,)", style=solid]; +"317 conv2d_31_zero_point_0" -> "319 quantize_per_channel_default_28" [label="(144,)", style=solid]; +"317 conv2d_31_zero_point_0" -> "320 dequantize_per_channel_default_28" [label="(144,)", style=solid]; +"318 compressed_weight_updated_constant21" -> "319 quantize_per_channel_default_28" [label="(144, 48, 1, 1)", style=solid]; +"319 quantize_per_channel_default_28" -> "320 dequantize_per_channel_default_28" [label="(144, 48, 1, 1)", style=solid]; +"320 dequantize_per_channel_default_28" -> "322 conv2d_31" [label="(144, 48, 1, 1)", style=solid]; +"321 features_8_block_0_0_weight_bias_0_0" -> "322 conv2d_31" [label="(144,)", style=solid]; +"322 conv2d_31" -> "325 hardswish__9" [label="(1, 144, 14, 14)", style=solid]; +"323 hardswish__9_scale_0" -> "326 quantize_per_channel_default_29" [label="(144,)", style=solid]; +"323 hardswish__9_scale_0" -> "327 dequantize_per_channel_default_29" [label="(144,)", style=solid]; +"324 hardswish__9_zero_point_0" -> "326 quantize_per_channel_default_29" [label="(144,)", style=solid]; +"324 hardswish__9_zero_point_0" -> "327 dequantize_per_channel_default_29" [label="(144,)", style=solid]; +"325 hardswish__9" -> "326 quantize_per_channel_default_29" [label="(1, 144, 14, 14)", style=solid]; +"326 quantize_per_channel_default_29" -> "327 dequantize_per_channel_default_29" [label="(1, 144, 14, 14)", style=solid]; +"327 dequantize_per_channel_default_29" -> "334 conv2d_32" [label="(1, 144, 14, 14)", style=solid]; +"328 conv2d_32_scale_0" -> "331 quantize_per_channel_default_30" [label="(144,)", style=solid]; +"328 conv2d_32_scale_0" -> "332 dequantize_per_channel_default_30" [label="(144,)", style=solid]; +"329 conv2d_32_zero_point_0" -> "331 quantize_per_channel_default_30" [label="(144,)", style=solid]; +"329 conv2d_32_zero_point_0" -> "332 dequantize_per_channel_default_30" [label="(144,)", style=solid]; +"330 compressed_weight_updated_constant22" -> "331 quantize_per_channel_default_30" [label="(144, 1, 5, 5)", style=solid]; +"331 quantize_per_channel_default_30" -> "332 dequantize_per_channel_default_30" [label="(144, 1, 5, 5)", style=solid]; +"332 dequantize_per_channel_default_30" -> "334 conv2d_32" [label="(144, 1, 5, 5)", style=solid]; +"333 features_8_block_1_0_weight_bias_0_0" -> "334 conv2d_32" [label="(144,)", style=solid]; +"334 conv2d_32" -> "335 hardswish__10" [label="(1, 144, 14, 14)", style=solid]; +"335 hardswish__10" -> "336 quantize_per_tensor_default_28" [label="(1, 144, 14, 14)", style=solid]; +"336 quantize_per_tensor_default_28" -> "337 dequantize_per_tensor_default_38" [label="(1, 144, 14, 14)", style=solid]; +"336 quantize_per_tensor_default_28" -> "338 dequantize_per_tensor_default_37" [label="(1, 144, 14, 14)", style=solid]; +"337 dequantize_per_tensor_default_38" -> "348 mul_5" [label="(1, 144, 14, 14)", style=solid]; +"338 dequantize_per_tensor_default_37" -> "339 adaptive_avg_pool2d_5" [label="(1, 144, 14, 14)", style=solid]; +"339 adaptive_avg_pool2d_5" -> "341 conv2d_33" [label="(1, 144, 1, 1)", style=solid]; +"340 features_8_block_2_fc1_bias_0_0" -> "341 conv2d_33" [label="(40,)", style=solid]; +"341 conv2d_33" -> "342 relu_5" [label="(1, 40, 1, 1)", style=solid]; +"342 relu_5" -> "344 conv2d_34" [label="(1, 40, 1, 1)", style=solid]; +"343 features_8_block_2_fc2_bias_0_0" -> "344 conv2d_34" [label="(144,)", style=solid]; +"344 conv2d_34" -> "345 hardsigmoid_5" [label="(1, 144, 1, 1)", style=solid]; +"345 hardsigmoid_5" -> "346 quantize_per_tensor_default_29" [label="(1, 144, 1, 1)", style=solid]; +"346 quantize_per_tensor_default_29" -> "347 dequantize_per_tensor_default_39" [label="(1, 144, 1, 1)", style=solid]; +"347 dequantize_per_tensor_default_39" -> "348 mul_5" [label="(1, 144, 1, 1)", style=solid]; +"348 mul_5" -> "349 quantize_per_tensor_default_30" [label="(1, 144, 14, 14)", style=solid]; +"349 quantize_per_tensor_default_30" -> "350 dequantize_per_tensor_default_40" [label="(1, 144, 14, 14)", style=solid]; +"350 dequantize_per_tensor_default_40" -> "357 conv2d_35" [label="(1, 144, 14, 14)", style=solid]; +"351 conv2d_35_scale_0" -> "354 quantize_per_channel_default_31" [label="(48,)", style=solid]; +"351 conv2d_35_scale_0" -> "355 dequantize_per_channel_default_31" [label="(48,)", style=solid]; +"352 conv2d_35_zero_point_0" -> "354 quantize_per_channel_default_31" [label="(48,)", style=solid]; +"352 conv2d_35_zero_point_0" -> "355 dequantize_per_channel_default_31" [label="(48,)", style=solid]; +"353 compressed_weight_updated_constant23" -> "354 quantize_per_channel_default_31" [label="(48, 144, 1, 1)", style=solid]; +"354 quantize_per_channel_default_31" -> "355 dequantize_per_channel_default_31" [label="(48, 144, 1, 1)", style=solid]; +"355 dequantize_per_channel_default_31" -> "357 conv2d_35" [label="(48, 144, 1, 1)", style=solid]; +"356 features_8_block_3_0_weight_bias_0_0" -> "357 conv2d_35" [label="(48,)", style=solid]; +"357 conv2d_35" -> "358 quantize_per_tensor_default_31" [label="(1, 48, 14, 14)", style=solid]; +"358 quantize_per_tensor_default_31" -> "359 dequantize_per_tensor_default_41" [label="(1, 48, 14, 14)", style=solid]; +"359 dequantize_per_tensor_default_41" -> "360 add__3" [label="(1, 48, 14, 14)", style=solid]; +"360 add__3" -> "361 quantize_per_tensor_default_32" [label="(1, 48, 14, 14)", style=solid]; +"361 quantize_per_tensor_default_32" -> "362 dequantize_per_tensor_default_42" [label="(1, 48, 14, 14)", style=solid]; +"362 dequantize_per_tensor_default_42" -> "369 conv2d_36" [label="(1, 48, 14, 14)", style=solid]; +"363 conv2d_36_scale_0" -> "366 quantize_per_channel_default_32" [label="(288,)", style=solid]; +"363 conv2d_36_scale_0" -> "367 dequantize_per_channel_default_32" [label="(288,)", style=solid]; +"364 conv2d_36_zero_point_0" -> "366 quantize_per_channel_default_32" [label="(288,)", style=solid]; +"364 conv2d_36_zero_point_0" -> "367 dequantize_per_channel_default_32" [label="(288,)", style=solid]; +"365 compressed_weight_updated_constant24" -> "366 quantize_per_channel_default_32" [label="(288, 48, 1, 1)", style=solid]; +"366 quantize_per_channel_default_32" -> "367 dequantize_per_channel_default_32" [label="(288, 48, 1, 1)", style=solid]; +"367 dequantize_per_channel_default_32" -> "369 conv2d_36" [label="(288, 48, 1, 1)", style=solid]; +"368 features_9_block_0_0_weight_bias_0_0" -> "369 conv2d_36" [label="(288,)", style=solid]; +"369 conv2d_36" -> "372 hardswish__11" [label="(1, 288, 14, 14)", style=solid]; +"370 hardswish__11_scale_0" -> "373 quantize_per_channel_default_33" [label="(288,)", style=solid]; +"370 hardswish__11_scale_0" -> "374 dequantize_per_channel_default_33" [label="(288,)", style=solid]; +"371 hardswish__11_zero_point_0" -> "373 quantize_per_channel_default_33" [label="(288,)", style=solid]; +"371 hardswish__11_zero_point_0" -> "374 dequantize_per_channel_default_33" [label="(288,)", style=solid]; +"372 hardswish__11" -> "373 quantize_per_channel_default_33" [label="(1, 288, 14, 14)", style=solid]; +"373 quantize_per_channel_default_33" -> "374 dequantize_per_channel_default_33" [label="(1, 288, 14, 14)", style=solid]; +"374 dequantize_per_channel_default_33" -> "381 conv2d_37" [label="(1, 288, 14, 14)", style=solid]; +"375 conv2d_37_scale_0" -> "378 quantize_per_channel_default_34" [label="(288,)", style=solid]; +"375 conv2d_37_scale_0" -> "379 dequantize_per_channel_default_34" [label="(288,)", style=solid]; +"376 conv2d_37_zero_point_0" -> "378 quantize_per_channel_default_34" [label="(288,)", style=solid]; +"376 conv2d_37_zero_point_0" -> "379 dequantize_per_channel_default_34" [label="(288,)", style=solid]; +"377 compressed_weight_updated_constant25" -> "378 quantize_per_channel_default_34" [label="(288, 1, 5, 5)", style=solid]; +"378 quantize_per_channel_default_34" -> "379 dequantize_per_channel_default_34" [label="(288, 1, 5, 5)", style=solid]; +"379 dequantize_per_channel_default_34" -> "381 conv2d_37" [label="(288, 1, 5, 5)", style=solid]; +"380 features_9_block_1_0_weight_bias_0_0" -> "381 conv2d_37" [label="(288,)", style=solid]; +"381 conv2d_37" -> "382 hardswish__12" [label="(1, 288, 7, 7)", style=solid]; +"382 hardswish__12" -> "383 quantize_per_tensor_default_33" [label="(1, 288, 7, 7)", style=solid]; +"383 quantize_per_tensor_default_33" -> "384 dequantize_per_tensor_default_44" [label="(1, 288, 7, 7)", style=solid]; +"383 quantize_per_tensor_default_33" -> "385 dequantize_per_tensor_default_43" [label="(1, 288, 7, 7)", style=solid]; +"384 dequantize_per_tensor_default_44" -> "395 mul_6" [label="(1, 288, 7, 7)", style=solid]; +"385 dequantize_per_tensor_default_43" -> "386 adaptive_avg_pool2d_6" [label="(1, 288, 7, 7)", style=solid]; +"386 adaptive_avg_pool2d_6" -> "388 conv2d_38" [label="(1, 288, 1, 1)", style=solid]; +"387 features_9_block_2_fc1_bias_0_0" -> "388 conv2d_38" [label="(72,)", style=solid]; +"388 conv2d_38" -> "389 relu_6" [label="(1, 72, 1, 1)", style=solid]; +"389 relu_6" -> "391 conv2d_39" [label="(1, 72, 1, 1)", style=solid]; +"390 features_9_block_2_fc2_bias_0_0" -> "391 conv2d_39" [label="(288,)", style=solid]; +"391 conv2d_39" -> "392 hardsigmoid_6" [label="(1, 288, 1, 1)", style=solid]; +"392 hardsigmoid_6" -> "393 quantize_per_tensor_default_34" [label="(1, 288, 1, 1)", style=solid]; +"393 quantize_per_tensor_default_34" -> "394 dequantize_per_tensor_default_45" [label="(1, 288, 1, 1)", style=solid]; +"394 dequantize_per_tensor_default_45" -> "395 mul_6" [label="(1, 288, 1, 1)", style=solid]; +"395 mul_6" -> "396 quantize_per_tensor_default_35" [label="(1, 288, 7, 7)", style=solid]; +"396 quantize_per_tensor_default_35" -> "397 dequantize_per_tensor_default_46" [label="(1, 288, 7, 7)", style=solid]; +"397 dequantize_per_tensor_default_46" -> "404 conv2d_40" [label="(1, 288, 7, 7)", style=solid]; +"398 conv2d_40_scale_0" -> "401 quantize_per_channel_default_35" [label="(96,)", style=solid]; +"398 conv2d_40_scale_0" -> "402 dequantize_per_channel_default_35" [label="(96,)", style=solid]; +"399 conv2d_40_zero_point_0" -> "401 quantize_per_channel_default_35" [label="(96,)", style=solid]; +"399 conv2d_40_zero_point_0" -> "402 dequantize_per_channel_default_35" [label="(96,)", style=solid]; +"400 compressed_weight_updated_constant26" -> "401 quantize_per_channel_default_35" [label="(96, 288, 1, 1)", style=solid]; +"401 quantize_per_channel_default_35" -> "402 dequantize_per_channel_default_35" [label="(96, 288, 1, 1)", style=solid]; +"402 dequantize_per_channel_default_35" -> "404 conv2d_40" [label="(96, 288, 1, 1)", style=solid]; +"403 features_9_block_3_0_weight_bias_0_0" -> "404 conv2d_40" [label="(96,)", style=solid]; +"404 conv2d_40" -> "405 quantize_per_tensor_default_36" [label="(1, 96, 7, 7)", style=solid]; +"405 quantize_per_tensor_default_36" -> "406 dequantize_per_tensor_default_48" [label="(1, 96, 7, 7)", style=solid]; +"405 quantize_per_tensor_default_36" -> "407 dequantize_per_tensor_default_47" [label="(1, 96, 7, 7)", style=solid]; +"406 dequantize_per_tensor_default_48" -> "452 add__4" [label="(1, 96, 7, 7)", style=solid]; +"407 dequantize_per_tensor_default_47" -> "414 conv2d_41" [label="(1, 96, 7, 7)", style=solid]; +"408 conv2d_41_scale_0" -> "411 quantize_per_channel_default_36" [label="(576,)", style=solid]; +"408 conv2d_41_scale_0" -> "412 dequantize_per_channel_default_36" [label="(576,)", style=solid]; +"409 conv2d_41_zero_point_0" -> "411 quantize_per_channel_default_36" [label="(576,)", style=solid]; +"409 conv2d_41_zero_point_0" -> "412 dequantize_per_channel_default_36" [label="(576,)", style=solid]; +"410 compressed_weight_updated_constant27" -> "411 quantize_per_channel_default_36" [label="(576, 96, 1, 1)", style=solid]; +"411 quantize_per_channel_default_36" -> "412 dequantize_per_channel_default_36" [label="(576, 96, 1, 1)", style=solid]; +"412 dequantize_per_channel_default_36" -> "414 conv2d_41" [label="(576, 96, 1, 1)", style=solid]; +"413 features_10_block_0_0_weight_bias_0_0" -> "414 conv2d_41" [label="(576,)", style=solid]; +"414 conv2d_41" -> "417 hardswish__13" [label="(1, 576, 7, 7)", style=solid]; +"415 hardswish__13_scale_0" -> "418 quantize_per_channel_default_37" [label="(576,)", style=solid]; +"415 hardswish__13_scale_0" -> "419 dequantize_per_channel_default_37" [label="(576,)", style=solid]; +"416 hardswish__13_zero_point_0" -> "418 quantize_per_channel_default_37" [label="(576,)", style=solid]; +"416 hardswish__13_zero_point_0" -> "419 dequantize_per_channel_default_37" [label="(576,)", style=solid]; +"417 hardswish__13" -> "418 quantize_per_channel_default_37" [label="(1, 576, 7, 7)", style=solid]; +"418 quantize_per_channel_default_37" -> "419 dequantize_per_channel_default_37" [label="(1, 576, 7, 7)", style=solid]; +"419 dequantize_per_channel_default_37" -> "426 conv2d_42" [label="(1, 576, 7, 7)", style=solid]; +"420 conv2d_42_scale_0" -> "423 quantize_per_channel_default_38" [label="(576,)", style=solid]; +"420 conv2d_42_scale_0" -> "424 dequantize_per_channel_default_38" [label="(576,)", style=solid]; +"421 conv2d_42_zero_point_0" -> "423 quantize_per_channel_default_38" [label="(576,)", style=solid]; +"421 conv2d_42_zero_point_0" -> "424 dequantize_per_channel_default_38" [label="(576,)", style=solid]; +"422 compressed_weight_updated_constant28" -> "423 quantize_per_channel_default_38" [label="(576, 1, 5, 5)", style=solid]; +"423 quantize_per_channel_default_38" -> "424 dequantize_per_channel_default_38" [label="(576, 1, 5, 5)", style=solid]; +"424 dequantize_per_channel_default_38" -> "426 conv2d_42" [label="(576, 1, 5, 5)", style=solid]; +"425 features_10_block_1_0_weight_bias_0_0" -> "426 conv2d_42" [label="(576,)", style=solid]; +"426 conv2d_42" -> "427 hardswish__14" [label="(1, 576, 7, 7)", style=solid]; +"427 hardswish__14" -> "428 quantize_per_tensor_default_37" [label="(1, 576, 7, 7)", style=solid]; +"428 quantize_per_tensor_default_37" -> "429 dequantize_per_tensor_default_50" [label="(1, 576, 7, 7)", style=solid]; +"428 quantize_per_tensor_default_37" -> "430 dequantize_per_tensor_default_49" [label="(1, 576, 7, 7)", style=solid]; +"429 dequantize_per_tensor_default_50" -> "440 mul_7" [label="(1, 576, 7, 7)", style=solid]; +"430 dequantize_per_tensor_default_49" -> "431 adaptive_avg_pool2d_7" [label="(1, 576, 7, 7)", style=solid]; +"431 adaptive_avg_pool2d_7" -> "433 conv2d_43" [label="(1, 576, 1, 1)", style=solid]; +"432 features_10_block_2_fc1_bias_0_0" -> "433 conv2d_43" [label="(144,)", style=solid]; +"433 conv2d_43" -> "434 relu_7" [label="(1, 144, 1, 1)", style=solid]; +"434 relu_7" -> "436 conv2d_44" [label="(1, 144, 1, 1)", style=solid]; +"435 features_10_block_2_fc2_bias_0_0" -> "436 conv2d_44" [label="(576,)", style=solid]; +"436 conv2d_44" -> "437 hardsigmoid_7" [label="(1, 576, 1, 1)", style=solid]; +"437 hardsigmoid_7" -> "438 quantize_per_tensor_default_38" [label="(1, 576, 1, 1)", style=solid]; +"438 quantize_per_tensor_default_38" -> "439 dequantize_per_tensor_default_51" [label="(1, 576, 1, 1)", style=solid]; +"439 dequantize_per_tensor_default_51" -> "440 mul_7" [label="(1, 576, 1, 1)", style=solid]; +"440 mul_7" -> "441 quantize_per_tensor_default_39" [label="(1, 576, 7, 7)", style=solid]; +"441 quantize_per_tensor_default_39" -> "442 dequantize_per_tensor_default_52" [label="(1, 576, 7, 7)", style=solid]; +"442 dequantize_per_tensor_default_52" -> "449 conv2d_45" [label="(1, 576, 7, 7)", style=solid]; +"443 conv2d_45_scale_0" -> "446 quantize_per_channel_default_39" [label="(96,)", style=solid]; +"443 conv2d_45_scale_0" -> "447 dequantize_per_channel_default_39" [label="(96,)", style=solid]; +"444 conv2d_45_zero_point_0" -> "446 quantize_per_channel_default_39" [label="(96,)", style=solid]; +"444 conv2d_45_zero_point_0" -> "447 dequantize_per_channel_default_39" [label="(96,)", style=solid]; +"445 compressed_weight_updated_constant29" -> "446 quantize_per_channel_default_39" [label="(96, 576, 1, 1)", style=solid]; +"446 quantize_per_channel_default_39" -> "447 dequantize_per_channel_default_39" [label="(96, 576, 1, 1)", style=solid]; +"447 dequantize_per_channel_default_39" -> "449 conv2d_45" [label="(96, 576, 1, 1)", style=solid]; +"448 features_10_block_3_0_weight_bias_0_0" -> "449 conv2d_45" [label="(96,)", style=solid]; +"449 conv2d_45" -> "450 quantize_per_tensor_default_40" [label="(1, 96, 7, 7)", style=solid]; +"450 quantize_per_tensor_default_40" -> "451 dequantize_per_tensor_default_53" [label="(1, 96, 7, 7)", style=solid]; +"451 dequantize_per_tensor_default_53" -> "452 add__4" [label="(1, 96, 7, 7)", style=solid]; +"452 add__4" -> "453 quantize_per_tensor_default_41" [label="(1, 96, 7, 7)", style=solid]; +"453 quantize_per_tensor_default_41" -> "454 dequantize_per_tensor_default_55" [label="(1, 96, 7, 7)", style=solid]; +"453 quantize_per_tensor_default_41" -> "455 dequantize_per_tensor_default_54" [label="(1, 96, 7, 7)", style=solid]; +"454 dequantize_per_tensor_default_55" -> "500 add__5" [label="(1, 96, 7, 7)", style=solid]; +"455 dequantize_per_tensor_default_54" -> "462 conv2d_46" [label="(1, 96, 7, 7)", style=solid]; +"456 conv2d_46_scale_0" -> "459 quantize_per_channel_default_40" [label="(576,)", style=solid]; +"456 conv2d_46_scale_0" -> "460 dequantize_per_channel_default_40" [label="(576,)", style=solid]; +"457 conv2d_46_zero_point_0" -> "459 quantize_per_channel_default_40" [label="(576,)", style=solid]; +"457 conv2d_46_zero_point_0" -> "460 dequantize_per_channel_default_40" [label="(576,)", style=solid]; +"458 compressed_weight_updated_constant30" -> "459 quantize_per_channel_default_40" [label="(576, 96, 1, 1)", style=solid]; +"459 quantize_per_channel_default_40" -> "460 dequantize_per_channel_default_40" [label="(576, 96, 1, 1)", style=solid]; +"460 dequantize_per_channel_default_40" -> "462 conv2d_46" [label="(576, 96, 1, 1)", style=solid]; +"461 features_11_block_0_0_weight_bias_0_0" -> "462 conv2d_46" [label="(576,)", style=solid]; +"462 conv2d_46" -> "465 hardswish__15" [label="(1, 576, 7, 7)", style=solid]; +"463 hardswish__15_scale_0" -> "466 quantize_per_channel_default_41" [label="(576,)", style=solid]; +"463 hardswish__15_scale_0" -> "467 dequantize_per_channel_default_41" [label="(576,)", style=solid]; +"464 hardswish__15_zero_point_0" -> "466 quantize_per_channel_default_41" [label="(576,)", style=solid]; +"464 hardswish__15_zero_point_0" -> "467 dequantize_per_channel_default_41" [label="(576,)", style=solid]; +"465 hardswish__15" -> "466 quantize_per_channel_default_41" [label="(1, 576, 7, 7)", style=solid]; +"466 quantize_per_channel_default_41" -> "467 dequantize_per_channel_default_41" [label="(1, 576, 7, 7)", style=solid]; +"467 dequantize_per_channel_default_41" -> "474 conv2d_47" [label="(1, 576, 7, 7)", style=solid]; +"468 conv2d_47_scale_0" -> "471 quantize_per_channel_default_42" [label="(576,)", style=solid]; +"468 conv2d_47_scale_0" -> "472 dequantize_per_channel_default_42" [label="(576,)", style=solid]; +"469 conv2d_47_zero_point_0" -> "471 quantize_per_channel_default_42" [label="(576,)", style=solid]; +"469 conv2d_47_zero_point_0" -> "472 dequantize_per_channel_default_42" [label="(576,)", style=solid]; +"470 compressed_weight_updated_constant31" -> "471 quantize_per_channel_default_42" [label="(576, 1, 5, 5)", style=solid]; +"471 quantize_per_channel_default_42" -> "472 dequantize_per_channel_default_42" [label="(576, 1, 5, 5)", style=solid]; +"472 dequantize_per_channel_default_42" -> "474 conv2d_47" [label="(576, 1, 5, 5)", style=solid]; +"473 features_11_block_1_0_weight_bias_0_0" -> "474 conv2d_47" [label="(576,)", style=solid]; +"474 conv2d_47" -> "475 hardswish__16" [label="(1, 576, 7, 7)", style=solid]; +"475 hardswish__16" -> "476 quantize_per_tensor_default_42" [label="(1, 576, 7, 7)", style=solid]; +"476 quantize_per_tensor_default_42" -> "477 dequantize_per_tensor_default_57" [label="(1, 576, 7, 7)", style=solid]; +"476 quantize_per_tensor_default_42" -> "478 dequantize_per_tensor_default_56" [label="(1, 576, 7, 7)", style=solid]; +"477 dequantize_per_tensor_default_57" -> "488 mul_8" [label="(1, 576, 7, 7)", style=solid]; +"478 dequantize_per_tensor_default_56" -> "479 adaptive_avg_pool2d_8" [label="(1, 576, 7, 7)", style=solid]; +"479 adaptive_avg_pool2d_8" -> "481 conv2d_48" [label="(1, 576, 1, 1)", style=solid]; +"480 features_11_block_2_fc1_bias_0_0" -> "481 conv2d_48" [label="(144,)", style=solid]; +"481 conv2d_48" -> "482 relu_8" [label="(1, 144, 1, 1)", style=solid]; +"482 relu_8" -> "484 conv2d_49" [label="(1, 144, 1, 1)", style=solid]; +"483 features_11_block_2_fc2_bias_0_0" -> "484 conv2d_49" [label="(576,)", style=solid]; +"484 conv2d_49" -> "485 hardsigmoid_8" [label="(1, 576, 1, 1)", style=solid]; +"485 hardsigmoid_8" -> "486 quantize_per_tensor_default_43" [label="(1, 576, 1, 1)", style=solid]; +"486 quantize_per_tensor_default_43" -> "487 dequantize_per_tensor_default_58" [label="(1, 576, 1, 1)", style=solid]; +"487 dequantize_per_tensor_default_58" -> "488 mul_8" [label="(1, 576, 1, 1)", style=solid]; +"488 mul_8" -> "489 quantize_per_tensor_default_44" [label="(1, 576, 7, 7)", style=solid]; +"489 quantize_per_tensor_default_44" -> "490 dequantize_per_tensor_default_59" [label="(1, 576, 7, 7)", style=solid]; +"490 dequantize_per_tensor_default_59" -> "497 conv2d_50" [label="(1, 576, 7, 7)", style=solid]; +"491 conv2d_50_scale_0" -> "494 quantize_per_channel_default_43" [label="(96,)", style=solid]; +"491 conv2d_50_scale_0" -> "495 dequantize_per_channel_default_43" [label="(96,)", style=solid]; +"492 conv2d_50_zero_point_0" -> "494 quantize_per_channel_default_43" [label="(96,)", style=solid]; +"492 conv2d_50_zero_point_0" -> "495 dequantize_per_channel_default_43" [label="(96,)", style=solid]; +"493 compressed_weight_updated_constant32" -> "494 quantize_per_channel_default_43" [label="(96, 576, 1, 1)", style=solid]; +"494 quantize_per_channel_default_43" -> "495 dequantize_per_channel_default_43" [label="(96, 576, 1, 1)", style=solid]; +"495 dequantize_per_channel_default_43" -> "497 conv2d_50" [label="(96, 576, 1, 1)", style=solid]; +"496 features_11_block_3_0_weight_bias_0_0" -> "497 conv2d_50" [label="(96,)", style=solid]; +"497 conv2d_50" -> "498 quantize_per_tensor_default_45" [label="(1, 96, 7, 7)", style=solid]; +"498 quantize_per_tensor_default_45" -> "499 dequantize_per_tensor_default_60" [label="(1, 96, 7, 7)", style=solid]; +"499 dequantize_per_tensor_default_60" -> "500 add__5" [label="(1, 96, 7, 7)", style=solid]; +"500 add__5" -> "501 quantize_per_tensor_default_46" [label="(1, 96, 7, 7)", style=solid]; +"501 quantize_per_tensor_default_46" -> "502 dequantize_per_tensor_default_61" [label="(1, 96, 7, 7)", style=solid]; +"502 dequantize_per_tensor_default_61" -> "509 conv2d_51" [label="(1, 96, 7, 7)", style=solid]; +"503 conv2d_51_scale_0" -> "506 quantize_per_channel_default_44" [label="(576,)", style=solid]; +"503 conv2d_51_scale_0" -> "507 dequantize_per_channel_default_44" [label="(576,)", style=solid]; +"504 conv2d_51_zero_point_0" -> "506 quantize_per_channel_default_44" [label="(576,)", style=solid]; +"504 conv2d_51_zero_point_0" -> "507 dequantize_per_channel_default_44" [label="(576,)", style=solid]; +"505 compressed_weight_updated_constant33" -> "506 quantize_per_channel_default_44" [label="(576, 96, 1, 1)", style=solid]; +"506 quantize_per_channel_default_44" -> "507 dequantize_per_channel_default_44" [label="(576, 96, 1, 1)", style=solid]; +"507 dequantize_per_channel_default_44" -> "509 conv2d_51" [label="(576, 96, 1, 1)", style=solid]; +"508 features_12_0_weight_bias_0_0" -> "509 conv2d_51" [label="(576,)", style=solid]; +"509 conv2d_51" -> "510 hardswish__17" [label="(1, 576, 7, 7)", style=solid]; +"510 hardswish__17" -> "511 quantize_per_tensor_default_47" [label="(1, 576, 7, 7)", style=solid]; +"511 quantize_per_tensor_default_47" -> "512 dequantize_per_tensor_default_62" [label="(1, 576, 7, 7)", style=solid]; +"512 dequantize_per_tensor_default_62" -> "513 adaptive_avg_pool2d_9" [label="(1, 576, 7, 7)", style=solid]; +"513 adaptive_avg_pool2d_9" -> "514 quantize_per_tensor_default_48" [label="(1, 576, 1, 1)", style=solid]; +"514 quantize_per_tensor_default_48" -> "515 dequantize_per_tensor_default_63" [label="(1, 576, 1, 1)", style=solid]; +"515 dequantize_per_tensor_default_63" -> "516 flatten" [label="(1, 576, 1, 1)", style=solid]; +"516 flatten" -> "523 linear" [label="(1, 576)", style=solid]; +"517 linear_scale_0" -> "520 quantize_per_channel_default_45" [label="(1024,)", style=solid]; +"517 linear_scale_0" -> "521 dequantize_per_channel_default_45" [label="(1024,)", style=solid]; +"518 linear_zero_point_0" -> "520 quantize_per_channel_default_45" [label="(1024,)", style=solid]; +"518 linear_zero_point_0" -> "521 dequantize_per_channel_default_45" [label="(1024,)", style=solid]; +"519 compressed_weight_updated_constant34" -> "520 quantize_per_channel_default_45" [label="(1024, 576)", style=solid]; +"520 quantize_per_channel_default_45" -> "521 dequantize_per_channel_default_45" [label="(1024, 576)", style=solid]; +"521 dequantize_per_channel_default_45" -> "523 linear" [label="(1024, 576)", style=solid]; +"522 classifier_0_bias_0_0" -> "523 linear" [label="(1024,)", style=solid]; +"523 linear" -> "524 hardswish__18" [label="(1, 1024)", style=solid]; +"524 hardswish__18" -> "525 quantize_per_tensor_default_49" [label="(1, 1024)", style=solid]; +"525 quantize_per_tensor_default_49" -> "526 dequantize_per_tensor_default_64" [label="(1, 1024)", style=solid]; +"526 dequantize_per_tensor_default_64" -> "527 dropout_" [label="(1, 1024)", style=solid]; +"527 dropout_" -> "534 linear_1" [label="(1, 1024)", style=solid]; +"528 linear_1_scale_0" -> "531 quantize_per_channel_default_46" [label="(1000,)", style=solid]; +"528 linear_1_scale_0" -> "532 dequantize_per_channel_default_46" [label="(1000,)", style=solid]; +"529 linear_1_zero_point_0" -> "531 quantize_per_channel_default_46" [label="(1000,)", style=solid]; +"529 linear_1_zero_point_0" -> "532 dequantize_per_channel_default_46" [label="(1000,)", style=solid]; +"530 compressed_weight_updated_constant35" -> "531 quantize_per_channel_default_46" [label="(1000, 1024)", style=solid]; +"531 quantize_per_channel_default_46" -> "532 dequantize_per_channel_default_46" [label="(1000, 1024)", style=solid]; +"532 dequantize_per_channel_default_46" -> "534 linear_1" [label="(1000, 1024)", style=solid]; +"533 classifier_3_bias_0_0" -> "534 linear_1" [label="(1000,)", style=solid]; +"534 linear_1" -> "535 output" [label="(1, 1000)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/quantized/resnet18.dot b/tests/torch/data/reference_graphs/fx/quantized/resnet18.dot index e7eefb3da1c..e6875129c47 100644 --- a/tests/torch/data/reference_graphs/fx/quantized/resnet18.dot +++ b/tests/torch/data/reference_graphs/fx/quantized/resnet18.dot @@ -1,10 +1,10 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; +"0 x" [id=0, type=input]; "1 quantize_per_tensor_default" [id=1, type=quantize_per_tensor]; "2 dequantize_per_tensor_default" [id=2, type=dequantize_per_tensor]; -"3 _param_constant0" [id=3, type=get_attr]; -"4 conv2d_scale_0" [id=4, type=get_attr]; -"5 conv2d_zero_point_0" [id=5, type=get_attr]; +"3 conv2d_scale_0" [id=3, type=get_attr]; +"4 conv2d_zero_point_0" [id=4, type=get_attr]; +"5 compressed_weight_updated_constant0" [id=5, type=get_attr]; "6 quantize_per_channel_default" [id=6, type=quantize_per_channel]; "7 dequantize_per_channel_default" [id=7, type=dequantize_per_channel]; "8 conv1_weight_bias_0_0" [id=8, type=get_attr]; @@ -13,9 +13,9 @@ strict digraph { "11 quantize_per_tensor_default_1" [id=11, type=quantize_per_tensor]; "12 dequantize_per_tensor_default_1" [id=12, type=dequantize_per_tensor]; "13 max_pool2d" [id=13, type=max_pool2d]; -"14 _param_constant3" [id=14, type=get_attr]; -"15 conv2d_1_scale_0" [id=15, type=get_attr]; -"16 conv2d_1_zero_point_0" [id=16, type=get_attr]; +"14 conv2d_1_scale_0" [id=14, type=get_attr]; +"15 conv2d_1_zero_point_0" [id=15, type=get_attr]; +"16 compressed_weight_updated_constant1" [id=16, type=get_attr]; "17 quantize_per_channel_default_1" [id=17, type=quantize_per_channel]; "18 dequantize_per_channel_default_1" [id=18, type=dequantize_per_channel]; "19 layer1_0_conv1_weight_bias_0_0" [id=19, type=get_attr]; @@ -23,9 +23,9 @@ strict digraph { "21 relu__1" [id=21, type=relu_]; "22 quantize_per_tensor_default_2" [id=22, type=quantize_per_tensor]; "23 dequantize_per_tensor_default_2" [id=23, type=dequantize_per_tensor]; -"24 _param_constant6" [id=24, type=get_attr]; -"25 conv2d_2_scale_0" [id=25, type=get_attr]; -"26 conv2d_2_zero_point_0" [id=26, type=get_attr]; +"24 conv2d_2_scale_0" [id=24, type=get_attr]; +"25 conv2d_2_zero_point_0" [id=25, type=get_attr]; +"26 compressed_weight_updated_constant2" [id=26, type=get_attr]; "27 quantize_per_channel_default_2" [id=27, type=quantize_per_channel]; "28 dequantize_per_channel_default_2" [id=28, type=dequantize_per_channel]; "29 layer1_0_conv2_weight_bias_0_0" [id=29, type=get_attr]; @@ -37,9 +37,9 @@ strict digraph { "35 quantize_per_tensor_default_4" [id=35, type=quantize_per_tensor]; "36 dequantize_per_tensor_default_5" [id=36, type=dequantize_per_tensor]; "37 dequantize_per_tensor_default_4" [id=37, type=dequantize_per_tensor]; -"38 _param_constant9" [id=38, type=get_attr]; -"39 conv2d_3_scale_0" [id=39, type=get_attr]; -"40 conv2d_3_zero_point_0" [id=40, type=get_attr]; +"38 conv2d_3_scale_0" [id=38, type=get_attr]; +"39 conv2d_3_zero_point_0" [id=39, type=get_attr]; +"40 compressed_weight_updated_constant3" [id=40, type=get_attr]; "41 quantize_per_channel_default_3" [id=41, type=quantize_per_channel]; "42 dequantize_per_channel_default_3" [id=42, type=dequantize_per_channel]; "43 layer1_1_conv1_weight_bias_0_0" [id=43, type=get_attr]; @@ -47,9 +47,9 @@ strict digraph { "45 relu__3" [id=45, type=relu_]; "46 quantize_per_tensor_default_5" [id=46, type=quantize_per_tensor]; "47 dequantize_per_tensor_default_6" [id=47, type=dequantize_per_tensor]; -"48 _param_constant12" [id=48, type=get_attr]; -"49 conv2d_4_scale_0" [id=49, type=get_attr]; -"50 conv2d_4_zero_point_0" [id=50, type=get_attr]; +"48 conv2d_4_scale_0" [id=48, type=get_attr]; +"49 conv2d_4_zero_point_0" [id=49, type=get_attr]; +"50 compressed_weight_updated_constant4" [id=50, type=get_attr]; "51 quantize_per_channel_default_4" [id=51, type=quantize_per_channel]; "52 dequantize_per_channel_default_4" [id=52, type=dequantize_per_channel]; "53 layer1_1_conv2_weight_bias_0_0" [id=53, type=get_attr]; @@ -61,9 +61,9 @@ strict digraph { "59 quantize_per_tensor_default_7" [id=59, type=quantize_per_tensor]; "60 dequantize_per_tensor_default_9" [id=60, type=dequantize_per_tensor]; "61 dequantize_per_tensor_default_8" [id=61, type=dequantize_per_tensor]; -"62 _param_constant15" [id=62, type=get_attr]; -"63 conv2d_5_scale_0" [id=63, type=get_attr]; -"64 conv2d_5_zero_point_0" [id=64, type=get_attr]; +"62 conv2d_5_scale_0" [id=62, type=get_attr]; +"63 conv2d_5_zero_point_0" [id=63, type=get_attr]; +"64 compressed_weight_updated_constant5" [id=64, type=get_attr]; "65 quantize_per_channel_default_5" [id=65, type=quantize_per_channel]; "66 dequantize_per_channel_default_5" [id=66, type=dequantize_per_channel]; "67 layer2_0_conv1_weight_bias_0_0" [id=67, type=get_attr]; @@ -71,18 +71,18 @@ strict digraph { "69 relu__5" [id=69, type=relu_]; "70 quantize_per_tensor_default_8" [id=70, type=quantize_per_tensor]; "71 dequantize_per_tensor_default_10" [id=71, type=dequantize_per_tensor]; -"72 _param_constant18" [id=72, type=get_attr]; -"73 conv2d_6_scale_0" [id=73, type=get_attr]; -"74 conv2d_6_zero_point_0" [id=74, type=get_attr]; +"72 conv2d_6_scale_0" [id=72, type=get_attr]; +"73 conv2d_6_zero_point_0" [id=73, type=get_attr]; +"74 compressed_weight_updated_constant6" [id=74, type=get_attr]; "75 quantize_per_channel_default_6" [id=75, type=quantize_per_channel]; "76 dequantize_per_channel_default_6" [id=76, type=dequantize_per_channel]; "77 layer2_0_conv2_weight_bias_0_0" [id=77, type=get_attr]; "78 conv2d_6" [id=78, type=conv2d]; "79 quantize_per_tensor_default_9" [id=79, type=quantize_per_tensor]; "80 dequantize_per_tensor_default_11" [id=80, type=dequantize_per_tensor]; -"81 _param_constant21" [id=81, type=get_attr]; -"82 conv2d_7_scale_0" [id=82, type=get_attr]; -"83 conv2d_7_zero_point_0" [id=83, type=get_attr]; +"81 conv2d_7_scale_0" [id=81, type=get_attr]; +"82 conv2d_7_zero_point_0" [id=82, type=get_attr]; +"83 compressed_weight_updated_constant7" [id=83, type=get_attr]; "84 quantize_per_channel_default_7" [id=84, type=quantize_per_channel]; "85 dequantize_per_channel_default_7" [id=85, type=dequantize_per_channel]; "86 layer2_0_downsample_0_weight_bias_0_0" [id=86, type=get_attr]; @@ -94,9 +94,9 @@ strict digraph { "92 quantize_per_tensor_default_11" [id=92, type=quantize_per_tensor]; "93 dequantize_per_tensor_default_14" [id=93, type=dequantize_per_tensor]; "94 dequantize_per_tensor_default_13" [id=94, type=dequantize_per_tensor]; -"95 _param_constant24" [id=95, type=get_attr]; -"96 conv2d_8_scale_0" [id=96, type=get_attr]; -"97 conv2d_8_zero_point_0" [id=97, type=get_attr]; +"95 conv2d_8_scale_0" [id=95, type=get_attr]; +"96 conv2d_8_zero_point_0" [id=96, type=get_attr]; +"97 compressed_weight_updated_constant8" [id=97, type=get_attr]; "98 quantize_per_channel_default_8" [id=98, type=quantize_per_channel]; "99 dequantize_per_channel_default_8" [id=99, type=dequantize_per_channel]; "100 layer2_1_conv1_weight_bias_0_0" [id=100, type=get_attr]; @@ -104,9 +104,9 @@ strict digraph { "102 relu__7" [id=102, type=relu_]; "103 quantize_per_tensor_default_12" [id=103, type=quantize_per_tensor]; "104 dequantize_per_tensor_default_15" [id=104, type=dequantize_per_tensor]; -"105 _param_constant27" [id=105, type=get_attr]; -"106 conv2d_9_scale_0" [id=106, type=get_attr]; -"107 conv2d_9_zero_point_0" [id=107, type=get_attr]; +"105 conv2d_9_scale_0" [id=105, type=get_attr]; +"106 conv2d_9_zero_point_0" [id=106, type=get_attr]; +"107 compressed_weight_updated_constant9" [id=107, type=get_attr]; "108 quantize_per_channel_default_9" [id=108, type=quantize_per_channel]; "109 dequantize_per_channel_default_9" [id=109, type=dequantize_per_channel]; "110 layer2_1_conv2_weight_bias_0_0" [id=110, type=get_attr]; @@ -118,9 +118,9 @@ strict digraph { "116 quantize_per_tensor_default_14" [id=116, type=quantize_per_tensor]; "117 dequantize_per_tensor_default_18" [id=117, type=dequantize_per_tensor]; "118 dequantize_per_tensor_default_17" [id=118, type=dequantize_per_tensor]; -"119 _param_constant30" [id=119, type=get_attr]; -"120 conv2d_10_scale_0" [id=120, type=get_attr]; -"121 conv2d_10_zero_point_0" [id=121, type=get_attr]; +"119 conv2d_10_scale_0" [id=119, type=get_attr]; +"120 conv2d_10_zero_point_0" [id=120, type=get_attr]; +"121 compressed_weight_updated_constant10" [id=121, type=get_attr]; "122 quantize_per_channel_default_10" [id=122, type=quantize_per_channel]; "123 dequantize_per_channel_default_10" [id=123, type=dequantize_per_channel]; "124 layer3_0_conv1_weight_bias_0_0" [id=124, type=get_attr]; @@ -128,18 +128,18 @@ strict digraph { "126 relu__9" [id=126, type=relu_]; "127 quantize_per_tensor_default_15" [id=127, type=quantize_per_tensor]; "128 dequantize_per_tensor_default_19" [id=128, type=dequantize_per_tensor]; -"129 _param_constant33" [id=129, type=get_attr]; -"130 conv2d_11_scale_0" [id=130, type=get_attr]; -"131 conv2d_11_zero_point_0" [id=131, type=get_attr]; +"129 conv2d_11_scale_0" [id=129, type=get_attr]; +"130 conv2d_11_zero_point_0" [id=130, type=get_attr]; +"131 compressed_weight_updated_constant11" [id=131, type=get_attr]; "132 quantize_per_channel_default_11" [id=132, type=quantize_per_channel]; "133 dequantize_per_channel_default_11" [id=133, type=dequantize_per_channel]; "134 layer3_0_conv2_weight_bias_0_0" [id=134, type=get_attr]; "135 conv2d_11" [id=135, type=conv2d]; "136 quantize_per_tensor_default_16" [id=136, type=quantize_per_tensor]; "137 dequantize_per_tensor_default_20" [id=137, type=dequantize_per_tensor]; -"138 _param_constant36" [id=138, type=get_attr]; -"139 conv2d_12_scale_0" [id=139, type=get_attr]; -"140 conv2d_12_zero_point_0" [id=140, type=get_attr]; +"138 conv2d_12_scale_0" [id=138, type=get_attr]; +"139 conv2d_12_zero_point_0" [id=139, type=get_attr]; +"140 compressed_weight_updated_constant12" [id=140, type=get_attr]; "141 quantize_per_channel_default_12" [id=141, type=quantize_per_channel]; "142 dequantize_per_channel_default_12" [id=142, type=dequantize_per_channel]; "143 layer3_0_downsample_0_weight_bias_0_0" [id=143, type=get_attr]; @@ -151,9 +151,9 @@ strict digraph { "149 quantize_per_tensor_default_18" [id=149, type=quantize_per_tensor]; "150 dequantize_per_tensor_default_23" [id=150, type=dequantize_per_tensor]; "151 dequantize_per_tensor_default_22" [id=151, type=dequantize_per_tensor]; -"152 _param_constant39" [id=152, type=get_attr]; -"153 conv2d_13_scale_0" [id=153, type=get_attr]; -"154 conv2d_13_zero_point_0" [id=154, type=get_attr]; +"152 conv2d_13_scale_0" [id=152, type=get_attr]; +"153 conv2d_13_zero_point_0" [id=153, type=get_attr]; +"154 compressed_weight_updated_constant13" [id=154, type=get_attr]; "155 quantize_per_channel_default_13" [id=155, type=quantize_per_channel]; "156 dequantize_per_channel_default_13" [id=156, type=dequantize_per_channel]; "157 layer3_1_conv1_weight_bias_0_0" [id=157, type=get_attr]; @@ -161,9 +161,9 @@ strict digraph { "159 relu__11" [id=159, type=relu_]; "160 quantize_per_tensor_default_19" [id=160, type=quantize_per_tensor]; "161 dequantize_per_tensor_default_24" [id=161, type=dequantize_per_tensor]; -"162 _param_constant42" [id=162, type=get_attr]; -"163 conv2d_14_scale_0" [id=163, type=get_attr]; -"164 conv2d_14_zero_point_0" [id=164, type=get_attr]; +"162 conv2d_14_scale_0" [id=162, type=get_attr]; +"163 conv2d_14_zero_point_0" [id=163, type=get_attr]; +"164 compressed_weight_updated_constant14" [id=164, type=get_attr]; "165 quantize_per_channel_default_14" [id=165, type=quantize_per_channel]; "166 dequantize_per_channel_default_14" [id=166, type=dequantize_per_channel]; "167 layer3_1_conv2_weight_bias_0_0" [id=167, type=get_attr]; @@ -175,9 +175,9 @@ strict digraph { "173 quantize_per_tensor_default_21" [id=173, type=quantize_per_tensor]; "174 dequantize_per_tensor_default_27" [id=174, type=dequantize_per_tensor]; "175 dequantize_per_tensor_default_26" [id=175, type=dequantize_per_tensor]; -"176 _param_constant45" [id=176, type=get_attr]; -"177 conv2d_15_scale_0" [id=177, type=get_attr]; -"178 conv2d_15_zero_point_0" [id=178, type=get_attr]; +"176 conv2d_15_scale_0" [id=176, type=get_attr]; +"177 conv2d_15_zero_point_0" [id=177, type=get_attr]; +"178 compressed_weight_updated_constant15" [id=178, type=get_attr]; "179 quantize_per_channel_default_15" [id=179, type=quantize_per_channel]; "180 dequantize_per_channel_default_15" [id=180, type=dequantize_per_channel]; "181 layer4_0_conv1_weight_bias_0_0" [id=181, type=get_attr]; @@ -185,18 +185,18 @@ strict digraph { "183 relu__13" [id=183, type=relu_]; "184 quantize_per_tensor_default_22" [id=184, type=quantize_per_tensor]; "185 dequantize_per_tensor_default_28" [id=185, type=dequantize_per_tensor]; -"186 _param_constant48" [id=186, type=get_attr]; -"187 conv2d_16_scale_0" [id=187, type=get_attr]; -"188 conv2d_16_zero_point_0" [id=188, type=get_attr]; +"186 conv2d_16_scale_0" [id=186, type=get_attr]; +"187 conv2d_16_zero_point_0" [id=187, type=get_attr]; +"188 compressed_weight_updated_constant16" [id=188, type=get_attr]; "189 quantize_per_channel_default_16" [id=189, type=quantize_per_channel]; "190 dequantize_per_channel_default_16" [id=190, type=dequantize_per_channel]; "191 layer4_0_conv2_weight_bias_0_0" [id=191, type=get_attr]; "192 conv2d_16" [id=192, type=conv2d]; "193 quantize_per_tensor_default_23" [id=193, type=quantize_per_tensor]; "194 dequantize_per_tensor_default_29" [id=194, type=dequantize_per_tensor]; -"195 _param_constant51" [id=195, type=get_attr]; -"196 conv2d_17_scale_0" [id=196, type=get_attr]; -"197 conv2d_17_zero_point_0" [id=197, type=get_attr]; +"195 conv2d_17_scale_0" [id=195, type=get_attr]; +"196 conv2d_17_zero_point_0" [id=196, type=get_attr]; +"197 compressed_weight_updated_constant17" [id=197, type=get_attr]; "198 quantize_per_channel_default_17" [id=198, type=quantize_per_channel]; "199 dequantize_per_channel_default_17" [id=199, type=dequantize_per_channel]; "200 layer4_0_downsample_0_weight_bias_0_0" [id=200, type=get_attr]; @@ -208,9 +208,9 @@ strict digraph { "206 quantize_per_tensor_default_25" [id=206, type=quantize_per_tensor]; "207 dequantize_per_tensor_default_32" [id=207, type=dequantize_per_tensor]; "208 dequantize_per_tensor_default_31" [id=208, type=dequantize_per_tensor]; -"209 _param_constant54" [id=209, type=get_attr]; -"210 conv2d_18_scale_0" [id=210, type=get_attr]; -"211 conv2d_18_zero_point_0" [id=211, type=get_attr]; +"209 conv2d_18_scale_0" [id=209, type=get_attr]; +"210 conv2d_18_zero_point_0" [id=210, type=get_attr]; +"211 compressed_weight_updated_constant18" [id=211, type=get_attr]; "212 quantize_per_channel_default_18" [id=212, type=quantize_per_channel]; "213 dequantize_per_channel_default_18" [id=213, type=dequantize_per_channel]; "214 layer4_1_conv1_weight_bias_0_0" [id=214, type=get_attr]; @@ -218,9 +218,9 @@ strict digraph { "216 relu__15" [id=216, type=relu_]; "217 quantize_per_tensor_default_26" [id=217, type=quantize_per_tensor]; "218 dequantize_per_tensor_default_33" [id=218, type=dequantize_per_tensor]; -"219 _param_constant57" [id=219, type=get_attr]; -"220 conv2d_19_scale_0" [id=220, type=get_attr]; -"221 conv2d_19_zero_point_0" [id=221, type=get_attr]; +"219 conv2d_19_scale_0" [id=219, type=get_attr]; +"220 conv2d_19_zero_point_0" [id=220, type=get_attr]; +"221 compressed_weight_updated_constant19" [id=221, type=get_attr]; "222 quantize_per_channel_default_19" [id=222, type=quantize_per_channel]; "223 dequantize_per_channel_default_19" [id=223, type=dequantize_per_channel]; "224 layer4_1_conv2_weight_bias_0_0" [id=224, type=get_attr]; @@ -235,305 +235,305 @@ strict digraph { "233 quantize_per_tensor_default_29" [id=233, type=quantize_per_tensor]; "234 dequantize_per_tensor_default_36" [id=234, type=dequantize_per_tensor]; "235 flatten" [id=235, type=flatten]; -"236 _param_constant60" [id=236, type=get_attr]; -"237 linear_scale_0" [id=237, type=get_attr]; -"238 linear_zero_point_0" [id=238, type=get_attr]; +"236 linear_scale_0" [id=236, type=get_attr]; +"237 linear_zero_point_0" [id=237, type=get_attr]; +"238 compressed_weight_updated_constant20" [id=238, type=get_attr]; "239 quantize_per_channel_default_20" [id=239, type=quantize_per_channel]; "240 dequantize_per_channel_default_20" [id=240, type=dequantize_per_channel]; -"241 _param_constant61_0_0" [id=241, type=get_attr]; +"241 fc_bias_0_0" [id=241, type=get_attr]; "242 linear" [id=242, type=linear]; "243 output" [id=243, type=output]; -"0 arg0_1" -> "1 quantize_per_tensor_default"; -"1 quantize_per_tensor_default" -> "2 dequantize_per_tensor_default"; -"2 dequantize_per_tensor_default" -> "9 conv2d"; -"3 _param_constant0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "7 dequantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "6 quantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "7 dequantize_per_channel_default"; -"6 quantize_per_channel_default" -> "7 dequantize_per_channel_default"; -"7 dequantize_per_channel_default" -> "9 conv2d"; -"8 conv1_weight_bias_0_0" -> "9 conv2d"; -"9 conv2d" -> "10 relu_"; -"10 relu_" -> "11 quantize_per_tensor_default_1"; -"11 quantize_per_tensor_default_1" -> "12 dequantize_per_tensor_default_1"; -"12 dequantize_per_tensor_default_1" -> "13 max_pool2d"; -"13 max_pool2d" -> "20 conv2d_1"; -"13 max_pool2d" -> "33 add_"; -"14 _param_constant3" -> "17 quantize_per_channel_default_1"; -"15 conv2d_1_scale_0" -> "17 quantize_per_channel_default_1"; -"15 conv2d_1_scale_0" -> "18 dequantize_per_channel_default_1"; -"16 conv2d_1_zero_point_0" -> "17 quantize_per_channel_default_1"; -"16 conv2d_1_zero_point_0" -> "18 dequantize_per_channel_default_1"; -"17 quantize_per_channel_default_1" -> "18 dequantize_per_channel_default_1"; -"18 dequantize_per_channel_default_1" -> "20 conv2d_1"; -"19 layer1_0_conv1_weight_bias_0_0" -> "20 conv2d_1"; -"20 conv2d_1" -> "21 relu__1"; -"21 relu__1" -> "22 quantize_per_tensor_default_2"; -"22 quantize_per_tensor_default_2" -> "23 dequantize_per_tensor_default_2"; -"23 dequantize_per_tensor_default_2" -> "30 conv2d_2"; -"24 _param_constant6" -> "27 quantize_per_channel_default_2"; -"25 conv2d_2_scale_0" -> "27 quantize_per_channel_default_2"; -"25 conv2d_2_scale_0" -> "28 dequantize_per_channel_default_2"; -"26 conv2d_2_zero_point_0" -> "27 quantize_per_channel_default_2"; -"26 conv2d_2_zero_point_0" -> "28 dequantize_per_channel_default_2"; -"27 quantize_per_channel_default_2" -> "28 dequantize_per_channel_default_2"; -"28 dequantize_per_channel_default_2" -> "30 conv2d_2"; -"29 layer1_0_conv2_weight_bias_0_0" -> "30 conv2d_2"; -"30 conv2d_2" -> "31 quantize_per_tensor_default_3"; -"31 quantize_per_tensor_default_3" -> "32 dequantize_per_tensor_default_3"; -"32 dequantize_per_tensor_default_3" -> "33 add_"; -"33 add_" -> "34 relu__2"; -"34 relu__2" -> "35 quantize_per_tensor_default_4"; -"35 quantize_per_tensor_default_4" -> "36 dequantize_per_tensor_default_5"; -"35 quantize_per_tensor_default_4" -> "37 dequantize_per_tensor_default_4"; -"36 dequantize_per_tensor_default_5" -> "57 add__1"; -"37 dequantize_per_tensor_default_4" -> "44 conv2d_3"; -"38 _param_constant9" -> "41 quantize_per_channel_default_3"; -"39 conv2d_3_scale_0" -> "41 quantize_per_channel_default_3"; -"39 conv2d_3_scale_0" -> "42 dequantize_per_channel_default_3"; -"40 conv2d_3_zero_point_0" -> "41 quantize_per_channel_default_3"; -"40 conv2d_3_zero_point_0" -> "42 dequantize_per_channel_default_3"; -"41 quantize_per_channel_default_3" -> "42 dequantize_per_channel_default_3"; -"42 dequantize_per_channel_default_3" -> "44 conv2d_3"; -"43 layer1_1_conv1_weight_bias_0_0" -> "44 conv2d_3"; -"44 conv2d_3" -> "45 relu__3"; -"45 relu__3" -> "46 quantize_per_tensor_default_5"; -"46 quantize_per_tensor_default_5" -> "47 dequantize_per_tensor_default_6"; -"47 dequantize_per_tensor_default_6" -> "54 conv2d_4"; -"48 _param_constant12" -> "51 quantize_per_channel_default_4"; -"49 conv2d_4_scale_0" -> "51 quantize_per_channel_default_4"; -"49 conv2d_4_scale_0" -> "52 dequantize_per_channel_default_4"; -"50 conv2d_4_zero_point_0" -> "51 quantize_per_channel_default_4"; -"50 conv2d_4_zero_point_0" -> "52 dequantize_per_channel_default_4"; -"51 quantize_per_channel_default_4" -> "52 dequantize_per_channel_default_4"; -"52 dequantize_per_channel_default_4" -> "54 conv2d_4"; -"53 layer1_1_conv2_weight_bias_0_0" -> "54 conv2d_4"; -"54 conv2d_4" -> "55 quantize_per_tensor_default_6"; -"55 quantize_per_tensor_default_6" -> "56 dequantize_per_tensor_default_7"; -"56 dequantize_per_tensor_default_7" -> "57 add__1"; -"57 add__1" -> "58 relu__4"; -"58 relu__4" -> "59 quantize_per_tensor_default_7"; -"59 quantize_per_tensor_default_7" -> "60 dequantize_per_tensor_default_9"; -"59 quantize_per_tensor_default_7" -> "61 dequantize_per_tensor_default_8"; -"60 dequantize_per_tensor_default_9" -> "87 conv2d_7"; -"61 dequantize_per_tensor_default_8" -> "68 conv2d_5"; -"62 _param_constant15" -> "65 quantize_per_channel_default_5"; -"63 conv2d_5_scale_0" -> "65 quantize_per_channel_default_5"; -"63 conv2d_5_scale_0" -> "66 dequantize_per_channel_default_5"; -"64 conv2d_5_zero_point_0" -> "65 quantize_per_channel_default_5"; -"64 conv2d_5_zero_point_0" -> "66 dequantize_per_channel_default_5"; -"65 quantize_per_channel_default_5" -> "66 dequantize_per_channel_default_5"; -"66 dequantize_per_channel_default_5" -> "68 conv2d_5"; -"67 layer2_0_conv1_weight_bias_0_0" -> "68 conv2d_5"; -"68 conv2d_5" -> "69 relu__5"; -"69 relu__5" -> "70 quantize_per_tensor_default_8"; -"70 quantize_per_tensor_default_8" -> "71 dequantize_per_tensor_default_10"; -"71 dequantize_per_tensor_default_10" -> "78 conv2d_6"; -"72 _param_constant18" -> "75 quantize_per_channel_default_6"; -"73 conv2d_6_scale_0" -> "75 quantize_per_channel_default_6"; -"73 conv2d_6_scale_0" -> "76 dequantize_per_channel_default_6"; -"74 conv2d_6_zero_point_0" -> "75 quantize_per_channel_default_6"; -"74 conv2d_6_zero_point_0" -> "76 dequantize_per_channel_default_6"; -"75 quantize_per_channel_default_6" -> "76 dequantize_per_channel_default_6"; -"76 dequantize_per_channel_default_6" -> "78 conv2d_6"; -"77 layer2_0_conv2_weight_bias_0_0" -> "78 conv2d_6"; -"78 conv2d_6" -> "79 quantize_per_tensor_default_9"; -"79 quantize_per_tensor_default_9" -> "80 dequantize_per_tensor_default_11"; -"80 dequantize_per_tensor_default_11" -> "90 add__2"; -"81 _param_constant21" -> "84 quantize_per_channel_default_7"; -"82 conv2d_7_scale_0" -> "84 quantize_per_channel_default_7"; -"82 conv2d_7_scale_0" -> "85 dequantize_per_channel_default_7"; -"83 conv2d_7_zero_point_0" -> "84 quantize_per_channel_default_7"; -"83 conv2d_7_zero_point_0" -> "85 dequantize_per_channel_default_7"; -"84 quantize_per_channel_default_7" -> "85 dequantize_per_channel_default_7"; -"85 dequantize_per_channel_default_7" -> "87 conv2d_7"; -"86 layer2_0_downsample_0_weight_bias_0_0" -> "87 conv2d_7"; -"87 conv2d_7" -> "88 quantize_per_tensor_default_10"; -"88 quantize_per_tensor_default_10" -> "89 dequantize_per_tensor_default_12"; -"89 dequantize_per_tensor_default_12" -> "90 add__2"; -"90 add__2" -> "91 relu__6"; -"91 relu__6" -> "92 quantize_per_tensor_default_11"; -"92 quantize_per_tensor_default_11" -> "93 dequantize_per_tensor_default_14"; -"92 quantize_per_tensor_default_11" -> "94 dequantize_per_tensor_default_13"; -"93 dequantize_per_tensor_default_14" -> "114 add__3"; -"94 dequantize_per_tensor_default_13" -> "101 conv2d_8"; -"95 _param_constant24" -> "98 quantize_per_channel_default_8"; -"96 conv2d_8_scale_0" -> "98 quantize_per_channel_default_8"; -"96 conv2d_8_scale_0" -> "99 dequantize_per_channel_default_8"; -"97 conv2d_8_zero_point_0" -> "98 quantize_per_channel_default_8"; -"97 conv2d_8_zero_point_0" -> "99 dequantize_per_channel_default_8"; -"98 quantize_per_channel_default_8" -> "99 dequantize_per_channel_default_8"; -"99 dequantize_per_channel_default_8" -> "101 conv2d_8"; -"100 layer2_1_conv1_weight_bias_0_0" -> "101 conv2d_8"; -"101 conv2d_8" -> "102 relu__7"; -"102 relu__7" -> "103 quantize_per_tensor_default_12"; -"103 quantize_per_tensor_default_12" -> "104 dequantize_per_tensor_default_15"; -"104 dequantize_per_tensor_default_15" -> "111 conv2d_9"; -"105 _param_constant27" -> "108 quantize_per_channel_default_9"; -"106 conv2d_9_scale_0" -> "108 quantize_per_channel_default_9"; -"106 conv2d_9_scale_0" -> "109 dequantize_per_channel_default_9"; -"107 conv2d_9_zero_point_0" -> "108 quantize_per_channel_default_9"; -"107 conv2d_9_zero_point_0" -> "109 dequantize_per_channel_default_9"; -"108 quantize_per_channel_default_9" -> "109 dequantize_per_channel_default_9"; -"109 dequantize_per_channel_default_9" -> "111 conv2d_9"; -"110 layer2_1_conv2_weight_bias_0_0" -> "111 conv2d_9"; -"111 conv2d_9" -> "112 quantize_per_tensor_default_13"; -"112 quantize_per_tensor_default_13" -> "113 dequantize_per_tensor_default_16"; -"113 dequantize_per_tensor_default_16" -> "114 add__3"; -"114 add__3" -> "115 relu__8"; -"115 relu__8" -> "116 quantize_per_tensor_default_14"; -"116 quantize_per_tensor_default_14" -> "117 dequantize_per_tensor_default_18"; -"116 quantize_per_tensor_default_14" -> "118 dequantize_per_tensor_default_17"; -"117 dequantize_per_tensor_default_18" -> "144 conv2d_12"; -"118 dequantize_per_tensor_default_17" -> "125 conv2d_10"; -"119 _param_constant30" -> "122 quantize_per_channel_default_10"; -"120 conv2d_10_scale_0" -> "122 quantize_per_channel_default_10"; -"120 conv2d_10_scale_0" -> "123 dequantize_per_channel_default_10"; -"121 conv2d_10_zero_point_0" -> "122 quantize_per_channel_default_10"; -"121 conv2d_10_zero_point_0" -> "123 dequantize_per_channel_default_10"; -"122 quantize_per_channel_default_10" -> "123 dequantize_per_channel_default_10"; -"123 dequantize_per_channel_default_10" -> "125 conv2d_10"; -"124 layer3_0_conv1_weight_bias_0_0" -> "125 conv2d_10"; -"125 conv2d_10" -> "126 relu__9"; -"126 relu__9" -> "127 quantize_per_tensor_default_15"; -"127 quantize_per_tensor_default_15" -> "128 dequantize_per_tensor_default_19"; -"128 dequantize_per_tensor_default_19" -> "135 conv2d_11"; -"129 _param_constant33" -> "132 quantize_per_channel_default_11"; -"130 conv2d_11_scale_0" -> "132 quantize_per_channel_default_11"; -"130 conv2d_11_scale_0" -> "133 dequantize_per_channel_default_11"; -"131 conv2d_11_zero_point_0" -> "132 quantize_per_channel_default_11"; -"131 conv2d_11_zero_point_0" -> "133 dequantize_per_channel_default_11"; -"132 quantize_per_channel_default_11" -> "133 dequantize_per_channel_default_11"; -"133 dequantize_per_channel_default_11" -> "135 conv2d_11"; -"134 layer3_0_conv2_weight_bias_0_0" -> "135 conv2d_11"; -"135 conv2d_11" -> "136 quantize_per_tensor_default_16"; -"136 quantize_per_tensor_default_16" -> "137 dequantize_per_tensor_default_20"; -"137 dequantize_per_tensor_default_20" -> "147 add__4"; -"138 _param_constant36" -> "141 quantize_per_channel_default_12"; -"139 conv2d_12_scale_0" -> "141 quantize_per_channel_default_12"; -"139 conv2d_12_scale_0" -> "142 dequantize_per_channel_default_12"; -"140 conv2d_12_zero_point_0" -> "141 quantize_per_channel_default_12"; -"140 conv2d_12_zero_point_0" -> "142 dequantize_per_channel_default_12"; -"141 quantize_per_channel_default_12" -> "142 dequantize_per_channel_default_12"; -"142 dequantize_per_channel_default_12" -> "144 conv2d_12"; -"143 layer3_0_downsample_0_weight_bias_0_0" -> "144 conv2d_12"; -"144 conv2d_12" -> "145 quantize_per_tensor_default_17"; -"145 quantize_per_tensor_default_17" -> "146 dequantize_per_tensor_default_21"; -"146 dequantize_per_tensor_default_21" -> "147 add__4"; -"147 add__4" -> "148 relu__10"; -"148 relu__10" -> "149 quantize_per_tensor_default_18"; -"149 quantize_per_tensor_default_18" -> "150 dequantize_per_tensor_default_23"; -"149 quantize_per_tensor_default_18" -> "151 dequantize_per_tensor_default_22"; -"150 dequantize_per_tensor_default_23" -> "171 add__5"; -"151 dequantize_per_tensor_default_22" -> "158 conv2d_13"; -"152 _param_constant39" -> "155 quantize_per_channel_default_13"; -"153 conv2d_13_scale_0" -> "155 quantize_per_channel_default_13"; -"153 conv2d_13_scale_0" -> "156 dequantize_per_channel_default_13"; -"154 conv2d_13_zero_point_0" -> "155 quantize_per_channel_default_13"; -"154 conv2d_13_zero_point_0" -> "156 dequantize_per_channel_default_13"; -"155 quantize_per_channel_default_13" -> "156 dequantize_per_channel_default_13"; -"156 dequantize_per_channel_default_13" -> "158 conv2d_13"; -"157 layer3_1_conv1_weight_bias_0_0" -> "158 conv2d_13"; -"158 conv2d_13" -> "159 relu__11"; -"159 relu__11" -> "160 quantize_per_tensor_default_19"; -"160 quantize_per_tensor_default_19" -> "161 dequantize_per_tensor_default_24"; -"161 dequantize_per_tensor_default_24" -> "168 conv2d_14"; -"162 _param_constant42" -> "165 quantize_per_channel_default_14"; -"163 conv2d_14_scale_0" -> "165 quantize_per_channel_default_14"; -"163 conv2d_14_scale_0" -> "166 dequantize_per_channel_default_14"; -"164 conv2d_14_zero_point_0" -> "165 quantize_per_channel_default_14"; -"164 conv2d_14_zero_point_0" -> "166 dequantize_per_channel_default_14"; -"165 quantize_per_channel_default_14" -> "166 dequantize_per_channel_default_14"; -"166 dequantize_per_channel_default_14" -> "168 conv2d_14"; -"167 layer3_1_conv2_weight_bias_0_0" -> "168 conv2d_14"; -"168 conv2d_14" -> "169 quantize_per_tensor_default_20"; -"169 quantize_per_tensor_default_20" -> "170 dequantize_per_tensor_default_25"; -"170 dequantize_per_tensor_default_25" -> "171 add__5"; -"171 add__5" -> "172 relu__12"; -"172 relu__12" -> "173 quantize_per_tensor_default_21"; -"173 quantize_per_tensor_default_21" -> "174 dequantize_per_tensor_default_27"; -"173 quantize_per_tensor_default_21" -> "175 dequantize_per_tensor_default_26"; -"174 dequantize_per_tensor_default_27" -> "201 conv2d_17"; -"175 dequantize_per_tensor_default_26" -> "182 conv2d_15"; -"176 _param_constant45" -> "179 quantize_per_channel_default_15"; -"177 conv2d_15_scale_0" -> "179 quantize_per_channel_default_15"; -"177 conv2d_15_scale_0" -> "180 dequantize_per_channel_default_15"; -"178 conv2d_15_zero_point_0" -> "179 quantize_per_channel_default_15"; -"178 conv2d_15_zero_point_0" -> "180 dequantize_per_channel_default_15"; -"179 quantize_per_channel_default_15" -> "180 dequantize_per_channel_default_15"; -"180 dequantize_per_channel_default_15" -> "182 conv2d_15"; -"181 layer4_0_conv1_weight_bias_0_0" -> "182 conv2d_15"; -"182 conv2d_15" -> "183 relu__13"; -"183 relu__13" -> "184 quantize_per_tensor_default_22"; -"184 quantize_per_tensor_default_22" -> "185 dequantize_per_tensor_default_28"; -"185 dequantize_per_tensor_default_28" -> "192 conv2d_16"; -"186 _param_constant48" -> "189 quantize_per_channel_default_16"; -"187 conv2d_16_scale_0" -> "189 quantize_per_channel_default_16"; -"187 conv2d_16_scale_0" -> "190 dequantize_per_channel_default_16"; -"188 conv2d_16_zero_point_0" -> "189 quantize_per_channel_default_16"; -"188 conv2d_16_zero_point_0" -> "190 dequantize_per_channel_default_16"; -"189 quantize_per_channel_default_16" -> "190 dequantize_per_channel_default_16"; -"190 dequantize_per_channel_default_16" -> "192 conv2d_16"; -"191 layer4_0_conv2_weight_bias_0_0" -> "192 conv2d_16"; -"192 conv2d_16" -> "193 quantize_per_tensor_default_23"; -"193 quantize_per_tensor_default_23" -> "194 dequantize_per_tensor_default_29"; -"194 dequantize_per_tensor_default_29" -> "204 add__6"; -"195 _param_constant51" -> "198 quantize_per_channel_default_17"; -"196 conv2d_17_scale_0" -> "198 quantize_per_channel_default_17"; -"196 conv2d_17_scale_0" -> "199 dequantize_per_channel_default_17"; -"197 conv2d_17_zero_point_0" -> "198 quantize_per_channel_default_17"; -"197 conv2d_17_zero_point_0" -> "199 dequantize_per_channel_default_17"; -"198 quantize_per_channel_default_17" -> "199 dequantize_per_channel_default_17"; -"199 dequantize_per_channel_default_17" -> "201 conv2d_17"; -"200 layer4_0_downsample_0_weight_bias_0_0" -> "201 conv2d_17"; -"201 conv2d_17" -> "202 quantize_per_tensor_default_24"; -"202 quantize_per_tensor_default_24" -> "203 dequantize_per_tensor_default_30"; -"203 dequantize_per_tensor_default_30" -> "204 add__6"; -"204 add__6" -> "205 relu__14"; -"205 relu__14" -> "206 quantize_per_tensor_default_25"; -"206 quantize_per_tensor_default_25" -> "207 dequantize_per_tensor_default_32"; -"206 quantize_per_tensor_default_25" -> "208 dequantize_per_tensor_default_31"; -"207 dequantize_per_tensor_default_32" -> "228 add__7"; -"208 dequantize_per_tensor_default_31" -> "215 conv2d_18"; -"209 _param_constant54" -> "212 quantize_per_channel_default_18"; -"210 conv2d_18_scale_0" -> "212 quantize_per_channel_default_18"; -"210 conv2d_18_scale_0" -> "213 dequantize_per_channel_default_18"; -"211 conv2d_18_zero_point_0" -> "212 quantize_per_channel_default_18"; -"211 conv2d_18_zero_point_0" -> "213 dequantize_per_channel_default_18"; -"212 quantize_per_channel_default_18" -> "213 dequantize_per_channel_default_18"; -"213 dequantize_per_channel_default_18" -> "215 conv2d_18"; -"214 layer4_1_conv1_weight_bias_0_0" -> "215 conv2d_18"; -"215 conv2d_18" -> "216 relu__15"; -"216 relu__15" -> "217 quantize_per_tensor_default_26"; -"217 quantize_per_tensor_default_26" -> "218 dequantize_per_tensor_default_33"; -"218 dequantize_per_tensor_default_33" -> "225 conv2d_19"; -"219 _param_constant57" -> "222 quantize_per_channel_default_19"; -"220 conv2d_19_scale_0" -> "222 quantize_per_channel_default_19"; -"220 conv2d_19_scale_0" -> "223 dequantize_per_channel_default_19"; -"221 conv2d_19_zero_point_0" -> "222 quantize_per_channel_default_19"; -"221 conv2d_19_zero_point_0" -> "223 dequantize_per_channel_default_19"; -"222 quantize_per_channel_default_19" -> "223 dequantize_per_channel_default_19"; -"223 dequantize_per_channel_default_19" -> "225 conv2d_19"; -"224 layer4_1_conv2_weight_bias_0_0" -> "225 conv2d_19"; -"225 conv2d_19" -> "226 quantize_per_tensor_default_27"; -"226 quantize_per_tensor_default_27" -> "227 dequantize_per_tensor_default_34"; -"227 dequantize_per_tensor_default_34" -> "228 add__7"; -"228 add__7" -> "229 relu__16"; -"229 relu__16" -> "230 quantize_per_tensor_default_28"; -"230 quantize_per_tensor_default_28" -> "231 dequantize_per_tensor_default_35"; -"231 dequantize_per_tensor_default_35" -> "232 adaptive_avg_pool2d"; -"232 adaptive_avg_pool2d" -> "233 quantize_per_tensor_default_29"; -"233 quantize_per_tensor_default_29" -> "234 dequantize_per_tensor_default_36"; -"234 dequantize_per_tensor_default_36" -> "235 flatten"; -"235 flatten" -> "242 linear"; -"236 _param_constant60" -> "239 quantize_per_channel_default_20"; -"237 linear_scale_0" -> "239 quantize_per_channel_default_20"; -"237 linear_scale_0" -> "240 dequantize_per_channel_default_20"; -"238 linear_zero_point_0" -> "239 quantize_per_channel_default_20"; -"238 linear_zero_point_0" -> "240 dequantize_per_channel_default_20"; -"239 quantize_per_channel_default_20" -> "240 dequantize_per_channel_default_20"; -"240 dequantize_per_channel_default_20" -> "242 linear"; -"241 _param_constant61_0_0" -> "242 linear"; -"242 linear" -> "243 output"; +"0 x" -> "1 quantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"1 quantize_per_tensor_default" -> "2 dequantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"2 dequantize_per_tensor_default" -> "9 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"3 conv2d_scale_0" -> "6 quantize_per_channel_default" [label="(64,)", style=solid]; +"3 conv2d_scale_0" -> "7 dequantize_per_channel_default" [label="(64,)", style=solid]; +"4 conv2d_zero_point_0" -> "6 quantize_per_channel_default" [label="(64,)", style=solid]; +"4 conv2d_zero_point_0" -> "7 dequantize_per_channel_default" [label="(64,)", style=solid]; +"5 compressed_weight_updated_constant0" -> "6 quantize_per_channel_default" [label="(64, 3, 7, 7)", style=solid]; +"6 quantize_per_channel_default" -> "7 dequantize_per_channel_default" [label="(64, 3, 7, 7)", style=solid]; +"7 dequantize_per_channel_default" -> "9 conv2d" [label="(64, 3, 7, 7)", style=solid]; +"8 conv1_weight_bias_0_0" -> "9 conv2d" [label="(64,)", style=solid]; +"9 conv2d" -> "10 relu_" [label="(1, 64, 112, 112)", style=solid]; +"10 relu_" -> "11 quantize_per_tensor_default_1" [label="(1, 64, 112, 112)", style=solid]; +"11 quantize_per_tensor_default_1" -> "12 dequantize_per_tensor_default_1" [label="(1, 64, 112, 112)", style=solid]; +"12 dequantize_per_tensor_default_1" -> "13 max_pool2d" [label="(1, 64, 112, 112)", style=solid]; +"13 max_pool2d" -> "20 conv2d_1" [label="(1, 64, 56, 56)", style=solid]; +"13 max_pool2d" -> "33 add_" [label="(1, 64, 56, 56)", style=solid]; +"14 conv2d_1_scale_0" -> "17 quantize_per_channel_default_1" [label="(64,)", style=solid]; +"14 conv2d_1_scale_0" -> "18 dequantize_per_channel_default_1" [label="(64,)", style=solid]; +"15 conv2d_1_zero_point_0" -> "17 quantize_per_channel_default_1" [label="(64,)", style=solid]; +"15 conv2d_1_zero_point_0" -> "18 dequantize_per_channel_default_1" [label="(64,)", style=solid]; +"16 compressed_weight_updated_constant1" -> "17 quantize_per_channel_default_1" [label="(64, 64, 3, 3)", style=solid]; +"17 quantize_per_channel_default_1" -> "18 dequantize_per_channel_default_1" [label="(64, 64, 3, 3)", style=solid]; +"18 dequantize_per_channel_default_1" -> "20 conv2d_1" [label="(64, 64, 3, 3)", style=solid]; +"19 layer1_0_conv1_weight_bias_0_0" -> "20 conv2d_1" [label="(64,)", style=solid]; +"20 conv2d_1" -> "21 relu__1" [label="(1, 64, 56, 56)", style=solid]; +"21 relu__1" -> "22 quantize_per_tensor_default_2" [label="(1, 64, 56, 56)", style=solid]; +"22 quantize_per_tensor_default_2" -> "23 dequantize_per_tensor_default_2" [label="(1, 64, 56, 56)", style=solid]; +"23 dequantize_per_tensor_default_2" -> "30 conv2d_2" [label="(1, 64, 56, 56)", style=solid]; +"24 conv2d_2_scale_0" -> "27 quantize_per_channel_default_2" [label="(64,)", style=solid]; +"24 conv2d_2_scale_0" -> "28 dequantize_per_channel_default_2" [label="(64,)", style=solid]; +"25 conv2d_2_zero_point_0" -> "27 quantize_per_channel_default_2" [label="(64,)", style=solid]; +"25 conv2d_2_zero_point_0" -> "28 dequantize_per_channel_default_2" [label="(64,)", style=solid]; +"26 compressed_weight_updated_constant2" -> "27 quantize_per_channel_default_2" [label="(64, 64, 3, 3)", style=solid]; +"27 quantize_per_channel_default_2" -> "28 dequantize_per_channel_default_2" [label="(64, 64, 3, 3)", style=solid]; +"28 dequantize_per_channel_default_2" -> "30 conv2d_2" [label="(64, 64, 3, 3)", style=solid]; +"29 layer1_0_conv2_weight_bias_0_0" -> "30 conv2d_2" [label="(64,)", style=solid]; +"30 conv2d_2" -> "31 quantize_per_tensor_default_3" [label="(1, 64, 56, 56)", style=solid]; +"31 quantize_per_tensor_default_3" -> "32 dequantize_per_tensor_default_3" [label="(1, 64, 56, 56)", style=solid]; +"32 dequantize_per_tensor_default_3" -> "33 add_" [label="(1, 64, 56, 56)", style=solid]; +"33 add_" -> "34 relu__2" [label="(1, 64, 56, 56)", style=solid]; +"34 relu__2" -> "35 quantize_per_tensor_default_4" [label="(1, 64, 56, 56)", style=solid]; +"35 quantize_per_tensor_default_4" -> "36 dequantize_per_tensor_default_5" [label="(1, 64, 56, 56)", style=solid]; +"35 quantize_per_tensor_default_4" -> "37 dequantize_per_tensor_default_4" [label="(1, 64, 56, 56)", style=solid]; +"36 dequantize_per_tensor_default_5" -> "57 add__1" [label="(1, 64, 56, 56)", style=solid]; +"37 dequantize_per_tensor_default_4" -> "44 conv2d_3" [label="(1, 64, 56, 56)", style=solid]; +"38 conv2d_3_scale_0" -> "41 quantize_per_channel_default_3" [label="(64,)", style=solid]; +"38 conv2d_3_scale_0" -> "42 dequantize_per_channel_default_3" [label="(64,)", style=solid]; +"39 conv2d_3_zero_point_0" -> "41 quantize_per_channel_default_3" [label="(64,)", style=solid]; +"39 conv2d_3_zero_point_0" -> "42 dequantize_per_channel_default_3" [label="(64,)", style=solid]; +"40 compressed_weight_updated_constant3" -> "41 quantize_per_channel_default_3" [label="(64, 64, 3, 3)", style=solid]; +"41 quantize_per_channel_default_3" -> "42 dequantize_per_channel_default_3" [label="(64, 64, 3, 3)", style=solid]; +"42 dequantize_per_channel_default_3" -> "44 conv2d_3" [label="(64, 64, 3, 3)", style=solid]; +"43 layer1_1_conv1_weight_bias_0_0" -> "44 conv2d_3" [label="(64,)", style=solid]; +"44 conv2d_3" -> "45 relu__3" [label="(1, 64, 56, 56)", style=solid]; +"45 relu__3" -> "46 quantize_per_tensor_default_5" [label="(1, 64, 56, 56)", style=solid]; +"46 quantize_per_tensor_default_5" -> "47 dequantize_per_tensor_default_6" [label="(1, 64, 56, 56)", style=solid]; +"47 dequantize_per_tensor_default_6" -> "54 conv2d_4" [label="(1, 64, 56, 56)", style=solid]; +"48 conv2d_4_scale_0" -> "51 quantize_per_channel_default_4" [label="(64,)", style=solid]; +"48 conv2d_4_scale_0" -> "52 dequantize_per_channel_default_4" [label="(64,)", style=solid]; +"49 conv2d_4_zero_point_0" -> "51 quantize_per_channel_default_4" [label="(64,)", style=solid]; +"49 conv2d_4_zero_point_0" -> "52 dequantize_per_channel_default_4" [label="(64,)", style=solid]; +"50 compressed_weight_updated_constant4" -> "51 quantize_per_channel_default_4" [label="(64, 64, 3, 3)", style=solid]; +"51 quantize_per_channel_default_4" -> "52 dequantize_per_channel_default_4" [label="(64, 64, 3, 3)", style=solid]; +"52 dequantize_per_channel_default_4" -> "54 conv2d_4" [label="(64, 64, 3, 3)", style=solid]; +"53 layer1_1_conv2_weight_bias_0_0" -> "54 conv2d_4" [label="(64,)", style=solid]; +"54 conv2d_4" -> "55 quantize_per_tensor_default_6" [label="(1, 64, 56, 56)", style=solid]; +"55 quantize_per_tensor_default_6" -> "56 dequantize_per_tensor_default_7" [label="(1, 64, 56, 56)", style=solid]; +"56 dequantize_per_tensor_default_7" -> "57 add__1" [label="(1, 64, 56, 56)", style=solid]; +"57 add__1" -> "58 relu__4" [label="(1, 64, 56, 56)", style=solid]; +"58 relu__4" -> "59 quantize_per_tensor_default_7" [label="(1, 64, 56, 56)", style=solid]; +"59 quantize_per_tensor_default_7" -> "60 dequantize_per_tensor_default_9" [label="(1, 64, 56, 56)", style=solid]; +"59 quantize_per_tensor_default_7" -> "61 dequantize_per_tensor_default_8" [label="(1, 64, 56, 56)", style=solid]; +"60 dequantize_per_tensor_default_9" -> "87 conv2d_7" [label="(1, 64, 56, 56)", style=solid]; +"61 dequantize_per_tensor_default_8" -> "68 conv2d_5" [label="(1, 64, 56, 56)", style=solid]; +"62 conv2d_5_scale_0" -> "65 quantize_per_channel_default_5" [label="(128,)", style=solid]; +"62 conv2d_5_scale_0" -> "66 dequantize_per_channel_default_5" [label="(128,)", style=solid]; +"63 conv2d_5_zero_point_0" -> "65 quantize_per_channel_default_5" [label="(128,)", style=solid]; +"63 conv2d_5_zero_point_0" -> "66 dequantize_per_channel_default_5" [label="(128,)", style=solid]; +"64 compressed_weight_updated_constant5" -> "65 quantize_per_channel_default_5" [label="(128, 64, 3, 3)", style=solid]; +"65 quantize_per_channel_default_5" -> "66 dequantize_per_channel_default_5" [label="(128, 64, 3, 3)", style=solid]; +"66 dequantize_per_channel_default_5" -> "68 conv2d_5" [label="(128, 64, 3, 3)", style=solid]; +"67 layer2_0_conv1_weight_bias_0_0" -> "68 conv2d_5" [label="(128,)", style=solid]; +"68 conv2d_5" -> "69 relu__5" [label="(1, 128, 28, 28)", style=solid]; +"69 relu__5" -> "70 quantize_per_tensor_default_8" [label="(1, 128, 28, 28)", style=solid]; +"70 quantize_per_tensor_default_8" -> "71 dequantize_per_tensor_default_10" [label="(1, 128, 28, 28)", style=solid]; +"71 dequantize_per_tensor_default_10" -> "78 conv2d_6" [label="(1, 128, 28, 28)", style=solid]; +"72 conv2d_6_scale_0" -> "75 quantize_per_channel_default_6" [label="(128,)", style=solid]; +"72 conv2d_6_scale_0" -> "76 dequantize_per_channel_default_6" [label="(128,)", style=solid]; +"73 conv2d_6_zero_point_0" -> "75 quantize_per_channel_default_6" [label="(128,)", style=solid]; +"73 conv2d_6_zero_point_0" -> "76 dequantize_per_channel_default_6" [label="(128,)", style=solid]; +"74 compressed_weight_updated_constant6" -> "75 quantize_per_channel_default_6" [label="(128, 128, 3, 3)", style=solid]; +"75 quantize_per_channel_default_6" -> "76 dequantize_per_channel_default_6" [label="(128, 128, 3, 3)", style=solid]; +"76 dequantize_per_channel_default_6" -> "78 conv2d_6" [label="(128, 128, 3, 3)", style=solid]; +"77 layer2_0_conv2_weight_bias_0_0" -> "78 conv2d_6" [label="(128,)", style=solid]; +"78 conv2d_6" -> "79 quantize_per_tensor_default_9" [label="(1, 128, 28, 28)", style=solid]; +"79 quantize_per_tensor_default_9" -> "80 dequantize_per_tensor_default_11" [label="(1, 128, 28, 28)", style=solid]; +"80 dequantize_per_tensor_default_11" -> "90 add__2" [label="(1, 128, 28, 28)", style=solid]; +"81 conv2d_7_scale_0" -> "84 quantize_per_channel_default_7" [label="(128,)", style=solid]; +"81 conv2d_7_scale_0" -> "85 dequantize_per_channel_default_7" [label="(128,)", style=solid]; +"82 conv2d_7_zero_point_0" -> "84 quantize_per_channel_default_7" [label="(128,)", style=solid]; +"82 conv2d_7_zero_point_0" -> "85 dequantize_per_channel_default_7" [label="(128,)", style=solid]; +"83 compressed_weight_updated_constant7" -> "84 quantize_per_channel_default_7" [label="(128, 64, 1, 1)", style=solid]; +"84 quantize_per_channel_default_7" -> "85 dequantize_per_channel_default_7" [label="(128, 64, 1, 1)", style=solid]; +"85 dequantize_per_channel_default_7" -> "87 conv2d_7" [label="(128, 64, 1, 1)", style=solid]; +"86 layer2_0_downsample_0_weight_bias_0_0" -> "87 conv2d_7" [label="(128,)", style=solid]; +"87 conv2d_7" -> "88 quantize_per_tensor_default_10" [label="(1, 128, 28, 28)", style=solid]; +"88 quantize_per_tensor_default_10" -> "89 dequantize_per_tensor_default_12" [label="(1, 128, 28, 28)", style=solid]; +"89 dequantize_per_tensor_default_12" -> "90 add__2" [label="(1, 128, 28, 28)", style=solid]; +"90 add__2" -> "91 relu__6" [label="(1, 128, 28, 28)", style=solid]; +"91 relu__6" -> "92 quantize_per_tensor_default_11" [label="(1, 128, 28, 28)", style=solid]; +"92 quantize_per_tensor_default_11" -> "93 dequantize_per_tensor_default_14" [label="(1, 128, 28, 28)", style=solid]; +"92 quantize_per_tensor_default_11" -> "94 dequantize_per_tensor_default_13" [label="(1, 128, 28, 28)", style=solid]; +"93 dequantize_per_tensor_default_14" -> "114 add__3" [label="(1, 128, 28, 28)", style=solid]; +"94 dequantize_per_tensor_default_13" -> "101 conv2d_8" [label="(1, 128, 28, 28)", style=solid]; +"95 conv2d_8_scale_0" -> "98 quantize_per_channel_default_8" [label="(128,)", style=solid]; +"95 conv2d_8_scale_0" -> "99 dequantize_per_channel_default_8" [label="(128,)", style=solid]; +"96 conv2d_8_zero_point_0" -> "98 quantize_per_channel_default_8" [label="(128,)", style=solid]; +"96 conv2d_8_zero_point_0" -> "99 dequantize_per_channel_default_8" [label="(128,)", style=solid]; +"97 compressed_weight_updated_constant8" -> "98 quantize_per_channel_default_8" [label="(128, 128, 3, 3)", style=solid]; +"98 quantize_per_channel_default_8" -> "99 dequantize_per_channel_default_8" [label="(128, 128, 3, 3)", style=solid]; +"99 dequantize_per_channel_default_8" -> "101 conv2d_8" [label="(128, 128, 3, 3)", style=solid]; +"100 layer2_1_conv1_weight_bias_0_0" -> "101 conv2d_8" [label="(128,)", style=solid]; +"101 conv2d_8" -> "102 relu__7" [label="(1, 128, 28, 28)", style=solid]; +"102 relu__7" -> "103 quantize_per_tensor_default_12" [label="(1, 128, 28, 28)", style=solid]; +"103 quantize_per_tensor_default_12" -> "104 dequantize_per_tensor_default_15" [label="(1, 128, 28, 28)", style=solid]; +"104 dequantize_per_tensor_default_15" -> "111 conv2d_9" [label="(1, 128, 28, 28)", style=solid]; +"105 conv2d_9_scale_0" -> "108 quantize_per_channel_default_9" [label="(128,)", style=solid]; +"105 conv2d_9_scale_0" -> "109 dequantize_per_channel_default_9" [label="(128,)", style=solid]; +"106 conv2d_9_zero_point_0" -> "108 quantize_per_channel_default_9" [label="(128,)", style=solid]; +"106 conv2d_9_zero_point_0" -> "109 dequantize_per_channel_default_9" [label="(128,)", style=solid]; +"107 compressed_weight_updated_constant9" -> "108 quantize_per_channel_default_9" [label="(128, 128, 3, 3)", style=solid]; +"108 quantize_per_channel_default_9" -> "109 dequantize_per_channel_default_9" [label="(128, 128, 3, 3)", style=solid]; +"109 dequantize_per_channel_default_9" -> "111 conv2d_9" [label="(128, 128, 3, 3)", style=solid]; +"110 layer2_1_conv2_weight_bias_0_0" -> "111 conv2d_9" [label="(128,)", style=solid]; +"111 conv2d_9" -> "112 quantize_per_tensor_default_13" [label="(1, 128, 28, 28)", style=solid]; +"112 quantize_per_tensor_default_13" -> "113 dequantize_per_tensor_default_16" [label="(1, 128, 28, 28)", style=solid]; +"113 dequantize_per_tensor_default_16" -> "114 add__3" [label="(1, 128, 28, 28)", style=solid]; +"114 add__3" -> "115 relu__8" [label="(1, 128, 28, 28)", style=solid]; +"115 relu__8" -> "116 quantize_per_tensor_default_14" [label="(1, 128, 28, 28)", style=solid]; +"116 quantize_per_tensor_default_14" -> "117 dequantize_per_tensor_default_18" [label="(1, 128, 28, 28)", style=solid]; +"116 quantize_per_tensor_default_14" -> "118 dequantize_per_tensor_default_17" [label="(1, 128, 28, 28)", style=solid]; +"117 dequantize_per_tensor_default_18" -> "144 conv2d_12" [label="(1, 128, 28, 28)", style=solid]; +"118 dequantize_per_tensor_default_17" -> "125 conv2d_10" [label="(1, 128, 28, 28)", style=solid]; +"119 conv2d_10_scale_0" -> "122 quantize_per_channel_default_10" [label="(256,)", style=solid]; +"119 conv2d_10_scale_0" -> "123 dequantize_per_channel_default_10" [label="(256,)", style=solid]; +"120 conv2d_10_zero_point_0" -> "122 quantize_per_channel_default_10" [label="(256,)", style=solid]; +"120 conv2d_10_zero_point_0" -> "123 dequantize_per_channel_default_10" [label="(256,)", style=solid]; +"121 compressed_weight_updated_constant10" -> "122 quantize_per_channel_default_10" [label="(256, 128, 3, 3)", style=solid]; +"122 quantize_per_channel_default_10" -> "123 dequantize_per_channel_default_10" [label="(256, 128, 3, 3)", style=solid]; +"123 dequantize_per_channel_default_10" -> "125 conv2d_10" [label="(256, 128, 3, 3)", style=solid]; +"124 layer3_0_conv1_weight_bias_0_0" -> "125 conv2d_10" [label="(256,)", style=solid]; +"125 conv2d_10" -> "126 relu__9" [label="(1, 256, 14, 14)", style=solid]; +"126 relu__9" -> "127 quantize_per_tensor_default_15" [label="(1, 256, 14, 14)", style=solid]; +"127 quantize_per_tensor_default_15" -> "128 dequantize_per_tensor_default_19" [label="(1, 256, 14, 14)", style=solid]; +"128 dequantize_per_tensor_default_19" -> "135 conv2d_11" [label="(1, 256, 14, 14)", style=solid]; +"129 conv2d_11_scale_0" -> "132 quantize_per_channel_default_11" [label="(256,)", style=solid]; +"129 conv2d_11_scale_0" -> "133 dequantize_per_channel_default_11" [label="(256,)", style=solid]; +"130 conv2d_11_zero_point_0" -> "132 quantize_per_channel_default_11" [label="(256,)", style=solid]; +"130 conv2d_11_zero_point_0" -> "133 dequantize_per_channel_default_11" [label="(256,)", style=solid]; +"131 compressed_weight_updated_constant11" -> "132 quantize_per_channel_default_11" [label="(256, 256, 3, 3)", style=solid]; +"132 quantize_per_channel_default_11" -> "133 dequantize_per_channel_default_11" [label="(256, 256, 3, 3)", style=solid]; +"133 dequantize_per_channel_default_11" -> "135 conv2d_11" [label="(256, 256, 3, 3)", style=solid]; +"134 layer3_0_conv2_weight_bias_0_0" -> "135 conv2d_11" [label="(256,)", style=solid]; +"135 conv2d_11" -> "136 quantize_per_tensor_default_16" [label="(1, 256, 14, 14)", style=solid]; +"136 quantize_per_tensor_default_16" -> "137 dequantize_per_tensor_default_20" [label="(1, 256, 14, 14)", style=solid]; +"137 dequantize_per_tensor_default_20" -> "147 add__4" [label="(1, 256, 14, 14)", style=solid]; +"138 conv2d_12_scale_0" -> "141 quantize_per_channel_default_12" [label="(256,)", style=solid]; +"138 conv2d_12_scale_0" -> "142 dequantize_per_channel_default_12" [label="(256,)", style=solid]; +"139 conv2d_12_zero_point_0" -> "141 quantize_per_channel_default_12" [label="(256,)", style=solid]; +"139 conv2d_12_zero_point_0" -> "142 dequantize_per_channel_default_12" [label="(256,)", style=solid]; +"140 compressed_weight_updated_constant12" -> "141 quantize_per_channel_default_12" [label="(256, 128, 1, 1)", style=solid]; +"141 quantize_per_channel_default_12" -> "142 dequantize_per_channel_default_12" [label="(256, 128, 1, 1)", style=solid]; +"142 dequantize_per_channel_default_12" -> "144 conv2d_12" [label="(256, 128, 1, 1)", style=solid]; +"143 layer3_0_downsample_0_weight_bias_0_0" -> "144 conv2d_12" [label="(256,)", style=solid]; +"144 conv2d_12" -> "145 quantize_per_tensor_default_17" [label="(1, 256, 14, 14)", style=solid]; +"145 quantize_per_tensor_default_17" -> "146 dequantize_per_tensor_default_21" [label="(1, 256, 14, 14)", style=solid]; +"146 dequantize_per_tensor_default_21" -> "147 add__4" [label="(1, 256, 14, 14)", style=solid]; +"147 add__4" -> "148 relu__10" [label="(1, 256, 14, 14)", style=solid]; +"148 relu__10" -> "149 quantize_per_tensor_default_18" [label="(1, 256, 14, 14)", style=solid]; +"149 quantize_per_tensor_default_18" -> "150 dequantize_per_tensor_default_23" [label="(1, 256, 14, 14)", style=solid]; +"149 quantize_per_tensor_default_18" -> "151 dequantize_per_tensor_default_22" [label="(1, 256, 14, 14)", style=solid]; +"150 dequantize_per_tensor_default_23" -> "171 add__5" [label="(1, 256, 14, 14)", style=solid]; +"151 dequantize_per_tensor_default_22" -> "158 conv2d_13" [label="(1, 256, 14, 14)", style=solid]; +"152 conv2d_13_scale_0" -> "155 quantize_per_channel_default_13" [label="(256,)", style=solid]; +"152 conv2d_13_scale_0" -> "156 dequantize_per_channel_default_13" [label="(256,)", style=solid]; +"153 conv2d_13_zero_point_0" -> "155 quantize_per_channel_default_13" [label="(256,)", style=solid]; +"153 conv2d_13_zero_point_0" -> "156 dequantize_per_channel_default_13" [label="(256,)", style=solid]; +"154 compressed_weight_updated_constant13" -> "155 quantize_per_channel_default_13" [label="(256, 256, 3, 3)", style=solid]; +"155 quantize_per_channel_default_13" -> "156 dequantize_per_channel_default_13" [label="(256, 256, 3, 3)", style=solid]; +"156 dequantize_per_channel_default_13" -> "158 conv2d_13" [label="(256, 256, 3, 3)", style=solid]; +"157 layer3_1_conv1_weight_bias_0_0" -> "158 conv2d_13" [label="(256,)", style=solid]; +"158 conv2d_13" -> "159 relu__11" [label="(1, 256, 14, 14)", style=solid]; +"159 relu__11" -> "160 quantize_per_tensor_default_19" [label="(1, 256, 14, 14)", style=solid]; +"160 quantize_per_tensor_default_19" -> "161 dequantize_per_tensor_default_24" [label="(1, 256, 14, 14)", style=solid]; +"161 dequantize_per_tensor_default_24" -> "168 conv2d_14" [label="(1, 256, 14, 14)", style=solid]; +"162 conv2d_14_scale_0" -> "165 quantize_per_channel_default_14" [label="(256,)", style=solid]; +"162 conv2d_14_scale_0" -> "166 dequantize_per_channel_default_14" [label="(256,)", style=solid]; +"163 conv2d_14_zero_point_0" -> "165 quantize_per_channel_default_14" [label="(256,)", style=solid]; +"163 conv2d_14_zero_point_0" -> "166 dequantize_per_channel_default_14" [label="(256,)", style=solid]; +"164 compressed_weight_updated_constant14" -> "165 quantize_per_channel_default_14" [label="(256, 256, 3, 3)", style=solid]; +"165 quantize_per_channel_default_14" -> "166 dequantize_per_channel_default_14" [label="(256, 256, 3, 3)", style=solid]; +"166 dequantize_per_channel_default_14" -> "168 conv2d_14" [label="(256, 256, 3, 3)", style=solid]; +"167 layer3_1_conv2_weight_bias_0_0" -> "168 conv2d_14" [label="(256,)", style=solid]; +"168 conv2d_14" -> "169 quantize_per_tensor_default_20" [label="(1, 256, 14, 14)", style=solid]; +"169 quantize_per_tensor_default_20" -> "170 dequantize_per_tensor_default_25" [label="(1, 256, 14, 14)", style=solid]; +"170 dequantize_per_tensor_default_25" -> "171 add__5" [label="(1, 256, 14, 14)", style=solid]; +"171 add__5" -> "172 relu__12" [label="(1, 256, 14, 14)", style=solid]; +"172 relu__12" -> "173 quantize_per_tensor_default_21" [label="(1, 256, 14, 14)", style=solid]; +"173 quantize_per_tensor_default_21" -> "174 dequantize_per_tensor_default_27" [label="(1, 256, 14, 14)", style=solid]; +"173 quantize_per_tensor_default_21" -> "175 dequantize_per_tensor_default_26" [label="(1, 256, 14, 14)", style=solid]; +"174 dequantize_per_tensor_default_27" -> "201 conv2d_17" [label="(1, 256, 14, 14)", style=solid]; +"175 dequantize_per_tensor_default_26" -> "182 conv2d_15" [label="(1, 256, 14, 14)", style=solid]; +"176 conv2d_15_scale_0" -> "179 quantize_per_channel_default_15" [label="(512,)", style=solid]; +"176 conv2d_15_scale_0" -> "180 dequantize_per_channel_default_15" [label="(512,)", style=solid]; +"177 conv2d_15_zero_point_0" -> "179 quantize_per_channel_default_15" [label="(512,)", style=solid]; +"177 conv2d_15_zero_point_0" -> "180 dequantize_per_channel_default_15" [label="(512,)", style=solid]; +"178 compressed_weight_updated_constant15" -> "179 quantize_per_channel_default_15" [label="(512, 256, 3, 3)", style=solid]; +"179 quantize_per_channel_default_15" -> "180 dequantize_per_channel_default_15" [label="(512, 256, 3, 3)", style=solid]; +"180 dequantize_per_channel_default_15" -> "182 conv2d_15" [label="(512, 256, 3, 3)", style=solid]; +"181 layer4_0_conv1_weight_bias_0_0" -> "182 conv2d_15" [label="(512,)", style=solid]; +"182 conv2d_15" -> "183 relu__13" [label="(1, 512, 7, 7)", style=solid]; +"183 relu__13" -> "184 quantize_per_tensor_default_22" [label="(1, 512, 7, 7)", style=solid]; +"184 quantize_per_tensor_default_22" -> "185 dequantize_per_tensor_default_28" [label="(1, 512, 7, 7)", style=solid]; +"185 dequantize_per_tensor_default_28" -> "192 conv2d_16" [label="(1, 512, 7, 7)", style=solid]; +"186 conv2d_16_scale_0" -> "189 quantize_per_channel_default_16" [label="(512,)", style=solid]; +"186 conv2d_16_scale_0" -> "190 dequantize_per_channel_default_16" [label="(512,)", style=solid]; +"187 conv2d_16_zero_point_0" -> "189 quantize_per_channel_default_16" [label="(512,)", style=solid]; +"187 conv2d_16_zero_point_0" -> "190 dequantize_per_channel_default_16" [label="(512,)", style=solid]; +"188 compressed_weight_updated_constant16" -> "189 quantize_per_channel_default_16" [label="(512, 512, 3, 3)", style=solid]; +"189 quantize_per_channel_default_16" -> "190 dequantize_per_channel_default_16" [label="(512, 512, 3, 3)", style=solid]; +"190 dequantize_per_channel_default_16" -> "192 conv2d_16" [label="(512, 512, 3, 3)", style=solid]; +"191 layer4_0_conv2_weight_bias_0_0" -> "192 conv2d_16" [label="(512,)", style=solid]; +"192 conv2d_16" -> "193 quantize_per_tensor_default_23" [label="(1, 512, 7, 7)", style=solid]; +"193 quantize_per_tensor_default_23" -> "194 dequantize_per_tensor_default_29" [label="(1, 512, 7, 7)", style=solid]; +"194 dequantize_per_tensor_default_29" -> "204 add__6" [label="(1, 512, 7, 7)", style=solid]; +"195 conv2d_17_scale_0" -> "198 quantize_per_channel_default_17" [label="(512,)", style=solid]; +"195 conv2d_17_scale_0" -> "199 dequantize_per_channel_default_17" [label="(512,)", style=solid]; +"196 conv2d_17_zero_point_0" -> "198 quantize_per_channel_default_17" [label="(512,)", style=solid]; +"196 conv2d_17_zero_point_0" -> "199 dequantize_per_channel_default_17" [label="(512,)", style=solid]; +"197 compressed_weight_updated_constant17" -> "198 quantize_per_channel_default_17" [label="(512, 256, 1, 1)", style=solid]; +"198 quantize_per_channel_default_17" -> "199 dequantize_per_channel_default_17" [label="(512, 256, 1, 1)", style=solid]; +"199 dequantize_per_channel_default_17" -> "201 conv2d_17" [label="(512, 256, 1, 1)", style=solid]; +"200 layer4_0_downsample_0_weight_bias_0_0" -> "201 conv2d_17" [label="(512,)", style=solid]; +"201 conv2d_17" -> "202 quantize_per_tensor_default_24" [label="(1, 512, 7, 7)", style=solid]; +"202 quantize_per_tensor_default_24" -> "203 dequantize_per_tensor_default_30" [label="(1, 512, 7, 7)", style=solid]; +"203 dequantize_per_tensor_default_30" -> "204 add__6" [label="(1, 512, 7, 7)", style=solid]; +"204 add__6" -> "205 relu__14" [label="(1, 512, 7, 7)", style=solid]; +"205 relu__14" -> "206 quantize_per_tensor_default_25" [label="(1, 512, 7, 7)", style=solid]; +"206 quantize_per_tensor_default_25" -> "207 dequantize_per_tensor_default_32" [label="(1, 512, 7, 7)", style=solid]; +"206 quantize_per_tensor_default_25" -> "208 dequantize_per_tensor_default_31" [label="(1, 512, 7, 7)", style=solid]; +"207 dequantize_per_tensor_default_32" -> "228 add__7" [label="(1, 512, 7, 7)", style=solid]; +"208 dequantize_per_tensor_default_31" -> "215 conv2d_18" [label="(1, 512, 7, 7)", style=solid]; +"209 conv2d_18_scale_0" -> "212 quantize_per_channel_default_18" [label="(512,)", style=solid]; +"209 conv2d_18_scale_0" -> "213 dequantize_per_channel_default_18" [label="(512,)", style=solid]; +"210 conv2d_18_zero_point_0" -> "212 quantize_per_channel_default_18" [label="(512,)", style=solid]; +"210 conv2d_18_zero_point_0" -> "213 dequantize_per_channel_default_18" [label="(512,)", style=solid]; +"211 compressed_weight_updated_constant18" -> "212 quantize_per_channel_default_18" [label="(512, 512, 3, 3)", style=solid]; +"212 quantize_per_channel_default_18" -> "213 dequantize_per_channel_default_18" [label="(512, 512, 3, 3)", style=solid]; +"213 dequantize_per_channel_default_18" -> "215 conv2d_18" [label="(512, 512, 3, 3)", style=solid]; +"214 layer4_1_conv1_weight_bias_0_0" -> "215 conv2d_18" [label="(512,)", style=solid]; +"215 conv2d_18" -> "216 relu__15" [label="(1, 512, 7, 7)", style=solid]; +"216 relu__15" -> "217 quantize_per_tensor_default_26" [label="(1, 512, 7, 7)", style=solid]; +"217 quantize_per_tensor_default_26" -> "218 dequantize_per_tensor_default_33" [label="(1, 512, 7, 7)", style=solid]; +"218 dequantize_per_tensor_default_33" -> "225 conv2d_19" [label="(1, 512, 7, 7)", style=solid]; +"219 conv2d_19_scale_0" -> "222 quantize_per_channel_default_19" [label="(512,)", style=solid]; +"219 conv2d_19_scale_0" -> "223 dequantize_per_channel_default_19" [label="(512,)", style=solid]; +"220 conv2d_19_zero_point_0" -> "222 quantize_per_channel_default_19" [label="(512,)", style=solid]; +"220 conv2d_19_zero_point_0" -> "223 dequantize_per_channel_default_19" [label="(512,)", style=solid]; +"221 compressed_weight_updated_constant19" -> "222 quantize_per_channel_default_19" [label="(512, 512, 3, 3)", style=solid]; +"222 quantize_per_channel_default_19" -> "223 dequantize_per_channel_default_19" [label="(512, 512, 3, 3)", style=solid]; +"223 dequantize_per_channel_default_19" -> "225 conv2d_19" [label="(512, 512, 3, 3)", style=solid]; +"224 layer4_1_conv2_weight_bias_0_0" -> "225 conv2d_19" [label="(512,)", style=solid]; +"225 conv2d_19" -> "226 quantize_per_tensor_default_27" [label="(1, 512, 7, 7)", style=solid]; +"226 quantize_per_tensor_default_27" -> "227 dequantize_per_tensor_default_34" [label="(1, 512, 7, 7)", style=solid]; +"227 dequantize_per_tensor_default_34" -> "228 add__7" [label="(1, 512, 7, 7)", style=solid]; +"228 add__7" -> "229 relu__16" [label="(1, 512, 7, 7)", style=solid]; +"229 relu__16" -> "230 quantize_per_tensor_default_28" [label="(1, 512, 7, 7)", style=solid]; +"230 quantize_per_tensor_default_28" -> "231 dequantize_per_tensor_default_35" [label="(1, 512, 7, 7)", style=solid]; +"231 dequantize_per_tensor_default_35" -> "232 adaptive_avg_pool2d" [label="(1, 512, 7, 7)", style=solid]; +"232 adaptive_avg_pool2d" -> "233 quantize_per_tensor_default_29" [label="(1, 512, 1, 1)", style=solid]; +"233 quantize_per_tensor_default_29" -> "234 dequantize_per_tensor_default_36" [label="(1, 512, 1, 1)", style=solid]; +"234 dequantize_per_tensor_default_36" -> "235 flatten" [label="(1, 512, 1, 1)", style=solid]; +"235 flatten" -> "242 linear" [label="(1, 512)", style=solid]; +"236 linear_scale_0" -> "239 quantize_per_channel_default_20" [label="(1000,)", style=solid]; +"236 linear_scale_0" -> "240 dequantize_per_channel_default_20" [label="(1000,)", style=solid]; +"237 linear_zero_point_0" -> "239 quantize_per_channel_default_20" [label="(1000,)", style=solid]; +"237 linear_zero_point_0" -> "240 dequantize_per_channel_default_20" [label="(1000,)", style=solid]; +"238 compressed_weight_updated_constant20" -> "239 quantize_per_channel_default_20" [label="(1000, 512)", style=solid]; +"239 quantize_per_channel_default_20" -> "240 dequantize_per_channel_default_20" [label="(1000, 512)", style=solid]; +"240 dequantize_per_channel_default_20" -> "242 linear" [label="(1000, 512)", style=solid]; +"241 fc_bias_0_0" -> "242 linear" [label="(1000,)", style=solid]; +"242 linear" -> "243 output" [label="(1, 1000)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/quantized/swin_v2_s.dot b/tests/torch/data/reference_graphs/fx/quantized/swin_v2_s.dot index ea39f9c0188..e45747bd6fa 100644 --- a/tests/torch/data/reference_graphs/fx/quantized/swin_v2_s.dot +++ b/tests/torch/data/reference_graphs/fx/quantized/swin_v2_s.dot @@ -1,6858 +1,5466 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; -"1 quantize_per_tensor_default" [id=1, type=quantize_per_tensor]; -"2 dequantize_per_tensor_default" [id=2, type=dequantize_per_tensor]; -"3 _param_constant0" [id=3, type=get_attr]; -"4 conv2d_scale_0" [id=4, type=get_attr]; -"5 conv2d_zero_point_0" [id=5, type=get_attr]; -"6 quantize_per_channel_default" [id=6, type=quantize_per_channel]; -"7 dequantize_per_channel_default" [id=7, type=dequantize_per_channel]; -"8 _param_constant1_0_0" [id=8, type=get_attr]; -"9 conv2d" [id=9, type=conv2d]; -"10 permute" [id=10, type=permute]; -"11 _param_constant2" [id=11, type=get_attr]; -"12 _param_constant3" [id=12, type=get_attr]; -"13 layer_norm" [id=13, type=layer_norm]; -"14 _tensor_constant0" [id=14, type=get_attr]; -"15 linear_updated_constant0" [id=15, type=get_attr]; -"16 _tensor_constant0_0_0_nncf_smooth_quant_0" [id=16, type=call_module]; -"17 linear_scale_0" [id=17, type=get_attr]; -"18 linear_zero_point_0" [id=18, type=get_attr]; -"19 quantize_per_channel_default_1" [id=19, type=quantize_per_channel]; -"20 dequantize_per_channel_default_1" [id=20, type=dequantize_per_channel]; -"21 _param_constant5_0_0" [id=21, type=get_attr]; -"22 linear" [id=22, type=linear]; -"23 relu_" [id=23, type=relu_]; -"24 linear_1_updated_constant0" [id=24, type=get_attr]; -"25 relu__0_0_nncf_smooth_quant_0" [id=25, type=call_module]; -"26 linear_1_scale_0" [id=26, type=get_attr]; -"27 linear_1_zero_point_0" [id=27, type=get_attr]; -"28 quantize_per_channel_default_2" [id=28, type=quantize_per_channel]; -"29 dequantize_per_channel_default_2" [id=29, type=dequantize_per_channel]; -"30 linear_1" [id=30, type=linear]; -"31 view" [id=31, type=view]; -"32 _tensor_constant1" [id=32, type=get_attr]; -"33 index" [id=33, type=index]; -"34 view_1" [id=34, type=view]; -"35 permute_1" [id=35, type=permute]; -"36 contiguous" [id=36, type=contiguous]; -"37 unsqueeze" [id=37, type=unsqueeze]; -"38 sigmoid" [id=38, type=sigmoid]; -"39 mul" [id=39, type=mul]; -"40 pad" [id=40, type=pad]; -"41 view_2" [id=41, type=view]; -"42 permute_2" [id=42, type=permute]; -"43 reshape" [id=43, type=reshape]; -"44 linear_2_updated_constant0" [id=44, type=get_attr]; -"45 reshape_0_0_nncf_smooth_quant_0" [id=45, type=call_module]; -"46 quantize_per_tensor_default_1" [id=46, type=quantize_per_tensor]; -"47 dequantize_per_tensor_default_1" [id=47, type=dequantize_per_tensor]; -"48 linear_2_scale_0" [id=48, type=get_attr]; -"49 linear_2_zero_point_0" [id=49, type=get_attr]; -"50 quantize_per_channel_default_3" [id=50, type=quantize_per_channel]; -"51 dequantize_per_channel_default_3" [id=51, type=dequantize_per_channel]; -"52 _param_constant7_0_0" [id=52, type=get_attr]; -"53 linear_2" [id=53, type=linear]; -"54 reshape_1" [id=54, type=reshape]; -"55 permute_3" [id=55, type=permute]; -"56 select" [id=56, type=select]; -"57 select_1" [id=57, type=select]; -"58 select_2" [id=58, type=select]; -"59 linalg_vector_norm" [id=59, type=linalg_vector_norm]; -"60 clamp_min" [id=60, type=clamp_min]; -"61 expand_as" [id=61, type=expand_as]; -"62 div" [id=62, type=div]; -"63 quantize_per_tensor_default_2" [id=63, type=quantize_per_tensor]; -"64 dequantize_per_tensor_default_2" [id=64, type=dequantize_per_tensor]; -"65 linalg_vector_norm_1" [id=65, type=linalg_vector_norm]; -"66 clamp_min_1" [id=66, type=clamp_min]; -"67 expand_as_1" [id=67, type=expand_as]; -"68 div_1" [id=68, type=div]; -"69 quantize_per_tensor_default_3" [id=69, type=quantize_per_tensor]; -"70 dequantize_per_tensor_default_3" [id=70, type=dequantize_per_tensor]; -"71 transpose" [id=71, type=transpose]; -"72 matmul" [id=72, type=matmul]; -"73 _param_constant9" [id=73, type=get_attr]; -"74 clamp" [id=74, type=clamp]; -"75 exp" [id=75, type=exp]; -"76 mul_1" [id=76, type=mul]; -"77 add" [id=77, type=add]; -"78 softmax" [id=78, type=softmax]; -"79 dropout" [id=79, type=dropout]; -"80 matmul_1" [id=80, type=matmul]; -"81 transpose_1" [id=81, type=transpose]; -"82 reshape_2" [id=82, type=reshape]; -"83 linear_3_updated_constant0" [id=83, type=get_attr]; -"84 reshape_2_0_0_nncf_smooth_quant_0" [id=84, type=call_module]; -"85 quantize_per_tensor_default_4" [id=85, type=quantize_per_tensor]; -"86 dequantize_per_tensor_default_4" [id=86, type=dequantize_per_tensor]; -"87 linear_3_scale_0" [id=87, type=get_attr]; -"88 linear_3_zero_point_0" [id=88, type=get_attr]; -"89 quantize_per_channel_default_4" [id=89, type=quantize_per_channel]; -"90 dequantize_per_channel_default_4" [id=90, type=dequantize_per_channel]; -"91 _param_constant11_0_0" [id=91, type=get_attr]; -"92 linear_3" [id=92, type=linear]; -"93 dropout_1" [id=93, type=dropout]; -"94 view_3" [id=94, type=view]; -"95 permute_4" [id=95, type=permute]; -"96 reshape_3" [id=96, type=reshape]; -"97 slice_2" [id=97, type=slice]; -"98 slice_3" [id=98, type=slice]; -"99 _param_constant12" [id=99, type=get_attr]; -"100 _param_constant13" [id=100, type=get_attr]; -"101 layer_norm_1" [id=101, type=layer_norm]; -"102 add_1" [id=102, type=add]; -"103 linear_4_updated_constant0" [id=103, type=get_attr]; -"104 add_1_0_0_nncf_smooth_quant_0" [id=104, type=call_module]; -"105 quantize_per_tensor_default_5" [id=105, type=quantize_per_tensor]; -"106 dequantize_per_tensor_default_5" [id=106, type=dequantize_per_tensor]; -"107 linear_4_scale_0" [id=107, type=get_attr]; -"108 linear_4_zero_point_0" [id=108, type=get_attr]; -"109 quantize_per_channel_default_5" [id=109, type=quantize_per_channel]; -"110 dequantize_per_channel_default_5" [id=110, type=dequantize_per_channel]; -"111 _param_constant15_0_0" [id=111, type=get_attr]; -"112 linear_4" [id=112, type=linear]; -"113 gelu" [id=113, type=gelu]; -"114 dropout_2" [id=114, type=dropout]; -"115 linear_5_updated_constant0" [id=115, type=get_attr]; -"116 dropout_2_0_0_nncf_smooth_quant_0" [id=116, type=call_module]; -"117 quantize_per_tensor_default_6" [id=117, type=quantize_per_tensor]; -"118 dequantize_per_tensor_default_6" [id=118, type=dequantize_per_tensor]; -"119 linear_5_scale_0" [id=119, type=get_attr]; -"120 linear_5_zero_point_0" [id=120, type=get_attr]; -"121 quantize_per_channel_default_6" [id=121, type=quantize_per_channel]; -"122 dequantize_per_channel_default_6" [id=122, type=dequantize_per_channel]; -"123 _param_constant17_0_0" [id=123, type=get_attr]; -"124 linear_5" [id=124, type=linear]; -"125 dropout_3" [id=125, type=dropout]; -"126 _param_constant18" [id=126, type=get_attr]; -"127 _param_constant19" [id=127, type=get_attr]; -"128 layer_norm_2" [id=128, type=layer_norm]; -"129 add_2" [id=129, type=add]; -"130 _tensor_constant2" [id=130, type=get_attr]; -"131 linear_6_updated_constant0" [id=131, type=get_attr]; -"132 _tensor_constant2_0_0_nncf_smooth_quant_0" [id=132, type=call_module]; -"133 linear_6_scale_0" [id=133, type=get_attr]; -"134 linear_6_zero_point_0" [id=134, type=get_attr]; -"135 quantize_per_channel_default_7" [id=135, type=quantize_per_channel]; -"136 dequantize_per_channel_default_7" [id=136, type=dequantize_per_channel]; -"137 _param_constant21_0_0" [id=137, type=get_attr]; -"138 linear_6" [id=138, type=linear]; -"139 relu__1" [id=139, type=relu_]; -"140 linear_7_updated_constant0" [id=140, type=get_attr]; -"141 relu__1_0_0_nncf_smooth_quant_0" [id=141, type=call_module]; -"142 linear_7_scale_0" [id=142, type=get_attr]; -"143 linear_7_zero_point_0" [id=143, type=get_attr]; -"144 quantize_per_channel_default_8" [id=144, type=quantize_per_channel]; -"145 dequantize_per_channel_default_8" [id=145, type=dequantize_per_channel]; -"146 linear_7" [id=146, type=linear]; -"147 view_4" [id=147, type=view]; -"148 _tensor_constant3" [id=148, type=get_attr]; -"149 index_1" [id=149, type=index]; -"150 view_5" [id=150, type=view]; -"151 permute_5" [id=151, type=permute]; -"152 contiguous_1" [id=152, type=contiguous]; -"153 unsqueeze_1" [id=153, type=unsqueeze]; -"154 sigmoid_1" [id=154, type=sigmoid]; -"155 mul_2" [id=155, type=mul]; -"156 pad_1" [id=156, type=pad]; -"157 roll" [id=157, type=roll]; -"158 view_6" [id=158, type=view]; -"159 permute_6" [id=159, type=permute]; -"160 reshape_4" [id=160, type=reshape]; -"161 linear_8_updated_constant0" [id=161, type=get_attr]; -"162 reshape_4_0_0_nncf_smooth_quant_0" [id=162, type=call_module]; -"163 quantize_per_tensor_default_7" [id=163, type=quantize_per_tensor]; -"164 dequantize_per_tensor_default_7" [id=164, type=dequantize_per_tensor]; -"165 linear_8_scale_0" [id=165, type=get_attr]; -"166 linear_8_zero_point_0" [id=166, type=get_attr]; -"167 quantize_per_channel_default_9" [id=167, type=quantize_per_channel]; -"168 dequantize_per_channel_default_9" [id=168, type=dequantize_per_channel]; -"169 _param_constant23_0_0" [id=169, type=get_attr]; -"170 linear_8" [id=170, type=linear]; -"171 reshape_5" [id=171, type=reshape]; -"172 permute_7" [id=172, type=permute]; -"173 select_3" [id=173, type=select]; -"174 select_4" [id=174, type=select]; -"175 select_5" [id=175, type=select]; -"176 linalg_vector_norm_2" [id=176, type=linalg_vector_norm]; -"177 clamp_min_2" [id=177, type=clamp_min]; -"178 expand_as_2" [id=178, type=expand_as]; -"179 div_2" [id=179, type=div]; -"180 quantize_per_tensor_default_8" [id=180, type=quantize_per_tensor]; -"181 dequantize_per_tensor_default_8" [id=181, type=dequantize_per_tensor]; -"182 linalg_vector_norm_3" [id=182, type=linalg_vector_norm]; -"183 clamp_min_3" [id=183, type=clamp_min]; -"184 expand_as_3" [id=184, type=expand_as]; -"185 div_3" [id=185, type=div]; -"186 quantize_per_tensor_default_9" [id=186, type=quantize_per_tensor]; -"187 dequantize_per_tensor_default_9" [id=187, type=dequantize_per_tensor]; -"188 transpose_2" [id=188, type=transpose]; -"189 matmul_2" [id=189, type=matmul]; -"190 _param_constant25" [id=190, type=get_attr]; -"191 clamp_1" [id=191, type=clamp]; -"192 exp_1" [id=192, type=exp]; -"193 mul_3" [id=193, type=mul]; -"194 add_3" [id=194, type=add]; -"195 new_zeros" [id=195, type=new_zeros]; -"196 view_7" [id=196, type=view]; -"197 permute_8" [id=197, type=permute]; -"198 reshape_6" [id=198, type=reshape]; -"199 unsqueeze_2" [id=199, type=unsqueeze]; -"200 unsqueeze_3" [id=200, type=unsqueeze]; -"201 sub" [id=201, type=sub]; -"202 ne" [id=202, type=ne]; -"203 masked_fill" [id=203, type=masked_fill]; -"204 eq" [id=204, type=eq]; -"205 masked_fill_1" [id=205, type=masked_fill]; -"206 view_8" [id=206, type=view]; -"207 unsqueeze_4" [id=207, type=unsqueeze]; -"208 unsqueeze_5" [id=208, type=unsqueeze]; -"209 add_4" [id=209, type=add]; -"210 view_9" [id=210, type=view]; -"211 softmax_1" [id=211, type=softmax]; -"212 dropout_4" [id=212, type=dropout]; -"213 matmul_3" [id=213, type=matmul]; -"214 transpose_3" [id=214, type=transpose]; -"215 reshape_7" [id=215, type=reshape]; -"216 linear_9_updated_constant0" [id=216, type=get_attr]; -"217 reshape_7_0_0_nncf_smooth_quant_0" [id=217, type=call_module]; -"218 quantize_per_tensor_default_10" [id=218, type=quantize_per_tensor]; -"219 dequantize_per_tensor_default_10" [id=219, type=dequantize_per_tensor]; -"220 linear_9_scale_0" [id=220, type=get_attr]; -"221 linear_9_zero_point_0" [id=221, type=get_attr]; -"222 quantize_per_channel_default_10" [id=222, type=quantize_per_channel]; -"223 dequantize_per_channel_default_10" [id=223, type=dequantize_per_channel]; -"224 _param_constant27_0_0" [id=224, type=get_attr]; -"225 linear_9" [id=225, type=linear]; -"226 dropout_5" [id=226, type=dropout]; -"227 view_10" [id=227, type=view]; -"228 permute_9" [id=228, type=permute]; -"229 reshape_8" [id=229, type=reshape]; -"230 roll_1" [id=230, type=roll]; -"231 slice_23" [id=231, type=slice]; -"232 slice_24" [id=232, type=slice]; -"233 _param_constant28" [id=233, type=get_attr]; -"234 _param_constant29" [id=234, type=get_attr]; -"235 layer_norm_3" [id=235, type=layer_norm]; -"236 add_5" [id=236, type=add]; -"237 linear_10_updated_constant0" [id=237, type=get_attr]; -"238 add_5_0_0_nncf_smooth_quant_0" [id=238, type=call_module]; -"239 quantize_per_tensor_default_11" [id=239, type=quantize_per_tensor]; -"240 dequantize_per_tensor_default_11" [id=240, type=dequantize_per_tensor]; -"241 linear_10_scale_0" [id=241, type=get_attr]; -"242 linear_10_zero_point_0" [id=242, type=get_attr]; -"243 quantize_per_channel_default_11" [id=243, type=quantize_per_channel]; -"244 dequantize_per_channel_default_11" [id=244, type=dequantize_per_channel]; -"245 _param_constant31_0_0" [id=245, type=get_attr]; -"246 linear_10" [id=246, type=linear]; -"247 gelu_1" [id=247, type=gelu]; -"248 dropout_6" [id=248, type=dropout]; -"249 linear_11_updated_constant0" [id=249, type=get_attr]; -"250 dropout_6_0_0_nncf_smooth_quant_0" [id=250, type=call_module]; -"251 quantize_per_tensor_default_12" [id=251, type=quantize_per_tensor]; -"252 dequantize_per_tensor_default_12" [id=252, type=dequantize_per_tensor]; -"253 linear_11_scale_0" [id=253, type=get_attr]; -"254 linear_11_zero_point_0" [id=254, type=get_attr]; -"255 quantize_per_channel_default_12" [id=255, type=quantize_per_channel]; -"256 dequantize_per_channel_default_12" [id=256, type=dequantize_per_channel]; -"257 _param_constant33_0_0" [id=257, type=get_attr]; -"258 linear_11" [id=258, type=linear]; -"259 dropout_7" [id=259, type=dropout]; -"260 _param_constant34" [id=260, type=get_attr]; -"261 _param_constant35" [id=261, type=get_attr]; -"262 layer_norm_4" [id=262, type=layer_norm]; -"263 add_6" [id=263, type=add]; -"264 pad_2" [id=264, type=pad]; -"265 slice_25" [id=265, type=slice]; -"266 slice_26" [id=266, type=slice]; -"267 slice_27" [id=267, type=slice]; -"268 slice_28" [id=268, type=slice]; -"269 slice_29" [id=269, type=slice]; -"270 slice_30" [id=270, type=slice]; -"271 slice_31" [id=271, type=slice]; -"272 slice_32" [id=272, type=slice]; -"273 slice_33" [id=273, type=slice]; -"274 slice_34" [id=274, type=slice]; -"275 slice_35" [id=275, type=slice]; -"276 slice_36" [id=276, type=slice]; -"277 cat" [id=277, type=cat]; -"278 linear_12_updated_constant0" [id=278, type=get_attr]; -"279 cat_0_0_nncf_smooth_quant_0" [id=279, type=call_module]; -"280 quantize_per_tensor_default_13" [id=280, type=quantize_per_tensor]; -"281 dequantize_per_tensor_default_13" [id=281, type=dequantize_per_tensor]; -"282 linear_12_scale_0" [id=282, type=get_attr]; -"283 linear_12_zero_point_0" [id=283, type=get_attr]; -"284 quantize_per_channel_default_13" [id=284, type=quantize_per_channel]; -"285 dequantize_per_channel_default_13" [id=285, type=dequantize_per_channel]; -"286 linear_12" [id=286, type=linear]; -"287 _param_constant37" [id=287, type=get_attr]; -"288 _param_constant38" [id=288, type=get_attr]; -"289 layer_norm_5" [id=289, type=layer_norm]; -"290 _tensor_constant13" [id=290, type=get_attr]; -"291 linear_13_updated_constant0" [id=291, type=get_attr]; -"292 _tensor_constant13_0_0_nncf_smooth_quant_0" [id=292, type=call_module]; -"293 linear_13_scale_0" [id=293, type=get_attr]; -"294 linear_13_zero_point_0" [id=294, type=get_attr]; -"295 quantize_per_channel_default_14" [id=295, type=quantize_per_channel]; -"296 dequantize_per_channel_default_14" [id=296, type=dequantize_per_channel]; -"297 _param_constant40_0_0" [id=297, type=get_attr]; -"298 linear_13" [id=298, type=linear]; -"299 relu__2" [id=299, type=relu_]; -"300 linear_14_updated_constant0" [id=300, type=get_attr]; -"301 relu__2_0_0_nncf_smooth_quant_0" [id=301, type=call_module]; -"302 linear_14_scale_0" [id=302, type=get_attr]; -"303 linear_14_zero_point_0" [id=303, type=get_attr]; -"304 quantize_per_channel_default_15" [id=304, type=quantize_per_channel]; -"305 dequantize_per_channel_default_15" [id=305, type=dequantize_per_channel]; -"306 linear_14" [id=306, type=linear]; -"307 view_11" [id=307, type=view]; -"308 _tensor_constant14" [id=308, type=get_attr]; -"309 index_2" [id=309, type=index]; -"310 view_12" [id=310, type=view]; -"311 permute_10" [id=311, type=permute]; -"312 contiguous_2" [id=312, type=contiguous]; -"313 unsqueeze_6" [id=313, type=unsqueeze]; -"314 sigmoid_2" [id=314, type=sigmoid]; -"315 mul_4" [id=315, type=mul]; -"316 pad_3" [id=316, type=pad]; -"317 view_13" [id=317, type=view]; -"318 permute_11" [id=318, type=permute]; -"319 reshape_9" [id=319, type=reshape]; -"320 linear_15_updated_constant0" [id=320, type=get_attr]; -"321 reshape_9_0_0_nncf_smooth_quant_0" [id=321, type=call_module]; -"322 quantize_per_tensor_default_14" [id=322, type=quantize_per_tensor]; -"323 dequantize_per_tensor_default_14" [id=323, type=dequantize_per_tensor]; -"324 linear_15_scale_0" [id=324, type=get_attr]; -"325 linear_15_zero_point_0" [id=325, type=get_attr]; -"326 quantize_per_channel_default_16" [id=326, type=quantize_per_channel]; -"327 dequantize_per_channel_default_16" [id=327, type=dequantize_per_channel]; -"328 _param_constant42_0_0" [id=328, type=get_attr]; -"329 linear_15" [id=329, type=linear]; -"330 reshape_10" [id=330, type=reshape]; -"331 permute_12" [id=331, type=permute]; -"332 select_6" [id=332, type=select]; -"333 select_7" [id=333, type=select]; -"334 select_8" [id=334, type=select]; -"335 linalg_vector_norm_4" [id=335, type=linalg_vector_norm]; -"336 clamp_min_4" [id=336, type=clamp_min]; -"337 expand_as_4" [id=337, type=expand_as]; -"338 div_4" [id=338, type=div]; -"339 quantize_per_tensor_default_15" [id=339, type=quantize_per_tensor]; -"340 dequantize_per_tensor_default_15" [id=340, type=dequantize_per_tensor]; -"341 linalg_vector_norm_5" [id=341, type=linalg_vector_norm]; -"342 clamp_min_5" [id=342, type=clamp_min]; -"343 expand_as_5" [id=343, type=expand_as]; -"344 div_5" [id=344, type=div]; -"345 quantize_per_tensor_default_16" [id=345, type=quantize_per_tensor]; -"346 dequantize_per_tensor_default_16" [id=346, type=dequantize_per_tensor]; -"347 transpose_4" [id=347, type=transpose]; -"348 matmul_4" [id=348, type=matmul]; -"349 _param_constant44" [id=349, type=get_attr]; -"350 clamp_2" [id=350, type=clamp]; -"351 exp_2" [id=351, type=exp]; -"352 mul_5" [id=352, type=mul]; -"353 add_7" [id=353, type=add]; -"354 softmax_2" [id=354, type=softmax]; -"355 dropout_8" [id=355, type=dropout]; -"356 matmul_5" [id=356, type=matmul]; -"357 transpose_5" [id=357, type=transpose]; -"358 reshape_11" [id=358, type=reshape]; -"359 linear_16_updated_constant0" [id=359, type=get_attr]; -"360 reshape_11_0_0_nncf_smooth_quant_0" [id=360, type=call_module]; -"361 quantize_per_tensor_default_17" [id=361, type=quantize_per_tensor]; -"362 dequantize_per_tensor_default_17" [id=362, type=dequantize_per_tensor]; -"363 linear_16_scale_0" [id=363, type=get_attr]; -"364 linear_16_zero_point_0" [id=364, type=get_attr]; -"365 quantize_per_channel_default_17" [id=365, type=quantize_per_channel]; -"366 dequantize_per_channel_default_17" [id=366, type=dequantize_per_channel]; -"367 _param_constant46_0_0" [id=367, type=get_attr]; -"368 linear_16" [id=368, type=linear]; -"369 dropout_9" [id=369, type=dropout]; -"370 view_14" [id=370, type=view]; -"371 permute_13" [id=371, type=permute]; -"372 reshape_12" [id=372, type=reshape]; -"373 slice_38" [id=373, type=slice]; -"374 slice_39" [id=374, type=slice]; -"375 slice_40" [id=375, type=slice]; -"376 slice_41" [id=376, type=slice]; -"377 contiguous_3" [id=377, type=contiguous]; -"378 _param_constant47" [id=378, type=get_attr]; -"379 _param_constant48" [id=379, type=get_attr]; -"380 layer_norm_6" [id=380, type=layer_norm]; -"381 add_8" [id=381, type=add]; -"382 linear_17_updated_constant0" [id=382, type=get_attr]; -"383 add_8_0_0_nncf_smooth_quant_0" [id=383, type=call_module]; -"384 quantize_per_tensor_default_18" [id=384, type=quantize_per_tensor]; -"385 dequantize_per_tensor_default_18" [id=385, type=dequantize_per_tensor]; -"386 linear_17_scale_0" [id=386, type=get_attr]; -"387 linear_17_zero_point_0" [id=387, type=get_attr]; -"388 quantize_per_channel_default_18" [id=388, type=quantize_per_channel]; -"389 dequantize_per_channel_default_18" [id=389, type=dequantize_per_channel]; -"390 _param_constant50_0_0" [id=390, type=get_attr]; -"391 linear_17" [id=391, type=linear]; -"392 gelu_2" [id=392, type=gelu]; -"393 dropout_10" [id=393, type=dropout]; -"394 linear_18_updated_constant0" [id=394, type=get_attr]; -"395 dropout_10_0_0_nncf_smooth_quant_0" [id=395, type=call_module]; -"396 quantize_per_tensor_default_19" [id=396, type=quantize_per_tensor]; -"397 dequantize_per_tensor_default_19" [id=397, type=dequantize_per_tensor]; -"398 linear_18_scale_0" [id=398, type=get_attr]; -"399 linear_18_zero_point_0" [id=399, type=get_attr]; -"400 quantize_per_channel_default_19" [id=400, type=quantize_per_channel]; -"401 dequantize_per_channel_default_19" [id=401, type=dequantize_per_channel]; -"402 _param_constant52_0_0" [id=402, type=get_attr]; -"403 linear_18" [id=403, type=linear]; -"404 dropout_11" [id=404, type=dropout]; -"405 _param_constant53" [id=405, type=get_attr]; -"406 _param_constant54" [id=406, type=get_attr]; -"407 layer_norm_7" [id=407, type=layer_norm]; -"408 add_9" [id=408, type=add]; -"409 _tensor_constant15" [id=409, type=get_attr]; -"410 linear_19_updated_constant0" [id=410, type=get_attr]; -"411 _tensor_constant15_0_0_nncf_smooth_quant_0" [id=411, type=call_module]; -"412 linear_19_scale_0" [id=412, type=get_attr]; -"413 linear_19_zero_point_0" [id=413, type=get_attr]; -"414 quantize_per_channel_default_20" [id=414, type=quantize_per_channel]; -"415 dequantize_per_channel_default_20" [id=415, type=dequantize_per_channel]; -"416 _param_constant56_0_0" [id=416, type=get_attr]; -"417 linear_19" [id=417, type=linear]; -"418 relu__3" [id=418, type=relu_]; -"419 linear_20_updated_constant0" [id=419, type=get_attr]; -"420 relu__3_0_0_nncf_smooth_quant_0" [id=420, type=call_module]; -"421 linear_20_scale_0" [id=421, type=get_attr]; -"422 linear_20_zero_point_0" [id=422, type=get_attr]; -"423 quantize_per_channel_default_21" [id=423, type=quantize_per_channel]; -"424 dequantize_per_channel_default_21" [id=424, type=dequantize_per_channel]; -"425 linear_20" [id=425, type=linear]; -"426 view_15" [id=426, type=view]; -"427 _tensor_constant16" [id=427, type=get_attr]; -"428 index_3" [id=428, type=index]; -"429 view_16" [id=429, type=view]; -"430 permute_14" [id=430, type=permute]; -"431 contiguous_4" [id=431, type=contiguous]; -"432 unsqueeze_7" [id=432, type=unsqueeze]; -"433 sigmoid_3" [id=433, type=sigmoid]; -"434 mul_6" [id=434, type=mul]; -"435 pad_4" [id=435, type=pad]; -"436 roll_2" [id=436, type=roll]; -"437 view_17" [id=437, type=view]; -"438 permute_15" [id=438, type=permute]; -"439 reshape_13" [id=439, type=reshape]; -"440 linear_21_updated_constant0" [id=440, type=get_attr]; -"441 reshape_13_0_0_nncf_smooth_quant_0" [id=441, type=call_module]; -"442 quantize_per_tensor_default_20" [id=442, type=quantize_per_tensor]; -"443 dequantize_per_tensor_default_20" [id=443, type=dequantize_per_tensor]; -"444 linear_21_scale_0" [id=444, type=get_attr]; -"445 linear_21_zero_point_0" [id=445, type=get_attr]; -"446 quantize_per_channel_default_22" [id=446, type=quantize_per_channel]; -"447 dequantize_per_channel_default_22" [id=447, type=dequantize_per_channel]; -"448 _param_constant58_0_0" [id=448, type=get_attr]; -"449 linear_21" [id=449, type=linear]; -"450 reshape_14" [id=450, type=reshape]; -"451 permute_16" [id=451, type=permute]; -"452 select_9" [id=452, type=select]; -"453 select_10" [id=453, type=select]; -"454 select_11" [id=454, type=select]; -"455 linalg_vector_norm_6" [id=455, type=linalg_vector_norm]; -"456 clamp_min_6" [id=456, type=clamp_min]; -"457 expand_as_6" [id=457, type=expand_as]; -"458 div_6" [id=458, type=div]; -"459 quantize_per_tensor_default_21" [id=459, type=quantize_per_tensor]; -"460 dequantize_per_tensor_default_21" [id=460, type=dequantize_per_tensor]; -"461 linalg_vector_norm_7" [id=461, type=linalg_vector_norm]; -"462 clamp_min_7" [id=462, type=clamp_min]; -"463 expand_as_7" [id=463, type=expand_as]; -"464 div_7" [id=464, type=div]; -"465 quantize_per_tensor_default_22" [id=465, type=quantize_per_tensor]; -"466 dequantize_per_tensor_default_22" [id=466, type=dequantize_per_tensor]; -"467 transpose_6" [id=467, type=transpose]; -"468 matmul_6" [id=468, type=matmul]; -"469 _param_constant60" [id=469, type=get_attr]; -"470 clamp_3" [id=470, type=clamp]; -"471 exp_3" [id=471, type=exp]; -"472 mul_7" [id=472, type=mul]; -"473 add_10" [id=473, type=add]; -"474 new_zeros_1" [id=474, type=new_zeros]; -"475 view_18" [id=475, type=view]; -"476 permute_17" [id=476, type=permute]; -"477 reshape_15" [id=477, type=reshape]; -"478 unsqueeze_8" [id=478, type=unsqueeze]; -"479 unsqueeze_9" [id=479, type=unsqueeze]; -"480 sub_1" [id=480, type=sub]; -"481 ne_1" [id=481, type=ne]; -"482 masked_fill_2" [id=482, type=masked_fill]; -"483 eq_1" [id=483, type=eq]; -"484 masked_fill_3" [id=484, type=masked_fill]; -"485 view_19" [id=485, type=view]; -"486 unsqueeze_10" [id=486, type=unsqueeze]; -"487 unsqueeze_11" [id=487, type=unsqueeze]; -"488 add_11" [id=488, type=add]; -"489 view_20" [id=489, type=view]; -"490 softmax_3" [id=490, type=softmax]; -"491 dropout_12" [id=491, type=dropout]; -"492 matmul_7" [id=492, type=matmul]; -"493 transpose_7" [id=493, type=transpose]; -"494 reshape_16" [id=494, type=reshape]; -"495 linear_22_updated_constant0" [id=495, type=get_attr]; -"496 reshape_16_0_0_nncf_smooth_quant_0" [id=496, type=call_module]; -"497 quantize_per_tensor_default_23" [id=497, type=quantize_per_tensor]; -"498 dequantize_per_tensor_default_23" [id=498, type=dequantize_per_tensor]; -"499 linear_22_scale_0" [id=499, type=get_attr]; -"500 linear_22_zero_point_0" [id=500, type=get_attr]; -"501 quantize_per_channel_default_23" [id=501, type=quantize_per_channel]; -"502 dequantize_per_channel_default_23" [id=502, type=dequantize_per_channel]; -"503 _param_constant62_0_0" [id=503, type=get_attr]; -"504 linear_22" [id=504, type=linear]; -"505 dropout_13" [id=505, type=dropout]; -"506 view_21" [id=506, type=view]; -"507 permute_18" [id=507, type=permute]; -"508 reshape_17" [id=508, type=reshape]; -"509 roll_3" [id=509, type=roll]; -"510 slice_61" [id=510, type=slice]; -"511 slice_62" [id=511, type=slice]; -"512 slice_63" [id=512, type=slice]; -"513 slice_64" [id=513, type=slice]; -"514 contiguous_5" [id=514, type=contiguous]; -"515 _param_constant63" [id=515, type=get_attr]; -"516 _param_constant64" [id=516, type=get_attr]; -"517 layer_norm_8" [id=517, type=layer_norm]; -"518 add_12" [id=518, type=add]; -"519 linear_23_updated_constant0" [id=519, type=get_attr]; -"520 add_12_0_0_nncf_smooth_quant_0" [id=520, type=call_module]; -"521 quantize_per_tensor_default_24" [id=521, type=quantize_per_tensor]; -"522 dequantize_per_tensor_default_24" [id=522, type=dequantize_per_tensor]; -"523 linear_23_scale_0" [id=523, type=get_attr]; -"524 linear_23_zero_point_0" [id=524, type=get_attr]; -"525 quantize_per_channel_default_24" [id=525, type=quantize_per_channel]; -"526 dequantize_per_channel_default_24" [id=526, type=dequantize_per_channel]; -"527 _param_constant66_0_0" [id=527, type=get_attr]; -"528 linear_23" [id=528, type=linear]; -"529 gelu_3" [id=529, type=gelu]; -"530 dropout_14" [id=530, type=dropout]; -"531 linear_24_updated_constant0" [id=531, type=get_attr]; -"532 dropout_14_0_0_nncf_smooth_quant_0" [id=532, type=call_module]; -"533 quantize_per_tensor_default_25" [id=533, type=quantize_per_tensor]; -"534 dequantize_per_tensor_default_25" [id=534, type=dequantize_per_tensor]; -"535 linear_24_scale_0" [id=535, type=get_attr]; -"536 linear_24_zero_point_0" [id=536, type=get_attr]; -"537 quantize_per_channel_default_25" [id=537, type=quantize_per_channel]; -"538 dequantize_per_channel_default_25" [id=538, type=dequantize_per_channel]; -"539 _param_constant68_0_0" [id=539, type=get_attr]; -"540 linear_24" [id=540, type=linear]; -"541 dropout_15" [id=541, type=dropout]; -"542 _param_constant69" [id=542, type=get_attr]; -"543 _param_constant70" [id=543, type=get_attr]; -"544 layer_norm_9" [id=544, type=layer_norm]; -"545 add_13" [id=545, type=add]; -"546 pad_5" [id=546, type=pad]; -"547 slice_65" [id=547, type=slice]; -"548 slice_66" [id=548, type=slice]; -"549 slice_67" [id=549, type=slice]; -"550 slice_68" [id=550, type=slice]; -"551 slice_69" [id=551, type=slice]; -"552 slice_70" [id=552, type=slice]; -"553 slice_71" [id=553, type=slice]; -"554 slice_72" [id=554, type=slice]; -"555 slice_73" [id=555, type=slice]; -"556 slice_74" [id=556, type=slice]; -"557 slice_75" [id=557, type=slice]; -"558 slice_76" [id=558, type=slice]; -"559 cat_1" [id=559, type=cat]; -"560 linear_25_updated_constant0" [id=560, type=get_attr]; -"561 cat_1_0_0_nncf_smooth_quant_0" [id=561, type=call_module]; -"562 quantize_per_tensor_default_26" [id=562, type=quantize_per_tensor]; -"563 dequantize_per_tensor_default_26" [id=563, type=dequantize_per_tensor]; -"564 linear_25_scale_0" [id=564, type=get_attr]; -"565 linear_25_zero_point_0" [id=565, type=get_attr]; -"566 quantize_per_channel_default_26" [id=566, type=quantize_per_channel]; -"567 dequantize_per_channel_default_26" [id=567, type=dequantize_per_channel]; -"568 linear_25" [id=568, type=linear]; -"569 _param_constant72" [id=569, type=get_attr]; -"570 _param_constant73" [id=570, type=get_attr]; -"571 layer_norm_10" [id=571, type=layer_norm]; -"572 _tensor_constant26" [id=572, type=get_attr]; -"573 linear_26_updated_constant0" [id=573, type=get_attr]; -"574 _tensor_constant26_0_0_nncf_smooth_quant_0" [id=574, type=call_module]; -"575 linear_26_scale_0" [id=575, type=get_attr]; -"576 linear_26_zero_point_0" [id=576, type=get_attr]; -"577 quantize_per_channel_default_27" [id=577, type=quantize_per_channel]; -"578 dequantize_per_channel_default_27" [id=578, type=dequantize_per_channel]; -"579 _param_constant75_0_0" [id=579, type=get_attr]; -"580 linear_26" [id=580, type=linear]; -"581 relu__4" [id=581, type=relu_]; -"582 linear_27_updated_constant0" [id=582, type=get_attr]; -"583 relu__4_0_0_nncf_smooth_quant_0" [id=583, type=call_module]; -"584 linear_27_scale_0" [id=584, type=get_attr]; -"585 linear_27_zero_point_0" [id=585, type=get_attr]; -"586 quantize_per_channel_default_28" [id=586, type=quantize_per_channel]; -"587 dequantize_per_channel_default_28" [id=587, type=dequantize_per_channel]; -"588 linear_27" [id=588, type=linear]; -"589 view_22" [id=589, type=view]; -"590 _tensor_constant27" [id=590, type=get_attr]; -"591 index_4" [id=591, type=index]; -"592 view_23" [id=592, type=view]; -"593 permute_19" [id=593, type=permute]; -"594 contiguous_6" [id=594, type=contiguous]; -"595 unsqueeze_12" [id=595, type=unsqueeze]; -"596 sigmoid_4" [id=596, type=sigmoid]; -"597 mul_8" [id=597, type=mul]; -"598 pad_6" [id=598, type=pad]; -"599 view_24" [id=599, type=view]; -"600 permute_20" [id=600, type=permute]; -"601 reshape_18" [id=601, type=reshape]; -"602 linear_28_updated_constant0" [id=602, type=get_attr]; -"603 reshape_18_0_0_nncf_smooth_quant_0" [id=603, type=call_module]; -"604 quantize_per_tensor_default_27" [id=604, type=quantize_per_tensor]; -"605 dequantize_per_tensor_default_27" [id=605, type=dequantize_per_tensor]; -"606 linear_28_scale_0" [id=606, type=get_attr]; -"607 linear_28_zero_point_0" [id=607, type=get_attr]; -"608 quantize_per_channel_default_29" [id=608, type=quantize_per_channel]; -"609 dequantize_per_channel_default_29" [id=609, type=dequantize_per_channel]; -"610 _param_constant77_0_0" [id=610, type=get_attr]; -"611 linear_28" [id=611, type=linear]; -"612 reshape_19" [id=612, type=reshape]; -"613 permute_21" [id=613, type=permute]; -"614 select_12" [id=614, type=select]; -"615 select_13" [id=615, type=select]; -"616 select_14" [id=616, type=select]; -"617 linalg_vector_norm_8" [id=617, type=linalg_vector_norm]; -"618 clamp_min_8" [id=618, type=clamp_min]; -"619 expand_as_8" [id=619, type=expand_as]; -"620 div_8" [id=620, type=div]; -"621 quantize_per_tensor_default_28" [id=621, type=quantize_per_tensor]; -"622 dequantize_per_tensor_default_28" [id=622, type=dequantize_per_tensor]; -"623 linalg_vector_norm_9" [id=623, type=linalg_vector_norm]; -"624 clamp_min_9" [id=624, type=clamp_min]; -"625 expand_as_9" [id=625, type=expand_as]; -"626 div_9" [id=626, type=div]; -"627 quantize_per_tensor_default_29" [id=627, type=quantize_per_tensor]; -"628 dequantize_per_tensor_default_29" [id=628, type=dequantize_per_tensor]; -"629 transpose_8" [id=629, type=transpose]; -"630 matmul_8" [id=630, type=matmul]; -"631 _param_constant79" [id=631, type=get_attr]; -"632 clamp_4" [id=632, type=clamp]; -"633 exp_4" [id=633, type=exp]; -"634 mul_9" [id=634, type=mul]; -"635 add_14" [id=635, type=add]; -"636 softmax_4" [id=636, type=softmax]; -"637 dropout_16" [id=637, type=dropout]; -"638 matmul_9" [id=638, type=matmul]; -"639 transpose_9" [id=639, type=transpose]; -"640 reshape_20" [id=640, type=reshape]; -"641 linear_29_updated_constant0" [id=641, type=get_attr]; -"642 reshape_20_0_0_nncf_smooth_quant_0" [id=642, type=call_module]; -"643 quantize_per_tensor_default_30" [id=643, type=quantize_per_tensor]; -"644 dequantize_per_tensor_default_30" [id=644, type=dequantize_per_tensor]; -"645 linear_29_scale_0" [id=645, type=get_attr]; -"646 linear_29_zero_point_0" [id=646, type=get_attr]; -"647 quantize_per_channel_default_30" [id=647, type=quantize_per_channel]; -"648 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quantize_per_channel_default_90" [id=1927, type=quantize_per_channel]; -"1928 dequantize_per_channel_default_90" [id=1928, type=dequantize_per_channel]; -"1929 _param_constant241_0_0" [id=1929, type=get_attr]; -"1930 linear_89" [id=1930, type=linear]; -"1931 dropout_57" [id=1931, type=dropout]; -"1932 view_80" [id=1932, type=view]; -"1933 permute_67" [id=1933, type=permute]; -"1934 reshape_66" [id=1934, type=reshape]; -"1935 slice_218" [id=1935, type=slice]; -"1936 slice_219" [id=1936, type=slice]; -"1937 slice_220" [id=1937, type=slice]; -"1938 slice_221" [id=1938, type=slice]; -"1939 contiguous_27" [id=1939, type=contiguous]; -"1940 _param_constant242" [id=1940, type=get_attr]; -"1941 _param_constant243" [id=1941, type=get_attr]; -"1942 layer_norm_31" [id=1942, type=layer_norm]; -"1943 add_50" [id=1943, type=add]; -"1944 linear_90_updated_constant0" [id=1944, type=get_attr]; -"1945 add_50_0_0_nncf_smooth_quant_0" [id=1945, type=call_module]; -"1946 quantize_per_tensor_default_91" [id=1946, type=quantize_per_tensor]; -"1947 dequantize_per_tensor_default_91" [id=1947, type=dequantize_per_tensor]; -"1948 linear_90_scale_0" [id=1948, type=get_attr]; -"1949 linear_90_zero_point_0" [id=1949, type=get_attr]; -"1950 quantize_per_channel_default_91" [id=1950, type=quantize_per_channel]; -"1951 dequantize_per_channel_default_91" [id=1951, type=dequantize_per_channel]; -"1952 _param_constant245_0_0" [id=1952, type=get_attr]; -"1953 linear_90" [id=1953, type=linear]; -"1954 gelu_14" [id=1954, type=gelu]; -"1955 dropout_58" [id=1955, type=dropout]; -"1956 linear_91_updated_constant0" [id=1956, type=get_attr]; -"1957 dropout_58_0_0_nncf_smooth_quant_0" [id=1957, type=call_module]; -"1958 quantize_per_tensor_default_92" [id=1958, type=quantize_per_tensor]; -"1959 dequantize_per_tensor_default_92" [id=1959, type=dequantize_per_tensor]; -"1960 linear_91_scale_0" [id=1960, type=get_attr]; -"1961 linear_91_zero_point_0" [id=1961, type=get_attr]; -"1962 quantize_per_channel_default_92" [id=1962, type=quantize_per_channel]; -"1963 dequantize_per_channel_default_92" [id=1963, type=dequantize_per_channel]; -"1964 _param_constant247_0_0" [id=1964, type=get_attr]; -"1965 linear_91" [id=1965, type=linear]; -"1966 dropout_59" [id=1966, type=dropout]; -"1967 _param_constant248" [id=1967, type=get_attr]; -"1968 _param_constant249" [id=1968, type=get_attr]; -"1969 layer_norm_32" [id=1969, type=layer_norm]; -"1970 add_51" [id=1970, type=add]; -"1971 _tensor_constant93" [id=1971, type=get_attr]; -"1972 linear_92_updated_constant0" [id=1972, type=get_attr]; -"1973 _tensor_constant93_0_0_nncf_smooth_quant_0" [id=1973, type=call_module]; -"1974 linear_92_scale_0" [id=1974, type=get_attr]; -"1975 linear_92_zero_point_0" [id=1975, type=get_attr]; -"1976 quantize_per_channel_default_93" [id=1976, type=quantize_per_channel]; -"1977 dequantize_per_channel_default_93" [id=1977, type=dequantize_per_channel]; -"1978 _param_constant251_0_0" [id=1978, type=get_attr]; -"1979 linear_92" [id=1979, type=linear]; -"1980 relu__15" [id=1980, type=relu_]; -"1981 linear_93_updated_constant0" [id=1981, type=get_attr]; -"1982 relu__15_0_0_nncf_smooth_quant_0" [id=1982, type=call_module]; -"1983 linear_93_scale_0" [id=1983, type=get_attr]; -"1984 linear_93_zero_point_0" [id=1984, type=get_attr]; -"1985 quantize_per_channel_default_94" [id=1985, type=quantize_per_channel]; -"1986 dequantize_per_channel_default_94" [id=1986, type=dequantize_per_channel]; -"1987 linear_93" [id=1987, type=linear]; -"1988 view_81" [id=1988, type=view]; -"1989 _tensor_constant94" [id=1989, type=get_attr]; -"1990 index_15" [id=1990, type=index]; -"1991 view_82" [id=1991, type=view]; -"1992 permute_68" [id=1992, type=permute]; -"1993 contiguous_28" [id=1993, type=contiguous]; -"1994 unsqueeze_43" [id=1994, type=unsqueeze]; -"1995 sigmoid_15" [id=1995, type=sigmoid]; -"1996 mul_30" [id=1996, type=mul]; -"1997 pad_17" [id=1997, type=pad]; -"1998 roll_14" [id=1998, type=roll]; -"1999 view_83" [id=1999, type=view]; -"2000 permute_69" [id=2000, type=permute]; -"2001 reshape_67" [id=2001, type=reshape]; -"2002 linear_94_updated_constant0" [id=2002, type=get_attr]; -"2003 reshape_67_0_0_nncf_smooth_quant_0" [id=2003, type=call_module]; -"2004 quantize_per_tensor_default_93" [id=2004, type=quantize_per_tensor]; -"2005 dequantize_per_tensor_default_93" [id=2005, type=dequantize_per_tensor]; -"2006 linear_94_scale_0" [id=2006, type=get_attr]; -"2007 linear_94_zero_point_0" [id=2007, type=get_attr]; -"2008 quantize_per_channel_default_95" [id=2008, type=quantize_per_channel]; -"2009 dequantize_per_channel_default_95" [id=2009, type=dequantize_per_channel]; -"2010 _param_constant253_0_0" [id=2010, type=get_attr]; -"2011 linear_94" [id=2011, type=linear]; -"2012 reshape_68" [id=2012, type=reshape]; -"2013 permute_70" [id=2013, type=permute]; -"2014 select_45" [id=2014, type=select]; -"2015 select_46" [id=2015, type=select]; -"2016 select_47" [id=2016, type=select]; -"2017 linalg_vector_norm_30" [id=2017, type=linalg_vector_norm]; -"2018 clamp_min_30" [id=2018, type=clamp_min]; -"2019 expand_as_30" [id=2019, type=expand_as]; -"2020 div_30" [id=2020, type=div]; -"2021 quantize_per_tensor_default_94" [id=2021, type=quantize_per_tensor]; -"2022 dequantize_per_tensor_default_94" [id=2022, type=dequantize_per_tensor]; -"2023 linalg_vector_norm_31" [id=2023, type=linalg_vector_norm]; -"2024 clamp_min_31" [id=2024, type=clamp_min]; -"2025 expand_as_31" [id=2025, type=expand_as]; -"2026 div_31" [id=2026, type=div]; -"2027 quantize_per_tensor_default_95" [id=2027, type=quantize_per_tensor]; -"2028 dequantize_per_tensor_default_95" [id=2028, type=dequantize_per_tensor]; -"2029 transpose_30" [id=2029, type=transpose]; -"2030 matmul_30" [id=2030, type=matmul]; -"2031 _param_constant255" [id=2031, type=get_attr]; -"2032 clamp_15" [id=2032, type=clamp]; -"2033 exp_15" [id=2033, type=exp]; -"2034 mul_31" [id=2034, type=mul]; -"2035 add_52" 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reshape_70_0_0_nncf_smooth_quant_0" [id=2058, type=call_module]; -"2059 quantize_per_tensor_default_96" [id=2059, type=quantize_per_tensor]; -"2060 dequantize_per_tensor_default_96" [id=2060, type=dequantize_per_tensor]; -"2061 linear_95_scale_0" [id=2061, type=get_attr]; -"2062 linear_95_zero_point_0" [id=2062, type=get_attr]; -"2063 quantize_per_channel_default_96" [id=2063, type=quantize_per_channel]; -"2064 dequantize_per_channel_default_96" [id=2064, type=dequantize_per_channel]; -"2065 _param_constant257_0_0" [id=2065, type=get_attr]; -"2066 linear_95" [id=2066, type=linear]; -"2067 dropout_61" [id=2067, type=dropout]; -"2068 view_87" [id=2068, type=view]; -"2069 permute_72" [id=2069, type=permute]; -"2070 reshape_71" [id=2070, type=reshape]; -"2071 roll_15" [id=2071, type=roll]; -"2072 slice_241" [id=2072, type=slice]; -"2073 slice_242" [id=2073, type=slice]; -"2074 slice_243" [id=2074, type=slice]; -"2075 slice_244" [id=2075, type=slice]; -"2076 contiguous_29" [id=2076, type=contiguous]; -"2077 _param_constant258" [id=2077, type=get_attr]; -"2078 _param_constant259" [id=2078, type=get_attr]; -"2079 layer_norm_33" [id=2079, type=layer_norm]; -"2080 add_54" [id=2080, type=add]; -"2081 linear_96_updated_constant0" [id=2081, type=get_attr]; -"2082 add_54_0_0_nncf_smooth_quant_0" [id=2082, type=call_module]; -"2083 quantize_per_tensor_default_97" [id=2083, type=quantize_per_tensor]; -"2084 dequantize_per_tensor_default_97" [id=2084, type=dequantize_per_tensor]; -"2085 linear_96_scale_0" [id=2085, type=get_attr]; -"2086 linear_96_zero_point_0" [id=2086, type=get_attr]; -"2087 quantize_per_channel_default_97" [id=2087, type=quantize_per_channel]; -"2088 dequantize_per_channel_default_97" [id=2088, type=dequantize_per_channel]; -"2089 _param_constant261_0_0" [id=2089, type=get_attr]; -"2090 linear_96" [id=2090, type=linear]; -"2091 gelu_15" [id=2091, type=gelu]; -"2092 dropout_62" [id=2092, type=dropout]; -"2093 linear_97_updated_constant0" [id=2093, 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dequantize_per_channel_default_143" [id=3036, type=dequantize_per_channel]; -"3037 linear_142" [id=3037, type=linear]; -"3038 view_125" [id=3038, type=view]; -"3039 _tensor_constant146" [id=3039, type=get_attr]; -"3040 index_23" [id=3040, type=index]; -"3041 view_126" [id=3041, type=view]; -"3042 permute_104" [id=3042, type=permute]; -"3043 contiguous_44" [id=3043, type=contiguous]; -"3044 unsqueeze_67" [id=3044, type=unsqueeze]; -"3045 sigmoid_23" [id=3045, type=sigmoid]; -"3046 mul_46" [id=3046, type=mul]; -"3047 pad_26" [id=3047, type=pad]; -"3048 view_127" [id=3048, type=view]; -"3049 permute_105" [id=3049, type=permute]; -"3050 reshape_103" [id=3050, type=reshape]; -"3051 linear_143_updated_constant0" [id=3051, type=get_attr]; -"3052 reshape_103_0_0_nncf_smooth_quant_0" [id=3052, type=call_module]; -"3053 quantize_per_tensor_default_142" [id=3053, type=quantize_per_tensor]; -"3054 dequantize_per_tensor_default_142" [id=3054, type=dequantize_per_tensor]; -"3055 linear_143_scale_0" [id=3055, type=get_attr]; -"3056 linear_143_zero_point_0" [id=3056, type=get_attr]; -"3057 quantize_per_channel_default_144" [id=3057, type=quantize_per_channel]; -"3058 dequantize_per_channel_default_144" [id=3058, type=dequantize_per_channel]; -"3059 _param_constant384_0_0" [id=3059, type=get_attr]; -"3060 linear_143" [id=3060, type=linear]; -"3061 reshape_104" [id=3061, type=reshape]; -"3062 permute_106" [id=3062, type=permute]; -"3063 select_69" [id=3063, type=select]; -"3064 select_70" [id=3064, type=select]; -"3065 select_71" [id=3065, type=select]; -"3066 linalg_vector_norm_46" [id=3066, type=linalg_vector_norm]; -"3067 clamp_min_46" [id=3067, type=clamp_min]; -"3068 expand_as_46" [id=3068, type=expand_as]; -"3069 div_46" [id=3069, type=div]; -"3070 quantize_per_tensor_default_143" [id=3070, type=quantize_per_tensor]; -"3071 dequantize_per_tensor_default_143" [id=3071, type=dequantize_per_tensor]; -"3072 linalg_vector_norm_47" [id=3072, type=linalg_vector_norm]; -"3073 clamp_min_47" [id=3073, type=clamp_min]; -"3074 expand_as_47" [id=3074, type=expand_as]; -"3075 div_47" [id=3075, type=div]; -"3076 quantize_per_tensor_default_144" [id=3076, type=quantize_per_tensor]; -"3077 dequantize_per_tensor_default_144" [id=3077, type=dequantize_per_tensor]; -"3078 transpose_46" [id=3078, type=transpose]; -"3079 matmul_46" [id=3079, type=matmul]; -"3080 _param_constant386" [id=3080, type=get_attr]; -"3081 clamp_23" [id=3081, type=clamp]; -"3082 exp_23" [id=3082, type=exp]; -"3083 mul_47" [id=3083, type=mul]; -"3084 add_80" [id=3084, type=add]; -"3085 softmax_23" [id=3085, type=softmax]; -"3086 dropout_92" [id=3086, type=dropout]; -"3087 matmul_47" [id=3087, type=matmul]; -"3088 transpose_47" [id=3088, type=transpose]; -"3089 reshape_105" [id=3089, type=reshape]; -"3090 linear_144_updated_constant0" [id=3090, type=get_attr]; -"3091 reshape_105_0_0_nncf_smooth_quant_0" [id=3091, type=call_module]; -"3092 quantize_per_tensor_default_145" [id=3092, type=quantize_per_tensor]; -"3093 dequantize_per_tensor_default_145" [id=3093, type=dequantize_per_tensor]; -"3094 linear_144_scale_0" [id=3094, type=get_attr]; -"3095 linear_144_zero_point_0" [id=3095, type=get_attr]; -"3096 quantize_per_channel_default_145" [id=3096, type=quantize_per_channel]; -"3097 dequantize_per_channel_default_145" [id=3097, type=dequantize_per_channel]; -"3098 _param_constant388_0_0" [id=3098, type=get_attr]; -"3099 linear_144" [id=3099, type=linear]; -"3100 dropout_93" [id=3100, type=dropout]; -"3101 view_128" [id=3101, type=view]; -"3102 permute_107" [id=3102, type=permute]; -"3103 reshape_106" [id=3103, type=reshape]; -"3104 slice_347" [id=3104, type=slice]; -"3105 slice_348" [id=3105, type=slice]; -"3106 slice_349" [id=3106, type=slice]; -"3107 slice_350" [id=3107, type=slice]; -"3108 contiguous_45" [id=3108, type=contiguous]; -"3109 _param_constant389" [id=3109, type=get_attr]; -"3110 _param_constant390" [id=3110, type=get_attr]; -"3111 layer_norm_50" [id=3111, type=layer_norm]; -"3112 add_81" [id=3112, type=add]; -"3113 linear_145_updated_constant0" [id=3113, type=get_attr]; -"3114 add_81_0_0_nncf_smooth_quant_0" [id=3114, type=call_module]; -"3115 quantize_per_tensor_default_146" [id=3115, type=quantize_per_tensor]; -"3116 dequantize_per_tensor_default_146" [id=3116, type=dequantize_per_tensor]; -"3117 linear_145_scale_0" [id=3117, type=get_attr]; -"3118 linear_145_zero_point_0" [id=3118, type=get_attr]; -"3119 quantize_per_channel_default_146" [id=3119, type=quantize_per_channel]; -"3120 dequantize_per_channel_default_146" [id=3120, type=dequantize_per_channel]; -"3121 _param_constant392_0_0" [id=3121, type=get_attr]; -"3122 linear_145" [id=3122, type=linear]; -"3123 gelu_23" [id=3123, type=gelu]; -"3124 dropout_94" [id=3124, type=dropout]; -"3125 linear_146_updated_constant0" [id=3125, type=get_attr]; -"3126 dropout_94_0_0_nncf_smooth_quant_0" [id=3126, type=call_module]; -"3127 quantize_per_tensor_default_147" [id=3127, type=quantize_per_tensor]; -"3128 dequantize_per_tensor_default_147" [id=3128, type=dequantize_per_tensor]; -"3129 linear_146_scale_0" [id=3129, type=get_attr]; -"3130 linear_146_zero_point_0" [id=3130, type=get_attr]; -"3131 quantize_per_channel_default_147" [id=3131, type=quantize_per_channel]; -"3132 dequantize_per_channel_default_147" [id=3132, type=dequantize_per_channel]; -"3133 _param_constant394_0_0" [id=3133, type=get_attr]; -"3134 linear_146" [id=3134, type=linear]; -"3135 dropout_95" [id=3135, type=dropout]; -"3136 _param_constant395" [id=3136, type=get_attr]; -"3137 _param_constant396" [id=3137, type=get_attr]; -"3138 layer_norm_51" [id=3138, type=layer_norm]; -"3139 add_82" [id=3139, type=add]; -"3140 _param_constant397" [id=3140, type=get_attr]; -"3141 _param_constant398" [id=3141, type=get_attr]; -"3142 layer_norm_52" [id=3142, type=layer_norm]; -"3143 permute_108" [id=3143, type=permute]; -"3144 adaptive_avg_pool2d" [id=3144, type=adaptive_avg_pool2d]; -"3145 flatten" [id=3145, type=flatten]; -"3146 linear_147_updated_constant0" [id=3146, type=get_attr]; -"3147 flatten_0_0_nncf_smooth_quant_0" [id=3147, type=call_module]; -"3148 quantize_per_tensor_default_148" [id=3148, type=quantize_per_tensor]; -"3149 dequantize_per_tensor_default_148" [id=3149, type=dequantize_per_tensor]; -"3150 linear_147_scale_0" [id=3150, type=get_attr]; -"3151 linear_147_zero_point_0" [id=3151, type=get_attr]; -"3152 quantize_per_channel_default_148" [id=3152, type=quantize_per_channel]; -"3153 dequantize_per_channel_default_148" [id=3153, type=dequantize_per_channel]; -"3154 _param_constant400_0_0" [id=3154, type=get_attr]; -"3155 linear_147" [id=3155, type=linear]; -"3156 output" [id=3156, type=output]; -"0 arg0_1" -> "1 quantize_per_tensor_default"; -"1 quantize_per_tensor_default" -> "2 dequantize_per_tensor_default"; -"2 dequantize_per_tensor_default" -> "9 conv2d"; -"3 _param_constant0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "7 dequantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "6 quantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "7 dequantize_per_channel_default"; -"6 quantize_per_channel_default" -> "7 dequantize_per_channel_default"; -"7 dequantize_per_channel_default" -> "9 conv2d"; -"8 _param_constant1_0_0" -> "9 conv2d"; -"9 conv2d" -> "10 permute"; -"10 permute" -> "13 layer_norm"; -"11 _param_constant2" -> "13 layer_norm"; -"12 _param_constant3" -> "13 layer_norm"; -"13 layer_norm" -> "40 pad"; -"13 layer_norm" -> "102 add_1"; -"14 _tensor_constant0" -> "16 _tensor_constant0_0_0_nncf_smooth_quant_0"; -"15 linear_updated_constant0" -> "19 quantize_per_channel_default_1"; -"16 _tensor_constant0_0_0_nncf_smooth_quant_0" -> "22 linear"; -"17 linear_scale_0" -> "19 quantize_per_channel_default_1"; -"17 linear_scale_0" -> "20 dequantize_per_channel_default_1"; -"18 linear_zero_point_0" -> "19 quantize_per_channel_default_1"; -"18 linear_zero_point_0" -> "20 dequantize_per_channel_default_1"; -"19 quantize_per_channel_default_1" -> "20 dequantize_per_channel_default_1"; -"20 dequantize_per_channel_default_1" -> "22 linear"; -"21 _param_constant5_0_0" -> "22 linear"; -"22 linear" -> "23 relu_"; -"23 relu_" -> "25 relu__0_0_nncf_smooth_quant_0"; -"24 linear_1_updated_constant0" -> "28 quantize_per_channel_default_2"; -"25 relu__0_0_nncf_smooth_quant_0" -> "30 linear_1"; -"26 linear_1_scale_0" -> "28 quantize_per_channel_default_2"; -"26 linear_1_scale_0" -> "29 dequantize_per_channel_default_2"; -"27 linear_1_zero_point_0" -> "28 quantize_per_channel_default_2"; -"27 linear_1_zero_point_0" -> "29 dequantize_per_channel_default_2"; -"28 quantize_per_channel_default_2" -> "29 dequantize_per_channel_default_2"; -"29 dequantize_per_channel_default_2" -> "30 linear_1"; -"30 linear_1" -> "31 view"; -"31 view" -> "33 index"; -"32 _tensor_constant1" -> "33 index"; -"33 index" -> "34 view_1"; -"34 view_1" -> "35 permute_1"; -"35 permute_1" -> "36 contiguous"; -"36 contiguous" -> "37 unsqueeze"; -"37 unsqueeze" -> "38 sigmoid"; -"38 sigmoid" -> "39 mul"; -"39 mul" -> "77 add"; -"40 pad" -> "41 view_2"; -"41 view_2" -> "42 permute_2"; -"42 permute_2" -> "43 reshape"; -"43 reshape" -> "45 reshape_0_0_nncf_smooth_quant_0"; -"44 linear_2_updated_constant0" -> "50 quantize_per_channel_default_3"; -"45 reshape_0_0_nncf_smooth_quant_0" -> "46 quantize_per_tensor_default_1"; -"46 quantize_per_tensor_default_1" -> "47 dequantize_per_tensor_default_1"; -"47 dequantize_per_tensor_default_1" -> "53 linear_2"; -"48 linear_2_scale_0" -> "50 quantize_per_channel_default_3"; -"48 linear_2_scale_0" -> "51 dequantize_per_channel_default_3"; -"49 linear_2_zero_point_0" -> "50 quantize_per_channel_default_3"; -"49 linear_2_zero_point_0" -> "51 dequantize_per_channel_default_3"; -"50 quantize_per_channel_default_3" -> "51 dequantize_per_channel_default_3"; -"51 dequantize_per_channel_default_3" -> "53 linear_2"; -"52 _param_constant7_0_0" -> "53 linear_2"; -"53 linear_2" -> "54 reshape_1"; -"54 reshape_1" -> "55 permute_3"; -"55 permute_3" -> "56 select"; -"55 permute_3" -> "57 select_1"; -"55 permute_3" -> "58 select_2"; -"56 select" -> "59 linalg_vector_norm"; -"56 select" -> "61 expand_as"; -"56 select" -> "62 div"; -"57 select_1" -> "65 linalg_vector_norm_1"; -"57 select_1" -> "67 expand_as_1"; -"57 select_1" -> "68 div_1"; -"58 select_2" -> "80 matmul_1"; -"59 linalg_vector_norm" -> "60 clamp_min"; -"60 clamp_min" -> "61 expand_as"; -"61 expand_as" -> "62 div"; -"62 div" -> "63 quantize_per_tensor_default_2"; -"63 quantize_per_tensor_default_2" -> "64 dequantize_per_tensor_default_2"; -"64 dequantize_per_tensor_default_2" -> "72 matmul"; -"65 linalg_vector_norm_1" -> "66 clamp_min_1"; -"66 clamp_min_1" -> "67 expand_as_1"; -"67 expand_as_1" -> "68 div_1"; -"68 div_1" -> "69 quantize_per_tensor_default_3"; -"69 quantize_per_tensor_default_3" -> "70 dequantize_per_tensor_default_3"; -"70 dequantize_per_tensor_default_3" -> "71 transpose"; -"71 transpose" -> "72 matmul"; -"72 matmul" -> "76 mul_1"; -"73 _param_constant9" -> "74 clamp"; -"74 clamp" -> "75 exp"; -"75 exp" -> "76 mul_1"; -"76 mul_1" -> "77 add"; -"77 add" -> "78 softmax"; -"78 softmax" -> "79 dropout"; -"79 dropout" -> "80 matmul_1"; -"80 matmul_1" -> "81 transpose_1"; -"81 transpose_1" -> "82 reshape_2"; -"82 reshape_2" -> "84 reshape_2_0_0_nncf_smooth_quant_0"; -"83 linear_3_updated_constant0" -> "89 quantize_per_channel_default_4"; -"84 reshape_2_0_0_nncf_smooth_quant_0" -> "85 quantize_per_tensor_default_4"; -"85 quantize_per_tensor_default_4" -> "86 dequantize_per_tensor_default_4"; -"86 dequantize_per_tensor_default_4" -> "92 linear_3"; -"87 linear_3_scale_0" -> "89 quantize_per_channel_default_4"; -"87 linear_3_scale_0" -> "90 dequantize_per_channel_default_4"; -"88 linear_3_zero_point_0" -> "89 quantize_per_channel_default_4"; -"88 linear_3_zero_point_0" -> "90 dequantize_per_channel_default_4"; -"89 quantize_per_channel_default_4" -> "90 dequantize_per_channel_default_4"; -"90 dequantize_per_channel_default_4" -> "92 linear_3"; -"91 _param_constant11_0_0" -> "92 linear_3"; -"92 linear_3" -> "93 dropout_1"; -"93 dropout_1" -> "94 view_3"; -"94 view_3" -> "95 permute_4"; -"95 permute_4" -> "96 reshape_3"; -"96 reshape_3" -> "97 slice_2"; -"97 slice_2" -> "98 slice_3"; -"98 slice_3" -> "101 layer_norm_1"; -"99 _param_constant12" -> "101 layer_norm_1"; -"100 _param_constant13" -> "101 layer_norm_1"; -"101 layer_norm_1" -> "102 add_1"; -"102 add_1" -> "104 add_1_0_0_nncf_smooth_quant_0"; -"102 add_1" -> "129 add_2"; -"103 linear_4_updated_constant0" -> "109 quantize_per_channel_default_5"; -"104 add_1_0_0_nncf_smooth_quant_0" -> "105 quantize_per_tensor_default_5"; -"105 quantize_per_tensor_default_5" -> "106 dequantize_per_tensor_default_5"; -"106 dequantize_per_tensor_default_5" -> "112 linear_4"; -"107 linear_4_scale_0" -> "109 quantize_per_channel_default_5"; -"107 linear_4_scale_0" -> "110 dequantize_per_channel_default_5"; -"108 linear_4_zero_point_0" -> "109 quantize_per_channel_default_5"; -"108 linear_4_zero_point_0" -> "110 dequantize_per_channel_default_5"; -"109 quantize_per_channel_default_5" -> "110 dequantize_per_channel_default_5"; -"110 dequantize_per_channel_default_5" -> "112 linear_4"; -"111 _param_constant15_0_0" -> "112 linear_4"; -"112 linear_4" -> "113 gelu"; -"113 gelu" -> "114 dropout_2"; -"114 dropout_2" -> "116 dropout_2_0_0_nncf_smooth_quant_0"; -"115 linear_5_updated_constant0" -> "121 quantize_per_channel_default_6"; -"116 dropout_2_0_0_nncf_smooth_quant_0" -> "117 quantize_per_tensor_default_6"; -"117 quantize_per_tensor_default_6" -> "118 dequantize_per_tensor_default_6"; -"118 dequantize_per_tensor_default_6" -> "124 linear_5"; -"119 linear_5_scale_0" -> "121 quantize_per_channel_default_6"; -"119 linear_5_scale_0" -> "122 dequantize_per_channel_default_6"; -"120 linear_5_zero_point_0" -> "121 quantize_per_channel_default_6"; -"120 linear_5_zero_point_0" -> "122 dequantize_per_channel_default_6"; -"121 quantize_per_channel_default_6" -> "122 dequantize_per_channel_default_6"; -"122 dequantize_per_channel_default_6" -> "124 linear_5"; -"123 _param_constant17_0_0" -> "124 linear_5"; -"124 linear_5" -> "125 dropout_3"; -"125 dropout_3" -> "128 layer_norm_2"; -"126 _param_constant18" -> "128 layer_norm_2"; -"127 _param_constant19" -> "128 layer_norm_2"; -"128 layer_norm_2" -> "129 add_2"; -"129 add_2" -> "156 pad_1"; -"129 add_2" -> "236 add_5"; -"130 _tensor_constant2" -> "132 _tensor_constant2_0_0_nncf_smooth_quant_0"; -"131 linear_6_updated_constant0" -> "135 quantize_per_channel_default_7"; -"132 _tensor_constant2_0_0_nncf_smooth_quant_0" -> "138 linear_6"; -"133 linear_6_scale_0" -> "135 quantize_per_channel_default_7"; -"133 linear_6_scale_0" -> "136 dequantize_per_channel_default_7"; -"134 linear_6_zero_point_0" -> "135 quantize_per_channel_default_7"; -"134 linear_6_zero_point_0" -> "136 dequantize_per_channel_default_7"; -"135 quantize_per_channel_default_7" -> "136 dequantize_per_channel_default_7"; -"136 dequantize_per_channel_default_7" -> "138 linear_6"; -"137 _param_constant21_0_0" -> "138 linear_6"; -"138 linear_6" -> "139 relu__1"; -"139 relu__1" -> "141 relu__1_0_0_nncf_smooth_quant_0"; -"140 linear_7_updated_constant0" -> "144 quantize_per_channel_default_8"; -"141 relu__1_0_0_nncf_smooth_quant_0" -> "146 linear_7"; -"142 linear_7_scale_0" -> "144 quantize_per_channel_default_8"; -"142 linear_7_scale_0" -> "145 dequantize_per_channel_default_8"; -"143 linear_7_zero_point_0" -> "144 quantize_per_channel_default_8"; -"143 linear_7_zero_point_0" -> "145 dequantize_per_channel_default_8"; -"144 quantize_per_channel_default_8" -> "145 dequantize_per_channel_default_8"; -"145 dequantize_per_channel_default_8" -> "146 linear_7"; -"146 linear_7" -> "147 view_4"; -"147 view_4" -> "149 index_1"; -"148 _tensor_constant3" -> "149 index_1"; -"149 index_1" -> "150 view_5"; -"150 view_5" -> "151 permute_5"; -"151 permute_5" -> "152 contiguous_1"; -"152 contiguous_1" -> "153 unsqueeze_1"; -"153 unsqueeze_1" -> "154 sigmoid_1"; -"154 sigmoid_1" -> "155 mul_2"; -"155 mul_2" -> "194 add_3"; -"156 pad_1" -> "157 roll"; -"157 roll" -> "158 view_6"; -"158 view_6" -> "159 permute_6"; -"159 permute_6" -> "160 reshape_4"; -"160 reshape_4" -> "162 reshape_4_0_0_nncf_smooth_quant_0"; -"160 reshape_4" -> "195 new_zeros"; -"161 linear_8_updated_constant0" -> "167 quantize_per_channel_default_9"; -"162 reshape_4_0_0_nncf_smooth_quant_0" -> "163 quantize_per_tensor_default_7"; -"163 quantize_per_tensor_default_7" -> "164 dequantize_per_tensor_default_7"; -"164 dequantize_per_tensor_default_7" -> "170 linear_8"; -"165 linear_8_scale_0" -> "167 quantize_per_channel_default_9"; -"165 linear_8_scale_0" -> "168 dequantize_per_channel_default_9"; -"166 linear_8_zero_point_0" -> "167 quantize_per_channel_default_9"; -"166 linear_8_zero_point_0" -> "168 dequantize_per_channel_default_9"; -"167 quantize_per_channel_default_9" -> "168 dequantize_per_channel_default_9"; -"168 dequantize_per_channel_default_9" -> "170 linear_8"; -"169 _param_constant23_0_0" -> "170 linear_8"; -"170 linear_8" -> "171 reshape_5"; -"171 reshape_5" -> "172 permute_7"; -"172 permute_7" -> "173 select_3"; -"172 permute_7" -> "174 select_4"; -"172 permute_7" -> "175 select_5"; -"173 select_3" -> "176 linalg_vector_norm_2"; -"173 select_3" -> "178 expand_as_2"; -"173 select_3" -> "179 div_2"; -"174 select_4" -> "182 linalg_vector_norm_3"; -"174 select_4" -> "184 expand_as_3"; -"174 select_4" -> "185 div_3"; -"175 select_5" -> "213 matmul_3"; -"176 linalg_vector_norm_2" -> "177 clamp_min_2"; -"177 clamp_min_2" -> "178 expand_as_2"; -"178 expand_as_2" -> "179 div_2"; -"179 div_2" -> "180 quantize_per_tensor_default_8"; -"180 quantize_per_tensor_default_8" -> "181 dequantize_per_tensor_default_8"; -"181 dequantize_per_tensor_default_8" -> "189 matmul_2"; -"182 linalg_vector_norm_3" -> "183 clamp_min_3"; -"183 clamp_min_3" -> "184 expand_as_3"; -"184 expand_as_3" -> "185 div_3"; -"185 div_3" -> "186 quantize_per_tensor_default_9"; -"186 quantize_per_tensor_default_9" -> "187 dequantize_per_tensor_default_9"; -"187 dequantize_per_tensor_default_9" -> "188 transpose_2"; -"188 transpose_2" -> "189 matmul_2"; -"189 matmul_2" -> "193 mul_3"; -"190 _param_constant25" -> "191 clamp_1"; -"191 clamp_1" -> "192 exp_1"; -"192 exp_1" -> "193 mul_3"; -"193 mul_3" -> "194 add_3"; -"194 add_3" -> "206 view_8"; -"195 new_zeros" -> "196 view_7"; -"196 view_7" -> "197 permute_8"; -"197 permute_8" -> "198 reshape_6"; -"198 reshape_6" -> "199 unsqueeze_2"; -"198 reshape_6" -> "200 unsqueeze_3"; -"199 unsqueeze_2" -> "201 sub"; -"200 unsqueeze_3" -> "201 sub"; -"201 sub" -> "202 ne"; -"201 sub" -> "203 masked_fill"; -"201 sub" -> "204 eq"; -"202 ne" -> "203 masked_fill"; -"203 masked_fill" -> "205 masked_fill_1"; -"204 eq" -> "205 masked_fill_1"; -"205 masked_fill_1" -> "207 unsqueeze_4"; -"206 view_8" -> "209 add_4"; -"207 unsqueeze_4" -> "208 unsqueeze_5"; -"208 unsqueeze_5" -> "209 add_4"; -"209 add_4" -> "210 view_9"; -"210 view_9" -> "211 softmax_1"; -"211 softmax_1" -> "212 dropout_4"; -"212 dropout_4" -> "213 matmul_3"; -"213 matmul_3" -> "214 transpose_3"; -"214 transpose_3" -> "215 reshape_7"; -"215 reshape_7" -> "217 reshape_7_0_0_nncf_smooth_quant_0"; -"216 linear_9_updated_constant0" -> "222 quantize_per_channel_default_10"; -"217 reshape_7_0_0_nncf_smooth_quant_0" -> "218 quantize_per_tensor_default_10"; -"218 quantize_per_tensor_default_10" -> "219 dequantize_per_tensor_default_10"; -"219 dequantize_per_tensor_default_10" -> "225 linear_9"; -"220 linear_9_scale_0" -> "222 quantize_per_channel_default_10"; -"220 linear_9_scale_0" -> "223 dequantize_per_channel_default_10"; -"221 linear_9_zero_point_0" -> "222 quantize_per_channel_default_10"; -"221 linear_9_zero_point_0" -> "223 dequantize_per_channel_default_10"; -"222 quantize_per_channel_default_10" -> "223 dequantize_per_channel_default_10"; -"223 dequantize_per_channel_default_10" -> "225 linear_9"; -"224 _param_constant27_0_0" -> "225 linear_9"; -"225 linear_9" -> "226 dropout_5"; -"226 dropout_5" -> "227 view_10"; -"227 view_10" -> "228 permute_9"; -"228 permute_9" -> "229 reshape_8"; -"229 reshape_8" -> "230 roll_1"; -"230 roll_1" -> "231 slice_23"; -"231 slice_23" -> "232 slice_24"; -"232 slice_24" -> "235 layer_norm_3"; -"233 _param_constant28" -> "235 layer_norm_3"; -"234 _param_constant29" -> "235 layer_norm_3"; -"235 layer_norm_3" -> "236 add_5"; -"236 add_5" -> "238 add_5_0_0_nncf_smooth_quant_0"; -"236 add_5" -> "263 add_6"; -"237 linear_10_updated_constant0" -> "243 quantize_per_channel_default_11"; -"238 add_5_0_0_nncf_smooth_quant_0" -> "239 quantize_per_tensor_default_11"; -"239 quantize_per_tensor_default_11" -> "240 dequantize_per_tensor_default_11"; -"240 dequantize_per_tensor_default_11" -> "246 linear_10"; -"241 linear_10_scale_0" -> "243 quantize_per_channel_default_11"; -"241 linear_10_scale_0" -> "244 dequantize_per_channel_default_11"; -"242 linear_10_zero_point_0" -> "243 quantize_per_channel_default_11"; -"242 linear_10_zero_point_0" -> "244 dequantize_per_channel_default_11"; -"243 quantize_per_channel_default_11" -> "244 dequantize_per_channel_default_11"; -"244 dequantize_per_channel_default_11" -> "246 linear_10"; -"245 _param_constant31_0_0" -> "246 linear_10"; -"246 linear_10" -> "247 gelu_1"; -"247 gelu_1" -> "248 dropout_6"; -"248 dropout_6" -> "250 dropout_6_0_0_nncf_smooth_quant_0"; -"249 linear_11_updated_constant0" -> "255 quantize_per_channel_default_12"; -"250 dropout_6_0_0_nncf_smooth_quant_0" -> "251 quantize_per_tensor_default_12"; -"251 quantize_per_tensor_default_12" -> "252 dequantize_per_tensor_default_12"; -"252 dequantize_per_tensor_default_12" -> "258 linear_11"; -"253 linear_11_scale_0" -> "255 quantize_per_channel_default_12"; -"253 linear_11_scale_0" -> "256 dequantize_per_channel_default_12"; -"254 linear_11_zero_point_0" -> "255 quantize_per_channel_default_12"; -"254 linear_11_zero_point_0" -> "256 dequantize_per_channel_default_12"; -"255 quantize_per_channel_default_12" -> "256 dequantize_per_channel_default_12"; -"256 dequantize_per_channel_default_12" -> "258 linear_11"; -"257 _param_constant33_0_0" -> "258 linear_11"; -"258 linear_11" -> "259 dropout_7"; -"259 dropout_7" -> "262 layer_norm_4"; -"260 _param_constant34" -> "262 layer_norm_4"; -"261 _param_constant35" -> "262 layer_norm_4"; -"262 layer_norm_4" -> "263 add_6"; -"263 add_6" -> "264 pad_2"; -"264 pad_2" -> "265 slice_25"; -"264 pad_2" -> "268 slice_28"; -"264 pad_2" -> "271 slice_31"; -"264 pad_2" -> "274 slice_34"; -"265 slice_25" -> "266 slice_26"; -"266 slice_26" -> "267 slice_27"; -"267 slice_27" -> "277 cat"; -"268 slice_28" -> "269 slice_29"; -"269 slice_29" -> "270 slice_30"; -"270 slice_30" -> "277 cat"; -"271 slice_31" -> "272 slice_32"; -"272 slice_32" -> "273 slice_33"; -"273 slice_33" -> "277 cat"; -"274 slice_34" -> "275 slice_35"; -"275 slice_35" -> "276 slice_36"; -"276 slice_36" -> "277 cat"; -"277 cat" -> "279 cat_0_0_nncf_smooth_quant_0"; -"278 linear_12_updated_constant0" -> "284 quantize_per_channel_default_13"; -"279 cat_0_0_nncf_smooth_quant_0" -> "280 quantize_per_tensor_default_13"; -"280 quantize_per_tensor_default_13" -> "281 dequantize_per_tensor_default_13"; -"281 dequantize_per_tensor_default_13" -> "286 linear_12"; -"282 linear_12_scale_0" -> "284 quantize_per_channel_default_13"; -"282 linear_12_scale_0" -> "285 dequantize_per_channel_default_13"; -"283 linear_12_zero_point_0" -> "284 quantize_per_channel_default_13"; -"283 linear_12_zero_point_0" -> "285 dequantize_per_channel_default_13"; -"284 quantize_per_channel_default_13" -> "285 dequantize_per_channel_default_13"; -"285 dequantize_per_channel_default_13" -> "286 linear_12"; -"286 linear_12" -> "289 layer_norm_5"; -"287 _param_constant37" -> "289 layer_norm_5"; -"288 _param_constant38" -> "289 layer_norm_5"; -"289 layer_norm_5" -> "316 pad_3"; -"289 layer_norm_5" -> "381 add_8"; -"290 _tensor_constant13" -> "292 _tensor_constant13_0_0_nncf_smooth_quant_0"; -"291 linear_13_updated_constant0" -> "295 quantize_per_channel_default_14"; -"292 _tensor_constant13_0_0_nncf_smooth_quant_0" -> "298 linear_13"; -"293 linear_13_scale_0" -> "295 quantize_per_channel_default_14"; -"293 linear_13_scale_0" -> "296 dequantize_per_channel_default_14"; -"294 linear_13_zero_point_0" -> "295 quantize_per_channel_default_14"; -"294 linear_13_zero_point_0" -> "296 dequantize_per_channel_default_14"; -"295 quantize_per_channel_default_14" -> "296 dequantize_per_channel_default_14"; -"296 dequantize_per_channel_default_14" -> "298 linear_13"; -"297 _param_constant40_0_0" -> "298 linear_13"; -"298 linear_13" -> "299 relu__2"; -"299 relu__2" -> "301 relu__2_0_0_nncf_smooth_quant_0"; -"300 linear_14_updated_constant0" -> "304 quantize_per_channel_default_15"; -"301 relu__2_0_0_nncf_smooth_quant_0" -> "306 linear_14"; -"302 linear_14_scale_0" -> "304 quantize_per_channel_default_15"; -"302 linear_14_scale_0" -> "305 dequantize_per_channel_default_15"; -"303 linear_14_zero_point_0" -> "304 quantize_per_channel_default_15"; -"303 linear_14_zero_point_0" -> "305 dequantize_per_channel_default_15"; -"304 quantize_per_channel_default_15" -> "305 dequantize_per_channel_default_15"; -"305 dequantize_per_channel_default_15" -> "306 linear_14"; -"306 linear_14" -> "307 view_11"; -"307 view_11" -> "309 index_2"; -"308 _tensor_constant14" -> "309 index_2"; -"309 index_2" -> "310 view_12"; -"310 view_12" -> "311 permute_10"; -"311 permute_10" -> "312 contiguous_2"; -"312 contiguous_2" -> "313 unsqueeze_6"; -"313 unsqueeze_6" -> "314 sigmoid_2"; -"314 sigmoid_2" -> "315 mul_4"; -"315 mul_4" -> "353 add_7"; -"316 pad_3" -> "317 view_13"; -"317 view_13" -> "318 permute_11"; -"318 permute_11" -> "319 reshape_9"; -"319 reshape_9" -> "321 reshape_9_0_0_nncf_smooth_quant_0"; -"320 linear_15_updated_constant0" -> "326 quantize_per_channel_default_16"; -"321 reshape_9_0_0_nncf_smooth_quant_0" -> "322 quantize_per_tensor_default_14"; -"322 quantize_per_tensor_default_14" -> "323 dequantize_per_tensor_default_14"; -"323 dequantize_per_tensor_default_14" -> "329 linear_15"; -"324 linear_15_scale_0" -> "326 quantize_per_channel_default_16"; -"324 linear_15_scale_0" -> "327 dequantize_per_channel_default_16"; -"325 linear_15_zero_point_0" -> "326 quantize_per_channel_default_16"; -"325 linear_15_zero_point_0" -> "327 dequantize_per_channel_default_16"; -"326 quantize_per_channel_default_16" -> "327 dequantize_per_channel_default_16"; -"327 dequantize_per_channel_default_16" -> "329 linear_15"; -"328 _param_constant42_0_0" -> "329 linear_15"; -"329 linear_15" -> "330 reshape_10"; -"330 reshape_10" -> "331 permute_12"; -"331 permute_12" -> "332 select_6"; -"331 permute_12" -> "333 select_7"; -"331 permute_12" -> "334 select_8"; -"332 select_6" -> "335 linalg_vector_norm_4"; -"332 select_6" -> "337 expand_as_4"; -"332 select_6" -> "338 div_4"; -"333 select_7" -> "341 linalg_vector_norm_5"; -"333 select_7" -> "343 expand_as_5"; -"333 select_7" -> "344 div_5"; -"334 select_8" -> "356 matmul_5"; -"335 linalg_vector_norm_4" -> "336 clamp_min_4"; -"336 clamp_min_4" -> "337 expand_as_4"; -"337 expand_as_4" -> "338 div_4"; -"338 div_4" -> "339 quantize_per_tensor_default_15"; -"339 quantize_per_tensor_default_15" -> "340 dequantize_per_tensor_default_15"; -"340 dequantize_per_tensor_default_15" -> "348 matmul_4"; -"341 linalg_vector_norm_5" -> "342 clamp_min_5"; -"342 clamp_min_5" -> "343 expand_as_5"; -"343 expand_as_5" -> "344 div_5"; -"344 div_5" -> "345 quantize_per_tensor_default_16"; -"345 quantize_per_tensor_default_16" -> "346 dequantize_per_tensor_default_16"; -"346 dequantize_per_tensor_default_16" -> "347 transpose_4"; -"347 transpose_4" -> "348 matmul_4"; -"348 matmul_4" -> "352 mul_5"; -"349 _param_constant44" -> "350 clamp_2"; -"350 clamp_2" -> "351 exp_2"; -"351 exp_2" -> "352 mul_5"; -"352 mul_5" -> "353 add_7"; -"353 add_7" -> "354 softmax_2"; -"354 softmax_2" -> "355 dropout_8"; -"355 dropout_8" -> "356 matmul_5"; -"356 matmul_5" -> "357 transpose_5"; -"357 transpose_5" -> "358 reshape_11"; -"358 reshape_11" -> "360 reshape_11_0_0_nncf_smooth_quant_0"; -"359 linear_16_updated_constant0" -> "365 quantize_per_channel_default_17"; -"360 reshape_11_0_0_nncf_smooth_quant_0" -> "361 quantize_per_tensor_default_17"; -"361 quantize_per_tensor_default_17" -> "362 dequantize_per_tensor_default_17"; -"362 dequantize_per_tensor_default_17" -> "368 linear_16"; -"363 linear_16_scale_0" -> "365 quantize_per_channel_default_17"; -"363 linear_16_scale_0" -> "366 dequantize_per_channel_default_17"; -"364 linear_16_zero_point_0" -> "365 quantize_per_channel_default_17"; -"364 linear_16_zero_point_0" -> "366 dequantize_per_channel_default_17"; -"365 quantize_per_channel_default_17" -> "366 dequantize_per_channel_default_17"; -"366 dequantize_per_channel_default_17" -> "368 linear_16"; -"367 _param_constant46_0_0" -> "368 linear_16"; -"368 linear_16" -> "369 dropout_9"; -"369 dropout_9" -> "370 view_14"; -"370 view_14" -> "371 permute_13"; -"371 permute_13" -> "372 reshape_12"; -"372 reshape_12" -> "373 slice_38"; -"373 slice_38" -> "374 slice_39"; -"374 slice_39" -> "375 slice_40"; -"375 slice_40" -> "376 slice_41"; -"376 slice_41" -> "377 contiguous_3"; -"377 contiguous_3" -> "380 layer_norm_6"; -"378 _param_constant47" -> "380 layer_norm_6"; -"379 _param_constant48" -> "380 layer_norm_6"; -"380 layer_norm_6" -> "381 add_8"; -"381 add_8" -> "383 add_8_0_0_nncf_smooth_quant_0"; -"381 add_8" -> "408 add_9"; -"382 linear_17_updated_constant0" -> "388 quantize_per_channel_default_18"; -"383 add_8_0_0_nncf_smooth_quant_0" -> "384 quantize_per_tensor_default_18"; -"384 quantize_per_tensor_default_18" -> "385 dequantize_per_tensor_default_18"; -"385 dequantize_per_tensor_default_18" -> "391 linear_17"; -"386 linear_17_scale_0" -> "388 quantize_per_channel_default_18"; -"386 linear_17_scale_0" -> "389 dequantize_per_channel_default_18"; -"387 linear_17_zero_point_0" -> "388 quantize_per_channel_default_18"; -"387 linear_17_zero_point_0" -> "389 dequantize_per_channel_default_18"; -"388 quantize_per_channel_default_18" -> "389 dequantize_per_channel_default_18"; -"389 dequantize_per_channel_default_18" -> "391 linear_17"; -"390 _param_constant50_0_0" -> "391 linear_17"; -"391 linear_17" -> "392 gelu_2"; -"392 gelu_2" -> "393 dropout_10"; -"393 dropout_10" -> "395 dropout_10_0_0_nncf_smooth_quant_0"; -"394 linear_18_updated_constant0" -> "400 quantize_per_channel_default_19"; -"395 dropout_10_0_0_nncf_smooth_quant_0" -> "396 quantize_per_tensor_default_19"; -"396 quantize_per_tensor_default_19" -> "397 dequantize_per_tensor_default_19"; -"397 dequantize_per_tensor_default_19" -> "403 linear_18"; -"398 linear_18_scale_0" -> "400 quantize_per_channel_default_19"; -"398 linear_18_scale_0" -> "401 dequantize_per_channel_default_19"; -"399 linear_18_zero_point_0" -> "400 quantize_per_channel_default_19"; -"399 linear_18_zero_point_0" -> "401 dequantize_per_channel_default_19"; -"400 quantize_per_channel_default_19" -> "401 dequantize_per_channel_default_19"; -"401 dequantize_per_channel_default_19" -> "403 linear_18"; -"402 _param_constant52_0_0" -> "403 linear_18"; -"403 linear_18" -> "404 dropout_11"; -"404 dropout_11" -> "407 layer_norm_7"; -"405 _param_constant53" -> "407 layer_norm_7"; -"406 _param_constant54" -> "407 layer_norm_7"; -"407 layer_norm_7" -> "408 add_9"; -"408 add_9" -> "435 pad_4"; -"408 add_9" -> "518 add_12"; -"409 _tensor_constant15" -> "411 _tensor_constant15_0_0_nncf_smooth_quant_0"; -"410 linear_19_updated_constant0" -> "414 quantize_per_channel_default_20"; -"411 _tensor_constant15_0_0_nncf_smooth_quant_0" -> "417 linear_19"; -"412 linear_19_scale_0" -> "414 quantize_per_channel_default_20"; -"412 linear_19_scale_0" -> "415 dequantize_per_channel_default_20"; -"413 linear_19_zero_point_0" -> "414 quantize_per_channel_default_20"; -"413 linear_19_zero_point_0" -> "415 dequantize_per_channel_default_20"; -"414 quantize_per_channel_default_20" -> "415 dequantize_per_channel_default_20"; -"415 dequantize_per_channel_default_20" -> "417 linear_19"; -"416 _param_constant56_0_0" -> "417 linear_19"; -"417 linear_19" -> "418 relu__3"; -"418 relu__3" -> "420 relu__3_0_0_nncf_smooth_quant_0"; -"419 linear_20_updated_constant0" -> "423 quantize_per_channel_default_21"; -"420 relu__3_0_0_nncf_smooth_quant_0" -> "425 linear_20"; -"421 linear_20_scale_0" -> "423 quantize_per_channel_default_21"; -"421 linear_20_scale_0" -> "424 dequantize_per_channel_default_21"; -"422 linear_20_zero_point_0" -> "423 quantize_per_channel_default_21"; -"422 linear_20_zero_point_0" -> "424 dequantize_per_channel_default_21"; -"423 quantize_per_channel_default_21" -> "424 dequantize_per_channel_default_21"; -"424 dequantize_per_channel_default_21" -> "425 linear_20"; -"425 linear_20" -> "426 view_15"; -"426 view_15" -> "428 index_3"; -"427 _tensor_constant16" -> "428 index_3"; -"428 index_3" -> "429 view_16"; -"429 view_16" -> "430 permute_14"; -"430 permute_14" -> "431 contiguous_4"; -"431 contiguous_4" -> "432 unsqueeze_7"; -"432 unsqueeze_7" -> "433 sigmoid_3"; -"433 sigmoid_3" -> "434 mul_6"; -"434 mul_6" -> "473 add_10"; -"435 pad_4" -> "436 roll_2"; -"436 roll_2" -> "437 view_17"; -"437 view_17" -> "438 permute_15"; -"438 permute_15" -> "439 reshape_13"; -"439 reshape_13" -> "441 reshape_13_0_0_nncf_smooth_quant_0"; -"439 reshape_13" -> "474 new_zeros_1"; -"440 linear_21_updated_constant0" -> "446 quantize_per_channel_default_22"; -"441 reshape_13_0_0_nncf_smooth_quant_0" -> "442 quantize_per_tensor_default_20"; -"442 quantize_per_tensor_default_20" -> "443 dequantize_per_tensor_default_20"; -"443 dequantize_per_tensor_default_20" -> "449 linear_21"; -"444 linear_21_scale_0" -> "446 quantize_per_channel_default_22"; -"444 linear_21_scale_0" -> "447 dequantize_per_channel_default_22"; -"445 linear_21_zero_point_0" -> "446 quantize_per_channel_default_22"; -"445 linear_21_zero_point_0" -> "447 dequantize_per_channel_default_22"; -"446 quantize_per_channel_default_22" -> "447 dequantize_per_channel_default_22"; -"447 dequantize_per_channel_default_22" -> "449 linear_21"; -"448 _param_constant58_0_0" -> "449 linear_21"; -"449 linear_21" -> "450 reshape_14"; -"450 reshape_14" -> "451 permute_16"; -"451 permute_16" -> "452 select_9"; -"451 permute_16" -> "453 select_10"; -"451 permute_16" -> "454 select_11"; -"452 select_9" -> "455 linalg_vector_norm_6"; -"452 select_9" -> "457 expand_as_6"; -"452 select_9" -> "458 div_6"; -"453 select_10" -> "461 linalg_vector_norm_7"; -"453 select_10" -> "463 expand_as_7"; -"453 select_10" -> "464 div_7"; -"454 select_11" -> "492 matmul_7"; -"455 linalg_vector_norm_6" -> "456 clamp_min_6"; -"456 clamp_min_6" -> "457 expand_as_6"; -"457 expand_as_6" -> "458 div_6"; -"458 div_6" -> "459 quantize_per_tensor_default_21"; -"459 quantize_per_tensor_default_21" -> "460 dequantize_per_tensor_default_21"; -"460 dequantize_per_tensor_default_21" -> "468 matmul_6"; -"461 linalg_vector_norm_7" -> "462 clamp_min_7"; -"462 clamp_min_7" -> "463 expand_as_7"; -"463 expand_as_7" -> "464 div_7"; -"464 div_7" -> "465 quantize_per_tensor_default_22"; -"465 quantize_per_tensor_default_22" -> "466 dequantize_per_tensor_default_22"; -"466 dequantize_per_tensor_default_22" -> "467 transpose_6"; -"467 transpose_6" -> "468 matmul_6"; -"468 matmul_6" -> "472 mul_7"; -"469 _param_constant60" -> "470 clamp_3"; -"470 clamp_3" -> "471 exp_3"; -"471 exp_3" -> "472 mul_7"; -"472 mul_7" -> "473 add_10"; -"473 add_10" -> "485 view_19"; -"474 new_zeros_1" -> "475 view_18"; -"475 view_18" -> "476 permute_17"; -"476 permute_17" -> "477 reshape_15"; -"477 reshape_15" -> "478 unsqueeze_8"; -"477 reshape_15" -> "479 unsqueeze_9"; -"478 unsqueeze_8" -> "480 sub_1"; -"479 unsqueeze_9" -> "480 sub_1"; -"480 sub_1" -> "481 ne_1"; -"480 sub_1" -> "482 masked_fill_2"; -"480 sub_1" -> "483 eq_1"; -"481 ne_1" -> "482 masked_fill_2"; -"482 masked_fill_2" -> "484 masked_fill_3"; -"483 eq_1" -> "484 masked_fill_3"; -"484 masked_fill_3" -> "486 unsqueeze_10"; -"485 view_19" -> "488 add_11"; -"486 unsqueeze_10" -> "487 unsqueeze_11"; -"487 unsqueeze_11" -> "488 add_11"; -"488 add_11" -> "489 view_20"; -"489 view_20" -> "490 softmax_3"; -"490 softmax_3" -> "491 dropout_12"; -"491 dropout_12" -> "492 matmul_7"; -"492 matmul_7" -> "493 transpose_7"; -"493 transpose_7" -> "494 reshape_16"; -"494 reshape_16" -> "496 reshape_16_0_0_nncf_smooth_quant_0"; -"495 linear_22_updated_constant0" -> "501 quantize_per_channel_default_23"; -"496 reshape_16_0_0_nncf_smooth_quant_0" -> "497 quantize_per_tensor_default_23"; -"497 quantize_per_tensor_default_23" -> "498 dequantize_per_tensor_default_23"; -"498 dequantize_per_tensor_default_23" -> "504 linear_22"; -"499 linear_22_scale_0" -> "501 quantize_per_channel_default_23"; -"499 linear_22_scale_0" -> "502 dequantize_per_channel_default_23"; -"500 linear_22_zero_point_0" -> "501 quantize_per_channel_default_23"; -"500 linear_22_zero_point_0" -> "502 dequantize_per_channel_default_23"; -"501 quantize_per_channel_default_23" -> "502 dequantize_per_channel_default_23"; -"502 dequantize_per_channel_default_23" -> "504 linear_22"; -"503 _param_constant62_0_0" -> "504 linear_22"; -"504 linear_22" -> "505 dropout_13"; -"505 dropout_13" -> "506 view_21"; -"506 view_21" -> "507 permute_18"; -"507 permute_18" -> "508 reshape_17"; -"508 reshape_17" -> "509 roll_3"; -"509 roll_3" -> "510 slice_61"; -"510 slice_61" -> "511 slice_62"; -"511 slice_62" -> "512 slice_63"; -"512 slice_63" -> "513 slice_64"; -"513 slice_64" -> "514 contiguous_5"; -"514 contiguous_5" -> "517 layer_norm_8"; -"515 _param_constant63" -> "517 layer_norm_8"; -"516 _param_constant64" -> "517 layer_norm_8"; -"517 layer_norm_8" -> "518 add_12"; -"518 add_12" -> "520 add_12_0_0_nncf_smooth_quant_0"; -"518 add_12" -> "545 add_13"; -"519 linear_23_updated_constant0" -> "525 quantize_per_channel_default_24"; -"520 add_12_0_0_nncf_smooth_quant_0" -> "521 quantize_per_tensor_default_24"; -"521 quantize_per_tensor_default_24" -> "522 dequantize_per_tensor_default_24"; -"522 dequantize_per_tensor_default_24" -> "528 linear_23"; -"523 linear_23_scale_0" -> "525 quantize_per_channel_default_24"; -"523 linear_23_scale_0" -> "526 dequantize_per_channel_default_24"; -"524 linear_23_zero_point_0" -> "525 quantize_per_channel_default_24"; -"524 linear_23_zero_point_0" -> "526 dequantize_per_channel_default_24"; -"525 quantize_per_channel_default_24" -> "526 dequantize_per_channel_default_24"; -"526 dequantize_per_channel_default_24" -> "528 linear_23"; -"527 _param_constant66_0_0" -> "528 linear_23"; -"528 linear_23" -> "529 gelu_3"; -"529 gelu_3" -> "530 dropout_14"; -"530 dropout_14" -> "532 dropout_14_0_0_nncf_smooth_quant_0"; -"531 linear_24_updated_constant0" -> "537 quantize_per_channel_default_25"; -"532 dropout_14_0_0_nncf_smooth_quant_0" -> "533 quantize_per_tensor_default_25"; -"533 quantize_per_tensor_default_25" -> "534 dequantize_per_tensor_default_25"; -"534 dequantize_per_tensor_default_25" -> "540 linear_24"; -"535 linear_24_scale_0" -> "537 quantize_per_channel_default_25"; -"535 linear_24_scale_0" -> "538 dequantize_per_channel_default_25"; -"536 linear_24_zero_point_0" -> "537 quantize_per_channel_default_25"; -"536 linear_24_zero_point_0" -> "538 dequantize_per_channel_default_25"; -"537 quantize_per_channel_default_25" -> "538 dequantize_per_channel_default_25"; -"538 dequantize_per_channel_default_25" -> "540 linear_24"; -"539 _param_constant68_0_0" -> "540 linear_24"; -"540 linear_24" -> "541 dropout_15"; -"541 dropout_15" -> "544 layer_norm_9"; -"542 _param_constant69" -> "544 layer_norm_9"; -"543 _param_constant70" -> "544 layer_norm_9"; -"544 layer_norm_9" -> "545 add_13"; -"545 add_13" -> "546 pad_5"; -"546 pad_5" -> "547 slice_65"; -"546 pad_5" -> "550 slice_68"; -"546 pad_5" -> "553 slice_71"; -"546 pad_5" -> "556 slice_74"; -"547 slice_65" -> "548 slice_66"; -"548 slice_66" -> "549 slice_67"; -"549 slice_67" -> "559 cat_1"; -"550 slice_68" -> "551 slice_69"; -"551 slice_69" -> "552 slice_70"; -"552 slice_70" -> "559 cat_1"; -"553 slice_71" -> "554 slice_72"; -"554 slice_72" -> "555 slice_73"; -"555 slice_73" -> "559 cat_1"; -"556 slice_74" -> "557 slice_75"; -"557 slice_75" -> "558 slice_76"; -"558 slice_76" -> "559 cat_1"; -"559 cat_1" -> "561 cat_1_0_0_nncf_smooth_quant_0"; -"560 linear_25_updated_constant0" -> "566 quantize_per_channel_default_26"; -"561 cat_1_0_0_nncf_smooth_quant_0" -> "562 quantize_per_tensor_default_26"; -"562 quantize_per_tensor_default_26" -> "563 dequantize_per_tensor_default_26"; -"563 dequantize_per_tensor_default_26" -> "568 linear_25"; -"564 linear_25_scale_0" -> "566 quantize_per_channel_default_26"; -"564 linear_25_scale_0" -> "567 dequantize_per_channel_default_26"; -"565 linear_25_zero_point_0" -> "566 quantize_per_channel_default_26"; -"565 linear_25_zero_point_0" -> "567 dequantize_per_channel_default_26"; -"566 quantize_per_channel_default_26" -> "567 dequantize_per_channel_default_26"; -"567 dequantize_per_channel_default_26" -> "568 linear_25"; -"568 linear_25" -> "571 layer_norm_10"; -"569 _param_constant72" -> "571 layer_norm_10"; -"570 _param_constant73" -> "571 layer_norm_10"; -"571 layer_norm_10" -> "598 pad_6"; -"571 layer_norm_10" -> "663 add_15"; -"572 _tensor_constant26" -> "574 _tensor_constant26_0_0_nncf_smooth_quant_0"; -"573 linear_26_updated_constant0" -> "577 quantize_per_channel_default_27"; -"574 _tensor_constant26_0_0_nncf_smooth_quant_0" -> "580 linear_26"; -"575 linear_26_scale_0" -> "577 quantize_per_channel_default_27"; -"575 linear_26_scale_0" -> "578 dequantize_per_channel_default_27"; -"576 linear_26_zero_point_0" -> "577 quantize_per_channel_default_27"; -"576 linear_26_zero_point_0" -> "578 dequantize_per_channel_default_27"; -"577 quantize_per_channel_default_27" -> "578 dequantize_per_channel_default_27"; -"578 dequantize_per_channel_default_27" -> "580 linear_26"; -"579 _param_constant75_0_0" -> "580 linear_26"; -"580 linear_26" -> "581 relu__4"; -"581 relu__4" -> "583 relu__4_0_0_nncf_smooth_quant_0"; -"582 linear_27_updated_constant0" -> "586 quantize_per_channel_default_28"; -"583 relu__4_0_0_nncf_smooth_quant_0" -> "588 linear_27"; -"584 linear_27_scale_0" -> "586 quantize_per_channel_default_28"; -"584 linear_27_scale_0" -> "587 dequantize_per_channel_default_28"; -"585 linear_27_zero_point_0" -> "586 quantize_per_channel_default_28"; -"585 linear_27_zero_point_0" -> "587 dequantize_per_channel_default_28"; -"586 quantize_per_channel_default_28" -> "587 dequantize_per_channel_default_28"; -"587 dequantize_per_channel_default_28" -> "588 linear_27"; -"588 linear_27" -> "589 view_22"; -"589 view_22" -> "591 index_4"; -"590 _tensor_constant27" -> "591 index_4"; -"591 index_4" -> "592 view_23"; -"592 view_23" -> "593 permute_19"; -"593 permute_19" -> "594 contiguous_6"; -"594 contiguous_6" -> "595 unsqueeze_12"; -"595 unsqueeze_12" -> "596 sigmoid_4"; -"596 sigmoid_4" -> "597 mul_8"; -"597 mul_8" -> "635 add_14"; -"598 pad_6" -> "599 view_24"; -"599 view_24" -> "600 permute_20"; -"600 permute_20" -> "601 reshape_18"; -"601 reshape_18" -> "603 reshape_18_0_0_nncf_smooth_quant_0"; -"602 linear_28_updated_constant0" -> "608 quantize_per_channel_default_29"; -"603 reshape_18_0_0_nncf_smooth_quant_0" -> "604 quantize_per_tensor_default_27"; -"604 quantize_per_tensor_default_27" -> "605 dequantize_per_tensor_default_27"; -"605 dequantize_per_tensor_default_27" -> "611 linear_28"; -"606 linear_28_scale_0" -> "608 quantize_per_channel_default_29"; -"606 linear_28_scale_0" -> "609 dequantize_per_channel_default_29"; -"607 linear_28_zero_point_0" -> "608 quantize_per_channel_default_29"; -"607 linear_28_zero_point_0" -> "609 dequantize_per_channel_default_29"; -"608 quantize_per_channel_default_29" -> "609 dequantize_per_channel_default_29"; -"609 dequantize_per_channel_default_29" -> "611 linear_28"; -"610 _param_constant77_0_0" -> "611 linear_28"; -"611 linear_28" -> "612 reshape_19"; -"612 reshape_19" -> "613 permute_21"; -"613 permute_21" -> "614 select_12"; -"613 permute_21" -> "615 select_13"; -"613 permute_21" -> "616 select_14"; -"614 select_12" -> "617 linalg_vector_norm_8"; -"614 select_12" -> "619 expand_as_8"; -"614 select_12" -> "620 div_8"; -"615 select_13" -> "623 linalg_vector_norm_9"; -"615 select_13" -> "625 expand_as_9"; -"615 select_13" -> "626 div_9"; -"616 select_14" -> "638 matmul_9"; -"617 linalg_vector_norm_8" -> "618 clamp_min_8"; -"618 clamp_min_8" -> "619 expand_as_8"; -"619 expand_as_8" -> "620 div_8"; -"620 div_8" -> "621 quantize_per_tensor_default_28"; -"621 quantize_per_tensor_default_28" -> "622 dequantize_per_tensor_default_28"; -"622 dequantize_per_tensor_default_28" -> "630 matmul_8"; -"623 linalg_vector_norm_9" -> "624 clamp_min_9"; -"624 clamp_min_9" -> "625 expand_as_9"; -"625 expand_as_9" -> "626 div_9"; -"626 div_9" -> "627 quantize_per_tensor_default_29"; -"627 quantize_per_tensor_default_29" -> "628 dequantize_per_tensor_default_29"; -"628 dequantize_per_tensor_default_29" -> "629 transpose_8"; -"629 transpose_8" -> "630 matmul_8"; -"630 matmul_8" -> "634 mul_9"; -"631 _param_constant79" -> "632 clamp_4"; -"632 clamp_4" -> "633 exp_4"; -"633 exp_4" -> "634 mul_9"; -"634 mul_9" -> "635 add_14"; -"635 add_14" -> "636 softmax_4"; -"636 softmax_4" -> "637 dropout_16"; -"637 dropout_16" -> "638 matmul_9"; -"638 matmul_9" -> "639 transpose_9"; -"639 transpose_9" -> "640 reshape_20"; -"640 reshape_20" -> "642 reshape_20_0_0_nncf_smooth_quant_0"; -"641 linear_29_updated_constant0" -> "647 quantize_per_channel_default_30"; -"642 reshape_20_0_0_nncf_smooth_quant_0" -> "643 quantize_per_tensor_default_30"; -"643 quantize_per_tensor_default_30" -> "644 dequantize_per_tensor_default_30"; -"644 dequantize_per_tensor_default_30" -> "650 linear_29"; -"645 linear_29_scale_0" -> "647 quantize_per_channel_default_30"; -"645 linear_29_scale_0" -> "648 dequantize_per_channel_default_30"; -"646 linear_29_zero_point_0" -> "647 quantize_per_channel_default_30"; -"646 linear_29_zero_point_0" -> "648 dequantize_per_channel_default_30"; -"647 quantize_per_channel_default_30" -> "648 dequantize_per_channel_default_30"; -"648 dequantize_per_channel_default_30" -> "650 linear_29"; -"649 _param_constant81_0_0" -> "650 linear_29"; -"650 linear_29" -> "651 dropout_17"; -"651 dropout_17" -> "652 view_25"; -"652 view_25" -> "653 permute_22"; -"653 permute_22" -> "654 reshape_21"; -"654 reshape_21" -> "655 slice_78"; -"655 slice_78" -> "656 slice_79"; -"656 slice_79" -> "657 slice_80"; -"657 slice_80" -> "658 slice_81"; -"658 slice_81" -> "659 contiguous_7"; -"659 contiguous_7" -> "662 layer_norm_11"; -"660 _param_constant82" -> "662 layer_norm_11"; -"661 _param_constant83" -> "662 layer_norm_11"; -"662 layer_norm_11" -> "663 add_15"; -"663 add_15" -> "665 add_15_0_0_nncf_smooth_quant_0"; -"663 add_15" -> "690 add_16"; -"664 linear_30_updated_constant0" -> "670 quantize_per_channel_default_31"; -"665 add_15_0_0_nncf_smooth_quant_0" -> "666 quantize_per_tensor_default_31"; -"666 quantize_per_tensor_default_31" -> "667 dequantize_per_tensor_default_31"; -"667 dequantize_per_tensor_default_31" -> "673 linear_30"; -"668 linear_30_scale_0" -> "670 quantize_per_channel_default_31"; -"668 linear_30_scale_0" -> "671 dequantize_per_channel_default_31"; -"669 linear_30_zero_point_0" -> "670 quantize_per_channel_default_31"; -"669 linear_30_zero_point_0" -> "671 dequantize_per_channel_default_31"; -"670 quantize_per_channel_default_31" -> "671 dequantize_per_channel_default_31"; -"671 dequantize_per_channel_default_31" -> "673 linear_30"; -"672 _param_constant85_0_0" -> "673 linear_30"; -"673 linear_30" -> "674 gelu_4"; -"674 gelu_4" -> "675 dropout_18"; -"675 dropout_18" -> "677 dropout_18_0_0_nncf_smooth_quant_0"; -"676 linear_31_updated_constant0" -> "682 quantize_per_channel_default_32"; -"677 dropout_18_0_0_nncf_smooth_quant_0" -> "678 quantize_per_tensor_default_32"; -"678 quantize_per_tensor_default_32" -> "679 dequantize_per_tensor_default_32"; -"679 dequantize_per_tensor_default_32" -> "685 linear_31"; -"680 linear_31_scale_0" -> "682 quantize_per_channel_default_32"; -"680 linear_31_scale_0" -> "683 dequantize_per_channel_default_32"; -"681 linear_31_zero_point_0" -> "682 quantize_per_channel_default_32"; -"681 linear_31_zero_point_0" -> "683 dequantize_per_channel_default_32"; -"682 quantize_per_channel_default_32" -> "683 dequantize_per_channel_default_32"; -"683 dequantize_per_channel_default_32" -> "685 linear_31"; -"684 _param_constant87_0_0" -> "685 linear_31"; -"685 linear_31" -> "686 dropout_19"; -"686 dropout_19" -> "689 layer_norm_12"; -"687 _param_constant88" -> "689 layer_norm_12"; -"688 _param_constant89" -> "689 layer_norm_12"; -"689 layer_norm_12" -> "690 add_16"; -"690 add_16" -> "717 pad_7"; -"690 add_16" -> "800 add_19"; -"691 _tensor_constant28" -> "693 _tensor_constant28_0_0_nncf_smooth_quant_0"; -"692 linear_32_updated_constant0" -> "696 quantize_per_channel_default_33"; -"693 _tensor_constant28_0_0_nncf_smooth_quant_0" -> "699 linear_32"; -"694 linear_32_scale_0" -> "696 quantize_per_channel_default_33"; -"694 linear_32_scale_0" -> "697 dequantize_per_channel_default_33"; -"695 linear_32_zero_point_0" -> "696 quantize_per_channel_default_33"; -"695 linear_32_zero_point_0" -> "697 dequantize_per_channel_default_33"; -"696 quantize_per_channel_default_33" -> "697 dequantize_per_channel_default_33"; -"697 dequantize_per_channel_default_33" -> "699 linear_32"; -"698 _param_constant91_0_0" -> "699 linear_32"; -"699 linear_32" -> "700 relu__5"; -"700 relu__5" -> "702 relu__5_0_0_nncf_smooth_quant_0"; -"701 linear_33_updated_constant0" -> "705 quantize_per_channel_default_34"; -"702 relu__5_0_0_nncf_smooth_quant_0" -> "707 linear_33"; -"703 linear_33_scale_0" -> "705 quantize_per_channel_default_34"; -"703 linear_33_scale_0" -> "706 dequantize_per_channel_default_34"; -"704 linear_33_zero_point_0" -> "705 quantize_per_channel_default_34"; -"704 linear_33_zero_point_0" -> "706 dequantize_per_channel_default_34"; -"705 quantize_per_channel_default_34" -> "706 dequantize_per_channel_default_34"; -"706 dequantize_per_channel_default_34" -> "707 linear_33"; -"707 linear_33" -> "708 view_26"; -"708 view_26" -> "710 index_5"; -"709 _tensor_constant29" -> "710 index_5"; -"710 index_5" -> "711 view_27"; -"711 view_27" -> "712 permute_23"; -"712 permute_23" -> "713 contiguous_8"; -"713 contiguous_8" -> "714 unsqueeze_13"; -"714 unsqueeze_13" -> "715 sigmoid_5"; -"715 sigmoid_5" -> "716 mul_10"; -"716 mul_10" -> "755 add_17"; -"717 pad_7" -> "718 roll_4"; -"718 roll_4" -> "719 view_28"; -"719 view_28" -> "720 permute_24"; -"720 permute_24" -> "721 reshape_22"; -"721 reshape_22" -> "723 reshape_22_0_0_nncf_smooth_quant_0"; -"721 reshape_22" -> "756 new_zeros_2"; -"722 linear_34_updated_constant0" -> "728 quantize_per_channel_default_35"; -"723 reshape_22_0_0_nncf_smooth_quant_0" -> "724 quantize_per_tensor_default_33"; -"724 quantize_per_tensor_default_33" -> "725 dequantize_per_tensor_default_33"; -"725 dequantize_per_tensor_default_33" -> "731 linear_34"; -"726 linear_34_scale_0" -> "728 quantize_per_channel_default_35"; -"726 linear_34_scale_0" -> "729 dequantize_per_channel_default_35"; -"727 linear_34_zero_point_0" -> "728 quantize_per_channel_default_35"; -"727 linear_34_zero_point_0" -> "729 dequantize_per_channel_default_35"; -"728 quantize_per_channel_default_35" -> "729 dequantize_per_channel_default_35"; -"729 dequantize_per_channel_default_35" -> "731 linear_34"; -"730 _param_constant93_0_0" -> "731 linear_34"; -"731 linear_34" -> "732 reshape_23"; -"732 reshape_23" -> "733 permute_25"; -"733 permute_25" -> "734 select_15"; -"733 permute_25" -> "735 select_16"; -"733 permute_25" -> "736 select_17"; -"734 select_15" -> "737 linalg_vector_norm_10"; -"734 select_15" -> "739 expand_as_10"; -"734 select_15" -> "740 div_10"; -"735 select_16" -> "743 linalg_vector_norm_11"; -"735 select_16" -> "745 expand_as_11"; -"735 select_16" -> "746 div_11"; -"736 select_17" -> "774 matmul_11"; -"737 linalg_vector_norm_10" -> "738 clamp_min_10"; -"738 clamp_min_10" -> "739 expand_as_10"; -"739 expand_as_10" -> "740 div_10"; -"740 div_10" -> "741 quantize_per_tensor_default_34"; -"741 quantize_per_tensor_default_34" -> "742 dequantize_per_tensor_default_34"; -"742 dequantize_per_tensor_default_34" -> "750 matmul_10"; -"743 linalg_vector_norm_11" -> "744 clamp_min_11"; -"744 clamp_min_11" -> "745 expand_as_11"; -"745 expand_as_11" -> "746 div_11"; -"746 div_11" -> "747 quantize_per_tensor_default_35"; -"747 quantize_per_tensor_default_35" -> "748 dequantize_per_tensor_default_35"; -"748 dequantize_per_tensor_default_35" -> "749 transpose_10"; -"749 transpose_10" -> "750 matmul_10"; -"750 matmul_10" -> "754 mul_11"; -"751 _param_constant95" -> "752 clamp_5"; -"752 clamp_5" -> "753 exp_5"; -"753 exp_5" -> "754 mul_11"; -"754 mul_11" -> "755 add_17"; -"755 add_17" -> "767 view_30"; -"756 new_zeros_2" -> "757 view_29"; -"757 view_29" -> "758 permute_26"; -"758 permute_26" -> "759 reshape_24"; -"759 reshape_24" -> "760 unsqueeze_14"; -"759 reshape_24" -> "761 unsqueeze_15"; -"760 unsqueeze_14" -> "762 sub_2"; -"761 unsqueeze_15" -> "762 sub_2"; -"762 sub_2" -> "763 ne_2"; -"762 sub_2" -> "764 masked_fill_4"; -"762 sub_2" -> "765 eq_2"; -"763 ne_2" -> "764 masked_fill_4"; -"764 masked_fill_4" -> "766 masked_fill_5"; -"765 eq_2" -> "766 masked_fill_5"; -"766 masked_fill_5" -> "768 unsqueeze_16"; -"767 view_30" -> "770 add_18"; -"768 unsqueeze_16" -> "769 unsqueeze_17"; -"769 unsqueeze_17" -> "770 add_18"; -"770 add_18" -> "771 view_31"; -"771 view_31" -> "772 softmax_5"; -"772 softmax_5" -> "773 dropout_20"; -"773 dropout_20" -> "774 matmul_11"; -"774 matmul_11" -> "775 transpose_11"; -"775 transpose_11" -> "776 reshape_25"; -"776 reshape_25" -> "778 reshape_25_0_0_nncf_smooth_quant_0"; -"777 linear_35_updated_constant0" -> "783 quantize_per_channel_default_36"; -"778 reshape_25_0_0_nncf_smooth_quant_0" -> "779 quantize_per_tensor_default_36"; -"779 quantize_per_tensor_default_36" -> "780 dequantize_per_tensor_default_36"; -"780 dequantize_per_tensor_default_36" -> "786 linear_35"; -"781 linear_35_scale_0" -> "783 quantize_per_channel_default_36"; -"781 linear_35_scale_0" -> "784 dequantize_per_channel_default_36"; -"782 linear_35_zero_point_0" -> "783 quantize_per_channel_default_36"; -"782 linear_35_zero_point_0" -> "784 dequantize_per_channel_default_36"; -"783 quantize_per_channel_default_36" -> "784 dequantize_per_channel_default_36"; -"784 dequantize_per_channel_default_36" -> "786 linear_35"; -"785 _param_constant97_0_0" -> "786 linear_35"; -"786 linear_35" -> "787 dropout_21"; -"787 dropout_21" -> "788 view_32"; -"788 view_32" -> "789 permute_27"; -"789 permute_27" -> "790 reshape_26"; -"790 reshape_26" -> "791 roll_5"; -"791 roll_5" -> "792 slice_101"; -"792 slice_101" -> "793 slice_102"; -"793 slice_102" -> "794 slice_103"; -"794 slice_103" -> "795 slice_104"; -"795 slice_104" -> "796 contiguous_9"; -"796 contiguous_9" -> "799 layer_norm_13"; -"797 _param_constant98" -> "799 layer_norm_13"; -"798 _param_constant99" -> "799 layer_norm_13"; -"799 layer_norm_13" -> "800 add_19"; -"800 add_19" -> "802 add_19_0_0_nncf_smooth_quant_0"; -"800 add_19" -> "827 add_20"; -"801 linear_36_updated_constant0" -> "807 quantize_per_channel_default_37"; -"802 add_19_0_0_nncf_smooth_quant_0" -> "803 quantize_per_tensor_default_37"; -"803 quantize_per_tensor_default_37" -> "804 dequantize_per_tensor_default_37"; -"804 dequantize_per_tensor_default_37" -> "810 linear_36"; -"805 linear_36_scale_0" -> "807 quantize_per_channel_default_37"; -"805 linear_36_scale_0" -> "808 dequantize_per_channel_default_37"; -"806 linear_36_zero_point_0" -> "807 quantize_per_channel_default_37"; -"806 linear_36_zero_point_0" -> "808 dequantize_per_channel_default_37"; -"807 quantize_per_channel_default_37" -> "808 dequantize_per_channel_default_37"; -"808 dequantize_per_channel_default_37" -> "810 linear_36"; -"809 _param_constant101_0_0" -> "810 linear_36"; -"810 linear_36" -> "811 gelu_5"; -"811 gelu_5" -> "812 dropout_22"; -"812 dropout_22" -> "814 dropout_22_0_0_nncf_smooth_quant_0"; -"813 linear_37_updated_constant0" -> "819 quantize_per_channel_default_38"; -"814 dropout_22_0_0_nncf_smooth_quant_0" -> "815 quantize_per_tensor_default_38"; -"815 quantize_per_tensor_default_38" -> "816 dequantize_per_tensor_default_38"; -"816 dequantize_per_tensor_default_38" -> "822 linear_37"; -"817 linear_37_scale_0" -> "819 quantize_per_channel_default_38"; -"817 linear_37_scale_0" -> "820 dequantize_per_channel_default_38"; -"818 linear_37_zero_point_0" -> "819 quantize_per_channel_default_38"; -"818 linear_37_zero_point_0" -> "820 dequantize_per_channel_default_38"; -"819 quantize_per_channel_default_38" -> "820 dequantize_per_channel_default_38"; -"820 dequantize_per_channel_default_38" -> "822 linear_37"; -"821 _param_constant103_0_0" -> "822 linear_37"; -"822 linear_37" -> "823 dropout_23"; -"823 dropout_23" -> "826 layer_norm_14"; -"824 _param_constant104" -> "826 layer_norm_14"; -"825 _param_constant105" -> "826 layer_norm_14"; -"826 layer_norm_14" -> "827 add_20"; -"827 add_20" -> "854 pad_8"; -"827 add_20" -> "919 add_22"; -"828 _tensor_constant39" -> "830 _tensor_constant39_0_0_nncf_smooth_quant_0"; -"829 linear_38_updated_constant0" -> "833 quantize_per_channel_default_39"; -"830 _tensor_constant39_0_0_nncf_smooth_quant_0" -> "836 linear_38"; -"831 linear_38_scale_0" -> "833 quantize_per_channel_default_39"; -"831 linear_38_scale_0" -> "834 dequantize_per_channel_default_39"; -"832 linear_38_zero_point_0" -> "833 quantize_per_channel_default_39"; -"832 linear_38_zero_point_0" -> "834 dequantize_per_channel_default_39"; -"833 quantize_per_channel_default_39" -> "834 dequantize_per_channel_default_39"; -"834 dequantize_per_channel_default_39" -> "836 linear_38"; -"835 _param_constant107_0_0" -> "836 linear_38"; -"836 linear_38" -> "837 relu__6"; -"837 relu__6" -> "839 relu__6_0_0_nncf_smooth_quant_0"; -"838 linear_39_updated_constant0" -> "842 quantize_per_channel_default_40"; -"839 relu__6_0_0_nncf_smooth_quant_0" -> "844 linear_39"; -"840 linear_39_scale_0" -> "842 quantize_per_channel_default_40"; -"840 linear_39_scale_0" -> "843 dequantize_per_channel_default_40"; -"841 linear_39_zero_point_0" -> "842 quantize_per_channel_default_40"; -"841 linear_39_zero_point_0" -> "843 dequantize_per_channel_default_40"; -"842 quantize_per_channel_default_40" -> "843 dequantize_per_channel_default_40"; -"843 dequantize_per_channel_default_40" -> "844 linear_39"; -"844 linear_39" -> "845 view_33"; -"845 view_33" -> "847 index_6"; -"846 _tensor_constant40" -> "847 index_6"; -"847 index_6" -> "848 view_34"; -"848 view_34" -> "849 permute_28"; -"849 permute_28" -> "850 contiguous_10"; -"850 contiguous_10" -> "851 unsqueeze_18"; -"851 unsqueeze_18" -> "852 sigmoid_6"; -"852 sigmoid_6" -> "853 mul_12"; -"853 mul_12" -> "891 add_21"; -"854 pad_8" -> "855 view_35"; -"855 view_35" -> "856 permute_29"; -"856 permute_29" -> "857 reshape_27"; -"857 reshape_27" -> "859 reshape_27_0_0_nncf_smooth_quant_0"; -"858 linear_40_updated_constant0" -> "864 quantize_per_channel_default_41"; -"859 reshape_27_0_0_nncf_smooth_quant_0" -> "860 quantize_per_tensor_default_39"; -"860 quantize_per_tensor_default_39" -> "861 dequantize_per_tensor_default_39"; -"861 dequantize_per_tensor_default_39" -> "867 linear_40"; -"862 linear_40_scale_0" -> "864 quantize_per_channel_default_41"; -"862 linear_40_scale_0" -> "865 dequantize_per_channel_default_41"; -"863 linear_40_zero_point_0" -> "864 quantize_per_channel_default_41"; -"863 linear_40_zero_point_0" -> "865 dequantize_per_channel_default_41"; -"864 quantize_per_channel_default_41" -> "865 dequantize_per_channel_default_41"; -"865 dequantize_per_channel_default_41" -> "867 linear_40"; -"866 _param_constant109_0_0" -> "867 linear_40"; -"867 linear_40" -> "868 reshape_28"; -"868 reshape_28" -> "869 permute_30"; -"869 permute_30" -> "870 select_18"; -"869 permute_30" -> "871 select_19"; -"869 permute_30" -> "872 select_20"; -"870 select_18" -> "873 linalg_vector_norm_12"; -"870 select_18" -> "875 expand_as_12"; -"870 select_18" -> "876 div_12"; -"871 select_19" -> "879 linalg_vector_norm_13"; -"871 select_19" -> "881 expand_as_13"; -"871 select_19" -> "882 div_13"; -"872 select_20" -> "894 matmul_13"; -"873 linalg_vector_norm_12" -> "874 clamp_min_12"; -"874 clamp_min_12" -> "875 expand_as_12"; -"875 expand_as_12" -> "876 div_12"; -"876 div_12" -> "877 quantize_per_tensor_default_40"; -"877 quantize_per_tensor_default_40" -> "878 dequantize_per_tensor_default_40"; -"878 dequantize_per_tensor_default_40" -> "886 matmul_12"; -"879 linalg_vector_norm_13" -> "880 clamp_min_13"; -"880 clamp_min_13" -> "881 expand_as_13"; -"881 expand_as_13" -> "882 div_13"; -"882 div_13" -> "883 quantize_per_tensor_default_41"; -"883 quantize_per_tensor_default_41" -> "884 dequantize_per_tensor_default_41"; -"884 dequantize_per_tensor_default_41" -> "885 transpose_12"; -"885 transpose_12" -> "886 matmul_12"; -"886 matmul_12" -> "890 mul_13"; -"887 _param_constant111" -> "888 clamp_6"; -"888 clamp_6" -> "889 exp_6"; -"889 exp_6" -> "890 mul_13"; -"890 mul_13" -> "891 add_21"; -"891 add_21" -> "892 softmax_6"; -"892 softmax_6" -> "893 dropout_24"; -"893 dropout_24" -> "894 matmul_13"; -"894 matmul_13" -> "895 transpose_13"; -"895 transpose_13" -> "896 reshape_29"; -"896 reshape_29" -> "898 reshape_29_0_0_nncf_smooth_quant_0"; -"897 linear_41_updated_constant0" -> "903 quantize_per_channel_default_42"; -"898 reshape_29_0_0_nncf_smooth_quant_0" -> "899 quantize_per_tensor_default_42"; -"899 quantize_per_tensor_default_42" -> "900 dequantize_per_tensor_default_42"; -"900 dequantize_per_tensor_default_42" -> "906 linear_41"; -"901 linear_41_scale_0" -> "903 quantize_per_channel_default_42"; -"901 linear_41_scale_0" -> "904 dequantize_per_channel_default_42"; -"902 linear_41_zero_point_0" -> "903 quantize_per_channel_default_42"; -"902 linear_41_zero_point_0" -> "904 dequantize_per_channel_default_42"; -"903 quantize_per_channel_default_42" -> "904 dequantize_per_channel_default_42"; -"904 dequantize_per_channel_default_42" -> "906 linear_41"; -"905 _param_constant113_0_0" -> "906 linear_41"; -"906 linear_41" -> "907 dropout_25"; -"907 dropout_25" -> "908 view_36"; -"908 view_36" -> "909 permute_31"; -"909 permute_31" -> "910 reshape_30"; -"910 reshape_30" -> "911 slice_106"; -"911 slice_106" -> "912 slice_107"; -"912 slice_107" -> "913 slice_108"; -"913 slice_108" -> "914 slice_109"; -"914 slice_109" -> "915 contiguous_11"; -"915 contiguous_11" -> "918 layer_norm_15"; -"916 _param_constant114" -> "918 layer_norm_15"; -"917 _param_constant115" -> "918 layer_norm_15"; -"918 layer_norm_15" -> "919 add_22"; -"919 add_22" -> "921 add_22_0_0_nncf_smooth_quant_0"; -"919 add_22" -> "946 add_23"; -"920 linear_42_updated_constant0" -> "926 quantize_per_channel_default_43"; -"921 add_22_0_0_nncf_smooth_quant_0" -> "922 quantize_per_tensor_default_43"; -"922 quantize_per_tensor_default_43" -> "923 dequantize_per_tensor_default_43"; -"923 dequantize_per_tensor_default_43" -> "929 linear_42"; -"924 linear_42_scale_0" -> "926 quantize_per_channel_default_43"; -"924 linear_42_scale_0" -> "927 dequantize_per_channel_default_43"; -"925 linear_42_zero_point_0" -> "926 quantize_per_channel_default_43"; -"925 linear_42_zero_point_0" -> "927 dequantize_per_channel_default_43"; -"926 quantize_per_channel_default_43" -> "927 dequantize_per_channel_default_43"; -"927 dequantize_per_channel_default_43" -> "929 linear_42"; -"928 _param_constant117_0_0" -> "929 linear_42"; -"929 linear_42" -> "930 gelu_6"; -"930 gelu_6" -> "931 dropout_26"; -"931 dropout_26" -> "933 dropout_26_0_0_nncf_smooth_quant_0"; -"932 linear_43_updated_constant0" -> "938 quantize_per_channel_default_44"; -"933 dropout_26_0_0_nncf_smooth_quant_0" -> "934 quantize_per_tensor_default_44"; -"934 quantize_per_tensor_default_44" -> "935 dequantize_per_tensor_default_44"; -"935 dequantize_per_tensor_default_44" -> "941 linear_43"; -"936 linear_43_scale_0" -> "938 quantize_per_channel_default_44"; -"936 linear_43_scale_0" -> "939 dequantize_per_channel_default_44"; -"937 linear_43_zero_point_0" -> "938 quantize_per_channel_default_44"; -"937 linear_43_zero_point_0" -> "939 dequantize_per_channel_default_44"; -"938 quantize_per_channel_default_44" -> "939 dequantize_per_channel_default_44"; -"939 dequantize_per_channel_default_44" -> "941 linear_43"; -"940 _param_constant119_0_0" -> "941 linear_43"; -"941 linear_43" -> "942 dropout_27"; -"942 dropout_27" -> "945 layer_norm_16"; -"943 _param_constant120" -> "945 layer_norm_16"; -"944 _param_constant121" -> "945 layer_norm_16"; -"945 layer_norm_16" -> "946 add_23"; -"946 add_23" -> "973 pad_9"; -"946 add_23" -> "1056 add_26"; -"947 _tensor_constant41" -> "949 _tensor_constant41_0_0_nncf_smooth_quant_0"; -"948 linear_44_updated_constant0" -> "952 quantize_per_channel_default_45"; -"949 _tensor_constant41_0_0_nncf_smooth_quant_0" -> "955 linear_44"; -"950 linear_44_scale_0" -> "952 quantize_per_channel_default_45"; -"950 linear_44_scale_0" -> "953 dequantize_per_channel_default_45"; -"951 linear_44_zero_point_0" -> "952 quantize_per_channel_default_45"; -"951 linear_44_zero_point_0" -> "953 dequantize_per_channel_default_45"; -"952 quantize_per_channel_default_45" -> "953 dequantize_per_channel_default_45"; -"953 dequantize_per_channel_default_45" -> "955 linear_44"; -"954 _param_constant123_0_0" -> "955 linear_44"; -"955 linear_44" -> "956 relu__7"; -"956 relu__7" -> "958 relu__7_0_0_nncf_smooth_quant_0"; -"957 linear_45_updated_constant0" -> "961 quantize_per_channel_default_46"; -"958 relu__7_0_0_nncf_smooth_quant_0" -> "963 linear_45"; -"959 linear_45_scale_0" -> "961 quantize_per_channel_default_46"; -"959 linear_45_scale_0" -> "962 dequantize_per_channel_default_46"; -"960 linear_45_zero_point_0" -> "961 quantize_per_channel_default_46"; -"960 linear_45_zero_point_0" -> "962 dequantize_per_channel_default_46"; -"961 quantize_per_channel_default_46" -> "962 dequantize_per_channel_default_46"; -"962 dequantize_per_channel_default_46" -> "963 linear_45"; -"963 linear_45" -> "964 view_37"; -"964 view_37" -> "966 index_7"; -"965 _tensor_constant42" -> "966 index_7"; -"966 index_7" -> "967 view_38"; -"967 view_38" -> "968 permute_32"; -"968 permute_32" -> "969 contiguous_12"; -"969 contiguous_12" -> "970 unsqueeze_19"; -"970 unsqueeze_19" -> "971 sigmoid_7"; -"971 sigmoid_7" -> "972 mul_14"; -"972 mul_14" -> "1011 add_24"; -"973 pad_9" -> "974 roll_6"; -"974 roll_6" -> "975 view_39"; -"975 view_39" -> "976 permute_33"; -"976 permute_33" -> "977 reshape_31"; -"977 reshape_31" -> "979 reshape_31_0_0_nncf_smooth_quant_0"; -"977 reshape_31" -> "1012 new_zeros_3"; -"978 linear_46_updated_constant0" -> "984 quantize_per_channel_default_47"; -"979 reshape_31_0_0_nncf_smooth_quant_0" -> "980 quantize_per_tensor_default_45"; -"980 quantize_per_tensor_default_45" -> "981 dequantize_per_tensor_default_45"; -"981 dequantize_per_tensor_default_45" -> "987 linear_46"; -"982 linear_46_scale_0" -> "984 quantize_per_channel_default_47"; -"982 linear_46_scale_0" -> "985 dequantize_per_channel_default_47"; -"983 linear_46_zero_point_0" -> "984 quantize_per_channel_default_47"; -"983 linear_46_zero_point_0" -> "985 dequantize_per_channel_default_47"; -"984 quantize_per_channel_default_47" -> "985 dequantize_per_channel_default_47"; -"985 dequantize_per_channel_default_47" -> "987 linear_46"; -"986 _param_constant125_0_0" -> "987 linear_46"; -"987 linear_46" -> "988 reshape_32"; -"988 reshape_32" -> "989 permute_34"; -"989 permute_34" -> "990 select_21"; -"989 permute_34" -> "991 select_22"; -"989 permute_34" -> "992 select_23"; -"990 select_21" -> "993 linalg_vector_norm_14"; -"990 select_21" -> "995 expand_as_14"; -"990 select_21" -> "996 div_14"; -"991 select_22" -> "999 linalg_vector_norm_15"; -"991 select_22" -> "1001 expand_as_15"; -"991 select_22" -> "1002 div_15"; -"992 select_23" -> "1030 matmul_15"; -"993 linalg_vector_norm_14" -> "994 clamp_min_14"; -"994 clamp_min_14" -> "995 expand_as_14"; -"995 expand_as_14" -> "996 div_14"; -"996 div_14" -> "997 quantize_per_tensor_default_46"; -"997 quantize_per_tensor_default_46" -> "998 dequantize_per_tensor_default_46"; -"998 dequantize_per_tensor_default_46" -> "1006 matmul_14"; -"999 linalg_vector_norm_15" -> "1000 clamp_min_15"; -"1000 clamp_min_15" -> "1001 expand_as_15"; -"1001 expand_as_15" -> "1002 div_15"; -"1002 div_15" -> "1003 quantize_per_tensor_default_47"; -"1003 quantize_per_tensor_default_47" -> "1004 dequantize_per_tensor_default_47"; -"1004 dequantize_per_tensor_default_47" -> "1005 transpose_14"; -"1005 transpose_14" -> "1006 matmul_14"; -"1006 matmul_14" -> "1010 mul_15"; -"1007 _param_constant127" -> "1008 clamp_7"; -"1008 clamp_7" -> "1009 exp_7"; -"1009 exp_7" -> "1010 mul_15"; -"1010 mul_15" -> "1011 add_24"; -"1011 add_24" -> "1023 view_41"; -"1012 new_zeros_3" -> "1013 view_40"; -"1013 view_40" -> "1014 permute_35"; -"1014 permute_35" -> "1015 reshape_33"; -"1015 reshape_33" -> "1016 unsqueeze_20"; -"1015 reshape_33" -> "1017 unsqueeze_21"; -"1016 unsqueeze_20" -> "1018 sub_3"; -"1017 unsqueeze_21" -> "1018 sub_3"; -"1018 sub_3" -> "1019 ne_3"; -"1018 sub_3" -> "1020 masked_fill_6"; -"1018 sub_3" -> "1021 eq_3"; -"1019 ne_3" -> "1020 masked_fill_6"; -"1020 masked_fill_6" -> "1022 masked_fill_7"; -"1021 eq_3" -> "1022 masked_fill_7"; -"1022 masked_fill_7" -> "1024 unsqueeze_22"; -"1023 view_41" -> "1026 add_25"; -"1024 unsqueeze_22" -> "1025 unsqueeze_23"; -"1025 unsqueeze_23" -> "1026 add_25"; -"1026 add_25" -> "1027 view_42"; -"1027 view_42" -> "1028 softmax_7"; -"1028 softmax_7" -> "1029 dropout_28"; -"1029 dropout_28" -> "1030 matmul_15"; -"1030 matmul_15" -> "1031 transpose_15"; -"1031 transpose_15" -> "1032 reshape_34"; -"1032 reshape_34" -> "1034 reshape_34_0_0_nncf_smooth_quant_0"; -"1033 linear_47_updated_constant0" -> "1039 quantize_per_channel_default_48"; -"1034 reshape_34_0_0_nncf_smooth_quant_0" -> "1035 quantize_per_tensor_default_48"; -"1035 quantize_per_tensor_default_48" -> "1036 dequantize_per_tensor_default_48"; -"1036 dequantize_per_tensor_default_48" -> "1042 linear_47"; -"1037 linear_47_scale_0" -> "1039 quantize_per_channel_default_48"; -"1037 linear_47_scale_0" -> "1040 dequantize_per_channel_default_48"; -"1038 linear_47_zero_point_0" -> "1039 quantize_per_channel_default_48"; -"1038 linear_47_zero_point_0" -> "1040 dequantize_per_channel_default_48"; -"1039 quantize_per_channel_default_48" -> "1040 dequantize_per_channel_default_48"; -"1040 dequantize_per_channel_default_48" -> "1042 linear_47"; -"1041 _param_constant129_0_0" -> "1042 linear_47"; -"1042 linear_47" -> "1043 dropout_29"; -"1043 dropout_29" -> "1044 view_43"; -"1044 view_43" -> "1045 permute_36"; -"1045 permute_36" -> "1046 reshape_35"; -"1046 reshape_35" -> "1047 roll_7"; -"1047 roll_7" -> "1048 slice_129"; -"1048 slice_129" -> "1049 slice_130"; -"1049 slice_130" -> "1050 slice_131"; -"1050 slice_131" -> "1051 slice_132"; -"1051 slice_132" -> "1052 contiguous_13"; -"1052 contiguous_13" -> "1055 layer_norm_17"; -"1053 _param_constant130" -> "1055 layer_norm_17"; -"1054 _param_constant131" -> "1055 layer_norm_17"; -"1055 layer_norm_17" -> "1056 add_26"; -"1056 add_26" -> "1058 add_26_0_0_nncf_smooth_quant_0"; -"1056 add_26" -> "1083 add_27"; -"1057 linear_48_updated_constant0" -> "1063 quantize_per_channel_default_49"; -"1058 add_26_0_0_nncf_smooth_quant_0" -> "1059 quantize_per_tensor_default_49"; -"1059 quantize_per_tensor_default_49" -> "1060 dequantize_per_tensor_default_49"; -"1060 dequantize_per_tensor_default_49" -> "1066 linear_48"; -"1061 linear_48_scale_0" -> "1063 quantize_per_channel_default_49"; -"1061 linear_48_scale_0" -> "1064 dequantize_per_channel_default_49"; -"1062 linear_48_zero_point_0" -> "1063 quantize_per_channel_default_49"; -"1062 linear_48_zero_point_0" -> "1064 dequantize_per_channel_default_49"; -"1063 quantize_per_channel_default_49" -> "1064 dequantize_per_channel_default_49"; -"1064 dequantize_per_channel_default_49" -> "1066 linear_48"; -"1065 _param_constant133_0_0" -> "1066 linear_48"; -"1066 linear_48" -> "1067 gelu_7"; -"1067 gelu_7" -> "1068 dropout_30"; -"1068 dropout_30" -> "1070 dropout_30_0_0_nncf_smooth_quant_0"; -"1069 linear_49_updated_constant0" -> "1075 quantize_per_channel_default_50"; -"1070 dropout_30_0_0_nncf_smooth_quant_0" -> "1071 quantize_per_tensor_default_50"; -"1071 quantize_per_tensor_default_50" -> "1072 dequantize_per_tensor_default_50"; -"1072 dequantize_per_tensor_default_50" -> "1078 linear_49"; -"1073 linear_49_scale_0" -> "1075 quantize_per_channel_default_50"; -"1073 linear_49_scale_0" -> "1076 dequantize_per_channel_default_50"; -"1074 linear_49_zero_point_0" -> "1075 quantize_per_channel_default_50"; -"1074 linear_49_zero_point_0" -> "1076 dequantize_per_channel_default_50"; -"1075 quantize_per_channel_default_50" -> "1076 dequantize_per_channel_default_50"; -"1076 dequantize_per_channel_default_50" -> "1078 linear_49"; -"1077 _param_constant135_0_0" -> "1078 linear_49"; -"1078 linear_49" -> "1079 dropout_31"; -"1079 dropout_31" -> "1082 layer_norm_18"; -"1080 _param_constant136" -> "1082 layer_norm_18"; -"1081 _param_constant137" -> "1082 layer_norm_18"; -"1082 layer_norm_18" -> "1083 add_27"; -"1083 add_27" -> "1110 pad_10"; -"1083 add_27" -> "1175 add_29"; -"1084 _tensor_constant52" -> "1086 _tensor_constant52_0_0_nncf_smooth_quant_0"; -"1085 linear_50_updated_constant0" -> "1089 quantize_per_channel_default_51"; -"1086 _tensor_constant52_0_0_nncf_smooth_quant_0" -> "1092 linear_50"; -"1087 linear_50_scale_0" -> "1089 quantize_per_channel_default_51"; -"1087 linear_50_scale_0" -> "1090 dequantize_per_channel_default_51"; -"1088 linear_50_zero_point_0" -> "1089 quantize_per_channel_default_51"; -"1088 linear_50_zero_point_0" -> "1090 dequantize_per_channel_default_51"; -"1089 quantize_per_channel_default_51" -> "1090 dequantize_per_channel_default_51"; -"1090 dequantize_per_channel_default_51" -> "1092 linear_50"; -"1091 _param_constant139_0_0" -> "1092 linear_50"; -"1092 linear_50" -> "1093 relu__8"; -"1093 relu__8" -> "1095 relu__8_0_0_nncf_smooth_quant_0"; -"1094 linear_51_updated_constant0" -> "1098 quantize_per_channel_default_52"; -"1095 relu__8_0_0_nncf_smooth_quant_0" -> "1100 linear_51"; -"1096 linear_51_scale_0" -> "1098 quantize_per_channel_default_52"; -"1096 linear_51_scale_0" -> "1099 dequantize_per_channel_default_52"; -"1097 linear_51_zero_point_0" -> "1098 quantize_per_channel_default_52"; -"1097 linear_51_zero_point_0" -> "1099 dequantize_per_channel_default_52"; -"1098 quantize_per_channel_default_52" -> "1099 dequantize_per_channel_default_52"; -"1099 dequantize_per_channel_default_52" -> "1100 linear_51"; -"1100 linear_51" -> "1101 view_44"; -"1101 view_44" -> "1103 index_8"; -"1102 _tensor_constant53" -> "1103 index_8"; -"1103 index_8" -> "1104 view_45"; -"1104 view_45" -> "1105 permute_37"; -"1105 permute_37" -> "1106 contiguous_14"; -"1106 contiguous_14" -> "1107 unsqueeze_24"; -"1107 unsqueeze_24" -> "1108 sigmoid_8"; -"1108 sigmoid_8" -> "1109 mul_16"; -"1109 mul_16" -> "1147 add_28"; -"1110 pad_10" -> "1111 view_46"; -"1111 view_46" -> "1112 permute_38"; -"1112 permute_38" -> "1113 reshape_36"; -"1113 reshape_36" -> "1115 reshape_36_0_0_nncf_smooth_quant_0"; -"1114 linear_52_updated_constant0" -> "1120 quantize_per_channel_default_53"; -"1115 reshape_36_0_0_nncf_smooth_quant_0" -> "1116 quantize_per_tensor_default_51"; -"1116 quantize_per_tensor_default_51" -> "1117 dequantize_per_tensor_default_51"; -"1117 dequantize_per_tensor_default_51" -> "1123 linear_52"; -"1118 linear_52_scale_0" -> "1120 quantize_per_channel_default_53"; -"1118 linear_52_scale_0" -> "1121 dequantize_per_channel_default_53"; -"1119 linear_52_zero_point_0" -> "1120 quantize_per_channel_default_53"; -"1119 linear_52_zero_point_0" -> "1121 dequantize_per_channel_default_53"; -"1120 quantize_per_channel_default_53" -> "1121 dequantize_per_channel_default_53"; -"1121 dequantize_per_channel_default_53" -> "1123 linear_52"; -"1122 _param_constant141_0_0" -> "1123 linear_52"; -"1123 linear_52" -> "1124 reshape_37"; -"1124 reshape_37" -> "1125 permute_39"; -"1125 permute_39" -> "1126 select_24"; -"1125 permute_39" -> "1127 select_25"; -"1125 permute_39" -> "1128 select_26"; -"1126 select_24" -> "1129 linalg_vector_norm_16"; -"1126 select_24" -> "1131 expand_as_16"; -"1126 select_24" -> "1132 div_16"; -"1127 select_25" -> "1135 linalg_vector_norm_17"; -"1127 select_25" -> "1137 expand_as_17"; -"1127 select_25" -> "1138 div_17"; -"1128 select_26" -> "1150 matmul_17"; -"1129 linalg_vector_norm_16" -> "1130 clamp_min_16"; -"1130 clamp_min_16" -> "1131 expand_as_16"; -"1131 expand_as_16" -> "1132 div_16"; -"1132 div_16" -> "1133 quantize_per_tensor_default_52"; -"1133 quantize_per_tensor_default_52" -> "1134 dequantize_per_tensor_default_52"; -"1134 dequantize_per_tensor_default_52" -> "1142 matmul_16"; -"1135 linalg_vector_norm_17" -> "1136 clamp_min_17"; -"1136 clamp_min_17" -> "1137 expand_as_17"; -"1137 expand_as_17" -> "1138 div_17"; -"1138 div_17" -> "1139 quantize_per_tensor_default_53"; -"1139 quantize_per_tensor_default_53" -> "1140 dequantize_per_tensor_default_53"; -"1140 dequantize_per_tensor_default_53" -> "1141 transpose_16"; -"1141 transpose_16" -> "1142 matmul_16"; -"1142 matmul_16" -> "1146 mul_17"; -"1143 _param_constant143" -> "1144 clamp_8"; -"1144 clamp_8" -> "1145 exp_8"; -"1145 exp_8" -> "1146 mul_17"; -"1146 mul_17" -> "1147 add_28"; -"1147 add_28" -> "1148 softmax_8"; -"1148 softmax_8" -> "1149 dropout_32"; -"1149 dropout_32" -> "1150 matmul_17"; -"1150 matmul_17" -> "1151 transpose_17"; -"1151 transpose_17" -> "1152 reshape_38"; -"1152 reshape_38" -> "1154 reshape_38_0_0_nncf_smooth_quant_0"; -"1153 linear_53_updated_constant0" -> "1159 quantize_per_channel_default_54"; -"1154 reshape_38_0_0_nncf_smooth_quant_0" -> "1155 quantize_per_tensor_default_54"; -"1155 quantize_per_tensor_default_54" -> "1156 dequantize_per_tensor_default_54"; -"1156 dequantize_per_tensor_default_54" -> "1162 linear_53"; -"1157 linear_53_scale_0" -> "1159 quantize_per_channel_default_54"; -"1157 linear_53_scale_0" -> "1160 dequantize_per_channel_default_54"; -"1158 linear_53_zero_point_0" -> "1159 quantize_per_channel_default_54"; -"1158 linear_53_zero_point_0" -> "1160 dequantize_per_channel_default_54"; -"1159 quantize_per_channel_default_54" -> "1160 dequantize_per_channel_default_54"; -"1160 dequantize_per_channel_default_54" -> "1162 linear_53"; -"1161 _param_constant145_0_0" -> "1162 linear_53"; -"1162 linear_53" -> "1163 dropout_33"; -"1163 dropout_33" -> "1164 view_47"; -"1164 view_47" -> "1165 permute_40"; -"1165 permute_40" -> "1166 reshape_39"; -"1166 reshape_39" -> "1167 slice_134"; -"1167 slice_134" -> "1168 slice_135"; -"1168 slice_135" -> "1169 slice_136"; -"1169 slice_136" -> "1170 slice_137"; -"1170 slice_137" -> "1171 contiguous_15"; -"1171 contiguous_15" -> "1174 layer_norm_19"; -"1172 _param_constant146" -> "1174 layer_norm_19"; -"1173 _param_constant147" -> "1174 layer_norm_19"; -"1174 layer_norm_19" -> "1175 add_29"; -"1175 add_29" -> "1177 add_29_0_0_nncf_smooth_quant_0"; -"1175 add_29" -> "1202 add_30"; -"1176 linear_54_updated_constant0" -> "1182 quantize_per_channel_default_55"; -"1177 add_29_0_0_nncf_smooth_quant_0" -> "1178 quantize_per_tensor_default_55"; -"1178 quantize_per_tensor_default_55" -> "1179 dequantize_per_tensor_default_55"; -"1179 dequantize_per_tensor_default_55" -> "1185 linear_54"; -"1180 linear_54_scale_0" -> "1182 quantize_per_channel_default_55"; -"1180 linear_54_scale_0" -> "1183 dequantize_per_channel_default_55"; -"1181 linear_54_zero_point_0" -> "1182 quantize_per_channel_default_55"; -"1181 linear_54_zero_point_0" -> "1183 dequantize_per_channel_default_55"; -"1182 quantize_per_channel_default_55" -> "1183 dequantize_per_channel_default_55"; -"1183 dequantize_per_channel_default_55" -> "1185 linear_54"; -"1184 _param_constant149_0_0" -> "1185 linear_54"; -"1185 linear_54" -> "1186 gelu_8"; -"1186 gelu_8" -> "1187 dropout_34"; -"1187 dropout_34" -> "1189 dropout_34_0_0_nncf_smooth_quant_0"; -"1188 linear_55_updated_constant0" -> "1194 quantize_per_channel_default_56"; -"1189 dropout_34_0_0_nncf_smooth_quant_0" -> "1190 quantize_per_tensor_default_56"; -"1190 quantize_per_tensor_default_56" -> "1191 dequantize_per_tensor_default_56"; -"1191 dequantize_per_tensor_default_56" -> "1197 linear_55"; -"1192 linear_55_scale_0" -> "1194 quantize_per_channel_default_56"; -"1192 linear_55_scale_0" -> "1195 dequantize_per_channel_default_56"; -"1193 linear_55_zero_point_0" -> "1194 quantize_per_channel_default_56"; -"1193 linear_55_zero_point_0" -> "1195 dequantize_per_channel_default_56"; -"1194 quantize_per_channel_default_56" -> "1195 dequantize_per_channel_default_56"; -"1195 dequantize_per_channel_default_56" -> "1197 linear_55"; -"1196 _param_constant151_0_0" -> "1197 linear_55"; -"1197 linear_55" -> "1198 dropout_35"; -"1198 dropout_35" -> "1201 layer_norm_20"; -"1199 _param_constant152" -> "1201 layer_norm_20"; -"1200 _param_constant153" -> "1201 layer_norm_20"; -"1201 layer_norm_20" -> "1202 add_30"; -"1202 add_30" -> "1229 pad_11"; -"1202 add_30" -> "1312 add_33"; -"1203 _tensor_constant54" -> "1205 _tensor_constant54_0_0_nncf_smooth_quant_0"; -"1204 linear_56_updated_constant0" -> "1208 quantize_per_channel_default_57"; -"1205 _tensor_constant54_0_0_nncf_smooth_quant_0" -> "1211 linear_56"; -"1206 linear_56_scale_0" -> "1208 quantize_per_channel_default_57"; -"1206 linear_56_scale_0" -> "1209 dequantize_per_channel_default_57"; -"1207 linear_56_zero_point_0" -> "1208 quantize_per_channel_default_57"; -"1207 linear_56_zero_point_0" -> "1209 dequantize_per_channel_default_57"; -"1208 quantize_per_channel_default_57" -> "1209 dequantize_per_channel_default_57"; -"1209 dequantize_per_channel_default_57" -> "1211 linear_56"; -"1210 _param_constant155_0_0" -> "1211 linear_56"; -"1211 linear_56" -> "1212 relu__9"; -"1212 relu__9" -> "1214 relu__9_0_0_nncf_smooth_quant_0"; -"1213 linear_57_updated_constant0" -> "1217 quantize_per_channel_default_58"; -"1214 relu__9_0_0_nncf_smooth_quant_0" -> "1219 linear_57"; -"1215 linear_57_scale_0" -> "1217 quantize_per_channel_default_58"; -"1215 linear_57_scale_0" -> "1218 dequantize_per_channel_default_58"; -"1216 linear_57_zero_point_0" -> "1217 quantize_per_channel_default_58"; -"1216 linear_57_zero_point_0" -> "1218 dequantize_per_channel_default_58"; -"1217 quantize_per_channel_default_58" -> "1218 dequantize_per_channel_default_58"; -"1218 dequantize_per_channel_default_58" -> "1219 linear_57"; -"1219 linear_57" -> "1220 view_48"; -"1220 view_48" -> "1222 index_9"; -"1221 _tensor_constant55" -> "1222 index_9"; -"1222 index_9" -> "1223 view_49"; -"1223 view_49" -> "1224 permute_41"; -"1224 permute_41" -> "1225 contiguous_16"; -"1225 contiguous_16" -> "1226 unsqueeze_25"; -"1226 unsqueeze_25" -> "1227 sigmoid_9"; -"1227 sigmoid_9" -> "1228 mul_18"; -"1228 mul_18" -> "1267 add_31"; -"1229 pad_11" -> "1230 roll_8"; -"1230 roll_8" -> "1231 view_50"; -"1231 view_50" -> "1232 permute_42"; -"1232 permute_42" -> "1233 reshape_40"; -"1233 reshape_40" -> "1235 reshape_40_0_0_nncf_smooth_quant_0"; -"1233 reshape_40" -> "1268 new_zeros_4"; -"1234 linear_58_updated_constant0" -> "1240 quantize_per_channel_default_59"; -"1235 reshape_40_0_0_nncf_smooth_quant_0" -> "1236 quantize_per_tensor_default_57"; -"1236 quantize_per_tensor_default_57" -> "1237 dequantize_per_tensor_default_57"; -"1237 dequantize_per_tensor_default_57" -> "1243 linear_58"; -"1238 linear_58_scale_0" -> "1240 quantize_per_channel_default_59"; -"1238 linear_58_scale_0" -> "1241 dequantize_per_channel_default_59"; -"1239 linear_58_zero_point_0" -> "1240 quantize_per_channel_default_59"; -"1239 linear_58_zero_point_0" -> "1241 dequantize_per_channel_default_59"; -"1240 quantize_per_channel_default_59" -> "1241 dequantize_per_channel_default_59"; -"1241 dequantize_per_channel_default_59" -> "1243 linear_58"; -"1242 _param_constant157_0_0" -> "1243 linear_58"; -"1243 linear_58" -> "1244 reshape_41"; -"1244 reshape_41" -> "1245 permute_43"; -"1245 permute_43" -> "1246 select_27"; -"1245 permute_43" -> "1247 select_28"; -"1245 permute_43" -> "1248 select_29"; -"1246 select_27" -> "1249 linalg_vector_norm_18"; -"1246 select_27" -> "1251 expand_as_18"; -"1246 select_27" -> "1252 div_18"; -"1247 select_28" -> "1255 linalg_vector_norm_19"; -"1247 select_28" -> "1257 expand_as_19"; -"1247 select_28" -> "1258 div_19"; -"1248 select_29" -> "1286 matmul_19"; -"1249 linalg_vector_norm_18" -> "1250 clamp_min_18"; -"1250 clamp_min_18" -> "1251 expand_as_18"; -"1251 expand_as_18" -> "1252 div_18"; -"1252 div_18" -> "1253 quantize_per_tensor_default_58"; -"1253 quantize_per_tensor_default_58" -> "1254 dequantize_per_tensor_default_58"; -"1254 dequantize_per_tensor_default_58" -> "1262 matmul_18"; -"1255 linalg_vector_norm_19" -> "1256 clamp_min_19"; -"1256 clamp_min_19" -> "1257 expand_as_19"; -"1257 expand_as_19" -> "1258 div_19"; -"1258 div_19" -> "1259 quantize_per_tensor_default_59"; -"1259 quantize_per_tensor_default_59" -> "1260 dequantize_per_tensor_default_59"; -"1260 dequantize_per_tensor_default_59" -> "1261 transpose_18"; -"1261 transpose_18" -> "1262 matmul_18"; -"1262 matmul_18" -> "1266 mul_19"; -"1263 _param_constant159" -> "1264 clamp_9"; -"1264 clamp_9" -> "1265 exp_9"; -"1265 exp_9" -> "1266 mul_19"; -"1266 mul_19" -> "1267 add_31"; -"1267 add_31" -> "1279 view_52"; -"1268 new_zeros_4" -> "1269 view_51"; -"1269 view_51" -> "1270 permute_44"; -"1270 permute_44" -> "1271 reshape_42"; -"1271 reshape_42" -> "1272 unsqueeze_26"; -"1271 reshape_42" -> "1273 unsqueeze_27"; -"1272 unsqueeze_26" -> "1274 sub_4"; -"1273 unsqueeze_27" -> "1274 sub_4"; -"1274 sub_4" -> "1275 ne_4"; -"1274 sub_4" -> "1276 masked_fill_8"; -"1274 sub_4" -> "1277 eq_4"; -"1275 ne_4" -> "1276 masked_fill_8"; -"1276 masked_fill_8" -> "1278 masked_fill_9"; -"1277 eq_4" -> "1278 masked_fill_9"; -"1278 masked_fill_9" -> "1280 unsqueeze_28"; -"1279 view_52" -> "1282 add_32"; -"1280 unsqueeze_28" -> "1281 unsqueeze_29"; -"1281 unsqueeze_29" -> "1282 add_32"; -"1282 add_32" -> "1283 view_53"; -"1283 view_53" -> "1284 softmax_9"; -"1284 softmax_9" -> "1285 dropout_36"; -"1285 dropout_36" -> "1286 matmul_19"; -"1286 matmul_19" -> "1287 transpose_19"; -"1287 transpose_19" -> "1288 reshape_43"; -"1288 reshape_43" -> "1290 reshape_43_0_0_nncf_smooth_quant_0"; -"1289 linear_59_updated_constant0" -> "1295 quantize_per_channel_default_60"; -"1290 reshape_43_0_0_nncf_smooth_quant_0" -> "1291 quantize_per_tensor_default_60"; -"1291 quantize_per_tensor_default_60" -> "1292 dequantize_per_tensor_default_60"; -"1292 dequantize_per_tensor_default_60" -> "1298 linear_59"; -"1293 linear_59_scale_0" -> "1295 quantize_per_channel_default_60"; -"1293 linear_59_scale_0" -> "1296 dequantize_per_channel_default_60"; -"1294 linear_59_zero_point_0" -> "1295 quantize_per_channel_default_60"; -"1294 linear_59_zero_point_0" -> "1296 dequantize_per_channel_default_60"; -"1295 quantize_per_channel_default_60" -> "1296 dequantize_per_channel_default_60"; -"1296 dequantize_per_channel_default_60" -> "1298 linear_59"; -"1297 _param_constant161_0_0" -> "1298 linear_59"; -"1298 linear_59" -> "1299 dropout_37"; -"1299 dropout_37" -> "1300 view_54"; -"1300 view_54" -> "1301 permute_45"; -"1301 permute_45" -> "1302 reshape_44"; -"1302 reshape_44" -> "1303 roll_9"; -"1303 roll_9" -> "1304 slice_157"; -"1304 slice_157" -> "1305 slice_158"; -"1305 slice_158" -> "1306 slice_159"; -"1306 slice_159" -> "1307 slice_160"; -"1307 slice_160" -> "1308 contiguous_17"; -"1308 contiguous_17" -> "1311 layer_norm_21"; -"1309 _param_constant162" -> "1311 layer_norm_21"; -"1310 _param_constant163" -> "1311 layer_norm_21"; -"1311 layer_norm_21" -> "1312 add_33"; -"1312 add_33" -> "1314 add_33_0_0_nncf_smooth_quant_0"; -"1312 add_33" -> "1339 add_34"; -"1313 linear_60_updated_constant0" -> "1319 quantize_per_channel_default_61"; -"1314 add_33_0_0_nncf_smooth_quant_0" -> "1315 quantize_per_tensor_default_61"; -"1315 quantize_per_tensor_default_61" -> "1316 dequantize_per_tensor_default_61"; -"1316 dequantize_per_tensor_default_61" -> "1322 linear_60"; -"1317 linear_60_scale_0" -> "1319 quantize_per_channel_default_61"; -"1317 linear_60_scale_0" -> "1320 dequantize_per_channel_default_61"; -"1318 linear_60_zero_point_0" -> "1319 quantize_per_channel_default_61"; -"1318 linear_60_zero_point_0" -> "1320 dequantize_per_channel_default_61"; -"1319 quantize_per_channel_default_61" -> "1320 dequantize_per_channel_default_61"; -"1320 dequantize_per_channel_default_61" -> "1322 linear_60"; -"1321 _param_constant165_0_0" -> "1322 linear_60"; -"1322 linear_60" -> "1323 gelu_9"; -"1323 gelu_9" -> "1324 dropout_38"; -"1324 dropout_38" -> "1326 dropout_38_0_0_nncf_smooth_quant_0"; -"1325 linear_61_updated_constant0" -> "1331 quantize_per_channel_default_62"; -"1326 dropout_38_0_0_nncf_smooth_quant_0" -> "1327 quantize_per_tensor_default_62"; -"1327 quantize_per_tensor_default_62" -> "1328 dequantize_per_tensor_default_62"; -"1328 dequantize_per_tensor_default_62" -> "1334 linear_61"; -"1329 linear_61_scale_0" -> "1331 quantize_per_channel_default_62"; -"1329 linear_61_scale_0" -> "1332 dequantize_per_channel_default_62"; -"1330 linear_61_zero_point_0" -> "1331 quantize_per_channel_default_62"; -"1330 linear_61_zero_point_0" -> "1332 dequantize_per_channel_default_62"; -"1331 quantize_per_channel_default_62" -> "1332 dequantize_per_channel_default_62"; -"1332 dequantize_per_channel_default_62" -> "1334 linear_61"; -"1333 _param_constant167_0_0" -> "1334 linear_61"; -"1334 linear_61" -> "1335 dropout_39"; -"1335 dropout_39" -> "1338 layer_norm_22"; -"1336 _param_constant168" -> "1338 layer_norm_22"; -"1337 _param_constant169" -> "1338 layer_norm_22"; -"1338 layer_norm_22" -> "1339 add_34"; -"1339 add_34" -> "1366 pad_12"; -"1339 add_34" -> "1431 add_36"; -"1340 _tensor_constant65" -> "1342 _tensor_constant65_0_0_nncf_smooth_quant_0"; -"1341 linear_62_updated_constant0" -> "1345 quantize_per_channel_default_63"; -"1342 _tensor_constant65_0_0_nncf_smooth_quant_0" -> "1348 linear_62"; -"1343 linear_62_scale_0" -> "1345 quantize_per_channel_default_63"; -"1343 linear_62_scale_0" -> "1346 dequantize_per_channel_default_63"; -"1344 linear_62_zero_point_0" -> "1345 quantize_per_channel_default_63"; -"1344 linear_62_zero_point_0" -> "1346 dequantize_per_channel_default_63"; -"1345 quantize_per_channel_default_63" -> "1346 dequantize_per_channel_default_63"; -"1346 dequantize_per_channel_default_63" -> "1348 linear_62"; -"1347 _param_constant171_0_0" -> "1348 linear_62"; -"1348 linear_62" -> "1349 relu__10"; -"1349 relu__10" -> "1351 relu__10_0_0_nncf_smooth_quant_0"; -"1350 linear_63_updated_constant0" -> "1354 quantize_per_channel_default_64"; -"1351 relu__10_0_0_nncf_smooth_quant_0" -> "1356 linear_63"; -"1352 linear_63_scale_0" -> "1354 quantize_per_channel_default_64"; -"1352 linear_63_scale_0" -> "1355 dequantize_per_channel_default_64"; -"1353 linear_63_zero_point_0" -> "1354 quantize_per_channel_default_64"; -"1353 linear_63_zero_point_0" -> "1355 dequantize_per_channel_default_64"; -"1354 quantize_per_channel_default_64" -> "1355 dequantize_per_channel_default_64"; -"1355 dequantize_per_channel_default_64" -> "1356 linear_63"; -"1356 linear_63" -> "1357 view_55"; -"1357 view_55" -> "1359 index_10"; -"1358 _tensor_constant66" -> "1359 index_10"; -"1359 index_10" -> "1360 view_56"; -"1360 view_56" -> "1361 permute_46"; -"1361 permute_46" -> "1362 contiguous_18"; -"1362 contiguous_18" -> "1363 unsqueeze_30"; -"1363 unsqueeze_30" -> "1364 sigmoid_10"; -"1364 sigmoid_10" -> "1365 mul_20"; -"1365 mul_20" -> "1403 add_35"; -"1366 pad_12" -> "1367 view_57"; -"1367 view_57" -> "1368 permute_47"; -"1368 permute_47" -> "1369 reshape_45"; -"1369 reshape_45" -> "1371 reshape_45_0_0_nncf_smooth_quant_0"; -"1370 linear_64_updated_constant0" -> "1376 quantize_per_channel_default_65"; -"1371 reshape_45_0_0_nncf_smooth_quant_0" -> "1372 quantize_per_tensor_default_63"; -"1372 quantize_per_tensor_default_63" -> "1373 dequantize_per_tensor_default_63"; -"1373 dequantize_per_tensor_default_63" -> "1379 linear_64"; -"1374 linear_64_scale_0" -> "1376 quantize_per_channel_default_65"; -"1374 linear_64_scale_0" -> "1377 dequantize_per_channel_default_65"; -"1375 linear_64_zero_point_0" -> "1376 quantize_per_channel_default_65"; -"1375 linear_64_zero_point_0" -> "1377 dequantize_per_channel_default_65"; -"1376 quantize_per_channel_default_65" -> "1377 dequantize_per_channel_default_65"; -"1377 dequantize_per_channel_default_65" -> "1379 linear_64"; -"1378 _param_constant173_0_0" -> "1379 linear_64"; -"1379 linear_64" -> "1380 reshape_46"; -"1380 reshape_46" -> "1381 permute_48"; -"1381 permute_48" -> "1382 select_30"; -"1381 permute_48" -> "1383 select_31"; -"1381 permute_48" -> "1384 select_32"; -"1382 select_30" -> "1385 linalg_vector_norm_20"; -"1382 select_30" -> "1387 expand_as_20"; -"1382 select_30" -> "1388 div_20"; -"1383 select_31" -> "1391 linalg_vector_norm_21"; -"1383 select_31" -> "1393 expand_as_21"; -"1383 select_31" -> "1394 div_21"; -"1384 select_32" -> "1406 matmul_21"; -"1385 linalg_vector_norm_20" -> "1386 clamp_min_20"; -"1386 clamp_min_20" -> "1387 expand_as_20"; -"1387 expand_as_20" -> "1388 div_20"; -"1388 div_20" -> "1389 quantize_per_tensor_default_64"; -"1389 quantize_per_tensor_default_64" -> "1390 dequantize_per_tensor_default_64"; -"1390 dequantize_per_tensor_default_64" -> "1398 matmul_20"; -"1391 linalg_vector_norm_21" -> "1392 clamp_min_21"; -"1392 clamp_min_21" -> "1393 expand_as_21"; -"1393 expand_as_21" -> "1394 div_21"; -"1394 div_21" -> "1395 quantize_per_tensor_default_65"; -"1395 quantize_per_tensor_default_65" -> "1396 dequantize_per_tensor_default_65"; -"1396 dequantize_per_tensor_default_65" -> "1397 transpose_20"; -"1397 transpose_20" -> "1398 matmul_20"; -"1398 matmul_20" -> "1402 mul_21"; -"1399 _param_constant175" -> "1400 clamp_10"; -"1400 clamp_10" -> "1401 exp_10"; -"1401 exp_10" -> "1402 mul_21"; -"1402 mul_21" -> "1403 add_35"; -"1403 add_35" -> "1404 softmax_10"; -"1404 softmax_10" -> "1405 dropout_40"; -"1405 dropout_40" -> "1406 matmul_21"; -"1406 matmul_21" -> "1407 transpose_21"; -"1407 transpose_21" -> "1408 reshape_47"; -"1408 reshape_47" -> "1410 reshape_47_0_0_nncf_smooth_quant_0"; -"1409 linear_65_updated_constant0" -> "1415 quantize_per_channel_default_66"; -"1410 reshape_47_0_0_nncf_smooth_quant_0" -> "1411 quantize_per_tensor_default_66"; -"1411 quantize_per_tensor_default_66" -> "1412 dequantize_per_tensor_default_66"; -"1412 dequantize_per_tensor_default_66" -> "1418 linear_65"; -"1413 linear_65_scale_0" -> "1415 quantize_per_channel_default_66"; -"1413 linear_65_scale_0" -> "1416 dequantize_per_channel_default_66"; -"1414 linear_65_zero_point_0" -> "1415 quantize_per_channel_default_66"; -"1414 linear_65_zero_point_0" -> "1416 dequantize_per_channel_default_66"; -"1415 quantize_per_channel_default_66" -> "1416 dequantize_per_channel_default_66"; -"1416 dequantize_per_channel_default_66" -> "1418 linear_65"; -"1417 _param_constant177_0_0" -> "1418 linear_65"; -"1418 linear_65" -> "1419 dropout_41"; -"1419 dropout_41" -> "1420 view_58"; -"1420 view_58" -> "1421 permute_49"; -"1421 permute_49" -> "1422 reshape_48"; -"1422 reshape_48" -> "1423 slice_162"; -"1423 slice_162" -> "1424 slice_163"; -"1424 slice_163" -> "1425 slice_164"; -"1425 slice_164" -> "1426 slice_165"; -"1426 slice_165" -> "1427 contiguous_19"; -"1427 contiguous_19" -> "1430 layer_norm_23"; -"1428 _param_constant178" -> "1430 layer_norm_23"; -"1429 _param_constant179" -> "1430 layer_norm_23"; -"1430 layer_norm_23" -> "1431 add_36"; -"1431 add_36" -> "1433 add_36_0_0_nncf_smooth_quant_0"; -"1431 add_36" -> "1458 add_37"; -"1432 linear_66_updated_constant0" -> "1438 quantize_per_channel_default_67"; -"1433 add_36_0_0_nncf_smooth_quant_0" -> "1434 quantize_per_tensor_default_67"; -"1434 quantize_per_tensor_default_67" -> "1435 dequantize_per_tensor_default_67"; -"1435 dequantize_per_tensor_default_67" -> "1441 linear_66"; -"1436 linear_66_scale_0" -> "1438 quantize_per_channel_default_67"; -"1436 linear_66_scale_0" -> "1439 dequantize_per_channel_default_67"; -"1437 linear_66_zero_point_0" -> "1438 quantize_per_channel_default_67"; -"1437 linear_66_zero_point_0" -> "1439 dequantize_per_channel_default_67"; -"1438 quantize_per_channel_default_67" -> "1439 dequantize_per_channel_default_67"; -"1439 dequantize_per_channel_default_67" -> "1441 linear_66"; -"1440 _param_constant181_0_0" -> "1441 linear_66"; -"1441 linear_66" -> "1442 gelu_10"; -"1442 gelu_10" -> "1443 dropout_42"; -"1443 dropout_42" -> "1445 dropout_42_0_0_nncf_smooth_quant_0"; -"1444 linear_67_updated_constant0" -> "1450 quantize_per_channel_default_68"; -"1445 dropout_42_0_0_nncf_smooth_quant_0" -> "1446 quantize_per_tensor_default_68"; -"1446 quantize_per_tensor_default_68" -> "1447 dequantize_per_tensor_default_68"; -"1447 dequantize_per_tensor_default_68" -> "1453 linear_67"; -"1448 linear_67_scale_0" -> "1450 quantize_per_channel_default_68"; -"1448 linear_67_scale_0" -> "1451 dequantize_per_channel_default_68"; -"1449 linear_67_zero_point_0" -> "1450 quantize_per_channel_default_68"; -"1449 linear_67_zero_point_0" -> "1451 dequantize_per_channel_default_68"; -"1450 quantize_per_channel_default_68" -> "1451 dequantize_per_channel_default_68"; -"1451 dequantize_per_channel_default_68" -> "1453 linear_67"; -"1452 _param_constant183_0_0" -> "1453 linear_67"; -"1453 linear_67" -> "1454 dropout_43"; -"1454 dropout_43" -> "1457 layer_norm_24"; -"1455 _param_constant184" -> "1457 layer_norm_24"; -"1456 _param_constant185" -> "1457 layer_norm_24"; -"1457 layer_norm_24" -> "1458 add_37"; -"1458 add_37" -> "1485 pad_13"; -"1458 add_37" -> "1568 add_40"; -"1459 _tensor_constant67" -> "1461 _tensor_constant67_0_0_nncf_smooth_quant_0"; -"1460 linear_68_updated_constant0" -> "1464 quantize_per_channel_default_69"; -"1461 _tensor_constant67_0_0_nncf_smooth_quant_0" -> "1467 linear_68"; -"1462 linear_68_scale_0" -> "1464 quantize_per_channel_default_69"; -"1462 linear_68_scale_0" -> "1465 dequantize_per_channel_default_69"; -"1463 linear_68_zero_point_0" -> "1464 quantize_per_channel_default_69"; -"1463 linear_68_zero_point_0" -> "1465 dequantize_per_channel_default_69"; -"1464 quantize_per_channel_default_69" -> "1465 dequantize_per_channel_default_69"; -"1465 dequantize_per_channel_default_69" -> "1467 linear_68"; -"1466 _param_constant187_0_0" -> "1467 linear_68"; -"1467 linear_68" -> "1468 relu__11"; -"1468 relu__11" -> "1470 relu__11_0_0_nncf_smooth_quant_0"; -"1469 linear_69_updated_constant0" -> "1473 quantize_per_channel_default_70"; -"1470 relu__11_0_0_nncf_smooth_quant_0" -> "1475 linear_69"; -"1471 linear_69_scale_0" -> "1473 quantize_per_channel_default_70"; -"1471 linear_69_scale_0" -> "1474 dequantize_per_channel_default_70"; -"1472 linear_69_zero_point_0" -> "1473 quantize_per_channel_default_70"; -"1472 linear_69_zero_point_0" -> "1474 dequantize_per_channel_default_70"; -"1473 quantize_per_channel_default_70" -> "1474 dequantize_per_channel_default_70"; -"1474 dequantize_per_channel_default_70" -> "1475 linear_69"; -"1475 linear_69" -> "1476 view_59"; -"1476 view_59" -> "1478 index_11"; -"1477 _tensor_constant68" -> "1478 index_11"; -"1478 index_11" -> "1479 view_60"; -"1479 view_60" -> "1480 permute_50"; -"1480 permute_50" -> "1481 contiguous_20"; -"1481 contiguous_20" -> "1482 unsqueeze_31"; -"1482 unsqueeze_31" -> "1483 sigmoid_11"; -"1483 sigmoid_11" -> "1484 mul_22"; -"1484 mul_22" -> "1523 add_38"; -"1485 pad_13" -> "1486 roll_10"; -"1486 roll_10" -> "1487 view_61"; -"1487 view_61" -> "1488 permute_51"; -"1488 permute_51" -> "1489 reshape_49"; -"1489 reshape_49" -> "1491 reshape_49_0_0_nncf_smooth_quant_0"; -"1489 reshape_49" -> "1524 new_zeros_5"; -"1490 linear_70_updated_constant0" -> "1496 quantize_per_channel_default_71"; -"1491 reshape_49_0_0_nncf_smooth_quant_0" -> "1492 quantize_per_tensor_default_69"; -"1492 quantize_per_tensor_default_69" -> "1493 dequantize_per_tensor_default_69"; -"1493 dequantize_per_tensor_default_69" -> "1499 linear_70"; -"1494 linear_70_scale_0" -> "1496 quantize_per_channel_default_71"; -"1494 linear_70_scale_0" -> "1497 dequantize_per_channel_default_71"; -"1495 linear_70_zero_point_0" -> "1496 quantize_per_channel_default_71"; -"1495 linear_70_zero_point_0" -> "1497 dequantize_per_channel_default_71"; -"1496 quantize_per_channel_default_71" -> "1497 dequantize_per_channel_default_71"; -"1497 dequantize_per_channel_default_71" -> "1499 linear_70"; -"1498 _param_constant189_0_0" -> "1499 linear_70"; -"1499 linear_70" -> "1500 reshape_50"; -"1500 reshape_50" -> "1501 permute_52"; -"1501 permute_52" -> "1502 select_33"; -"1501 permute_52" -> "1503 select_34"; -"1501 permute_52" -> "1504 select_35"; -"1502 select_33" -> "1505 linalg_vector_norm_22"; -"1502 select_33" -> "1507 expand_as_22"; -"1502 select_33" -> "1508 div_22"; -"1503 select_34" -> "1511 linalg_vector_norm_23"; -"1503 select_34" -> "1513 expand_as_23"; -"1503 select_34" -> "1514 div_23"; -"1504 select_35" -> "1542 matmul_23"; -"1505 linalg_vector_norm_22" -> "1506 clamp_min_22"; -"1506 clamp_min_22" -> "1507 expand_as_22"; -"1507 expand_as_22" -> "1508 div_22"; -"1508 div_22" -> "1509 quantize_per_tensor_default_70"; -"1509 quantize_per_tensor_default_70" -> "1510 dequantize_per_tensor_default_70"; -"1510 dequantize_per_tensor_default_70" -> "1518 matmul_22"; -"1511 linalg_vector_norm_23" -> "1512 clamp_min_23"; -"1512 clamp_min_23" -> "1513 expand_as_23"; -"1513 expand_as_23" -> "1514 div_23"; -"1514 div_23" -> "1515 quantize_per_tensor_default_71"; -"1515 quantize_per_tensor_default_71" -> "1516 dequantize_per_tensor_default_71"; -"1516 dequantize_per_tensor_default_71" -> "1517 transpose_22"; -"1517 transpose_22" -> "1518 matmul_22"; -"1518 matmul_22" -> "1522 mul_23"; -"1519 _param_constant191" -> "1520 clamp_11"; -"1520 clamp_11" -> "1521 exp_11"; -"1521 exp_11" -> "1522 mul_23"; -"1522 mul_23" -> "1523 add_38"; -"1523 add_38" -> "1535 view_63"; -"1524 new_zeros_5" -> "1525 view_62"; -"1525 view_62" -> "1526 permute_53"; -"1526 permute_53" -> "1527 reshape_51"; -"1527 reshape_51" -> "1528 unsqueeze_32"; -"1527 reshape_51" -> "1529 unsqueeze_33"; -"1528 unsqueeze_32" -> "1530 sub_5"; -"1529 unsqueeze_33" -> "1530 sub_5"; -"1530 sub_5" -> "1531 ne_5"; -"1530 sub_5" -> "1532 masked_fill_10"; -"1530 sub_5" -> "1533 eq_5"; -"1531 ne_5" -> "1532 masked_fill_10"; -"1532 masked_fill_10" -> "1534 masked_fill_11"; -"1533 eq_5" -> "1534 masked_fill_11"; -"1534 masked_fill_11" -> "1536 unsqueeze_34"; -"1535 view_63" -> "1538 add_39"; -"1536 unsqueeze_34" -> "1537 unsqueeze_35"; -"1537 unsqueeze_35" -> "1538 add_39"; -"1538 add_39" -> "1539 view_64"; -"1539 view_64" -> "1540 softmax_11"; -"1540 softmax_11" -> "1541 dropout_44"; -"1541 dropout_44" -> "1542 matmul_23"; -"1542 matmul_23" -> "1543 transpose_23"; -"1543 transpose_23" -> "1544 reshape_52"; -"1544 reshape_52" -> "1546 reshape_52_0_0_nncf_smooth_quant_0"; -"1545 linear_71_updated_constant0" -> "1551 quantize_per_channel_default_72"; -"1546 reshape_52_0_0_nncf_smooth_quant_0" -> "1547 quantize_per_tensor_default_72"; -"1547 quantize_per_tensor_default_72" -> "1548 dequantize_per_tensor_default_72"; -"1548 dequantize_per_tensor_default_72" -> "1554 linear_71"; -"1549 linear_71_scale_0" -> "1551 quantize_per_channel_default_72"; -"1549 linear_71_scale_0" -> "1552 dequantize_per_channel_default_72"; -"1550 linear_71_zero_point_0" -> "1551 quantize_per_channel_default_72"; -"1550 linear_71_zero_point_0" -> "1552 dequantize_per_channel_default_72"; -"1551 quantize_per_channel_default_72" -> "1552 dequantize_per_channel_default_72"; -"1552 dequantize_per_channel_default_72" -> "1554 linear_71"; -"1553 _param_constant193_0_0" -> "1554 linear_71"; -"1554 linear_71" -> "1555 dropout_45"; -"1555 dropout_45" -> "1556 view_65"; -"1556 view_65" -> "1557 permute_54"; -"1557 permute_54" -> "1558 reshape_53"; -"1558 reshape_53" -> "1559 roll_11"; -"1559 roll_11" -> "1560 slice_185"; -"1560 slice_185" -> "1561 slice_186"; -"1561 slice_186" -> "1562 slice_187"; -"1562 slice_187" -> "1563 slice_188"; -"1563 slice_188" -> "1564 contiguous_21"; -"1564 contiguous_21" -> "1567 layer_norm_25"; -"1565 _param_constant194" -> "1567 layer_norm_25"; -"1566 _param_constant195" -> "1567 layer_norm_25"; -"1567 layer_norm_25" -> "1568 add_40"; -"1568 add_40" -> "1570 add_40_0_0_nncf_smooth_quant_0"; -"1568 add_40" -> "1595 add_41"; -"1569 linear_72_updated_constant0" -> "1575 quantize_per_channel_default_73"; -"1570 add_40_0_0_nncf_smooth_quant_0" -> "1571 quantize_per_tensor_default_73"; -"1571 quantize_per_tensor_default_73" -> "1572 dequantize_per_tensor_default_73"; -"1572 dequantize_per_tensor_default_73" -> "1578 linear_72"; -"1573 linear_72_scale_0" -> "1575 quantize_per_channel_default_73"; -"1573 linear_72_scale_0" -> "1576 dequantize_per_channel_default_73"; -"1574 linear_72_zero_point_0" -> "1575 quantize_per_channel_default_73"; -"1574 linear_72_zero_point_0" -> "1576 dequantize_per_channel_default_73"; -"1575 quantize_per_channel_default_73" -> "1576 dequantize_per_channel_default_73"; -"1576 dequantize_per_channel_default_73" -> "1578 linear_72"; -"1577 _param_constant197_0_0" -> "1578 linear_72"; -"1578 linear_72" -> "1579 gelu_11"; -"1579 gelu_11" -> "1580 dropout_46"; -"1580 dropout_46" -> "1582 dropout_46_0_0_nncf_smooth_quant_0"; -"1581 linear_73_updated_constant0" -> "1587 quantize_per_channel_default_74"; -"1582 dropout_46_0_0_nncf_smooth_quant_0" -> "1583 quantize_per_tensor_default_74"; -"1583 quantize_per_tensor_default_74" -> "1584 dequantize_per_tensor_default_74"; -"1584 dequantize_per_tensor_default_74" -> "1590 linear_73"; -"1585 linear_73_scale_0" -> "1587 quantize_per_channel_default_74"; -"1585 linear_73_scale_0" -> "1588 dequantize_per_channel_default_74"; -"1586 linear_73_zero_point_0" -> "1587 quantize_per_channel_default_74"; -"1586 linear_73_zero_point_0" -> "1588 dequantize_per_channel_default_74"; -"1587 quantize_per_channel_default_74" -> "1588 dequantize_per_channel_default_74"; -"1588 dequantize_per_channel_default_74" -> "1590 linear_73"; -"1589 _param_constant199_0_0" -> "1590 linear_73"; -"1590 linear_73" -> "1591 dropout_47"; -"1591 dropout_47" -> "1594 layer_norm_26"; -"1592 _param_constant200" -> "1594 layer_norm_26"; -"1593 _param_constant201" -> "1594 layer_norm_26"; -"1594 layer_norm_26" -> "1595 add_41"; -"1595 add_41" -> "1622 pad_14"; -"1595 add_41" -> "1687 add_43"; -"1596 _tensor_constant78" -> "1598 _tensor_constant78_0_0_nncf_smooth_quant_0"; -"1597 linear_74_updated_constant0" -> "1601 quantize_per_channel_default_75"; -"1598 _tensor_constant78_0_0_nncf_smooth_quant_0" -> "1604 linear_74"; -"1599 linear_74_scale_0" -> "1601 quantize_per_channel_default_75"; -"1599 linear_74_scale_0" -> "1602 dequantize_per_channel_default_75"; -"1600 linear_74_zero_point_0" -> "1601 quantize_per_channel_default_75"; -"1600 linear_74_zero_point_0" -> "1602 dequantize_per_channel_default_75"; -"1601 quantize_per_channel_default_75" -> "1602 dequantize_per_channel_default_75"; -"1602 dequantize_per_channel_default_75" -> "1604 linear_74"; -"1603 _param_constant203_0_0" -> "1604 linear_74"; -"1604 linear_74" -> "1605 relu__12"; -"1605 relu__12" -> "1607 relu__12_0_0_nncf_smooth_quant_0"; -"1606 linear_75_updated_constant0" -> "1610 quantize_per_channel_default_76"; -"1607 relu__12_0_0_nncf_smooth_quant_0" -> "1612 linear_75"; -"1608 linear_75_scale_0" -> "1610 quantize_per_channel_default_76"; -"1608 linear_75_scale_0" -> "1611 dequantize_per_channel_default_76"; -"1609 linear_75_zero_point_0" -> "1610 quantize_per_channel_default_76"; -"1609 linear_75_zero_point_0" -> "1611 dequantize_per_channel_default_76"; -"1610 quantize_per_channel_default_76" -> "1611 dequantize_per_channel_default_76"; -"1611 dequantize_per_channel_default_76" -> "1612 linear_75"; -"1612 linear_75" -> "1613 view_66"; -"1613 view_66" -> "1615 index_12"; -"1614 _tensor_constant79" -> "1615 index_12"; -"1615 index_12" -> "1616 view_67"; -"1616 view_67" -> "1617 permute_55"; -"1617 permute_55" -> "1618 contiguous_22"; -"1618 contiguous_22" -> "1619 unsqueeze_36"; -"1619 unsqueeze_36" -> "1620 sigmoid_12"; -"1620 sigmoid_12" -> "1621 mul_24"; -"1621 mul_24" -> "1659 add_42"; -"1622 pad_14" -> "1623 view_68"; -"1623 view_68" -> "1624 permute_56"; -"1624 permute_56" -> "1625 reshape_54"; -"1625 reshape_54" -> "1627 reshape_54_0_0_nncf_smooth_quant_0"; -"1626 linear_76_updated_constant0" -> "1632 quantize_per_channel_default_77"; -"1627 reshape_54_0_0_nncf_smooth_quant_0" -> "1628 quantize_per_tensor_default_75"; -"1628 quantize_per_tensor_default_75" -> "1629 dequantize_per_tensor_default_75"; -"1629 dequantize_per_tensor_default_75" -> "1635 linear_76"; -"1630 linear_76_scale_0" -> "1632 quantize_per_channel_default_77"; -"1630 linear_76_scale_0" -> "1633 dequantize_per_channel_default_77"; -"1631 linear_76_zero_point_0" -> "1632 quantize_per_channel_default_77"; -"1631 linear_76_zero_point_0" -> "1633 dequantize_per_channel_default_77"; -"1632 quantize_per_channel_default_77" -> "1633 dequantize_per_channel_default_77"; -"1633 dequantize_per_channel_default_77" -> "1635 linear_76"; -"1634 _param_constant205_0_0" -> "1635 linear_76"; -"1635 linear_76" -> "1636 reshape_55"; -"1636 reshape_55" -> "1637 permute_57"; -"1637 permute_57" -> "1638 select_36"; -"1637 permute_57" -> "1639 select_37"; -"1637 permute_57" -> "1640 select_38"; -"1638 select_36" -> "1641 linalg_vector_norm_24"; -"1638 select_36" -> "1643 expand_as_24"; -"1638 select_36" -> "1644 div_24"; -"1639 select_37" -> "1647 linalg_vector_norm_25"; -"1639 select_37" -> "1649 expand_as_25"; -"1639 select_37" -> "1650 div_25"; -"1640 select_38" -> "1662 matmul_25"; -"1641 linalg_vector_norm_24" -> "1642 clamp_min_24"; -"1642 clamp_min_24" -> "1643 expand_as_24"; -"1643 expand_as_24" -> "1644 div_24"; -"1644 div_24" -> "1645 quantize_per_tensor_default_76"; -"1645 quantize_per_tensor_default_76" -> "1646 dequantize_per_tensor_default_76"; -"1646 dequantize_per_tensor_default_76" -> "1654 matmul_24"; -"1647 linalg_vector_norm_25" -> "1648 clamp_min_25"; -"1648 clamp_min_25" -> "1649 expand_as_25"; -"1649 expand_as_25" -> "1650 div_25"; -"1650 div_25" -> "1651 quantize_per_tensor_default_77"; -"1651 quantize_per_tensor_default_77" -> "1652 dequantize_per_tensor_default_77"; -"1652 dequantize_per_tensor_default_77" -> "1653 transpose_24"; -"1653 transpose_24" -> "1654 matmul_24"; -"1654 matmul_24" -> "1658 mul_25"; -"1655 _param_constant207" -> "1656 clamp_12"; -"1656 clamp_12" -> "1657 exp_12"; -"1657 exp_12" -> "1658 mul_25"; -"1658 mul_25" -> "1659 add_42"; -"1659 add_42" -> "1660 softmax_12"; -"1660 softmax_12" -> "1661 dropout_48"; -"1661 dropout_48" -> "1662 matmul_25"; -"1662 matmul_25" -> "1663 transpose_25"; -"1663 transpose_25" -> "1664 reshape_56"; -"1664 reshape_56" -> "1666 reshape_56_0_0_nncf_smooth_quant_0"; -"1665 linear_77_updated_constant0" -> "1671 quantize_per_channel_default_78"; -"1666 reshape_56_0_0_nncf_smooth_quant_0" -> "1667 quantize_per_tensor_default_78"; -"1667 quantize_per_tensor_default_78" -> "1668 dequantize_per_tensor_default_78"; -"1668 dequantize_per_tensor_default_78" -> "1674 linear_77"; -"1669 linear_77_scale_0" -> "1671 quantize_per_channel_default_78"; -"1669 linear_77_scale_0" -> "1672 dequantize_per_channel_default_78"; -"1670 linear_77_zero_point_0" -> "1671 quantize_per_channel_default_78"; -"1670 linear_77_zero_point_0" -> "1672 dequantize_per_channel_default_78"; -"1671 quantize_per_channel_default_78" -> "1672 dequantize_per_channel_default_78"; -"1672 dequantize_per_channel_default_78" -> "1674 linear_77"; -"1673 _param_constant209_0_0" -> "1674 linear_77"; -"1674 linear_77" -> "1675 dropout_49"; -"1675 dropout_49" -> "1676 view_69"; -"1676 view_69" -> "1677 permute_58"; -"1677 permute_58" -> "1678 reshape_57"; -"1678 reshape_57" -> "1679 slice_190"; -"1679 slice_190" -> "1680 slice_191"; -"1680 slice_191" -> "1681 slice_192"; -"1681 slice_192" -> "1682 slice_193"; -"1682 slice_193" -> "1683 contiguous_23"; -"1683 contiguous_23" -> "1686 layer_norm_27"; -"1684 _param_constant210" -> "1686 layer_norm_27"; -"1685 _param_constant211" -> "1686 layer_norm_27"; -"1686 layer_norm_27" -> "1687 add_43"; -"1687 add_43" -> "1689 add_43_0_0_nncf_smooth_quant_0"; -"1687 add_43" -> "1714 add_44"; -"1688 linear_78_updated_constant0" -> "1694 quantize_per_channel_default_79"; -"1689 add_43_0_0_nncf_smooth_quant_0" -> "1690 quantize_per_tensor_default_79"; -"1690 quantize_per_tensor_default_79" -> "1691 dequantize_per_tensor_default_79"; -"1691 dequantize_per_tensor_default_79" -> "1697 linear_78"; -"1692 linear_78_scale_0" -> "1694 quantize_per_channel_default_79"; -"1692 linear_78_scale_0" -> "1695 dequantize_per_channel_default_79"; -"1693 linear_78_zero_point_0" -> "1694 quantize_per_channel_default_79"; -"1693 linear_78_zero_point_0" -> "1695 dequantize_per_channel_default_79"; -"1694 quantize_per_channel_default_79" -> "1695 dequantize_per_channel_default_79"; -"1695 dequantize_per_channel_default_79" -> "1697 linear_78"; -"1696 _param_constant213_0_0" -> "1697 linear_78"; -"1697 linear_78" -> "1698 gelu_12"; -"1698 gelu_12" -> "1699 dropout_50"; -"1699 dropout_50" -> "1701 dropout_50_0_0_nncf_smooth_quant_0"; -"1700 linear_79_updated_constant0" -> "1706 quantize_per_channel_default_80"; -"1701 dropout_50_0_0_nncf_smooth_quant_0" -> "1702 quantize_per_tensor_default_80"; -"1702 quantize_per_tensor_default_80" -> "1703 dequantize_per_tensor_default_80"; -"1703 dequantize_per_tensor_default_80" -> "1709 linear_79"; -"1704 linear_79_scale_0" -> "1706 quantize_per_channel_default_80"; -"1704 linear_79_scale_0" -> "1707 dequantize_per_channel_default_80"; -"1705 linear_79_zero_point_0" -> "1706 quantize_per_channel_default_80"; -"1705 linear_79_zero_point_0" -> "1707 dequantize_per_channel_default_80"; -"1706 quantize_per_channel_default_80" -> "1707 dequantize_per_channel_default_80"; -"1707 dequantize_per_channel_default_80" -> "1709 linear_79"; -"1708 _param_constant215_0_0" -> "1709 linear_79"; -"1709 linear_79" -> "1710 dropout_51"; -"1710 dropout_51" -> "1713 layer_norm_28"; -"1711 _param_constant216" -> "1713 layer_norm_28"; -"1712 _param_constant217" -> "1713 layer_norm_28"; -"1713 layer_norm_28" -> "1714 add_44"; -"1714 add_44" -> "1741 pad_15"; -"1714 add_44" -> "1824 add_47"; -"1715 _tensor_constant80" -> "1717 _tensor_constant80_0_0_nncf_smooth_quant_0"; -"1716 linear_80_updated_constant0" -> "1720 quantize_per_channel_default_81"; -"1717 _tensor_constant80_0_0_nncf_smooth_quant_0" -> "1723 linear_80"; -"1718 linear_80_scale_0" -> "1720 quantize_per_channel_default_81"; -"1718 linear_80_scale_0" -> "1721 dequantize_per_channel_default_81"; -"1719 linear_80_zero_point_0" -> "1720 quantize_per_channel_default_81"; -"1719 linear_80_zero_point_0" -> "1721 dequantize_per_channel_default_81"; -"1720 quantize_per_channel_default_81" -> "1721 dequantize_per_channel_default_81"; -"1721 dequantize_per_channel_default_81" -> "1723 linear_80"; -"1722 _param_constant219_0_0" -> "1723 linear_80"; -"1723 linear_80" -> "1724 relu__13"; -"1724 relu__13" -> "1726 relu__13_0_0_nncf_smooth_quant_0"; -"1725 linear_81_updated_constant0" -> "1729 quantize_per_channel_default_82"; -"1726 relu__13_0_0_nncf_smooth_quant_0" -> "1731 linear_81"; -"1727 linear_81_scale_0" -> "1729 quantize_per_channel_default_82"; -"1727 linear_81_scale_0" -> "1730 dequantize_per_channel_default_82"; -"1728 linear_81_zero_point_0" -> "1729 quantize_per_channel_default_82"; -"1728 linear_81_zero_point_0" -> "1730 dequantize_per_channel_default_82"; -"1729 quantize_per_channel_default_82" -> "1730 dequantize_per_channel_default_82"; -"1730 dequantize_per_channel_default_82" -> "1731 linear_81"; -"1731 linear_81" -> "1732 view_70"; -"1732 view_70" -> "1734 index_13"; -"1733 _tensor_constant81" -> "1734 index_13"; -"1734 index_13" -> "1735 view_71"; -"1735 view_71" -> "1736 permute_59"; -"1736 permute_59" -> "1737 contiguous_24"; -"1737 contiguous_24" -> "1738 unsqueeze_37"; -"1738 unsqueeze_37" -> "1739 sigmoid_13"; -"1739 sigmoid_13" -> "1740 mul_26"; -"1740 mul_26" -> "1779 add_45"; -"1741 pad_15" -> "1742 roll_12"; -"1742 roll_12" -> "1743 view_72"; -"1743 view_72" -> "1744 permute_60"; -"1744 permute_60" -> "1745 reshape_58"; -"1745 reshape_58" -> "1747 reshape_58_0_0_nncf_smooth_quant_0"; -"1745 reshape_58" -> "1780 new_zeros_6"; -"1746 linear_82_updated_constant0" -> "1752 quantize_per_channel_default_83"; -"1747 reshape_58_0_0_nncf_smooth_quant_0" -> "1748 quantize_per_tensor_default_81"; -"1748 quantize_per_tensor_default_81" -> "1749 dequantize_per_tensor_default_81"; -"1749 dequantize_per_tensor_default_81" -> "1755 linear_82"; -"1750 linear_82_scale_0" -> "1752 quantize_per_channel_default_83"; -"1750 linear_82_scale_0" -> "1753 dequantize_per_channel_default_83"; -"1751 linear_82_zero_point_0" -> "1752 quantize_per_channel_default_83"; -"1751 linear_82_zero_point_0" -> "1753 dequantize_per_channel_default_83"; -"1752 quantize_per_channel_default_83" -> "1753 dequantize_per_channel_default_83"; -"1753 dequantize_per_channel_default_83" -> "1755 linear_82"; -"1754 _param_constant221_0_0" -> "1755 linear_82"; -"1755 linear_82" -> "1756 reshape_59"; -"1756 reshape_59" -> "1757 permute_61"; -"1757 permute_61" -> "1758 select_39"; -"1757 permute_61" -> "1759 select_40"; -"1757 permute_61" -> "1760 select_41"; -"1758 select_39" -> "1761 linalg_vector_norm_26"; -"1758 select_39" -> "1763 expand_as_26"; -"1758 select_39" -> "1764 div_26"; -"1759 select_40" -> "1767 linalg_vector_norm_27"; -"1759 select_40" -> "1769 expand_as_27"; -"1759 select_40" -> "1770 div_27"; -"1760 select_41" -> "1798 matmul_27"; -"1761 linalg_vector_norm_26" -> "1762 clamp_min_26"; -"1762 clamp_min_26" -> "1763 expand_as_26"; -"1763 expand_as_26" -> "1764 div_26"; -"1764 div_26" -> "1765 quantize_per_tensor_default_82"; -"1765 quantize_per_tensor_default_82" -> "1766 dequantize_per_tensor_default_82"; -"1766 dequantize_per_tensor_default_82" -> "1774 matmul_26"; -"1767 linalg_vector_norm_27" -> "1768 clamp_min_27"; -"1768 clamp_min_27" -> "1769 expand_as_27"; -"1769 expand_as_27" -> "1770 div_27"; -"1770 div_27" -> "1771 quantize_per_tensor_default_83"; -"1771 quantize_per_tensor_default_83" -> "1772 dequantize_per_tensor_default_83"; -"1772 dequantize_per_tensor_default_83" -> "1773 transpose_26"; -"1773 transpose_26" -> "1774 matmul_26"; -"1774 matmul_26" -> "1778 mul_27"; -"1775 _param_constant223" -> "1776 clamp_13"; -"1776 clamp_13" -> "1777 exp_13"; -"1777 exp_13" -> "1778 mul_27"; -"1778 mul_27" -> "1779 add_45"; -"1779 add_45" -> "1791 view_74"; -"1780 new_zeros_6" -> "1781 view_73"; -"1781 view_73" -> "1782 permute_62"; -"1782 permute_62" -> "1783 reshape_60"; -"1783 reshape_60" -> "1784 unsqueeze_38"; -"1783 reshape_60" -> "1785 unsqueeze_39"; -"1784 unsqueeze_38" -> "1786 sub_6"; -"1785 unsqueeze_39" -> "1786 sub_6"; -"1786 sub_6" -> "1787 ne_6"; -"1786 sub_6" -> "1788 masked_fill_12"; -"1786 sub_6" -> "1789 eq_6"; -"1787 ne_6" -> "1788 masked_fill_12"; -"1788 masked_fill_12" -> "1790 masked_fill_13"; -"1789 eq_6" -> "1790 masked_fill_13"; -"1790 masked_fill_13" -> "1792 unsqueeze_40"; -"1791 view_74" -> "1794 add_46"; -"1792 unsqueeze_40" -> "1793 unsqueeze_41"; -"1793 unsqueeze_41" -> "1794 add_46"; -"1794 add_46" -> "1795 view_75"; -"1795 view_75" -> "1796 softmax_13"; -"1796 softmax_13" -> "1797 dropout_52"; -"1797 dropout_52" -> "1798 matmul_27"; -"1798 matmul_27" -> "1799 transpose_27"; -"1799 transpose_27" -> "1800 reshape_61"; -"1800 reshape_61" -> "1802 reshape_61_0_0_nncf_smooth_quant_0"; -"1801 linear_83_updated_constant0" -> "1807 quantize_per_channel_default_84"; -"1802 reshape_61_0_0_nncf_smooth_quant_0" -> "1803 quantize_per_tensor_default_84"; -"1803 quantize_per_tensor_default_84" -> "1804 dequantize_per_tensor_default_84"; -"1804 dequantize_per_tensor_default_84" -> "1810 linear_83"; -"1805 linear_83_scale_0" -> "1807 quantize_per_channel_default_84"; -"1805 linear_83_scale_0" -> "1808 dequantize_per_channel_default_84"; -"1806 linear_83_zero_point_0" -> "1807 quantize_per_channel_default_84"; -"1806 linear_83_zero_point_0" -> "1808 dequantize_per_channel_default_84"; -"1807 quantize_per_channel_default_84" -> "1808 dequantize_per_channel_default_84"; -"1808 dequantize_per_channel_default_84" -> "1810 linear_83"; -"1809 _param_constant225_0_0" -> "1810 linear_83"; -"1810 linear_83" -> "1811 dropout_53"; -"1811 dropout_53" -> "1812 view_76"; -"1812 view_76" -> "1813 permute_63"; -"1813 permute_63" -> "1814 reshape_62"; -"1814 reshape_62" -> "1815 roll_13"; -"1815 roll_13" -> "1816 slice_213"; -"1816 slice_213" -> "1817 slice_214"; -"1817 slice_214" -> "1818 slice_215"; -"1818 slice_215" -> "1819 slice_216"; -"1819 slice_216" -> "1820 contiguous_25"; -"1820 contiguous_25" -> "1823 layer_norm_29"; -"1821 _param_constant226" -> "1823 layer_norm_29"; -"1822 _param_constant227" -> "1823 layer_norm_29"; -"1823 layer_norm_29" -> "1824 add_47"; -"1824 add_47" -> "1826 add_47_0_0_nncf_smooth_quant_0"; -"1824 add_47" -> "1851 add_48"; -"1825 linear_84_updated_constant0" -> "1831 quantize_per_channel_default_85"; -"1826 add_47_0_0_nncf_smooth_quant_0" -> "1827 quantize_per_tensor_default_85"; -"1827 quantize_per_tensor_default_85" -> "1828 dequantize_per_tensor_default_85"; -"1828 dequantize_per_tensor_default_85" -> "1834 linear_84"; -"1829 linear_84_scale_0" -> "1831 quantize_per_channel_default_85"; -"1829 linear_84_scale_0" -> "1832 dequantize_per_channel_default_85"; -"1830 linear_84_zero_point_0" -> "1831 quantize_per_channel_default_85"; -"1830 linear_84_zero_point_0" -> "1832 dequantize_per_channel_default_85"; -"1831 quantize_per_channel_default_85" -> "1832 dequantize_per_channel_default_85"; -"1832 dequantize_per_channel_default_85" -> "1834 linear_84"; -"1833 _param_constant229_0_0" -> "1834 linear_84"; -"1834 linear_84" -> "1835 gelu_13"; -"1835 gelu_13" -> "1836 dropout_54"; -"1836 dropout_54" -> "1838 dropout_54_0_0_nncf_smooth_quant_0"; -"1837 linear_85_updated_constant0" -> "1843 quantize_per_channel_default_86"; -"1838 dropout_54_0_0_nncf_smooth_quant_0" -> "1839 quantize_per_tensor_default_86"; -"1839 quantize_per_tensor_default_86" -> "1840 dequantize_per_tensor_default_86"; -"1840 dequantize_per_tensor_default_86" -> "1846 linear_85"; -"1841 linear_85_scale_0" -> "1843 quantize_per_channel_default_86"; -"1841 linear_85_scale_0" -> "1844 dequantize_per_channel_default_86"; -"1842 linear_85_zero_point_0" -> "1843 quantize_per_channel_default_86"; -"1842 linear_85_zero_point_0" -> "1844 dequantize_per_channel_default_86"; -"1843 quantize_per_channel_default_86" -> "1844 dequantize_per_channel_default_86"; -"1844 dequantize_per_channel_default_86" -> "1846 linear_85"; -"1845 _param_constant231_0_0" -> "1846 linear_85"; -"1846 linear_85" -> "1847 dropout_55"; -"1847 dropout_55" -> "1850 layer_norm_30"; -"1848 _param_constant232" -> "1850 layer_norm_30"; -"1849 _param_constant233" -> "1850 layer_norm_30"; -"1850 layer_norm_30" -> "1851 add_48"; -"1851 add_48" -> "1878 pad_16"; -"1851 add_48" -> "1943 add_50"; -"1852 _tensor_constant91" -> "1854 _tensor_constant91_0_0_nncf_smooth_quant_0"; -"1853 linear_86_updated_constant0" -> "1857 quantize_per_channel_default_87"; -"1854 _tensor_constant91_0_0_nncf_smooth_quant_0" -> "1860 linear_86"; -"1855 linear_86_scale_0" -> "1857 quantize_per_channel_default_87"; -"1855 linear_86_scale_0" -> "1858 dequantize_per_channel_default_87"; -"1856 linear_86_zero_point_0" -> "1857 quantize_per_channel_default_87"; -"1856 linear_86_zero_point_0" -> "1858 dequantize_per_channel_default_87"; -"1857 quantize_per_channel_default_87" -> "1858 dequantize_per_channel_default_87"; -"1858 dequantize_per_channel_default_87" -> "1860 linear_86"; -"1859 _param_constant235_0_0" -> "1860 linear_86"; -"1860 linear_86" -> "1861 relu__14"; -"1861 relu__14" -> "1863 relu__14_0_0_nncf_smooth_quant_0"; -"1862 linear_87_updated_constant0" -> "1866 quantize_per_channel_default_88"; -"1863 relu__14_0_0_nncf_smooth_quant_0" -> "1868 linear_87"; -"1864 linear_87_scale_0" -> "1866 quantize_per_channel_default_88"; -"1864 linear_87_scale_0" -> "1867 dequantize_per_channel_default_88"; -"1865 linear_87_zero_point_0" -> "1866 quantize_per_channel_default_88"; -"1865 linear_87_zero_point_0" -> "1867 dequantize_per_channel_default_88"; -"1866 quantize_per_channel_default_88" -> "1867 dequantize_per_channel_default_88"; -"1867 dequantize_per_channel_default_88" -> "1868 linear_87"; -"1868 linear_87" -> "1869 view_77"; -"1869 view_77" -> "1871 index_14"; -"1870 _tensor_constant92" -> "1871 index_14"; -"1871 index_14" -> "1872 view_78"; -"1872 view_78" -> "1873 permute_64"; -"1873 permute_64" -> "1874 contiguous_26"; -"1874 contiguous_26" -> "1875 unsqueeze_42"; -"1875 unsqueeze_42" -> "1876 sigmoid_14"; -"1876 sigmoid_14" -> "1877 mul_28"; -"1877 mul_28" -> "1915 add_49"; -"1878 pad_16" -> "1879 view_79"; -"1879 view_79" -> "1880 permute_65"; -"1880 permute_65" -> "1881 reshape_63"; -"1881 reshape_63" -> "1883 reshape_63_0_0_nncf_smooth_quant_0"; -"1882 linear_88_updated_constant0" -> "1888 quantize_per_channel_default_89"; -"1883 reshape_63_0_0_nncf_smooth_quant_0" -> "1884 quantize_per_tensor_default_87"; -"1884 quantize_per_tensor_default_87" -> "1885 dequantize_per_tensor_default_87"; -"1885 dequantize_per_tensor_default_87" -> "1891 linear_88"; -"1886 linear_88_scale_0" -> "1888 quantize_per_channel_default_89"; -"1886 linear_88_scale_0" -> "1889 dequantize_per_channel_default_89"; -"1887 linear_88_zero_point_0" -> "1888 quantize_per_channel_default_89"; -"1887 linear_88_zero_point_0" -> "1889 dequantize_per_channel_default_89"; -"1888 quantize_per_channel_default_89" -> "1889 dequantize_per_channel_default_89"; -"1889 dequantize_per_channel_default_89" -> "1891 linear_88"; -"1890 _param_constant237_0_0" -> "1891 linear_88"; -"1891 linear_88" -> "1892 reshape_64"; -"1892 reshape_64" -> "1893 permute_66"; -"1893 permute_66" -> "1894 select_42"; -"1893 permute_66" -> "1895 select_43"; -"1893 permute_66" -> "1896 select_44"; -"1894 select_42" -> "1897 linalg_vector_norm_28"; -"1894 select_42" -> "1899 expand_as_28"; -"1894 select_42" -> "1900 div_28"; -"1895 select_43" -> "1903 linalg_vector_norm_29"; -"1895 select_43" -> "1905 expand_as_29"; -"1895 select_43" -> "1906 div_29"; -"1896 select_44" -> "1918 matmul_29"; -"1897 linalg_vector_norm_28" -> "1898 clamp_min_28"; -"1898 clamp_min_28" -> "1899 expand_as_28"; -"1899 expand_as_28" -> "1900 div_28"; -"1900 div_28" -> "1901 quantize_per_tensor_default_88"; -"1901 quantize_per_tensor_default_88" -> "1902 dequantize_per_tensor_default_88"; -"1902 dequantize_per_tensor_default_88" -> "1910 matmul_28"; -"1903 linalg_vector_norm_29" -> "1904 clamp_min_29"; -"1904 clamp_min_29" -> "1905 expand_as_29"; -"1905 expand_as_29" -> "1906 div_29"; -"1906 div_29" -> "1907 quantize_per_tensor_default_89"; -"1907 quantize_per_tensor_default_89" -> "1908 dequantize_per_tensor_default_89"; -"1908 dequantize_per_tensor_default_89" -> "1909 transpose_28"; -"1909 transpose_28" -> "1910 matmul_28"; -"1910 matmul_28" -> "1914 mul_29"; -"1911 _param_constant239" -> "1912 clamp_14"; -"1912 clamp_14" -> "1913 exp_14"; -"1913 exp_14" -> "1914 mul_29"; -"1914 mul_29" -> "1915 add_49"; -"1915 add_49" -> "1916 softmax_14"; -"1916 softmax_14" -> "1917 dropout_56"; -"1917 dropout_56" -> "1918 matmul_29"; -"1918 matmul_29" -> "1919 transpose_29"; -"1919 transpose_29" -> "1920 reshape_65"; -"1920 reshape_65" -> "1922 reshape_65_0_0_nncf_smooth_quant_0"; -"1921 linear_89_updated_constant0" -> "1927 quantize_per_channel_default_90"; -"1922 reshape_65_0_0_nncf_smooth_quant_0" -> "1923 quantize_per_tensor_default_90"; -"1923 quantize_per_tensor_default_90" -> "1924 dequantize_per_tensor_default_90"; -"1924 dequantize_per_tensor_default_90" -> "1930 linear_89"; -"1925 linear_89_scale_0" -> "1927 quantize_per_channel_default_90"; -"1925 linear_89_scale_0" -> "1928 dequantize_per_channel_default_90"; -"1926 linear_89_zero_point_0" -> "1927 quantize_per_channel_default_90"; -"1926 linear_89_zero_point_0" -> "1928 dequantize_per_channel_default_90"; -"1927 quantize_per_channel_default_90" -> "1928 dequantize_per_channel_default_90"; -"1928 dequantize_per_channel_default_90" -> "1930 linear_89"; -"1929 _param_constant241_0_0" -> "1930 linear_89"; -"1930 linear_89" -> "1931 dropout_57"; -"1931 dropout_57" -> "1932 view_80"; -"1932 view_80" -> "1933 permute_67"; -"1933 permute_67" -> "1934 reshape_66"; -"1934 reshape_66" -> "1935 slice_218"; -"1935 slice_218" -> "1936 slice_219"; -"1936 slice_219" -> "1937 slice_220"; -"1937 slice_220" -> "1938 slice_221"; -"1938 slice_221" -> "1939 contiguous_27"; -"1939 contiguous_27" -> "1942 layer_norm_31"; -"1940 _param_constant242" -> "1942 layer_norm_31"; -"1941 _param_constant243" -> "1942 layer_norm_31"; -"1942 layer_norm_31" -> "1943 add_50"; -"1943 add_50" -> "1945 add_50_0_0_nncf_smooth_quant_0"; -"1943 add_50" -> "1970 add_51"; -"1944 linear_90_updated_constant0" -> "1950 quantize_per_channel_default_91"; -"1945 add_50_0_0_nncf_smooth_quant_0" -> "1946 quantize_per_tensor_default_91"; -"1946 quantize_per_tensor_default_91" -> "1947 dequantize_per_tensor_default_91"; -"1947 dequantize_per_tensor_default_91" -> "1953 linear_90"; -"1948 linear_90_scale_0" -> "1950 quantize_per_channel_default_91"; -"1948 linear_90_scale_0" -> "1951 dequantize_per_channel_default_91"; -"1949 linear_90_zero_point_0" -> "1950 quantize_per_channel_default_91"; -"1949 linear_90_zero_point_0" -> "1951 dequantize_per_channel_default_91"; -"1950 quantize_per_channel_default_91" -> "1951 dequantize_per_channel_default_91"; -"1951 dequantize_per_channel_default_91" -> "1953 linear_90"; -"1952 _param_constant245_0_0" -> "1953 linear_90"; -"1953 linear_90" -> "1954 gelu_14"; -"1954 gelu_14" -> "1955 dropout_58"; -"1955 dropout_58" -> "1957 dropout_58_0_0_nncf_smooth_quant_0"; -"1956 linear_91_updated_constant0" -> "1962 quantize_per_channel_default_92"; -"1957 dropout_58_0_0_nncf_smooth_quant_0" -> "1958 quantize_per_tensor_default_92"; -"1958 quantize_per_tensor_default_92" -> "1959 dequantize_per_tensor_default_92"; -"1959 dequantize_per_tensor_default_92" -> "1965 linear_91"; -"1960 linear_91_scale_0" -> "1962 quantize_per_channel_default_92"; -"1960 linear_91_scale_0" -> "1963 dequantize_per_channel_default_92"; -"1961 linear_91_zero_point_0" -> "1962 quantize_per_channel_default_92"; -"1961 linear_91_zero_point_0" -> "1963 dequantize_per_channel_default_92"; -"1962 quantize_per_channel_default_92" -> "1963 dequantize_per_channel_default_92"; -"1963 dequantize_per_channel_default_92" -> "1965 linear_91"; -"1964 _param_constant247_0_0" -> "1965 linear_91"; -"1965 linear_91" -> "1966 dropout_59"; -"1966 dropout_59" -> "1969 layer_norm_32"; -"1967 _param_constant248" -> "1969 layer_norm_32"; -"1968 _param_constant249" -> "1969 layer_norm_32"; -"1969 layer_norm_32" -> "1970 add_51"; -"1970 add_51" -> "1997 pad_17"; -"1970 add_51" -> "2080 add_54"; -"1971 _tensor_constant93" -> "1973 _tensor_constant93_0_0_nncf_smooth_quant_0"; -"1972 linear_92_updated_constant0" -> "1976 quantize_per_channel_default_93"; -"1973 _tensor_constant93_0_0_nncf_smooth_quant_0" -> "1979 linear_92"; -"1974 linear_92_scale_0" -> "1976 quantize_per_channel_default_93"; -"1974 linear_92_scale_0" -> "1977 dequantize_per_channel_default_93"; -"1975 linear_92_zero_point_0" -> "1976 quantize_per_channel_default_93"; -"1975 linear_92_zero_point_0" -> "1977 dequantize_per_channel_default_93"; -"1976 quantize_per_channel_default_93" -> "1977 dequantize_per_channel_default_93"; -"1977 dequantize_per_channel_default_93" -> "1979 linear_92"; -"1978 _param_constant251_0_0" -> "1979 linear_92"; -"1979 linear_92" -> "1980 relu__15"; -"1980 relu__15" -> "1982 relu__15_0_0_nncf_smooth_quant_0"; -"1981 linear_93_updated_constant0" -> "1985 quantize_per_channel_default_94"; -"1982 relu__15_0_0_nncf_smooth_quant_0" -> "1987 linear_93"; -"1983 linear_93_scale_0" -> "1985 quantize_per_channel_default_94"; -"1983 linear_93_scale_0" -> "1986 dequantize_per_channel_default_94"; -"1984 linear_93_zero_point_0" -> "1985 quantize_per_channel_default_94"; -"1984 linear_93_zero_point_0" -> "1986 dequantize_per_channel_default_94"; -"1985 quantize_per_channel_default_94" -> "1986 dequantize_per_channel_default_94"; -"1986 dequantize_per_channel_default_94" -> "1987 linear_93"; -"1987 linear_93" -> "1988 view_81"; -"1988 view_81" -> "1990 index_15"; -"1989 _tensor_constant94" -> "1990 index_15"; -"1990 index_15" -> "1991 view_82"; -"1991 view_82" -> "1992 permute_68"; -"1992 permute_68" -> "1993 contiguous_28"; -"1993 contiguous_28" -> "1994 unsqueeze_43"; -"1994 unsqueeze_43" -> "1995 sigmoid_15"; -"1995 sigmoid_15" -> "1996 mul_30"; -"1996 mul_30" -> "2035 add_52"; -"1997 pad_17" -> "1998 roll_14"; -"1998 roll_14" -> "1999 view_83"; -"1999 view_83" -> "2000 permute_69"; -"2000 permute_69" -> "2001 reshape_67"; -"2001 reshape_67" -> "2003 reshape_67_0_0_nncf_smooth_quant_0"; -"2001 reshape_67" -> "2036 new_zeros_7"; -"2002 linear_94_updated_constant0" -> "2008 quantize_per_channel_default_95"; -"2003 reshape_67_0_0_nncf_smooth_quant_0" -> "2004 quantize_per_tensor_default_93"; -"2004 quantize_per_tensor_default_93" -> "2005 dequantize_per_tensor_default_93"; -"2005 dequantize_per_tensor_default_93" -> "2011 linear_94"; -"2006 linear_94_scale_0" -> "2008 quantize_per_channel_default_95"; -"2006 linear_94_scale_0" -> "2009 dequantize_per_channel_default_95"; -"2007 linear_94_zero_point_0" -> "2008 quantize_per_channel_default_95"; -"2007 linear_94_zero_point_0" -> "2009 dequantize_per_channel_default_95"; -"2008 quantize_per_channel_default_95" -> "2009 dequantize_per_channel_default_95"; -"2009 dequantize_per_channel_default_95" -> "2011 linear_94"; -"2010 _param_constant253_0_0" -> "2011 linear_94"; -"2011 linear_94" -> "2012 reshape_68"; -"2012 reshape_68" -> "2013 permute_70"; -"2013 permute_70" -> "2014 select_45"; -"2013 permute_70" -> "2015 select_46"; -"2013 permute_70" -> "2016 select_47"; -"2014 select_45" -> "2017 linalg_vector_norm_30"; -"2014 select_45" -> "2019 expand_as_30"; -"2014 select_45" -> "2020 div_30"; -"2015 select_46" -> "2023 linalg_vector_norm_31"; -"2015 select_46" -> "2025 expand_as_31"; -"2015 select_46" -> "2026 div_31"; -"2016 select_47" -> "2054 matmul_31"; -"2017 linalg_vector_norm_30" -> "2018 clamp_min_30"; -"2018 clamp_min_30" -> "2019 expand_as_30"; -"2019 expand_as_30" -> "2020 div_30"; -"2020 div_30" -> "2021 quantize_per_tensor_default_94"; -"2021 quantize_per_tensor_default_94" -> "2022 dequantize_per_tensor_default_94"; -"2022 dequantize_per_tensor_default_94" -> "2030 matmul_30"; -"2023 linalg_vector_norm_31" -> "2024 clamp_min_31"; -"2024 clamp_min_31" -> "2025 expand_as_31"; -"2025 expand_as_31" -> "2026 div_31"; -"2026 div_31" -> "2027 quantize_per_tensor_default_95"; -"2027 quantize_per_tensor_default_95" -> "2028 dequantize_per_tensor_default_95"; -"2028 dequantize_per_tensor_default_95" -> "2029 transpose_30"; -"2029 transpose_30" -> "2030 matmul_30"; -"2030 matmul_30" -> "2034 mul_31"; -"2031 _param_constant255" -> "2032 clamp_15"; -"2032 clamp_15" -> "2033 exp_15"; -"2033 exp_15" -> "2034 mul_31"; -"2034 mul_31" -> "2035 add_52"; -"2035 add_52" -> "2047 view_85"; -"2036 new_zeros_7" -> "2037 view_84"; -"2037 view_84" -> "2038 permute_71"; -"2038 permute_71" -> "2039 reshape_69"; -"2039 reshape_69" -> "2040 unsqueeze_44"; -"2039 reshape_69" -> "2041 unsqueeze_45"; -"2040 unsqueeze_44" -> "2042 sub_7"; -"2041 unsqueeze_45" -> "2042 sub_7"; -"2042 sub_7" -> "2043 ne_7"; -"2042 sub_7" -> "2044 masked_fill_14"; -"2042 sub_7" -> "2045 eq_7"; -"2043 ne_7" -> "2044 masked_fill_14"; -"2044 masked_fill_14" -> "2046 masked_fill_15"; -"2045 eq_7" -> "2046 masked_fill_15"; -"2046 masked_fill_15" -> "2048 unsqueeze_46"; -"2047 view_85" -> "2050 add_53"; -"2048 unsqueeze_46" -> "2049 unsqueeze_47"; -"2049 unsqueeze_47" -> "2050 add_53"; -"2050 add_53" -> "2051 view_86"; -"2051 view_86" -> "2052 softmax_15"; -"2052 softmax_15" -> "2053 dropout_60"; -"2053 dropout_60" -> "2054 matmul_31"; -"2054 matmul_31" -> "2055 transpose_31"; -"2055 transpose_31" -> "2056 reshape_70"; -"2056 reshape_70" -> "2058 reshape_70_0_0_nncf_smooth_quant_0"; -"2057 linear_95_updated_constant0" -> "2063 quantize_per_channel_default_96"; -"2058 reshape_70_0_0_nncf_smooth_quant_0" -> "2059 quantize_per_tensor_default_96"; -"2059 quantize_per_tensor_default_96" -> "2060 dequantize_per_tensor_default_96"; -"2060 dequantize_per_tensor_default_96" -> "2066 linear_95"; -"2061 linear_95_scale_0" -> "2063 quantize_per_channel_default_96"; -"2061 linear_95_scale_0" -> "2064 dequantize_per_channel_default_96"; -"2062 linear_95_zero_point_0" -> "2063 quantize_per_channel_default_96"; -"2062 linear_95_zero_point_0" -> "2064 dequantize_per_channel_default_96"; -"2063 quantize_per_channel_default_96" -> "2064 dequantize_per_channel_default_96"; -"2064 dequantize_per_channel_default_96" -> "2066 linear_95"; -"2065 _param_constant257_0_0" -> "2066 linear_95"; -"2066 linear_95" -> "2067 dropout_61"; -"2067 dropout_61" -> "2068 view_87"; -"2068 view_87" -> "2069 permute_72"; -"2069 permute_72" -> "2070 reshape_71"; -"2070 reshape_71" -> "2071 roll_15"; -"2071 roll_15" -> "2072 slice_241"; -"2072 slice_241" -> "2073 slice_242"; -"2073 slice_242" -> "2074 slice_243"; -"2074 slice_243" -> "2075 slice_244"; -"2075 slice_244" -> "2076 contiguous_29"; -"2076 contiguous_29" -> "2079 layer_norm_33"; -"2077 _param_constant258" -> "2079 layer_norm_33"; -"2078 _param_constant259" -> "2079 layer_norm_33"; -"2079 layer_norm_33" -> "2080 add_54"; -"2080 add_54" -> "2082 add_54_0_0_nncf_smooth_quant_0"; -"2080 add_54" -> "2107 add_55"; -"2081 linear_96_updated_constant0" -> "2087 quantize_per_channel_default_97"; -"2082 add_54_0_0_nncf_smooth_quant_0" -> "2083 quantize_per_tensor_default_97"; -"2083 quantize_per_tensor_default_97" -> "2084 dequantize_per_tensor_default_97"; -"2084 dequantize_per_tensor_default_97" -> "2090 linear_96"; -"2085 linear_96_scale_0" -> "2087 quantize_per_channel_default_97"; -"2085 linear_96_scale_0" -> "2088 dequantize_per_channel_default_97"; -"2086 linear_96_zero_point_0" -> "2087 quantize_per_channel_default_97"; -"2086 linear_96_zero_point_0" -> "2088 dequantize_per_channel_default_97"; -"2087 quantize_per_channel_default_97" -> "2088 dequantize_per_channel_default_97"; -"2088 dequantize_per_channel_default_97" -> "2090 linear_96"; -"2089 _param_constant261_0_0" -> "2090 linear_96"; -"2090 linear_96" -> "2091 gelu_15"; -"2091 gelu_15" -> "2092 dropout_62"; -"2092 dropout_62" -> "2094 dropout_62_0_0_nncf_smooth_quant_0"; -"2093 linear_97_updated_constant0" -> "2099 quantize_per_channel_default_98"; -"2094 dropout_62_0_0_nncf_smooth_quant_0" -> "2095 quantize_per_tensor_default_98"; -"2095 quantize_per_tensor_default_98" -> "2096 dequantize_per_tensor_default_98"; -"2096 dequantize_per_tensor_default_98" -> "2102 linear_97"; -"2097 linear_97_scale_0" -> "2099 quantize_per_channel_default_98"; -"2097 linear_97_scale_0" -> "2100 dequantize_per_channel_default_98"; -"2098 linear_97_zero_point_0" -> "2099 quantize_per_channel_default_98"; -"2098 linear_97_zero_point_0" -> "2100 dequantize_per_channel_default_98"; -"2099 quantize_per_channel_default_98" -> "2100 dequantize_per_channel_default_98"; -"2100 dequantize_per_channel_default_98" -> "2102 linear_97"; -"2101 _param_constant263_0_0" -> "2102 linear_97"; -"2102 linear_97" -> "2103 dropout_63"; -"2103 dropout_63" -> "2106 layer_norm_34"; -"2104 _param_constant264" -> "2106 layer_norm_34"; -"2105 _param_constant265" -> "2106 layer_norm_34"; -"2106 layer_norm_34" -> "2107 add_55"; -"2107 add_55" -> "2134 pad_18"; -"2107 add_55" -> "2199 add_57"; -"2108 _tensor_constant104" -> "2110 _tensor_constant104_0_0_nncf_smooth_quant_0"; -"2109 linear_98_updated_constant0" -> "2113 quantize_per_channel_default_99"; -"2110 _tensor_constant104_0_0_nncf_smooth_quant_0" -> "2116 linear_98"; -"2111 linear_98_scale_0" -> "2113 quantize_per_channel_default_99"; -"2111 linear_98_scale_0" -> "2114 dequantize_per_channel_default_99"; -"2112 linear_98_zero_point_0" -> "2113 quantize_per_channel_default_99"; -"2112 linear_98_zero_point_0" -> "2114 dequantize_per_channel_default_99"; -"2113 quantize_per_channel_default_99" -> "2114 dequantize_per_channel_default_99"; -"2114 dequantize_per_channel_default_99" -> "2116 linear_98"; -"2115 _param_constant267_0_0" -> "2116 linear_98"; -"2116 linear_98" -> "2117 relu__16"; -"2117 relu__16" -> "2119 relu__16_0_0_nncf_smooth_quant_0"; -"2118 linear_99_updated_constant0" -> "2122 quantize_per_channel_default_100"; -"2119 relu__16_0_0_nncf_smooth_quant_0" -> "2124 linear_99"; -"2120 linear_99_scale_0" -> "2122 quantize_per_channel_default_100"; -"2120 linear_99_scale_0" -> "2123 dequantize_per_channel_default_100"; -"2121 linear_99_zero_point_0" -> "2122 quantize_per_channel_default_100"; -"2121 linear_99_zero_point_0" -> "2123 dequantize_per_channel_default_100"; -"2122 quantize_per_channel_default_100" -> "2123 dequantize_per_channel_default_100"; -"2123 dequantize_per_channel_default_100" -> "2124 linear_99"; -"2124 linear_99" -> "2125 view_88"; -"2125 view_88" -> "2127 index_16"; -"2126 _tensor_constant105" -> "2127 index_16"; -"2127 index_16" -> "2128 view_89"; -"2128 view_89" -> "2129 permute_73"; -"2129 permute_73" -> "2130 contiguous_30"; -"2130 contiguous_30" -> "2131 unsqueeze_48"; -"2131 unsqueeze_48" -> "2132 sigmoid_16"; -"2132 sigmoid_16" -> "2133 mul_32"; -"2133 mul_32" -> "2171 add_56"; -"2134 pad_18" -> "2135 view_90"; -"2135 view_90" -> "2136 permute_74"; -"2136 permute_74" -> "2137 reshape_72"; -"2137 reshape_72" -> "2139 reshape_72_0_0_nncf_smooth_quant_0"; -"2138 linear_100_updated_constant0" -> "2144 quantize_per_channel_default_101"; -"2139 reshape_72_0_0_nncf_smooth_quant_0" -> "2140 quantize_per_tensor_default_99"; -"2140 quantize_per_tensor_default_99" -> "2141 dequantize_per_tensor_default_99"; -"2141 dequantize_per_tensor_default_99" -> "2147 linear_100"; -"2142 linear_100_scale_0" -> "2144 quantize_per_channel_default_101"; -"2142 linear_100_scale_0" -> "2145 dequantize_per_channel_default_101"; -"2143 linear_100_zero_point_0" -> "2144 quantize_per_channel_default_101"; -"2143 linear_100_zero_point_0" -> "2145 dequantize_per_channel_default_101"; -"2144 quantize_per_channel_default_101" -> "2145 dequantize_per_channel_default_101"; -"2145 dequantize_per_channel_default_101" -> "2147 linear_100"; -"2146 _param_constant269_0_0" -> "2147 linear_100"; -"2147 linear_100" -> "2148 reshape_73"; -"2148 reshape_73" -> "2149 permute_75"; -"2149 permute_75" -> "2150 select_48"; -"2149 permute_75" -> "2151 select_49"; -"2149 permute_75" -> "2152 select_50"; -"2150 select_48" -> "2153 linalg_vector_norm_32"; -"2150 select_48" -> "2155 expand_as_32"; -"2150 select_48" -> "2156 div_32"; -"2151 select_49" -> "2159 linalg_vector_norm_33"; -"2151 select_49" -> "2161 expand_as_33"; -"2151 select_49" -> "2162 div_33"; -"2152 select_50" -> "2174 matmul_33"; -"2153 linalg_vector_norm_32" -> "2154 clamp_min_32"; -"2154 clamp_min_32" -> "2155 expand_as_32"; -"2155 expand_as_32" -> "2156 div_32"; -"2156 div_32" -> "2157 quantize_per_tensor_default_100"; -"2157 quantize_per_tensor_default_100" -> "2158 dequantize_per_tensor_default_100"; -"2158 dequantize_per_tensor_default_100" -> "2166 matmul_32"; -"2159 linalg_vector_norm_33" -> "2160 clamp_min_33"; -"2160 clamp_min_33" -> "2161 expand_as_33"; -"2161 expand_as_33" -> "2162 div_33"; -"2162 div_33" -> "2163 quantize_per_tensor_default_101"; -"2163 quantize_per_tensor_default_101" -> "2164 dequantize_per_tensor_default_101"; -"2164 dequantize_per_tensor_default_101" -> "2165 transpose_32"; -"2165 transpose_32" -> "2166 matmul_32"; -"2166 matmul_32" -> "2170 mul_33"; -"2167 _param_constant271" -> "2168 clamp_16"; -"2168 clamp_16" -> "2169 exp_16"; -"2169 exp_16" -> "2170 mul_33"; -"2170 mul_33" -> "2171 add_56"; -"2171 add_56" -> "2172 softmax_16"; -"2172 softmax_16" -> "2173 dropout_64"; -"2173 dropout_64" -> "2174 matmul_33"; -"2174 matmul_33" -> "2175 transpose_33"; -"2175 transpose_33" -> "2176 reshape_74"; -"2176 reshape_74" -> "2178 reshape_74_0_0_nncf_smooth_quant_0"; -"2177 linear_101_updated_constant0" -> "2183 quantize_per_channel_default_102"; -"2178 reshape_74_0_0_nncf_smooth_quant_0" -> "2179 quantize_per_tensor_default_102"; -"2179 quantize_per_tensor_default_102" -> "2180 dequantize_per_tensor_default_102"; -"2180 dequantize_per_tensor_default_102" -> "2186 linear_101"; -"2181 linear_101_scale_0" -> "2183 quantize_per_channel_default_102"; -"2181 linear_101_scale_0" -> "2184 dequantize_per_channel_default_102"; -"2182 linear_101_zero_point_0" -> "2183 quantize_per_channel_default_102"; -"2182 linear_101_zero_point_0" -> "2184 dequantize_per_channel_default_102"; -"2183 quantize_per_channel_default_102" -> "2184 dequantize_per_channel_default_102"; -"2184 dequantize_per_channel_default_102" -> "2186 linear_101"; -"2185 _param_constant273_0_0" -> "2186 linear_101"; -"2186 linear_101" -> "2187 dropout_65"; -"2187 dropout_65" -> "2188 view_91"; -"2188 view_91" -> "2189 permute_76"; -"2189 permute_76" -> "2190 reshape_75"; -"2190 reshape_75" -> "2191 slice_246"; -"2191 slice_246" -> "2192 slice_247"; -"2192 slice_247" -> "2193 slice_248"; -"2193 slice_248" -> "2194 slice_249"; -"2194 slice_249" -> "2195 contiguous_31"; -"2195 contiguous_31" -> "2198 layer_norm_35"; -"2196 _param_constant274" -> "2198 layer_norm_35"; -"2197 _param_constant275" -> "2198 layer_norm_35"; -"2198 layer_norm_35" -> "2199 add_57"; -"2199 add_57" -> "2201 add_57_0_0_nncf_smooth_quant_0"; -"2199 add_57" -> "2226 add_58"; -"2200 linear_102_updated_constant0" -> "2206 quantize_per_channel_default_103"; -"2201 add_57_0_0_nncf_smooth_quant_0" -> "2202 quantize_per_tensor_default_103"; -"2202 quantize_per_tensor_default_103" -> "2203 dequantize_per_tensor_default_103"; -"2203 dequantize_per_tensor_default_103" -> "2209 linear_102"; -"2204 linear_102_scale_0" -> "2206 quantize_per_channel_default_103"; -"2204 linear_102_scale_0" -> "2207 dequantize_per_channel_default_103"; -"2205 linear_102_zero_point_0" -> "2206 quantize_per_channel_default_103"; -"2205 linear_102_zero_point_0" -> "2207 dequantize_per_channel_default_103"; -"2206 quantize_per_channel_default_103" -> "2207 dequantize_per_channel_default_103"; -"2207 dequantize_per_channel_default_103" -> "2209 linear_102"; -"2208 _param_constant277_0_0" -> "2209 linear_102"; -"2209 linear_102" -> "2210 gelu_16"; -"2210 gelu_16" -> "2211 dropout_66"; -"2211 dropout_66" -> "2213 dropout_66_0_0_nncf_smooth_quant_0"; -"2212 linear_103_updated_constant0" -> "2218 quantize_per_channel_default_104"; -"2213 dropout_66_0_0_nncf_smooth_quant_0" -> "2214 quantize_per_tensor_default_104"; -"2214 quantize_per_tensor_default_104" -> "2215 dequantize_per_tensor_default_104"; -"2215 dequantize_per_tensor_default_104" -> "2221 linear_103"; -"2216 linear_103_scale_0" -> "2218 quantize_per_channel_default_104"; -"2216 linear_103_scale_0" -> "2219 dequantize_per_channel_default_104"; -"2217 linear_103_zero_point_0" -> "2218 quantize_per_channel_default_104"; -"2217 linear_103_zero_point_0" -> "2219 dequantize_per_channel_default_104"; -"2218 quantize_per_channel_default_104" -> "2219 dequantize_per_channel_default_104"; -"2219 dequantize_per_channel_default_104" -> "2221 linear_103"; -"2220 _param_constant279_0_0" -> "2221 linear_103"; -"2221 linear_103" -> "2222 dropout_67"; -"2222 dropout_67" -> "2225 layer_norm_36"; -"2223 _param_constant280" -> "2225 layer_norm_36"; -"2224 _param_constant281" -> "2225 layer_norm_36"; -"2225 layer_norm_36" -> "2226 add_58"; -"2226 add_58" -> "2253 pad_19"; -"2226 add_58" -> "2336 add_61"; -"2227 _tensor_constant106" -> "2229 _tensor_constant106_0_0_nncf_smooth_quant_0"; -"2228 linear_104_updated_constant0" -> "2232 quantize_per_channel_default_105"; -"2229 _tensor_constant106_0_0_nncf_smooth_quant_0" -> "2235 linear_104"; -"2230 linear_104_scale_0" -> "2232 quantize_per_channel_default_105"; -"2230 linear_104_scale_0" -> "2233 dequantize_per_channel_default_105"; -"2231 linear_104_zero_point_0" -> "2232 quantize_per_channel_default_105"; -"2231 linear_104_zero_point_0" -> "2233 dequantize_per_channel_default_105"; -"2232 quantize_per_channel_default_105" -> "2233 dequantize_per_channel_default_105"; -"2233 dequantize_per_channel_default_105" -> "2235 linear_104"; -"2234 _param_constant283_0_0" -> "2235 linear_104"; -"2235 linear_104" -> "2236 relu__17"; -"2236 relu__17" -> "2238 relu__17_0_0_nncf_smooth_quant_0"; -"2237 linear_105_updated_constant0" -> "2241 quantize_per_channel_default_106"; -"2238 relu__17_0_0_nncf_smooth_quant_0" -> "2243 linear_105"; -"2239 linear_105_scale_0" -> "2241 quantize_per_channel_default_106"; -"2239 linear_105_scale_0" -> "2242 dequantize_per_channel_default_106"; -"2240 linear_105_zero_point_0" -> "2241 quantize_per_channel_default_106"; -"2240 linear_105_zero_point_0" -> "2242 dequantize_per_channel_default_106"; -"2241 quantize_per_channel_default_106" -> "2242 dequantize_per_channel_default_106"; -"2242 dequantize_per_channel_default_106" -> "2243 linear_105"; -"2243 linear_105" -> "2244 view_92"; -"2244 view_92" -> "2246 index_17"; -"2245 _tensor_constant107" -> "2246 index_17"; -"2246 index_17" -> "2247 view_93"; -"2247 view_93" -> "2248 permute_77"; -"2248 permute_77" -> "2249 contiguous_32"; -"2249 contiguous_32" -> "2250 unsqueeze_49"; -"2250 unsqueeze_49" -> "2251 sigmoid_17"; -"2251 sigmoid_17" -> "2252 mul_34"; -"2252 mul_34" -> "2291 add_59"; -"2253 pad_19" -> "2254 roll_16"; -"2254 roll_16" -> "2255 view_94"; -"2255 view_94" -> "2256 permute_78"; -"2256 permute_78" -> "2257 reshape_76"; -"2257 reshape_76" -> "2259 reshape_76_0_0_nncf_smooth_quant_0"; -"2257 reshape_76" -> "2292 new_zeros_8"; -"2258 linear_106_updated_constant0" -> "2264 quantize_per_channel_default_107"; -"2259 reshape_76_0_0_nncf_smooth_quant_0" -> "2260 quantize_per_tensor_default_105"; -"2260 quantize_per_tensor_default_105" -> "2261 dequantize_per_tensor_default_105"; -"2261 dequantize_per_tensor_default_105" -> "2267 linear_106"; -"2262 linear_106_scale_0" -> "2264 quantize_per_channel_default_107"; -"2262 linear_106_scale_0" -> "2265 dequantize_per_channel_default_107"; -"2263 linear_106_zero_point_0" -> "2264 quantize_per_channel_default_107"; -"2263 linear_106_zero_point_0" -> "2265 dequantize_per_channel_default_107"; -"2264 quantize_per_channel_default_107" -> "2265 dequantize_per_channel_default_107"; -"2265 dequantize_per_channel_default_107" -> "2267 linear_106"; -"2266 _param_constant285_0_0" -> "2267 linear_106"; -"2267 linear_106" -> "2268 reshape_77"; -"2268 reshape_77" -> "2269 permute_79"; -"2269 permute_79" -> "2270 select_51"; -"2269 permute_79" -> "2271 select_52"; -"2269 permute_79" -> "2272 select_53"; -"2270 select_51" -> "2273 linalg_vector_norm_34"; -"2270 select_51" -> "2275 expand_as_34"; -"2270 select_51" -> "2276 div_34"; -"2271 select_52" -> "2279 linalg_vector_norm_35"; -"2271 select_52" -> "2281 expand_as_35"; -"2271 select_52" -> "2282 div_35"; -"2272 select_53" -> "2310 matmul_35"; -"2273 linalg_vector_norm_34" -> "2274 clamp_min_34"; -"2274 clamp_min_34" -> "2275 expand_as_34"; -"2275 expand_as_34" -> "2276 div_34"; -"2276 div_34" -> "2277 quantize_per_tensor_default_106"; -"2277 quantize_per_tensor_default_106" -> "2278 dequantize_per_tensor_default_106"; -"2278 dequantize_per_tensor_default_106" -> "2286 matmul_34"; -"2279 linalg_vector_norm_35" -> "2280 clamp_min_35"; -"2280 clamp_min_35" -> "2281 expand_as_35"; -"2281 expand_as_35" -> "2282 div_35"; -"2282 div_35" -> "2283 quantize_per_tensor_default_107"; -"2283 quantize_per_tensor_default_107" -> "2284 dequantize_per_tensor_default_107"; -"2284 dequantize_per_tensor_default_107" -> "2285 transpose_34"; -"2285 transpose_34" -> "2286 matmul_34"; -"2286 matmul_34" -> "2290 mul_35"; -"2287 _param_constant287" -> "2288 clamp_17"; -"2288 clamp_17" -> "2289 exp_17"; -"2289 exp_17" -> "2290 mul_35"; -"2290 mul_35" -> "2291 add_59"; -"2291 add_59" -> "2303 view_96"; -"2292 new_zeros_8" -> "2293 view_95"; -"2293 view_95" -> "2294 permute_80"; -"2294 permute_80" -> "2295 reshape_78"; -"2295 reshape_78" -> "2296 unsqueeze_50"; -"2295 reshape_78" -> "2297 unsqueeze_51"; -"2296 unsqueeze_50" -> "2298 sub_8"; -"2297 unsqueeze_51" -> "2298 sub_8"; -"2298 sub_8" -> "2299 ne_8"; -"2298 sub_8" -> "2300 masked_fill_16"; -"2298 sub_8" -> "2301 eq_8"; -"2299 ne_8" -> "2300 masked_fill_16"; -"2300 masked_fill_16" -> "2302 masked_fill_17"; -"2301 eq_8" -> "2302 masked_fill_17"; -"2302 masked_fill_17" -> "2304 unsqueeze_52"; -"2303 view_96" -> "2306 add_60"; -"2304 unsqueeze_52" -> "2305 unsqueeze_53"; -"2305 unsqueeze_53" -> "2306 add_60"; -"2306 add_60" -> "2307 view_97"; -"2307 view_97" -> "2308 softmax_17"; -"2308 softmax_17" -> "2309 dropout_68"; -"2309 dropout_68" -> "2310 matmul_35"; -"2310 matmul_35" -> "2311 transpose_35"; -"2311 transpose_35" -> "2312 reshape_79"; -"2312 reshape_79" -> "2314 reshape_79_0_0_nncf_smooth_quant_0"; -"2313 linear_107_updated_constant0" -> "2319 quantize_per_channel_default_108"; -"2314 reshape_79_0_0_nncf_smooth_quant_0" -> "2315 quantize_per_tensor_default_108"; -"2315 quantize_per_tensor_default_108" -> "2316 dequantize_per_tensor_default_108"; -"2316 dequantize_per_tensor_default_108" -> "2322 linear_107"; -"2317 linear_107_scale_0" -> "2319 quantize_per_channel_default_108"; -"2317 linear_107_scale_0" -> "2320 dequantize_per_channel_default_108"; -"2318 linear_107_zero_point_0" -> "2319 quantize_per_channel_default_108"; -"2318 linear_107_zero_point_0" -> "2320 dequantize_per_channel_default_108"; -"2319 quantize_per_channel_default_108" -> "2320 dequantize_per_channel_default_108"; -"2320 dequantize_per_channel_default_108" -> "2322 linear_107"; -"2321 _param_constant289_0_0" -> "2322 linear_107"; -"2322 linear_107" -> "2323 dropout_69"; -"2323 dropout_69" -> "2324 view_98"; -"2324 view_98" -> "2325 permute_81"; -"2325 permute_81" -> "2326 reshape_80"; -"2326 reshape_80" -> "2327 roll_17"; -"2327 roll_17" -> "2328 slice_269"; -"2328 slice_269" -> "2329 slice_270"; -"2329 slice_270" -> "2330 slice_271"; -"2330 slice_271" -> "2331 slice_272"; -"2331 slice_272" -> "2332 contiguous_33"; -"2332 contiguous_33" -> "2335 layer_norm_37"; -"2333 _param_constant290" -> "2335 layer_norm_37"; -"2334 _param_constant291" -> "2335 layer_norm_37"; -"2335 layer_norm_37" -> "2336 add_61"; -"2336 add_61" -> "2338 add_61_0_0_nncf_smooth_quant_0"; -"2336 add_61" -> "2363 add_62"; -"2337 linear_108_updated_constant0" -> "2343 quantize_per_channel_default_109"; -"2338 add_61_0_0_nncf_smooth_quant_0" -> "2339 quantize_per_tensor_default_109"; -"2339 quantize_per_tensor_default_109" -> "2340 dequantize_per_tensor_default_109"; -"2340 dequantize_per_tensor_default_109" -> "2346 linear_108"; -"2341 linear_108_scale_0" -> "2343 quantize_per_channel_default_109"; -"2341 linear_108_scale_0" -> "2344 dequantize_per_channel_default_109"; -"2342 linear_108_zero_point_0" -> "2343 quantize_per_channel_default_109"; -"2342 linear_108_zero_point_0" -> "2344 dequantize_per_channel_default_109"; -"2343 quantize_per_channel_default_109" -> "2344 dequantize_per_channel_default_109"; -"2344 dequantize_per_channel_default_109" -> "2346 linear_108"; -"2345 _param_constant293_0_0" -> "2346 linear_108"; -"2346 linear_108" -> "2347 gelu_17"; -"2347 gelu_17" -> "2348 dropout_70"; -"2348 dropout_70" -> "2350 dropout_70_0_0_nncf_smooth_quant_0"; -"2349 linear_109_updated_constant0" -> "2355 quantize_per_channel_default_110"; -"2350 dropout_70_0_0_nncf_smooth_quant_0" -> "2351 quantize_per_tensor_default_110"; -"2351 quantize_per_tensor_default_110" -> "2352 dequantize_per_tensor_default_110"; -"2352 dequantize_per_tensor_default_110" -> "2358 linear_109"; -"2353 linear_109_scale_0" -> "2355 quantize_per_channel_default_110"; -"2353 linear_109_scale_0" -> "2356 dequantize_per_channel_default_110"; -"2354 linear_109_zero_point_0" -> "2355 quantize_per_channel_default_110"; -"2354 linear_109_zero_point_0" -> "2356 dequantize_per_channel_default_110"; -"2355 quantize_per_channel_default_110" -> "2356 dequantize_per_channel_default_110"; -"2356 dequantize_per_channel_default_110" -> "2358 linear_109"; -"2357 _param_constant295_0_0" -> "2358 linear_109"; -"2358 linear_109" -> "2359 dropout_71"; -"2359 dropout_71" -> "2362 layer_norm_38"; -"2360 _param_constant296" -> "2362 layer_norm_38"; -"2361 _param_constant297" -> "2362 layer_norm_38"; -"2362 layer_norm_38" -> "2363 add_62"; -"2363 add_62" -> "2390 pad_20"; -"2363 add_62" -> "2455 add_64"; -"2364 _tensor_constant117" -> "2366 _tensor_constant117_0_0_nncf_smooth_quant_0"; -"2365 linear_110_updated_constant0" -> "2369 quantize_per_channel_default_111"; -"2366 _tensor_constant117_0_0_nncf_smooth_quant_0" -> "2372 linear_110"; -"2367 linear_110_scale_0" -> "2369 quantize_per_channel_default_111"; -"2367 linear_110_scale_0" -> "2370 dequantize_per_channel_default_111"; -"2368 linear_110_zero_point_0" -> "2369 quantize_per_channel_default_111"; -"2368 linear_110_zero_point_0" -> "2370 dequantize_per_channel_default_111"; -"2369 quantize_per_channel_default_111" -> "2370 dequantize_per_channel_default_111"; -"2370 dequantize_per_channel_default_111" -> "2372 linear_110"; -"2371 _param_constant299_0_0" -> "2372 linear_110"; -"2372 linear_110" -> "2373 relu__18"; -"2373 relu__18" -> "2375 relu__18_0_0_nncf_smooth_quant_0"; -"2374 linear_111_updated_constant0" -> "2378 quantize_per_channel_default_112"; -"2375 relu__18_0_0_nncf_smooth_quant_0" -> "2380 linear_111"; -"2376 linear_111_scale_0" -> "2378 quantize_per_channel_default_112"; -"2376 linear_111_scale_0" -> "2379 dequantize_per_channel_default_112"; -"2377 linear_111_zero_point_0" -> "2378 quantize_per_channel_default_112"; -"2377 linear_111_zero_point_0" -> "2379 dequantize_per_channel_default_112"; -"2378 quantize_per_channel_default_112" -> "2379 dequantize_per_channel_default_112"; -"2379 dequantize_per_channel_default_112" -> "2380 linear_111"; -"2380 linear_111" -> "2381 view_99"; -"2381 view_99" -> "2383 index_18"; -"2382 _tensor_constant118" -> "2383 index_18"; -"2383 index_18" -> "2384 view_100"; -"2384 view_100" -> "2385 permute_82"; -"2385 permute_82" -> "2386 contiguous_34"; -"2386 contiguous_34" -> "2387 unsqueeze_54"; -"2387 unsqueeze_54" -> "2388 sigmoid_18"; -"2388 sigmoid_18" -> "2389 mul_36"; -"2389 mul_36" -> "2427 add_63"; -"2390 pad_20" -> "2391 view_101"; -"2391 view_101" -> "2392 permute_83"; -"2392 permute_83" -> "2393 reshape_81"; -"2393 reshape_81" -> "2395 reshape_81_0_0_nncf_smooth_quant_0"; -"2394 linear_112_updated_constant0" -> "2400 quantize_per_channel_default_113"; -"2395 reshape_81_0_0_nncf_smooth_quant_0" -> "2396 quantize_per_tensor_default_111"; -"2396 quantize_per_tensor_default_111" -> "2397 dequantize_per_tensor_default_111"; -"2397 dequantize_per_tensor_default_111" -> "2403 linear_112"; -"2398 linear_112_scale_0" -> "2400 quantize_per_channel_default_113"; -"2398 linear_112_scale_0" -> "2401 dequantize_per_channel_default_113"; -"2399 linear_112_zero_point_0" -> "2400 quantize_per_channel_default_113"; -"2399 linear_112_zero_point_0" -> "2401 dequantize_per_channel_default_113"; -"2400 quantize_per_channel_default_113" -> "2401 dequantize_per_channel_default_113"; -"2401 dequantize_per_channel_default_113" -> "2403 linear_112"; -"2402 _param_constant301_0_0" -> "2403 linear_112"; -"2403 linear_112" -> "2404 reshape_82"; -"2404 reshape_82" -> "2405 permute_84"; -"2405 permute_84" -> "2406 select_54"; -"2405 permute_84" -> "2407 select_55"; -"2405 permute_84" -> "2408 select_56"; -"2406 select_54" -> "2409 linalg_vector_norm_36"; -"2406 select_54" -> "2411 expand_as_36"; -"2406 select_54" -> "2412 div_36"; -"2407 select_55" -> "2415 linalg_vector_norm_37"; -"2407 select_55" -> "2417 expand_as_37"; -"2407 select_55" -> "2418 div_37"; -"2408 select_56" -> "2430 matmul_37"; -"2409 linalg_vector_norm_36" -> "2410 clamp_min_36"; -"2410 clamp_min_36" -> "2411 expand_as_36"; -"2411 expand_as_36" -> "2412 div_36"; -"2412 div_36" -> "2413 quantize_per_tensor_default_112"; -"2413 quantize_per_tensor_default_112" -> "2414 dequantize_per_tensor_default_112"; -"2414 dequantize_per_tensor_default_112" -> "2422 matmul_36"; -"2415 linalg_vector_norm_37" -> "2416 clamp_min_37"; -"2416 clamp_min_37" -> "2417 expand_as_37"; -"2417 expand_as_37" -> "2418 div_37"; -"2418 div_37" -> "2419 quantize_per_tensor_default_113"; -"2419 quantize_per_tensor_default_113" -> "2420 dequantize_per_tensor_default_113"; -"2420 dequantize_per_tensor_default_113" -> "2421 transpose_36"; -"2421 transpose_36" -> "2422 matmul_36"; -"2422 matmul_36" -> "2426 mul_37"; -"2423 _param_constant303" -> "2424 clamp_18"; -"2424 clamp_18" -> "2425 exp_18"; -"2425 exp_18" -> "2426 mul_37"; -"2426 mul_37" -> "2427 add_63"; -"2427 add_63" -> "2428 softmax_18"; -"2428 softmax_18" -> "2429 dropout_72"; -"2429 dropout_72" -> "2430 matmul_37"; -"2430 matmul_37" -> "2431 transpose_37"; -"2431 transpose_37" -> "2432 reshape_83"; -"2432 reshape_83" -> "2434 reshape_83_0_0_nncf_smooth_quant_0"; -"2433 linear_113_updated_constant0" -> "2439 quantize_per_channel_default_114"; -"2434 reshape_83_0_0_nncf_smooth_quant_0" -> "2435 quantize_per_tensor_default_114"; -"2435 quantize_per_tensor_default_114" -> "2436 dequantize_per_tensor_default_114"; -"2436 dequantize_per_tensor_default_114" -> "2442 linear_113"; -"2437 linear_113_scale_0" -> "2439 quantize_per_channel_default_114"; -"2437 linear_113_scale_0" -> "2440 dequantize_per_channel_default_114"; -"2438 linear_113_zero_point_0" -> "2439 quantize_per_channel_default_114"; -"2438 linear_113_zero_point_0" -> "2440 dequantize_per_channel_default_114"; -"2439 quantize_per_channel_default_114" -> "2440 dequantize_per_channel_default_114"; -"2440 dequantize_per_channel_default_114" -> "2442 linear_113"; -"2441 _param_constant305_0_0" -> "2442 linear_113"; -"2442 linear_113" -> "2443 dropout_73"; -"2443 dropout_73" -> "2444 view_102"; -"2444 view_102" -> "2445 permute_85"; -"2445 permute_85" -> "2446 reshape_84"; -"2446 reshape_84" -> "2447 slice_274"; -"2447 slice_274" -> "2448 slice_275"; -"2448 slice_275" -> "2449 slice_276"; -"2449 slice_276" -> "2450 slice_277"; -"2450 slice_277" -> "2451 contiguous_35"; -"2451 contiguous_35" -> "2454 layer_norm_39"; -"2452 _param_constant306" -> "2454 layer_norm_39"; -"2453 _param_constant307" -> "2454 layer_norm_39"; -"2454 layer_norm_39" -> "2455 add_64"; -"2455 add_64" -> "2457 add_64_0_0_nncf_smooth_quant_0"; -"2455 add_64" -> "2482 add_65"; -"2456 linear_114_updated_constant0" -> "2462 quantize_per_channel_default_115"; -"2457 add_64_0_0_nncf_smooth_quant_0" -> "2458 quantize_per_tensor_default_115"; -"2458 quantize_per_tensor_default_115" -> "2459 dequantize_per_tensor_default_115"; -"2459 dequantize_per_tensor_default_115" -> "2465 linear_114"; -"2460 linear_114_scale_0" -> "2462 quantize_per_channel_default_115"; -"2460 linear_114_scale_0" -> "2463 dequantize_per_channel_default_115"; -"2461 linear_114_zero_point_0" -> "2462 quantize_per_channel_default_115"; -"2461 linear_114_zero_point_0" -> "2463 dequantize_per_channel_default_115"; -"2462 quantize_per_channel_default_115" -> "2463 dequantize_per_channel_default_115"; -"2463 dequantize_per_channel_default_115" -> "2465 linear_114"; -"2464 _param_constant309_0_0" -> "2465 linear_114"; -"2465 linear_114" -> "2466 gelu_18"; -"2466 gelu_18" -> "2467 dropout_74"; -"2467 dropout_74" -> "2469 dropout_74_0_0_nncf_smooth_quant_0"; -"2468 linear_115_updated_constant0" -> "2474 quantize_per_channel_default_116"; -"2469 dropout_74_0_0_nncf_smooth_quant_0" -> "2470 quantize_per_tensor_default_116"; -"2470 quantize_per_tensor_default_116" -> "2471 dequantize_per_tensor_default_116"; -"2471 dequantize_per_tensor_default_116" -> "2477 linear_115"; -"2472 linear_115_scale_0" -> "2474 quantize_per_channel_default_116"; -"2472 linear_115_scale_0" -> "2475 dequantize_per_channel_default_116"; -"2473 linear_115_zero_point_0" -> "2474 quantize_per_channel_default_116"; -"2473 linear_115_zero_point_0" -> "2475 dequantize_per_channel_default_116"; -"2474 quantize_per_channel_default_116" -> "2475 dequantize_per_channel_default_116"; -"2475 dequantize_per_channel_default_116" -> "2477 linear_115"; -"2476 _param_constant311_0_0" -> "2477 linear_115"; -"2477 linear_115" -> "2478 dropout_75"; -"2478 dropout_75" -> "2481 layer_norm_40"; -"2479 _param_constant312" -> "2481 layer_norm_40"; -"2480 _param_constant313" -> "2481 layer_norm_40"; -"2481 layer_norm_40" -> "2482 add_65"; -"2482 add_65" -> "2509 pad_21"; -"2482 add_65" -> "2592 add_68"; -"2483 _tensor_constant119" -> "2485 _tensor_constant119_0_0_nncf_smooth_quant_0"; -"2484 linear_116_updated_constant0" -> "2488 quantize_per_channel_default_117"; -"2485 _tensor_constant119_0_0_nncf_smooth_quant_0" -> "2491 linear_116"; -"2486 linear_116_scale_0" -> "2488 quantize_per_channel_default_117"; -"2486 linear_116_scale_0" -> "2489 dequantize_per_channel_default_117"; -"2487 linear_116_zero_point_0" -> "2488 quantize_per_channel_default_117"; -"2487 linear_116_zero_point_0" -> "2489 dequantize_per_channel_default_117"; -"2488 quantize_per_channel_default_117" -> "2489 dequantize_per_channel_default_117"; -"2489 dequantize_per_channel_default_117" -> "2491 linear_116"; -"2490 _param_constant315_0_0" -> "2491 linear_116"; -"2491 linear_116" -> "2492 relu__19"; -"2492 relu__19" -> "2494 relu__19_0_0_nncf_smooth_quant_0"; -"2493 linear_117_updated_constant0" -> "2497 quantize_per_channel_default_118"; -"2494 relu__19_0_0_nncf_smooth_quant_0" -> "2499 linear_117"; -"2495 linear_117_scale_0" -> "2497 quantize_per_channel_default_118"; -"2495 linear_117_scale_0" -> "2498 dequantize_per_channel_default_118"; -"2496 linear_117_zero_point_0" -> "2497 quantize_per_channel_default_118"; -"2496 linear_117_zero_point_0" -> "2498 dequantize_per_channel_default_118"; -"2497 quantize_per_channel_default_118" -> "2498 dequantize_per_channel_default_118"; -"2498 dequantize_per_channel_default_118" -> "2499 linear_117"; -"2499 linear_117" -> "2500 view_103"; -"2500 view_103" -> "2502 index_19"; -"2501 _tensor_constant120" -> "2502 index_19"; -"2502 index_19" -> "2503 view_104"; -"2503 view_104" -> "2504 permute_86"; -"2504 permute_86" -> "2505 contiguous_36"; -"2505 contiguous_36" -> "2506 unsqueeze_55"; -"2506 unsqueeze_55" -> "2507 sigmoid_19"; -"2507 sigmoid_19" -> "2508 mul_38"; -"2508 mul_38" -> "2547 add_66"; -"2509 pad_21" -> "2510 roll_18"; -"2510 roll_18" -> "2511 view_105"; -"2511 view_105" -> "2512 permute_87"; -"2512 permute_87" -> "2513 reshape_85"; -"2513 reshape_85" -> "2515 reshape_85_0_0_nncf_smooth_quant_0"; -"2513 reshape_85" -> "2548 new_zeros_9"; -"2514 linear_118_updated_constant0" -> "2520 quantize_per_channel_default_119"; -"2515 reshape_85_0_0_nncf_smooth_quant_0" -> "2516 quantize_per_tensor_default_117"; -"2516 quantize_per_tensor_default_117" -> "2517 dequantize_per_tensor_default_117"; -"2517 dequantize_per_tensor_default_117" -> "2523 linear_118"; -"2518 linear_118_scale_0" -> "2520 quantize_per_channel_default_119"; -"2518 linear_118_scale_0" -> "2521 dequantize_per_channel_default_119"; -"2519 linear_118_zero_point_0" -> "2520 quantize_per_channel_default_119"; -"2519 linear_118_zero_point_0" -> "2521 dequantize_per_channel_default_119"; -"2520 quantize_per_channel_default_119" -> "2521 dequantize_per_channel_default_119"; -"2521 dequantize_per_channel_default_119" -> "2523 linear_118"; -"2522 _param_constant317_0_0" -> "2523 linear_118"; -"2523 linear_118" -> "2524 reshape_86"; -"2524 reshape_86" -> "2525 permute_88"; -"2525 permute_88" -> "2526 select_57"; -"2525 permute_88" -> "2527 select_58"; -"2525 permute_88" -> "2528 select_59"; -"2526 select_57" -> "2529 linalg_vector_norm_38"; -"2526 select_57" -> "2531 expand_as_38"; -"2526 select_57" -> "2532 div_38"; -"2527 select_58" -> "2535 linalg_vector_norm_39"; -"2527 select_58" -> "2537 expand_as_39"; -"2527 select_58" -> "2538 div_39"; -"2528 select_59" -> "2566 matmul_39"; -"2529 linalg_vector_norm_38" -> "2530 clamp_min_38"; -"2530 clamp_min_38" -> "2531 expand_as_38"; -"2531 expand_as_38" -> "2532 div_38"; -"2532 div_38" -> "2533 quantize_per_tensor_default_118"; -"2533 quantize_per_tensor_default_118" -> "2534 dequantize_per_tensor_default_118"; -"2534 dequantize_per_tensor_default_118" -> "2542 matmul_38"; -"2535 linalg_vector_norm_39" -> "2536 clamp_min_39"; -"2536 clamp_min_39" -> "2537 expand_as_39"; -"2537 expand_as_39" -> "2538 div_39"; -"2538 div_39" -> "2539 quantize_per_tensor_default_119"; -"2539 quantize_per_tensor_default_119" -> "2540 dequantize_per_tensor_default_119"; -"2540 dequantize_per_tensor_default_119" -> "2541 transpose_38"; -"2541 transpose_38" -> "2542 matmul_38"; -"2542 matmul_38" -> "2546 mul_39"; -"2543 _param_constant319" -> "2544 clamp_19"; -"2544 clamp_19" -> "2545 exp_19"; -"2545 exp_19" -> "2546 mul_39"; -"2546 mul_39" -> "2547 add_66"; -"2547 add_66" -> "2559 view_107"; -"2548 new_zeros_9" -> "2549 view_106"; -"2549 view_106" -> "2550 permute_89"; -"2550 permute_89" -> "2551 reshape_87"; -"2551 reshape_87" -> "2552 unsqueeze_56"; -"2551 reshape_87" -> "2553 unsqueeze_57"; -"2552 unsqueeze_56" -> "2554 sub_9"; -"2553 unsqueeze_57" -> "2554 sub_9"; -"2554 sub_9" -> "2555 ne_9"; -"2554 sub_9" -> "2556 masked_fill_18"; -"2554 sub_9" -> "2557 eq_9"; -"2555 ne_9" -> "2556 masked_fill_18"; -"2556 masked_fill_18" -> "2558 masked_fill_19"; -"2557 eq_9" -> "2558 masked_fill_19"; -"2558 masked_fill_19" -> "2560 unsqueeze_58"; -"2559 view_107" -> "2562 add_67"; -"2560 unsqueeze_58" -> "2561 unsqueeze_59"; -"2561 unsqueeze_59" -> "2562 add_67"; -"2562 add_67" -> "2563 view_108"; -"2563 view_108" -> "2564 softmax_19"; -"2564 softmax_19" -> "2565 dropout_76"; -"2565 dropout_76" -> "2566 matmul_39"; -"2566 matmul_39" -> "2567 transpose_39"; -"2567 transpose_39" -> "2568 reshape_88"; -"2568 reshape_88" -> "2570 reshape_88_0_0_nncf_smooth_quant_0"; -"2569 linear_119_updated_constant0" -> "2575 quantize_per_channel_default_120"; -"2570 reshape_88_0_0_nncf_smooth_quant_0" -> "2571 quantize_per_tensor_default_120"; -"2571 quantize_per_tensor_default_120" -> "2572 dequantize_per_tensor_default_120"; -"2572 dequantize_per_tensor_default_120" -> "2578 linear_119"; -"2573 linear_119_scale_0" -> "2575 quantize_per_channel_default_120"; -"2573 linear_119_scale_0" -> "2576 dequantize_per_channel_default_120"; -"2574 linear_119_zero_point_0" -> "2575 quantize_per_channel_default_120"; -"2574 linear_119_zero_point_0" -> "2576 dequantize_per_channel_default_120"; -"2575 quantize_per_channel_default_120" -> "2576 dequantize_per_channel_default_120"; -"2576 dequantize_per_channel_default_120" -> "2578 linear_119"; -"2577 _param_constant321_0_0" -> "2578 linear_119"; -"2578 linear_119" -> "2579 dropout_77"; -"2579 dropout_77" -> "2580 view_109"; -"2580 view_109" -> "2581 permute_90"; -"2581 permute_90" -> "2582 reshape_89"; -"2582 reshape_89" -> "2583 roll_19"; -"2583 roll_19" -> "2584 slice_297"; -"2584 slice_297" -> "2585 slice_298"; -"2585 slice_298" -> "2586 slice_299"; -"2586 slice_299" -> "2587 slice_300"; -"2587 slice_300" -> "2588 contiguous_37"; -"2588 contiguous_37" -> "2591 layer_norm_41"; -"2589 _param_constant322" -> "2591 layer_norm_41"; -"2590 _param_constant323" -> "2591 layer_norm_41"; -"2591 layer_norm_41" -> "2592 add_68"; -"2592 add_68" -> "2594 add_68_0_0_nncf_smooth_quant_0"; -"2592 add_68" -> "2619 add_69"; -"2593 linear_120_updated_constant0" -> "2599 quantize_per_channel_default_121"; -"2594 add_68_0_0_nncf_smooth_quant_0" -> "2595 quantize_per_tensor_default_121"; -"2595 quantize_per_tensor_default_121" -> "2596 dequantize_per_tensor_default_121"; -"2596 dequantize_per_tensor_default_121" -> "2602 linear_120"; -"2597 linear_120_scale_0" -> "2599 quantize_per_channel_default_121"; -"2597 linear_120_scale_0" -> "2600 dequantize_per_channel_default_121"; -"2598 linear_120_zero_point_0" -> "2599 quantize_per_channel_default_121"; -"2598 linear_120_zero_point_0" -> "2600 dequantize_per_channel_default_121"; -"2599 quantize_per_channel_default_121" -> "2600 dequantize_per_channel_default_121"; -"2600 dequantize_per_channel_default_121" -> "2602 linear_120"; -"2601 _param_constant325_0_0" -> "2602 linear_120"; -"2602 linear_120" -> "2603 gelu_19"; -"2603 gelu_19" -> "2604 dropout_78"; -"2604 dropout_78" -> "2606 dropout_78_0_0_nncf_smooth_quant_0"; -"2605 linear_121_updated_constant0" -> "2611 quantize_per_channel_default_122"; -"2606 dropout_78_0_0_nncf_smooth_quant_0" -> "2607 quantize_per_tensor_default_122"; -"2607 quantize_per_tensor_default_122" -> "2608 dequantize_per_tensor_default_122"; -"2608 dequantize_per_tensor_default_122" -> "2614 linear_121"; -"2609 linear_121_scale_0" -> "2611 quantize_per_channel_default_122"; -"2609 linear_121_scale_0" -> "2612 dequantize_per_channel_default_122"; -"2610 linear_121_zero_point_0" -> "2611 quantize_per_channel_default_122"; -"2610 linear_121_zero_point_0" -> "2612 dequantize_per_channel_default_122"; -"2611 quantize_per_channel_default_122" -> "2612 dequantize_per_channel_default_122"; -"2612 dequantize_per_channel_default_122" -> "2614 linear_121"; -"2613 _param_constant327_0_0" -> "2614 linear_121"; -"2614 linear_121" -> "2615 dropout_79"; -"2615 dropout_79" -> "2618 layer_norm_42"; -"2616 _param_constant328" -> "2618 layer_norm_42"; -"2617 _param_constant329" -> "2618 layer_norm_42"; -"2618 layer_norm_42" -> "2619 add_69"; -"2619 add_69" -> "2646 pad_22"; -"2619 add_69" -> "2711 add_71"; -"2620 _tensor_constant130" -> "2622 _tensor_constant130_0_0_nncf_smooth_quant_0"; -"2621 linear_122_updated_constant0" -> "2625 quantize_per_channel_default_123"; -"2622 _tensor_constant130_0_0_nncf_smooth_quant_0" -> "2628 linear_122"; -"2623 linear_122_scale_0" -> "2625 quantize_per_channel_default_123"; -"2623 linear_122_scale_0" -> "2626 dequantize_per_channel_default_123"; -"2624 linear_122_zero_point_0" -> "2625 quantize_per_channel_default_123"; -"2624 linear_122_zero_point_0" -> "2626 dequantize_per_channel_default_123"; -"2625 quantize_per_channel_default_123" -> "2626 dequantize_per_channel_default_123"; -"2626 dequantize_per_channel_default_123" -> "2628 linear_122"; -"2627 _param_constant331_0_0" -> "2628 linear_122"; -"2628 linear_122" -> "2629 relu__20"; -"2629 relu__20" -> "2631 relu__20_0_0_nncf_smooth_quant_0"; -"2630 linear_123_updated_constant0" -> "2634 quantize_per_channel_default_124"; -"2631 relu__20_0_0_nncf_smooth_quant_0" -> "2636 linear_123"; -"2632 linear_123_scale_0" -> "2634 quantize_per_channel_default_124"; -"2632 linear_123_scale_0" -> "2635 dequantize_per_channel_default_124"; -"2633 linear_123_zero_point_0" -> "2634 quantize_per_channel_default_124"; -"2633 linear_123_zero_point_0" -> "2635 dequantize_per_channel_default_124"; -"2634 quantize_per_channel_default_124" -> "2635 dequantize_per_channel_default_124"; -"2635 dequantize_per_channel_default_124" -> "2636 linear_123"; -"2636 linear_123" -> "2637 view_110"; -"2637 view_110" -> "2639 index_20"; -"2638 _tensor_constant131" -> "2639 index_20"; -"2639 index_20" -> "2640 view_111"; -"2640 view_111" -> "2641 permute_91"; -"2641 permute_91" -> "2642 contiguous_38"; -"2642 contiguous_38" -> "2643 unsqueeze_60"; -"2643 unsqueeze_60" -> "2644 sigmoid_20"; -"2644 sigmoid_20" -> "2645 mul_40"; -"2645 mul_40" -> "2683 add_70"; -"2646 pad_22" -> "2647 view_112"; -"2647 view_112" -> "2648 permute_92"; -"2648 permute_92" -> "2649 reshape_90"; -"2649 reshape_90" -> "2651 reshape_90_0_0_nncf_smooth_quant_0"; -"2650 linear_124_updated_constant0" -> "2656 quantize_per_channel_default_125"; -"2651 reshape_90_0_0_nncf_smooth_quant_0" -> "2652 quantize_per_tensor_default_123"; -"2652 quantize_per_tensor_default_123" -> "2653 dequantize_per_tensor_default_123"; -"2653 dequantize_per_tensor_default_123" -> "2659 linear_124"; -"2654 linear_124_scale_0" -> "2656 quantize_per_channel_default_125"; -"2654 linear_124_scale_0" -> "2657 dequantize_per_channel_default_125"; -"2655 linear_124_zero_point_0" -> "2656 quantize_per_channel_default_125"; -"2655 linear_124_zero_point_0" -> "2657 dequantize_per_channel_default_125"; -"2656 quantize_per_channel_default_125" -> "2657 dequantize_per_channel_default_125"; -"2657 dequantize_per_channel_default_125" -> "2659 linear_124"; -"2658 _param_constant333_0_0" -> "2659 linear_124"; -"2659 linear_124" -> "2660 reshape_91"; -"2660 reshape_91" -> "2661 permute_93"; -"2661 permute_93" -> "2662 select_60"; -"2661 permute_93" -> "2663 select_61"; -"2661 permute_93" -> "2664 select_62"; -"2662 select_60" -> "2665 linalg_vector_norm_40"; -"2662 select_60" -> "2667 expand_as_40"; -"2662 select_60" -> "2668 div_40"; -"2663 select_61" -> "2671 linalg_vector_norm_41"; -"2663 select_61" -> "2673 expand_as_41"; -"2663 select_61" -> "2674 div_41"; -"2664 select_62" -> "2686 matmul_41"; -"2665 linalg_vector_norm_40" -> "2666 clamp_min_40"; -"2666 clamp_min_40" -> "2667 expand_as_40"; -"2667 expand_as_40" -> "2668 div_40"; -"2668 div_40" -> "2669 quantize_per_tensor_default_124"; -"2669 quantize_per_tensor_default_124" -> "2670 dequantize_per_tensor_default_124"; -"2670 dequantize_per_tensor_default_124" -> "2678 matmul_40"; -"2671 linalg_vector_norm_41" -> "2672 clamp_min_41"; -"2672 clamp_min_41" -> "2673 expand_as_41"; -"2673 expand_as_41" -> "2674 div_41"; -"2674 div_41" -> "2675 quantize_per_tensor_default_125"; -"2675 quantize_per_tensor_default_125" -> "2676 dequantize_per_tensor_default_125"; -"2676 dequantize_per_tensor_default_125" -> "2677 transpose_40"; -"2677 transpose_40" -> "2678 matmul_40"; -"2678 matmul_40" -> "2682 mul_41"; -"2679 _param_constant335" -> "2680 clamp_20"; -"2680 clamp_20" -> "2681 exp_20"; -"2681 exp_20" -> "2682 mul_41"; -"2682 mul_41" -> "2683 add_70"; -"2683 add_70" -> "2684 softmax_20"; -"2684 softmax_20" -> "2685 dropout_80"; -"2685 dropout_80" -> "2686 matmul_41"; -"2686 matmul_41" -> "2687 transpose_41"; -"2687 transpose_41" -> "2688 reshape_92"; -"2688 reshape_92" -> "2690 reshape_92_0_0_nncf_smooth_quant_0"; -"2689 linear_125_updated_constant0" -> "2695 quantize_per_channel_default_126"; -"2690 reshape_92_0_0_nncf_smooth_quant_0" -> "2691 quantize_per_tensor_default_126"; -"2691 quantize_per_tensor_default_126" -> "2692 dequantize_per_tensor_default_126"; -"2692 dequantize_per_tensor_default_126" -> "2698 linear_125"; -"2693 linear_125_scale_0" -> "2695 quantize_per_channel_default_126"; -"2693 linear_125_scale_0" -> "2696 dequantize_per_channel_default_126"; -"2694 linear_125_zero_point_0" -> "2695 quantize_per_channel_default_126"; -"2694 linear_125_zero_point_0" -> "2696 dequantize_per_channel_default_126"; -"2695 quantize_per_channel_default_126" -> "2696 dequantize_per_channel_default_126"; -"2696 dequantize_per_channel_default_126" -> "2698 linear_125"; -"2697 _param_constant337_0_0" -> "2698 linear_125"; -"2698 linear_125" -> "2699 dropout_81"; -"2699 dropout_81" -> "2700 view_113"; -"2700 view_113" -> "2701 permute_94"; -"2701 permute_94" -> "2702 reshape_93"; -"2702 reshape_93" -> "2703 slice_302"; -"2703 slice_302" -> "2704 slice_303"; -"2704 slice_303" -> "2705 slice_304"; -"2705 slice_304" -> "2706 slice_305"; -"2706 slice_305" -> "2707 contiguous_39"; -"2707 contiguous_39" -> "2710 layer_norm_43"; -"2708 _param_constant338" -> "2710 layer_norm_43"; -"2709 _param_constant339" -> "2710 layer_norm_43"; -"2710 layer_norm_43" -> "2711 add_71"; -"2711 add_71" -> "2713 add_71_0_0_nncf_smooth_quant_0"; -"2711 add_71" -> "2738 add_72"; -"2712 linear_126_updated_constant0" -> "2718 quantize_per_channel_default_127"; -"2713 add_71_0_0_nncf_smooth_quant_0" -> "2714 quantize_per_tensor_default_127"; -"2714 quantize_per_tensor_default_127" -> "2715 dequantize_per_tensor_default_127"; -"2715 dequantize_per_tensor_default_127" -> "2721 linear_126"; -"2716 linear_126_scale_0" -> "2718 quantize_per_channel_default_127"; -"2716 linear_126_scale_0" -> "2719 dequantize_per_channel_default_127"; -"2717 linear_126_zero_point_0" -> "2718 quantize_per_channel_default_127"; -"2717 linear_126_zero_point_0" -> "2719 dequantize_per_channel_default_127"; -"2718 quantize_per_channel_default_127" -> "2719 dequantize_per_channel_default_127"; -"2719 dequantize_per_channel_default_127" -> "2721 linear_126"; -"2720 _param_constant341_0_0" -> "2721 linear_126"; -"2721 linear_126" -> "2722 gelu_20"; -"2722 gelu_20" -> "2723 dropout_82"; -"2723 dropout_82" -> "2725 dropout_82_0_0_nncf_smooth_quant_0"; -"2724 linear_127_updated_constant0" -> "2730 quantize_per_channel_default_128"; -"2725 dropout_82_0_0_nncf_smooth_quant_0" -> "2726 quantize_per_tensor_default_128"; -"2726 quantize_per_tensor_default_128" -> "2727 dequantize_per_tensor_default_128"; -"2727 dequantize_per_tensor_default_128" -> "2733 linear_127"; -"2728 linear_127_scale_0" -> "2730 quantize_per_channel_default_128"; -"2728 linear_127_scale_0" -> "2731 dequantize_per_channel_default_128"; -"2729 linear_127_zero_point_0" -> "2730 quantize_per_channel_default_128"; -"2729 linear_127_zero_point_0" -> "2731 dequantize_per_channel_default_128"; -"2730 quantize_per_channel_default_128" -> "2731 dequantize_per_channel_default_128"; -"2731 dequantize_per_channel_default_128" -> "2733 linear_127"; -"2732 _param_constant343_0_0" -> "2733 linear_127"; -"2733 linear_127" -> "2734 dropout_83"; -"2734 dropout_83" -> "2737 layer_norm_44"; -"2735 _param_constant344" -> "2737 layer_norm_44"; -"2736 _param_constant345" -> "2737 layer_norm_44"; -"2737 layer_norm_44" -> "2738 add_72"; -"2738 add_72" -> "2765 pad_23"; -"2738 add_72" -> "2848 add_75"; -"2739 _tensor_constant132" -> "2741 _tensor_constant132_0_0_nncf_smooth_quant_0"; -"2740 linear_128_updated_constant0" -> "2744 quantize_per_channel_default_129"; -"2741 _tensor_constant132_0_0_nncf_smooth_quant_0" -> "2747 linear_128"; -"2742 linear_128_scale_0" -> "2744 quantize_per_channel_default_129"; -"2742 linear_128_scale_0" -> "2745 dequantize_per_channel_default_129"; -"2743 linear_128_zero_point_0" -> "2744 quantize_per_channel_default_129"; -"2743 linear_128_zero_point_0" -> "2745 dequantize_per_channel_default_129"; -"2744 quantize_per_channel_default_129" -> "2745 dequantize_per_channel_default_129"; -"2745 dequantize_per_channel_default_129" -> "2747 linear_128"; -"2746 _param_constant347_0_0" -> "2747 linear_128"; -"2747 linear_128" -> "2748 relu__21"; -"2748 relu__21" -> "2750 relu__21_0_0_nncf_smooth_quant_0"; -"2749 linear_129_updated_constant0" -> "2753 quantize_per_channel_default_130"; -"2750 relu__21_0_0_nncf_smooth_quant_0" -> "2755 linear_129"; -"2751 linear_129_scale_0" -> "2753 quantize_per_channel_default_130"; -"2751 linear_129_scale_0" -> "2754 dequantize_per_channel_default_130"; -"2752 linear_129_zero_point_0" -> "2753 quantize_per_channel_default_130"; -"2752 linear_129_zero_point_0" -> "2754 dequantize_per_channel_default_130"; -"2753 quantize_per_channel_default_130" -> "2754 dequantize_per_channel_default_130"; -"2754 dequantize_per_channel_default_130" -> "2755 linear_129"; -"2755 linear_129" -> "2756 view_114"; -"2756 view_114" -> "2758 index_21"; -"2757 _tensor_constant133" -> "2758 index_21"; -"2758 index_21" -> "2759 view_115"; -"2759 view_115" -> "2760 permute_95"; -"2760 permute_95" -> "2761 contiguous_40"; -"2761 contiguous_40" -> "2762 unsqueeze_61"; -"2762 unsqueeze_61" -> "2763 sigmoid_21"; -"2763 sigmoid_21" -> "2764 mul_42"; -"2764 mul_42" -> "2803 add_73"; -"2765 pad_23" -> "2766 roll_20"; -"2766 roll_20" -> "2767 view_116"; -"2767 view_116" -> "2768 permute_96"; -"2768 permute_96" -> "2769 reshape_94"; -"2769 reshape_94" -> "2771 reshape_94_0_0_nncf_smooth_quant_0"; -"2769 reshape_94" -> "2804 new_zeros_10"; -"2770 linear_130_updated_constant0" -> "2776 quantize_per_channel_default_131"; -"2771 reshape_94_0_0_nncf_smooth_quant_0" -> "2772 quantize_per_tensor_default_129"; -"2772 quantize_per_tensor_default_129" -> "2773 dequantize_per_tensor_default_129"; -"2773 dequantize_per_tensor_default_129" -> "2779 linear_130"; -"2774 linear_130_scale_0" -> "2776 quantize_per_channel_default_131"; -"2774 linear_130_scale_0" -> "2777 dequantize_per_channel_default_131"; -"2775 linear_130_zero_point_0" -> "2776 quantize_per_channel_default_131"; -"2775 linear_130_zero_point_0" -> "2777 dequantize_per_channel_default_131"; -"2776 quantize_per_channel_default_131" -> "2777 dequantize_per_channel_default_131"; -"2777 dequantize_per_channel_default_131" -> "2779 linear_130"; -"2778 _param_constant349_0_0" -> "2779 linear_130"; -"2779 linear_130" -> "2780 reshape_95"; -"2780 reshape_95" -> "2781 permute_97"; -"2781 permute_97" -> "2782 select_63"; -"2781 permute_97" -> "2783 select_64"; -"2781 permute_97" -> "2784 select_65"; -"2782 select_63" -> "2785 linalg_vector_norm_42"; -"2782 select_63" -> "2787 expand_as_42"; -"2782 select_63" -> "2788 div_42"; -"2783 select_64" -> "2791 linalg_vector_norm_43"; -"2783 select_64" -> "2793 expand_as_43"; -"2783 select_64" -> "2794 div_43"; -"2784 select_65" -> "2822 matmul_43"; -"2785 linalg_vector_norm_42" -> "2786 clamp_min_42"; -"2786 clamp_min_42" -> "2787 expand_as_42"; -"2787 expand_as_42" -> "2788 div_42"; -"2788 div_42" -> "2789 quantize_per_tensor_default_130"; -"2789 quantize_per_tensor_default_130" -> "2790 dequantize_per_tensor_default_130"; -"2790 dequantize_per_tensor_default_130" -> "2798 matmul_42"; -"2791 linalg_vector_norm_43" -> "2792 clamp_min_43"; -"2792 clamp_min_43" -> "2793 expand_as_43"; -"2793 expand_as_43" -> "2794 div_43"; -"2794 div_43" -> "2795 quantize_per_tensor_default_131"; -"2795 quantize_per_tensor_default_131" -> "2796 dequantize_per_tensor_default_131"; -"2796 dequantize_per_tensor_default_131" -> "2797 transpose_42"; -"2797 transpose_42" -> "2798 matmul_42"; -"2798 matmul_42" -> "2802 mul_43"; -"2799 _param_constant351" -> "2800 clamp_21"; -"2800 clamp_21" -> "2801 exp_21"; -"2801 exp_21" -> "2802 mul_43"; -"2802 mul_43" -> "2803 add_73"; -"2803 add_73" -> "2815 view_118"; -"2804 new_zeros_10" -> "2805 view_117"; -"2805 view_117" -> "2806 permute_98"; -"2806 permute_98" -> "2807 reshape_96"; -"2807 reshape_96" -> "2808 unsqueeze_62"; -"2807 reshape_96" -> "2809 unsqueeze_63"; -"2808 unsqueeze_62" -> "2810 sub_10"; -"2809 unsqueeze_63" -> "2810 sub_10"; -"2810 sub_10" -> "2811 ne_10"; -"2810 sub_10" -> "2812 masked_fill_20"; -"2810 sub_10" -> "2813 eq_10"; -"2811 ne_10" -> "2812 masked_fill_20"; -"2812 masked_fill_20" -> "2814 masked_fill_21"; -"2813 eq_10" -> "2814 masked_fill_21"; -"2814 masked_fill_21" -> "2816 unsqueeze_64"; -"2815 view_118" -> "2818 add_74"; -"2816 unsqueeze_64" -> "2817 unsqueeze_65"; -"2817 unsqueeze_65" -> "2818 add_74"; -"2818 add_74" -> "2819 view_119"; -"2819 view_119" -> "2820 softmax_21"; -"2820 softmax_21" -> "2821 dropout_84"; -"2821 dropout_84" -> "2822 matmul_43"; -"2822 matmul_43" -> "2823 transpose_43"; -"2823 transpose_43" -> "2824 reshape_97"; -"2824 reshape_97" -> "2826 reshape_97_0_0_nncf_smooth_quant_0"; -"2825 linear_131_updated_constant0" -> "2831 quantize_per_channel_default_132"; -"2826 reshape_97_0_0_nncf_smooth_quant_0" -> "2827 quantize_per_tensor_default_132"; -"2827 quantize_per_tensor_default_132" -> "2828 dequantize_per_tensor_default_132"; -"2828 dequantize_per_tensor_default_132" -> "2834 linear_131"; -"2829 linear_131_scale_0" -> "2831 quantize_per_channel_default_132"; -"2829 linear_131_scale_0" -> "2832 dequantize_per_channel_default_132"; -"2830 linear_131_zero_point_0" -> "2831 quantize_per_channel_default_132"; -"2830 linear_131_zero_point_0" -> "2832 dequantize_per_channel_default_132"; -"2831 quantize_per_channel_default_132" -> "2832 dequantize_per_channel_default_132"; -"2832 dequantize_per_channel_default_132" -> "2834 linear_131"; -"2833 _param_constant353_0_0" -> "2834 linear_131"; -"2834 linear_131" -> "2835 dropout_85"; -"2835 dropout_85" -> "2836 view_120"; -"2836 view_120" -> "2837 permute_99"; -"2837 permute_99" -> "2838 reshape_98"; -"2838 reshape_98" -> "2839 roll_21"; -"2839 roll_21" -> "2840 slice_325"; -"2840 slice_325" -> "2841 slice_326"; -"2841 slice_326" -> "2842 slice_327"; -"2842 slice_327" -> "2843 slice_328"; -"2843 slice_328" -> "2844 contiguous_41"; -"2844 contiguous_41" -> "2847 layer_norm_45"; -"2845 _param_constant354" -> "2847 layer_norm_45"; -"2846 _param_constant355" -> "2847 layer_norm_45"; -"2847 layer_norm_45" -> "2848 add_75"; -"2848 add_75" -> "2850 add_75_0_0_nncf_smooth_quant_0"; -"2848 add_75" -> "2875 add_76"; -"2849 linear_132_updated_constant0" -> "2855 quantize_per_channel_default_133"; -"2850 add_75_0_0_nncf_smooth_quant_0" -> "2851 quantize_per_tensor_default_133"; -"2851 quantize_per_tensor_default_133" -> "2852 dequantize_per_tensor_default_133"; -"2852 dequantize_per_tensor_default_133" -> "2858 linear_132"; -"2853 linear_132_scale_0" -> "2855 quantize_per_channel_default_133"; -"2853 linear_132_scale_0" -> "2856 dequantize_per_channel_default_133"; -"2854 linear_132_zero_point_0" -> "2855 quantize_per_channel_default_133"; -"2854 linear_132_zero_point_0" -> "2856 dequantize_per_channel_default_133"; -"2855 quantize_per_channel_default_133" -> "2856 dequantize_per_channel_default_133"; -"2856 dequantize_per_channel_default_133" -> "2858 linear_132"; -"2857 _param_constant357_0_0" -> "2858 linear_132"; -"2858 linear_132" -> "2859 gelu_21"; -"2859 gelu_21" -> "2860 dropout_86"; -"2860 dropout_86" -> "2862 dropout_86_0_0_nncf_smooth_quant_0"; -"2861 linear_133_updated_constant0" -> "2867 quantize_per_channel_default_134"; -"2862 dropout_86_0_0_nncf_smooth_quant_0" -> "2863 quantize_per_tensor_default_134"; -"2863 quantize_per_tensor_default_134" -> "2864 dequantize_per_tensor_default_134"; -"2864 dequantize_per_tensor_default_134" -> "2870 linear_133"; -"2865 linear_133_scale_0" -> "2867 quantize_per_channel_default_134"; -"2865 linear_133_scale_0" -> "2868 dequantize_per_channel_default_134"; -"2866 linear_133_zero_point_0" -> "2867 quantize_per_channel_default_134"; -"2866 linear_133_zero_point_0" -> "2868 dequantize_per_channel_default_134"; -"2867 quantize_per_channel_default_134" -> "2868 dequantize_per_channel_default_134"; -"2868 dequantize_per_channel_default_134" -> "2870 linear_133"; -"2869 _param_constant359_0_0" -> "2870 linear_133"; -"2870 linear_133" -> "2871 dropout_87"; -"2871 dropout_87" -> "2874 layer_norm_46"; -"2872 _param_constant360" -> "2874 layer_norm_46"; -"2873 _param_constant361" -> "2874 layer_norm_46"; -"2874 layer_norm_46" -> "2875 add_76"; -"2875 add_76" -> "2876 pad_24"; -"2876 pad_24" -> "2877 slice_329"; -"2876 pad_24" -> "2880 slice_332"; -"2876 pad_24" -> "2883 slice_335"; -"2876 pad_24" -> "2886 slice_338"; -"2877 slice_329" -> "2878 slice_330"; -"2878 slice_330" -> "2879 slice_331"; -"2879 slice_331" -> "2889 cat_2"; -"2880 slice_332" -> "2881 slice_333"; -"2881 slice_333" -> "2882 slice_334"; -"2882 slice_334" -> "2889 cat_2"; -"2883 slice_335" -> "2884 slice_336"; -"2884 slice_336" -> "2885 slice_337"; -"2885 slice_337" -> "2889 cat_2"; -"2886 slice_338" -> "2887 slice_339"; -"2887 slice_339" -> "2888 slice_340"; -"2888 slice_340" -> "2889 cat_2"; -"2889 cat_2" -> "2891 cat_2_0_0_nncf_smooth_quant_0"; -"2890 linear_134_updated_constant0" -> "2896 quantize_per_channel_default_135"; -"2891 cat_2_0_0_nncf_smooth_quant_0" -> "2892 quantize_per_tensor_default_135"; -"2892 quantize_per_tensor_default_135" -> "2893 dequantize_per_tensor_default_135"; -"2893 dequantize_per_tensor_default_135" -> "2898 linear_134"; -"2894 linear_134_scale_0" -> "2896 quantize_per_channel_default_135"; -"2894 linear_134_scale_0" -> "2897 dequantize_per_channel_default_135"; -"2895 linear_134_zero_point_0" -> "2896 quantize_per_channel_default_135"; -"2895 linear_134_zero_point_0" -> "2897 dequantize_per_channel_default_135"; -"2896 quantize_per_channel_default_135" -> "2897 dequantize_per_channel_default_135"; -"2897 dequantize_per_channel_default_135" -> "2898 linear_134"; -"2898 linear_134" -> "2901 layer_norm_47"; -"2899 _param_constant363" -> "2901 layer_norm_47"; -"2900 _param_constant364" -> "2901 layer_norm_47"; -"2901 layer_norm_47" -> "2928 pad_25"; -"2901 layer_norm_47" -> "2993 add_78"; -"2902 _tensor_constant143" -> "2904 _tensor_constant143_0_0_nncf_smooth_quant_0"; -"2903 linear_135_updated_constant0" -> "2907 quantize_per_channel_default_136"; -"2904 _tensor_constant143_0_0_nncf_smooth_quant_0" -> "2910 linear_135"; -"2905 linear_135_scale_0" -> "2907 quantize_per_channel_default_136"; -"2905 linear_135_scale_0" -> "2908 dequantize_per_channel_default_136"; -"2906 linear_135_zero_point_0" -> "2907 quantize_per_channel_default_136"; -"2906 linear_135_zero_point_0" -> "2908 dequantize_per_channel_default_136"; -"2907 quantize_per_channel_default_136" -> "2908 dequantize_per_channel_default_136"; -"2908 dequantize_per_channel_default_136" -> "2910 linear_135"; -"2909 _param_constant366_0_0" -> "2910 linear_135"; -"2910 linear_135" -> "2911 relu__22"; -"2911 relu__22" -> "2913 relu__22_0_0_nncf_smooth_quant_0"; -"2912 linear_136_updated_constant0" -> "2916 quantize_per_channel_default_137"; -"2913 relu__22_0_0_nncf_smooth_quant_0" -> "2918 linear_136"; -"2914 linear_136_scale_0" -> "2916 quantize_per_channel_default_137"; -"2914 linear_136_scale_0" -> "2917 dequantize_per_channel_default_137"; -"2915 linear_136_zero_point_0" -> "2916 quantize_per_channel_default_137"; -"2915 linear_136_zero_point_0" -> "2917 dequantize_per_channel_default_137"; -"2916 quantize_per_channel_default_137" -> "2917 dequantize_per_channel_default_137"; -"2917 dequantize_per_channel_default_137" -> "2918 linear_136"; -"2918 linear_136" -> "2919 view_121"; -"2919 view_121" -> "2921 index_22"; -"2920 _tensor_constant144" -> "2921 index_22"; -"2921 index_22" -> "2922 view_122"; -"2922 view_122" -> "2923 permute_100"; -"2923 permute_100" -> "2924 contiguous_42"; -"2924 contiguous_42" -> "2925 unsqueeze_66"; -"2925 unsqueeze_66" -> "2926 sigmoid_22"; -"2926 sigmoid_22" -> "2927 mul_44"; -"2927 mul_44" -> "2965 add_77"; -"2928 pad_25" -> "2929 view_123"; -"2929 view_123" -> "2930 permute_101"; -"2930 permute_101" -> "2931 reshape_99"; -"2931 reshape_99" -> "2933 reshape_99_0_0_nncf_smooth_quant_0"; -"2932 linear_137_updated_constant0" -> "2938 quantize_per_channel_default_138"; -"2933 reshape_99_0_0_nncf_smooth_quant_0" -> "2934 quantize_per_tensor_default_136"; -"2934 quantize_per_tensor_default_136" -> "2935 dequantize_per_tensor_default_136"; -"2935 dequantize_per_tensor_default_136" -> "2941 linear_137"; -"2936 linear_137_scale_0" -> "2938 quantize_per_channel_default_138"; -"2936 linear_137_scale_0" -> "2939 dequantize_per_channel_default_138"; -"2937 linear_137_zero_point_0" -> "2938 quantize_per_channel_default_138"; -"2937 linear_137_zero_point_0" -> "2939 dequantize_per_channel_default_138"; -"2938 quantize_per_channel_default_138" -> "2939 dequantize_per_channel_default_138"; -"2939 dequantize_per_channel_default_138" -> "2941 linear_137"; -"2940 _param_constant368_0_0" -> "2941 linear_137"; -"2941 linear_137" -> "2942 reshape_100"; -"2942 reshape_100" -> "2943 permute_102"; -"2943 permute_102" -> "2944 select_66"; -"2943 permute_102" -> "2945 select_67"; -"2943 permute_102" -> "2946 select_68"; -"2944 select_66" -> "2947 linalg_vector_norm_44"; -"2944 select_66" -> "2949 expand_as_44"; -"2944 select_66" -> "2950 div_44"; -"2945 select_67" -> "2953 linalg_vector_norm_45"; -"2945 select_67" -> "2955 expand_as_45"; -"2945 select_67" -> "2956 div_45"; -"2946 select_68" -> "2968 matmul_45"; -"2947 linalg_vector_norm_44" -> "2948 clamp_min_44"; -"2948 clamp_min_44" -> "2949 expand_as_44"; -"2949 expand_as_44" -> "2950 div_44"; -"2950 div_44" -> "2951 quantize_per_tensor_default_137"; -"2951 quantize_per_tensor_default_137" -> "2952 dequantize_per_tensor_default_137"; -"2952 dequantize_per_tensor_default_137" -> "2960 matmul_44"; -"2953 linalg_vector_norm_45" -> "2954 clamp_min_45"; -"2954 clamp_min_45" -> "2955 expand_as_45"; -"2955 expand_as_45" -> "2956 div_45"; -"2956 div_45" -> "2957 quantize_per_tensor_default_138"; -"2957 quantize_per_tensor_default_138" -> "2958 dequantize_per_tensor_default_138"; -"2958 dequantize_per_tensor_default_138" -> "2959 transpose_44"; -"2959 transpose_44" -> "2960 matmul_44"; -"2960 matmul_44" -> "2964 mul_45"; -"2961 _param_constant370" -> "2962 clamp_22"; -"2962 clamp_22" -> "2963 exp_22"; -"2963 exp_22" -> "2964 mul_45"; -"2964 mul_45" -> "2965 add_77"; -"2965 add_77" -> "2966 softmax_22"; -"2966 softmax_22" -> "2967 dropout_88"; -"2967 dropout_88" -> "2968 matmul_45"; -"2968 matmul_45" -> "2969 transpose_45"; -"2969 transpose_45" -> "2970 reshape_101"; -"2970 reshape_101" -> "2972 reshape_101_0_0_nncf_smooth_quant_0"; -"2971 linear_138_updated_constant0" -> "2977 quantize_per_channel_default_139"; -"2972 reshape_101_0_0_nncf_smooth_quant_0" -> "2973 quantize_per_tensor_default_139"; -"2973 quantize_per_tensor_default_139" -> "2974 dequantize_per_tensor_default_139"; -"2974 dequantize_per_tensor_default_139" -> "2980 linear_138"; -"2975 linear_138_scale_0" -> "2977 quantize_per_channel_default_139"; -"2975 linear_138_scale_0" -> "2978 dequantize_per_channel_default_139"; -"2976 linear_138_zero_point_0" -> "2977 quantize_per_channel_default_139"; -"2976 linear_138_zero_point_0" -> "2978 dequantize_per_channel_default_139"; -"2977 quantize_per_channel_default_139" -> "2978 dequantize_per_channel_default_139"; -"2978 dequantize_per_channel_default_139" -> "2980 linear_138"; -"2979 _param_constant372_0_0" -> "2980 linear_138"; -"2980 linear_138" -> "2981 dropout_89"; -"2981 dropout_89" -> "2982 view_124"; -"2982 view_124" -> "2983 permute_103"; -"2983 permute_103" -> "2984 reshape_102"; -"2984 reshape_102" -> "2985 slice_342"; -"2985 slice_342" -> "2986 slice_343"; -"2986 slice_343" -> "2987 slice_344"; -"2987 slice_344" -> "2988 slice_345"; -"2988 slice_345" -> "2989 contiguous_43"; -"2989 contiguous_43" -> "2992 layer_norm_48"; -"2990 _param_constant373" -> "2992 layer_norm_48"; -"2991 _param_constant374" -> "2992 layer_norm_48"; -"2992 layer_norm_48" -> "2993 add_78"; -"2993 add_78" -> "2995 add_78_0_0_nncf_smooth_quant_0"; -"2993 add_78" -> "3020 add_79"; -"2994 linear_139_updated_constant0" -> "3000 quantize_per_channel_default_140"; -"2995 add_78_0_0_nncf_smooth_quant_0" -> "2996 quantize_per_tensor_default_140"; -"2996 quantize_per_tensor_default_140" -> "2997 dequantize_per_tensor_default_140"; -"2997 dequantize_per_tensor_default_140" -> "3003 linear_139"; -"2998 linear_139_scale_0" -> "3000 quantize_per_channel_default_140"; -"2998 linear_139_scale_0" -> "3001 dequantize_per_channel_default_140"; -"2999 linear_139_zero_point_0" -> "3000 quantize_per_channel_default_140"; -"2999 linear_139_zero_point_0" -> "3001 dequantize_per_channel_default_140"; -"3000 quantize_per_channel_default_140" -> "3001 dequantize_per_channel_default_140"; -"3001 dequantize_per_channel_default_140" -> "3003 linear_139"; -"3002 _param_constant376_0_0" -> "3003 linear_139"; -"3003 linear_139" -> "3004 gelu_22"; -"3004 gelu_22" -> "3005 dropout_90"; -"3005 dropout_90" -> "3007 dropout_90_0_0_nncf_smooth_quant_0"; -"3006 linear_140_updated_constant0" -> "3012 quantize_per_channel_default_141"; -"3007 dropout_90_0_0_nncf_smooth_quant_0" -> "3008 quantize_per_tensor_default_141"; -"3008 quantize_per_tensor_default_141" -> "3009 dequantize_per_tensor_default_141"; -"3009 dequantize_per_tensor_default_141" -> "3015 linear_140"; -"3010 linear_140_scale_0" -> "3012 quantize_per_channel_default_141"; -"3010 linear_140_scale_0" -> "3013 dequantize_per_channel_default_141"; -"3011 linear_140_zero_point_0" -> "3012 quantize_per_channel_default_141"; -"3011 linear_140_zero_point_0" -> "3013 dequantize_per_channel_default_141"; -"3012 quantize_per_channel_default_141" -> "3013 dequantize_per_channel_default_141"; -"3013 dequantize_per_channel_default_141" -> "3015 linear_140"; -"3014 _param_constant378_0_0" -> "3015 linear_140"; -"3015 linear_140" -> "3016 dropout_91"; -"3016 dropout_91" -> "3019 layer_norm_49"; -"3017 _param_constant379" -> "3019 layer_norm_49"; -"3018 _param_constant380" -> "3019 layer_norm_49"; -"3019 layer_norm_49" -> "3020 add_79"; -"3020 add_79" -> "3047 pad_26"; -"3020 add_79" -> "3112 add_81"; -"3021 _tensor_constant145" -> "3023 _tensor_constant145_0_0_nncf_smooth_quant_0"; -"3022 linear_141_updated_constant0" -> "3026 quantize_per_channel_default_142"; -"3023 _tensor_constant145_0_0_nncf_smooth_quant_0" -> "3029 linear_141"; -"3024 linear_141_scale_0" -> "3026 quantize_per_channel_default_142"; -"3024 linear_141_scale_0" -> "3027 dequantize_per_channel_default_142"; -"3025 linear_141_zero_point_0" -> "3026 quantize_per_channel_default_142"; -"3025 linear_141_zero_point_0" -> "3027 dequantize_per_channel_default_142"; -"3026 quantize_per_channel_default_142" -> "3027 dequantize_per_channel_default_142"; -"3027 dequantize_per_channel_default_142" -> "3029 linear_141"; -"3028 _param_constant382_0_0" -> "3029 linear_141"; -"3029 linear_141" -> "3030 relu__23"; -"3030 relu__23" -> "3032 relu__23_0_0_nncf_smooth_quant_0"; -"3031 linear_142_updated_constant0" -> "3035 quantize_per_channel_default_143"; -"3032 relu__23_0_0_nncf_smooth_quant_0" -> "3037 linear_142"; -"3033 linear_142_scale_0" -> "3035 quantize_per_channel_default_143"; -"3033 linear_142_scale_0" -> "3036 dequantize_per_channel_default_143"; -"3034 linear_142_zero_point_0" -> "3035 quantize_per_channel_default_143"; -"3034 linear_142_zero_point_0" -> "3036 dequantize_per_channel_default_143"; -"3035 quantize_per_channel_default_143" -> "3036 dequantize_per_channel_default_143"; -"3036 dequantize_per_channel_default_143" -> "3037 linear_142"; -"3037 linear_142" -> "3038 view_125"; -"3038 view_125" -> "3040 index_23"; -"3039 _tensor_constant146" -> "3040 index_23"; -"3040 index_23" -> "3041 view_126"; -"3041 view_126" -> "3042 permute_104"; -"3042 permute_104" -> "3043 contiguous_44"; -"3043 contiguous_44" -> "3044 unsqueeze_67"; -"3044 unsqueeze_67" -> "3045 sigmoid_23"; -"3045 sigmoid_23" -> "3046 mul_46"; -"3046 mul_46" -> "3084 add_80"; -"3047 pad_26" -> "3048 view_127"; -"3048 view_127" -> "3049 permute_105"; -"3049 permute_105" -> "3050 reshape_103"; -"3050 reshape_103" -> "3052 reshape_103_0_0_nncf_smooth_quant_0"; -"3051 linear_143_updated_constant0" -> "3057 quantize_per_channel_default_144"; -"3052 reshape_103_0_0_nncf_smooth_quant_0" -> "3053 quantize_per_tensor_default_142"; -"3053 quantize_per_tensor_default_142" -> "3054 dequantize_per_tensor_default_142"; -"3054 dequantize_per_tensor_default_142" -> "3060 linear_143"; -"3055 linear_143_scale_0" -> "3057 quantize_per_channel_default_144"; -"3055 linear_143_scale_0" -> "3058 dequantize_per_channel_default_144"; -"3056 linear_143_zero_point_0" -> "3057 quantize_per_channel_default_144"; -"3056 linear_143_zero_point_0" -> "3058 dequantize_per_channel_default_144"; -"3057 quantize_per_channel_default_144" -> "3058 dequantize_per_channel_default_144"; -"3058 dequantize_per_channel_default_144" -> "3060 linear_143"; -"3059 _param_constant384_0_0" -> "3060 linear_143"; -"3060 linear_143" -> "3061 reshape_104"; -"3061 reshape_104" -> "3062 permute_106"; -"3062 permute_106" -> "3063 select_69"; -"3062 permute_106" -> "3064 select_70"; -"3062 permute_106" -> "3065 select_71"; -"3063 select_69" -> "3066 linalg_vector_norm_46"; -"3063 select_69" -> "3068 expand_as_46"; -"3063 select_69" -> "3069 div_46"; -"3064 select_70" -> "3072 linalg_vector_norm_47"; -"3064 select_70" -> "3074 expand_as_47"; -"3064 select_70" -> "3075 div_47"; -"3065 select_71" -> "3087 matmul_47"; -"3066 linalg_vector_norm_46" -> "3067 clamp_min_46"; -"3067 clamp_min_46" -> "3068 expand_as_46"; -"3068 expand_as_46" -> "3069 div_46"; -"3069 div_46" -> "3070 quantize_per_tensor_default_143"; -"3070 quantize_per_tensor_default_143" -> "3071 dequantize_per_tensor_default_143"; -"3071 dequantize_per_tensor_default_143" -> "3079 matmul_46"; -"3072 linalg_vector_norm_47" -> "3073 clamp_min_47"; -"3073 clamp_min_47" -> "3074 expand_as_47"; -"3074 expand_as_47" -> "3075 div_47"; -"3075 div_47" -> "3076 quantize_per_tensor_default_144"; -"3076 quantize_per_tensor_default_144" -> "3077 dequantize_per_tensor_default_144"; -"3077 dequantize_per_tensor_default_144" -> "3078 transpose_46"; -"3078 transpose_46" -> "3079 matmul_46"; -"3079 matmul_46" -> "3083 mul_47"; -"3080 _param_constant386" -> "3081 clamp_23"; -"3081 clamp_23" -> "3082 exp_23"; -"3082 exp_23" -> "3083 mul_47"; -"3083 mul_47" -> "3084 add_80"; -"3084 add_80" -> "3085 softmax_23"; -"3085 softmax_23" -> "3086 dropout_92"; -"3086 dropout_92" -> "3087 matmul_47"; -"3087 matmul_47" -> "3088 transpose_47"; -"3088 transpose_47" -> "3089 reshape_105"; -"3089 reshape_105" -> "3091 reshape_105_0_0_nncf_smooth_quant_0"; -"3090 linear_144_updated_constant0" -> "3096 quantize_per_channel_default_145"; -"3091 reshape_105_0_0_nncf_smooth_quant_0" -> "3092 quantize_per_tensor_default_145"; -"3092 quantize_per_tensor_default_145" -> "3093 dequantize_per_tensor_default_145"; -"3093 dequantize_per_tensor_default_145" -> "3099 linear_144"; -"3094 linear_144_scale_0" -> "3096 quantize_per_channel_default_145"; -"3094 linear_144_scale_0" -> "3097 dequantize_per_channel_default_145"; -"3095 linear_144_zero_point_0" -> "3096 quantize_per_channel_default_145"; -"3095 linear_144_zero_point_0" -> "3097 dequantize_per_channel_default_145"; -"3096 quantize_per_channel_default_145" -> "3097 dequantize_per_channel_default_145"; -"3097 dequantize_per_channel_default_145" -> "3099 linear_144"; -"3098 _param_constant388_0_0" -> "3099 linear_144"; -"3099 linear_144" -> "3100 dropout_93"; -"3100 dropout_93" -> "3101 view_128"; -"3101 view_128" -> "3102 permute_107"; -"3102 permute_107" -> "3103 reshape_106"; -"3103 reshape_106" -> "3104 slice_347"; -"3104 slice_347" -> "3105 slice_348"; -"3105 slice_348" -> "3106 slice_349"; -"3106 slice_349" -> "3107 slice_350"; -"3107 slice_350" -> "3108 contiguous_45"; -"3108 contiguous_45" -> "3111 layer_norm_50"; -"3109 _param_constant389" -> "3111 layer_norm_50"; -"3110 _param_constant390" -> "3111 layer_norm_50"; -"3111 layer_norm_50" -> "3112 add_81"; -"3112 add_81" -> "3114 add_81_0_0_nncf_smooth_quant_0"; -"3112 add_81" -> "3139 add_82"; -"3113 linear_145_updated_constant0" -> "3119 quantize_per_channel_default_146"; -"3114 add_81_0_0_nncf_smooth_quant_0" -> "3115 quantize_per_tensor_default_146"; -"3115 quantize_per_tensor_default_146" -> "3116 dequantize_per_tensor_default_146"; -"3116 dequantize_per_tensor_default_146" -> "3122 linear_145"; -"3117 linear_145_scale_0" -> "3119 quantize_per_channel_default_146"; -"3117 linear_145_scale_0" -> "3120 dequantize_per_channel_default_146"; -"3118 linear_145_zero_point_0" -> "3119 quantize_per_channel_default_146"; -"3118 linear_145_zero_point_0" -> "3120 dequantize_per_channel_default_146"; -"3119 quantize_per_channel_default_146" -> "3120 dequantize_per_channel_default_146"; -"3120 dequantize_per_channel_default_146" -> "3122 linear_145"; -"3121 _param_constant392_0_0" -> "3122 linear_145"; -"3122 linear_145" -> "3123 gelu_23"; -"3123 gelu_23" -> "3124 dropout_94"; -"3124 dropout_94" -> "3126 dropout_94_0_0_nncf_smooth_quant_0"; -"3125 linear_146_updated_constant0" -> "3131 quantize_per_channel_default_147"; -"3126 dropout_94_0_0_nncf_smooth_quant_0" -> "3127 quantize_per_tensor_default_147"; -"3127 quantize_per_tensor_default_147" -> "3128 dequantize_per_tensor_default_147"; -"3128 dequantize_per_tensor_default_147" -> "3134 linear_146"; -"3129 linear_146_scale_0" -> "3131 quantize_per_channel_default_147"; -"3129 linear_146_scale_0" -> "3132 dequantize_per_channel_default_147"; -"3130 linear_146_zero_point_0" -> "3131 quantize_per_channel_default_147"; -"3130 linear_146_zero_point_0" -> "3132 dequantize_per_channel_default_147"; -"3131 quantize_per_channel_default_147" -> "3132 dequantize_per_channel_default_147"; -"3132 dequantize_per_channel_default_147" -> "3134 linear_146"; -"3133 _param_constant394_0_0" -> "3134 linear_146"; -"3134 linear_146" -> "3135 dropout_95"; -"3135 dropout_95" -> "3138 layer_norm_51"; -"3136 _param_constant395" -> "3138 layer_norm_51"; -"3137 _param_constant396" -> "3138 layer_norm_51"; -"3138 layer_norm_51" -> "3139 add_82"; -"3139 add_82" -> "3142 layer_norm_52"; -"3140 _param_constant397" -> "3142 layer_norm_52"; -"3141 _param_constant398" -> "3142 layer_norm_52"; -"3142 layer_norm_52" -> "3143 permute_108"; -"3143 permute_108" -> "3144 adaptive_avg_pool2d"; -"3144 adaptive_avg_pool2d" -> "3145 flatten"; -"3145 flatten" -> "3147 flatten_0_0_nncf_smooth_quant_0"; -"3146 linear_147_updated_constant0" -> "3152 quantize_per_channel_default_148"; -"3147 flatten_0_0_nncf_smooth_quant_0" -> "3148 quantize_per_tensor_default_148"; -"3148 quantize_per_tensor_default_148" -> "3149 dequantize_per_tensor_default_148"; -"3149 dequantize_per_tensor_default_148" -> "3155 linear_147"; -"3150 linear_147_scale_0" -> "3152 quantize_per_channel_default_148"; -"3150 linear_147_scale_0" -> "3153 dequantize_per_channel_default_148"; -"3151 linear_147_zero_point_0" -> "3152 quantize_per_channel_default_148"; -"3151 linear_147_zero_point_0" -> "3153 dequantize_per_channel_default_148"; -"3152 quantize_per_channel_default_148" -> "3153 dequantize_per_channel_default_148"; -"3153 dequantize_per_channel_default_148" -> "3155 linear_147"; -"3154 _param_constant400_0_0" -> "3155 linear_147"; -"3155 linear_147" -> "3156 output"; +"0 features_0_2_weight" [id=0, type=get_attr]; +"1 features_0_2_bias" [id=1, type=get_attr]; +"2 features_1_0_norm1_weight" [id=2, type=get_attr]; +"3 features_1_0_norm1_bias" [id=3, type=get_attr]; +"4 features_1_0_norm2_weight" [id=4, type=get_attr]; +"5 features_1_0_norm2_bias" [id=5, type=get_attr]; +"6 features_1_1_norm1_weight" [id=6, type=get_attr]; +"7 features_1_1_norm1_bias" [id=7, type=get_attr]; +"8 features_1_1_norm2_weight" [id=8, type=get_attr]; +"9 features_1_1_norm2_bias" 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dropout_10_0_0_nncf_smooth_quant_0" [id=405, type=call_module]; +"406 quantize_per_tensor_default_19" [id=406, type=quantize_per_tensor]; +"407 dequantize_per_tensor_default_19" [id=407, type=dequantize_per_tensor]; +"408 linear_18_scale_0" [id=408, type=get_attr]; +"409 linear_18_zero_point_0" [id=409, type=get_attr]; +"410 compressed_weight_updated_constant13" [id=410, type=get_attr]; +"411 quantize_per_channel_default_16" [id=411, type=quantize_per_channel]; +"412 dequantize_per_channel_default_16" [id=412, type=dequantize_per_channel]; +"413 features_3_0_mlp_3_bias_0_0" [id=413, type=get_attr]; +"414 linear_18" [id=414, type=linear]; +"415 dropout_11" [id=415, type=dropout]; +"416 layer_norm_7" [id=416, type=layer_norm]; +"417 add_9" [id=417, type=add]; +"418 _frozen_param21" [id=418, type=get_attr]; +"419 pad_4" [id=419, type=pad]; +"420 roll_2" [id=420, type=roll]; +"421 view_17" [id=421, type=view]; +"422 permute_15" [id=422, type=permute]; +"423 reshape_13" [id=423, 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sub_1" [id=462, type=sub]; +"463 ne_1" [id=463, type=ne]; +"464 masked_fill_2" [id=464, type=masked_fill]; +"465 eq_1" [id=465, type=eq]; +"466 masked_fill_3" [id=466, type=masked_fill]; +"467 view_19" [id=467, type=view]; +"468 unsqueeze_10" [id=468, type=unsqueeze]; +"469 unsqueeze_11" [id=469, type=unsqueeze]; +"470 add_11" [id=470, type=add]; +"471 view_20" [id=471, type=view]; +"472 softmax_3" [id=472, type=softmax]; +"473 dropout_12" [id=473, type=dropout]; +"474 matmul_7" [id=474, type=matmul]; +"475 transpose_7" [id=475, type=transpose]; +"476 reshape_16" [id=476, type=reshape]; +"477 reshape_16_0_0_nncf_smooth_quant_0" [id=477, type=call_module]; +"478 quantize_per_tensor_default_23" [id=478, type=quantize_per_tensor]; +"479 dequantize_per_tensor_default_23" [id=479, type=dequantize_per_tensor]; +"480 linear_22_scale_0" [id=480, type=get_attr]; +"481 linear_22_zero_point_0" [id=481, type=get_attr]; +"482 compressed_weight_updated_constant15" [id=482, type=get_attr]; +"483 quantize_per_channel_default_19" [id=483, type=quantize_per_channel]; +"484 dequantize_per_channel_default_19" [id=484, type=dequantize_per_channel]; +"485 features_3_1_attn_proj_bias_0_0" [id=485, type=get_attr]; +"486 linear_22" [id=486, type=linear]; +"487 dropout_13" [id=487, type=dropout]; +"488 view_21" [id=488, type=view]; +"489 permute_18" [id=489, type=permute]; +"490 reshape_17" [id=490, type=reshape]; +"491 roll_3" [id=491, type=roll]; +"492 slice_61" [id=492, type=slice]; +"493 slice_62" [id=493, type=slice]; +"494 slice_63" [id=494, type=slice]; +"495 slice_64" [id=495, type=slice]; +"496 contiguous_5" [id=496, type=contiguous]; +"497 layer_norm_8" [id=497, type=layer_norm]; +"498 add_12" [id=498, type=add]; +"499 add_12_0_0_nncf_smooth_quant_0" [id=499, type=call_module]; +"500 quantize_per_tensor_default_24" [id=500, type=quantize_per_tensor]; +"501 dequantize_per_tensor_default_24" [id=501, type=dequantize_per_tensor]; +"502 linear_23_scale_0" [id=502, type=get_attr]; 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type=dequantize_per_channel]; +"519 features_3_1_mlp_3_bias_0_0" [id=519, type=get_attr]; +"520 linear_24" [id=520, type=linear]; +"521 dropout_15" [id=521, type=dropout]; +"522 layer_norm_9" [id=522, type=layer_norm]; +"523 add_13" [id=523, type=add]; +"524 pad_5" [id=524, type=pad]; +"525 slice_65" [id=525, type=slice]; +"526 slice_66" [id=526, type=slice]; +"527 slice_67" [id=527, type=slice]; +"528 slice_68" [id=528, type=slice]; +"529 slice_69" [id=529, type=slice]; +"530 slice_70" [id=530, type=slice]; +"531 slice_71" [id=531, type=slice]; +"532 slice_72" [id=532, type=slice]; +"533 slice_73" [id=533, type=slice]; +"534 slice_74" [id=534, type=slice]; +"535 slice_75" [id=535, type=slice]; +"536 slice_76" [id=536, type=slice]; +"537 cat_1" [id=537, type=cat]; +"538 cat_1_0_0_nncf_smooth_quant_0" [id=538, type=call_module]; +"539 quantize_per_tensor_default_26" [id=539, type=quantize_per_tensor]; +"540 dequantize_per_tensor_default_26" [id=540, type=dequantize_per_tensor]; +"541 linear_25_scale_0" [id=541, type=get_attr]; +"542 linear_25_zero_point_0" [id=542, type=get_attr]; +"543 compressed_weight_updated_constant18" [id=543, type=get_attr]; +"544 quantize_per_channel_default_22" [id=544, type=quantize_per_channel]; +"545 dequantize_per_channel_default_22" [id=545, type=dequantize_per_channel]; +"546 linear_25" [id=546, type=linear]; +"547 layer_norm_10" [id=547, type=layer_norm]; +"548 _frozen_param34" [id=548, type=get_attr]; +"549 pad_6" [id=549, type=pad]; +"550 view_24" [id=550, type=view]; +"551 permute_20" [id=551, type=permute]; +"552 reshape_18" [id=552, type=reshape]; +"553 reshape_18_0_0_nncf_smooth_quant_0" [id=553, type=call_module]; +"554 quantize_per_tensor_default_27" [id=554, type=quantize_per_tensor]; +"555 dequantize_per_tensor_default_27" [id=555, type=dequantize_per_tensor]; +"556 linear_28_scale_0" [id=556, type=get_attr]; +"557 linear_28_zero_point_0" [id=557, type=get_attr]; +"558 compressed_weight_updated_constant19" [id=558, type=get_attr]; +"559 quantize_per_channel_default_24" [id=559, type=quantize_per_channel]; +"560 dequantize_per_channel_default_24" [id=560, type=dequantize_per_channel]; +"561 _frozen_param35_0_0" [id=561, type=get_attr]; +"562 linear_28" [id=562, type=linear]; +"563 reshape_19" [id=563, type=reshape]; +"564 permute_21" [id=564, type=permute]; +"565 select_12" [id=565, type=select]; +"566 select_13" [id=566, type=select]; +"567 select_14" [id=567, type=select]; +"568 linalg_vector_norm_8" [id=568, type=linalg_vector_norm]; +"569 clamp_min_8" [id=569, type=clamp_min]; +"570 expand_as_8" [id=570, type=expand_as]; +"571 div_8" [id=571, type=div]; +"572 quantize_per_tensor_default_28" [id=572, type=quantize_per_tensor]; +"573 dequantize_per_tensor_default_28" [id=573, type=dequantize_per_tensor]; +"574 linalg_vector_norm_9" [id=574, type=linalg_vector_norm]; +"575 clamp_min_9" [id=575, type=clamp_min]; +"576 expand_as_9" [id=576, type=expand_as]; +"577 div_9" [id=577, type=div]; +"578 quantize_per_tensor_default_29" [id=578, type=quantize_per_tensor]; +"579 dequantize_per_tensor_default_29" [id=579, type=dequantize_per_tensor]; +"580 transpose_8" [id=580, type=transpose]; +"581 matmul_8" [id=581, type=matmul]; +"582 _frozen_param37" [id=582, type=get_attr]; +"583 mul_9" [id=583, type=mul]; +"584 add_14" [id=584, type=add]; +"585 softmax_4" [id=585, type=softmax]; +"586 dropout_16" [id=586, type=dropout]; +"587 matmul_9" [id=587, type=matmul]; +"588 transpose_9" [id=588, type=transpose]; +"589 reshape_20" [id=589, type=reshape]; +"590 reshape_20_0_0_nncf_smooth_quant_0" [id=590, type=call_module]; +"591 quantize_per_tensor_default_30" [id=591, type=quantize_per_tensor]; +"592 dequantize_per_tensor_default_30" [id=592, type=dequantize_per_tensor]; +"593 linear_29_scale_0" [id=593, type=get_attr]; +"594 linear_29_zero_point_0" [id=594, type=get_attr]; +"595 compressed_weight_updated_constant20" [id=595, type=get_attr]; +"596 quantize_per_channel_default_25" [id=596, type=quantize_per_channel]; +"597 dequantize_per_channel_default_25" [id=597, type=dequantize_per_channel]; +"598 features_5_0_attn_proj_bias_0_0" [id=598, type=get_attr]; +"599 linear_29" [id=599, type=linear]; +"600 dropout_17" [id=600, type=dropout]; +"601 view_25" [id=601, type=view]; +"602 permute_22" [id=602, type=permute]; +"603 reshape_21" [id=603, type=reshape]; +"604 slice_78" [id=604, type=slice]; +"605 slice_79" [id=605, type=slice]; +"606 slice_80" [id=606, type=slice]; +"607 slice_81" [id=607, type=slice]; +"608 contiguous_7" [id=608, type=contiguous]; +"609 layer_norm_11" [id=609, type=layer_norm]; +"610 add_15" [id=610, type=add]; +"611 add_15_0_0_nncf_smooth_quant_0" [id=611, type=call_module]; +"612 quantize_per_tensor_default_31" [id=612, type=quantize_per_tensor]; +"613 dequantize_per_tensor_default_31" [id=613, type=dequantize_per_tensor]; +"614 linear_30_scale_0" [id=614, type=get_attr]; +"615 linear_30_zero_point_0" [id=615, type=get_attr]; +"616 compressed_weight_updated_constant21" [id=616, type=get_attr]; +"617 quantize_per_channel_default_26" [id=617, type=quantize_per_channel]; +"618 dequantize_per_channel_default_26" [id=618, type=dequantize_per_channel]; +"619 features_5_0_mlp_0_bias_0_0" [id=619, type=get_attr]; +"620 linear_30" [id=620, type=linear]; +"621 gelu_4" [id=621, type=gelu]; +"622 dropout_18" [id=622, type=dropout]; +"623 dropout_18_0_0_nncf_smooth_quant_0" [id=623, type=call_module]; +"624 quantize_per_tensor_default_32" [id=624, type=quantize_per_tensor]; +"625 dequantize_per_tensor_default_32" [id=625, type=dequantize_per_tensor]; +"626 linear_31_scale_0" [id=626, type=get_attr]; +"627 linear_31_zero_point_0" [id=627, type=get_attr]; +"628 compressed_weight_updated_constant22" [id=628, type=get_attr]; +"629 quantize_per_channel_default_27" [id=629, type=quantize_per_channel]; +"630 dequantize_per_channel_default_27" [id=630, type=dequantize_per_channel]; +"631 features_5_0_mlp_3_bias_0_0" [id=631, type=get_attr]; +"632 linear_31" [id=632, type=linear]; +"633 dropout_19" [id=633, type=dropout]; +"634 layer_norm_12" [id=634, type=layer_norm]; +"635 add_16" [id=635, type=add]; +"636 _frozen_param38" [id=636, type=get_attr]; +"637 pad_7" [id=637, type=pad]; +"638 roll_4" [id=638, type=roll]; +"639 view_28" [id=639, type=view]; +"640 permute_24" [id=640, type=permute]; +"641 reshape_22" [id=641, type=reshape]; +"642 reshape_22_0_0_nncf_smooth_quant_0" [id=642, type=call_module]; +"643 quantize_per_tensor_default_33" [id=643, type=quantize_per_tensor]; +"644 dequantize_per_tensor_default_33" [id=644, type=dequantize_per_tensor]; +"645 linear_34_scale_0" [id=645, type=get_attr]; +"646 linear_34_zero_point_0" [id=646, type=get_attr]; +"647 compressed_weight_updated_constant23" [id=647, type=get_attr]; +"648 quantize_per_channel_default_29" [id=648, type=quantize_per_channel]; +"649 dequantize_per_channel_default_29" [id=649, type=dequantize_per_channel]; +"650 _frozen_param39_0_0" [id=650, type=get_attr]; +"651 linear_34" [id=651, type=linear]; +"652 reshape_23" [id=652, type=reshape]; +"653 permute_25" [id=653, type=permute]; +"654 select_15" [id=654, type=select]; +"655 select_16" [id=655, type=select]; +"656 select_17" [id=656, type=select]; +"657 linalg_vector_norm_10" [id=657, type=linalg_vector_norm]; +"658 clamp_min_10" [id=658, type=clamp_min]; +"659 expand_as_10" [id=659, type=expand_as]; +"660 div_10" [id=660, type=div]; +"661 quantize_per_tensor_default_34" [id=661, type=quantize_per_tensor]; +"662 dequantize_per_tensor_default_34" [id=662, type=dequantize_per_tensor]; +"663 linalg_vector_norm_11" [id=663, type=linalg_vector_norm]; +"664 clamp_min_11" [id=664, type=clamp_min]; +"665 expand_as_11" [id=665, type=expand_as]; +"666 div_11" [id=666, type=div]; +"667 quantize_per_tensor_default_35" [id=667, type=quantize_per_tensor]; +"668 dequantize_per_tensor_default_35" [id=668, type=dequantize_per_tensor]; +"669 transpose_10" [id=669, type=transpose]; +"670 matmul_10" [id=670, type=matmul]; +"671 _frozen_param41" [id=671, type=get_attr]; +"672 mul_11" [id=672, type=mul]; +"673 add_17" [id=673, type=add]; +"674 new_zeros_2" [id=674, type=new_zeros]; +"675 view_29" [id=675, type=view]; +"676 permute_26" [id=676, type=permute]; +"677 reshape_24" [id=677, type=reshape]; +"678 unsqueeze_14" [id=678, type=unsqueeze]; +"679 unsqueeze_15" [id=679, type=unsqueeze]; +"680 sub_2" [id=680, type=sub]; +"681 ne_2" [id=681, type=ne]; +"682 masked_fill_4" [id=682, type=masked_fill]; +"683 eq_2" [id=683, type=eq]; +"684 masked_fill_5" [id=684, type=masked_fill]; +"685 view_30" [id=685, type=view]; +"686 unsqueeze_16" [id=686, type=unsqueeze]; +"687 unsqueeze_17" [id=687, type=unsqueeze]; +"688 add_18" [id=688, type=add]; +"689 view_31" [id=689, type=view]; +"690 softmax_5" [id=690, type=softmax]; +"691 dropout_20" [id=691, type=dropout]; +"692 matmul_11" [id=692, type=matmul]; +"693 transpose_11" [id=693, type=transpose]; +"694 reshape_25" [id=694, type=reshape]; +"695 reshape_25_0_0_nncf_smooth_quant_0" [id=695, type=call_module]; +"696 quantize_per_tensor_default_36" [id=696, type=quantize_per_tensor]; +"697 dequantize_per_tensor_default_36" [id=697, type=dequantize_per_tensor]; +"698 linear_35_scale_0" [id=698, type=get_attr]; +"699 linear_35_zero_point_0" [id=699, type=get_attr]; +"700 compressed_weight_updated_constant24" [id=700, type=get_attr]; +"701 quantize_per_channel_default_30" [id=701, type=quantize_per_channel]; +"702 dequantize_per_channel_default_30" [id=702, type=dequantize_per_channel]; +"703 features_5_1_attn_proj_bias_0_0" [id=703, type=get_attr]; +"704 linear_35" [id=704, type=linear]; +"705 dropout_21" [id=705, type=dropout]; +"706 view_32" [id=706, type=view]; +"707 permute_27" [id=707, type=permute]; +"708 reshape_26" [id=708, type=reshape]; +"709 roll_5" [id=709, type=roll]; +"710 slice_101" [id=710, type=slice]; +"711 slice_102" [id=711, type=slice]; +"712 slice_103" [id=712, type=slice]; +"713 slice_104" [id=713, type=slice]; +"714 contiguous_9" [id=714, type=contiguous]; +"715 layer_norm_13" [id=715, type=layer_norm]; +"716 add_19" [id=716, type=add]; +"717 add_19_0_0_nncf_smooth_quant_0" [id=717, type=call_module]; +"718 quantize_per_tensor_default_37" [id=718, type=quantize_per_tensor]; +"719 dequantize_per_tensor_default_37" [id=719, type=dequantize_per_tensor]; +"720 linear_36_scale_0" [id=720, type=get_attr]; +"721 linear_36_zero_point_0" [id=721, type=get_attr]; +"722 compressed_weight_updated_constant25" [id=722, type=get_attr]; +"723 quantize_per_channel_default_31" [id=723, type=quantize_per_channel]; +"724 dequantize_per_channel_default_31" [id=724, type=dequantize_per_channel]; +"725 features_5_1_mlp_0_bias_0_0" [id=725, type=get_attr]; +"726 linear_36" [id=726, type=linear]; +"727 gelu_5" [id=727, type=gelu]; +"728 dropout_22" [id=728, type=dropout]; +"729 dropout_22_0_0_nncf_smooth_quant_0" [id=729, type=call_module]; +"730 quantize_per_tensor_default_38" 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type=quantize_per_tensor]; +"749 dequantize_per_tensor_default_39" [id=749, type=dequantize_per_tensor]; +"750 linear_40_scale_0" [id=750, type=get_attr]; +"751 linear_40_zero_point_0" [id=751, type=get_attr]; +"752 compressed_weight_updated_constant27" [id=752, type=get_attr]; +"753 quantize_per_channel_default_34" [id=753, type=quantize_per_channel]; +"754 dequantize_per_channel_default_34" [id=754, type=dequantize_per_channel]; +"755 _frozen_param52_0_0" [id=755, type=get_attr]; +"756 linear_40" [id=756, type=linear]; +"757 reshape_28" [id=757, type=reshape]; +"758 permute_30" [id=758, type=permute]; +"759 select_18" [id=759, type=select]; +"760 select_19" [id=760, type=select]; +"761 select_20" [id=761, type=select]; +"762 linalg_vector_norm_12" [id=762, type=linalg_vector_norm]; +"763 clamp_min_12" [id=763, type=clamp_min]; +"764 expand_as_12" [id=764, type=expand_as]; +"765 div_12" [id=765, type=div]; +"766 quantize_per_tensor_default_40" [id=766, type=quantize_per_tensor]; +"767 dequantize_per_tensor_default_40" [id=767, type=dequantize_per_tensor]; +"768 linalg_vector_norm_13" [id=768, type=linalg_vector_norm]; +"769 clamp_min_13" [id=769, type=clamp_min]; +"770 expand_as_13" [id=770, type=expand_as]; +"771 div_13" [id=771, type=div]; +"772 quantize_per_tensor_default_41" [id=772, type=quantize_per_tensor]; +"773 dequantize_per_tensor_default_41" [id=773, type=dequantize_per_tensor]; +"774 transpose_12" [id=774, type=transpose]; +"775 matmul_12" [id=775, type=matmul]; +"776 _frozen_param54" [id=776, type=get_attr]; +"777 mul_13" [id=777, type=mul]; +"778 add_21" [id=778, type=add]; +"779 softmax_6" [id=779, type=softmax]; +"780 dropout_24" [id=780, type=dropout]; +"781 matmul_13" [id=781, type=matmul]; +"782 transpose_13" [id=782, type=transpose]; +"783 reshape_29" [id=783, type=reshape]; +"784 reshape_29_0_0_nncf_smooth_quant_0" [id=784, type=call_module]; +"785 quantize_per_tensor_default_42" [id=785, type=quantize_per_tensor]; +"786 dequantize_per_tensor_default_42" [id=786, type=dequantize_per_tensor]; +"787 linear_41_scale_0" [id=787, type=get_attr]; +"788 linear_41_zero_point_0" [id=788, type=get_attr]; +"789 compressed_weight_updated_constant28" [id=789, type=get_attr]; +"790 quantize_per_channel_default_35" [id=790, type=quantize_per_channel]; +"791 dequantize_per_channel_default_35" [id=791, type=dequantize_per_channel]; +"792 features_5_2_attn_proj_bias_0_0" [id=792, type=get_attr]; +"793 linear_41" [id=793, type=linear]; +"794 dropout_25" [id=794, type=dropout]; +"795 view_36" [id=795, type=view]; +"796 permute_31" [id=796, type=permute]; +"797 reshape_30" [id=797, type=reshape]; +"798 slice_106" [id=798, type=slice]; +"799 slice_107" [id=799, type=slice]; +"800 slice_108" [id=800, type=slice]; +"801 slice_109" [id=801, type=slice]; +"802 contiguous_11" [id=802, type=contiguous]; +"803 layer_norm_15" [id=803, type=layer_norm]; +"804 add_22" [id=804, type=add]; +"805 add_22_0_0_nncf_smooth_quant_0" [id=805, type=call_module]; +"806 quantize_per_tensor_default_43" [id=806, type=quantize_per_tensor]; +"807 dequantize_per_tensor_default_43" [id=807, type=dequantize_per_tensor]; +"808 linear_42_scale_0" [id=808, type=get_attr]; +"809 linear_42_zero_point_0" [id=809, type=get_attr]; +"810 compressed_weight_updated_constant29" [id=810, type=get_attr]; +"811 quantize_per_channel_default_36" [id=811, type=quantize_per_channel]; +"812 dequantize_per_channel_default_36" [id=812, type=dequantize_per_channel]; +"813 features_5_2_mlp_0_bias_0_0" [id=813, type=get_attr]; +"814 linear_42" [id=814, type=linear]; +"815 gelu_6" [id=815, type=gelu]; +"816 dropout_26" [id=816, type=dropout]; +"817 dropout_26_0_0_nncf_smooth_quant_0" [id=817, type=call_module]; +"818 quantize_per_tensor_default_44" [id=818, type=quantize_per_tensor]; +"819 dequantize_per_tensor_default_44" [id=819, type=dequantize_per_tensor]; +"820 linear_43_scale_0" [id=820, type=get_attr]; +"821 linear_43_zero_point_0" [id=821, type=get_attr]; +"822 compressed_weight_updated_constant30" [id=822, type=get_attr]; +"823 quantize_per_channel_default_37" [id=823, type=quantize_per_channel]; +"824 dequantize_per_channel_default_37" [id=824, type=dequantize_per_channel]; +"825 features_5_2_mlp_3_bias_0_0" [id=825, type=get_attr]; +"826 linear_43" [id=826, type=linear]; +"827 dropout_27" [id=827, type=dropout]; +"828 layer_norm_16" [id=828, type=layer_norm]; +"829 add_23" [id=829, type=add]; +"830 _frozen_param55" [id=830, type=get_attr]; +"831 pad_9" [id=831, type=pad]; +"832 roll_6" [id=832, type=roll]; +"833 view_39" [id=833, type=view]; +"834 permute_33" [id=834, type=permute]; +"835 reshape_31" [id=835, type=reshape]; +"836 reshape_31_0_0_nncf_smooth_quant_0" [id=836, type=call_module]; +"837 quantize_per_tensor_default_45" [id=837, type=quantize_per_tensor]; +"838 dequantize_per_tensor_default_45" [id=838, type=dequantize_per_tensor]; +"839 linear_46_scale_0" [id=839, type=get_attr]; +"840 linear_46_zero_point_0" [id=840, type=get_attr]; +"841 compressed_weight_updated_constant31" [id=841, type=get_attr]; +"842 quantize_per_channel_default_39" [id=842, type=quantize_per_channel]; +"843 dequantize_per_channel_default_39" [id=843, type=dequantize_per_channel]; +"844 _frozen_param56_0_0" [id=844, type=get_attr]; +"845 linear_46" [id=845, type=linear]; +"846 reshape_32" [id=846, type=reshape]; +"847 permute_34" [id=847, type=permute]; +"848 select_21" [id=848, type=select]; +"849 select_22" [id=849, type=select]; +"850 select_23" [id=850, type=select]; +"851 linalg_vector_norm_14" [id=851, type=linalg_vector_norm]; +"852 clamp_min_14" [id=852, type=clamp_min]; +"853 expand_as_14" [id=853, type=expand_as]; +"854 div_14" [id=854, type=div]; +"855 quantize_per_tensor_default_46" [id=855, type=quantize_per_tensor]; +"856 dequantize_per_tensor_default_46" [id=856, type=dequantize_per_tensor]; +"857 linalg_vector_norm_15" [id=857, type=linalg_vector_norm]; +"858 clamp_min_15" 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type=dequantize_per_channel]; +"919 features_5_3_mlp_0_bias_0_0" [id=919, type=get_attr]; +"920 linear_48" [id=920, type=linear]; +"921 gelu_7" [id=921, type=gelu]; +"922 dropout_30" [id=922, type=dropout]; +"923 dropout_30_0_0_nncf_smooth_quant_0" [id=923, type=call_module]; +"924 quantize_per_tensor_default_50" [id=924, type=quantize_per_tensor]; +"925 dequantize_per_tensor_default_50" [id=925, type=dequantize_per_tensor]; +"926 linear_49_scale_0" [id=926, type=get_attr]; +"927 linear_49_zero_point_0" [id=927, type=get_attr]; +"928 compressed_weight_updated_constant34" [id=928, type=get_attr]; +"929 quantize_per_channel_default_42" [id=929, type=quantize_per_channel]; +"930 dequantize_per_channel_default_42" [id=930, type=dequantize_per_channel]; +"931 features_5_3_mlp_3_bias_0_0" [id=931, type=get_attr]; +"932 linear_49" [id=932, type=linear]; +"933 dropout_31" [id=933, type=dropout]; +"934 layer_norm_18" [id=934, type=layer_norm]; +"935 add_27" [id=935, type=add]; +"936 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type=slice]; +"993 slice_135" [id=993, type=slice]; +"994 slice_136" [id=994, type=slice]; +"995 slice_137" [id=995, type=slice]; +"996 contiguous_15" [id=996, type=contiguous]; +"997 layer_norm_19" [id=997, type=layer_norm]; +"998 add_29" [id=998, type=add]; +"999 add_29_0_0_nncf_smooth_quant_0" [id=999, type=call_module]; +"1000 quantize_per_tensor_default_55" [id=1000, type=quantize_per_tensor]; +"1001 dequantize_per_tensor_default_55" [id=1001, type=dequantize_per_tensor]; +"1002 linear_54_scale_0" [id=1002, type=get_attr]; +"1003 linear_54_zero_point_0" [id=1003, type=get_attr]; +"1004 compressed_weight_updated_constant37" [id=1004, type=get_attr]; +"1005 quantize_per_channel_default_46" [id=1005, type=quantize_per_channel]; +"1006 dequantize_per_channel_default_46" [id=1006, type=dequantize_per_channel]; +"1007 features_5_4_mlp_0_bias_0_0" [id=1007, type=get_attr]; +"1008 linear_54" [id=1008, type=linear]; +"1009 gelu_8" [id=1009, type=gelu]; +"1010 dropout_34" [id=1010, 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style=solid]; +"136 select_1" -> "147 div_1" [label="(49, 3, 64, 32)", style=solid]; +"137 select_2" -> "157 matmul_1" [label="(49, 3, 64, 32)", style=solid]; +"138 linalg_vector_norm" -> "139 clamp_min" [label="(49, 3, 64, 1)", style=solid]; +"139 clamp_min" -> "140 expand_as" [label="(49, 3, 64, 1)", style=solid]; +"140 expand_as" -> "141 div" [label="(49, 3, 64, 32)", style=solid]; +"141 div" -> "142 quantize_per_tensor_default_2" [label="(49, 3, 64, 32)", style=solid]; +"142 quantize_per_tensor_default_2" -> "143 dequantize_per_tensor_default_2" [label="(49, 3, 64, 32)", style=solid]; +"143 dequantize_per_tensor_default_2" -> "151 matmul" [label="(49, 3, 64, 32)", style=solid]; +"144 linalg_vector_norm_1" -> "145 clamp_min_1" [label="(49, 3, 64, 1)", style=solid]; +"145 clamp_min_1" -> "146 expand_as_1" [label="(49, 3, 64, 1)", style=solid]; +"146 expand_as_1" -> "147 div_1" [label="(49, 3, 64, 32)", style=solid]; +"147 div_1" -> "148 quantize_per_tensor_default_3" [label="(49, 3, 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reshape_2_0_0_nncf_smooth_quant_0" -> "161 quantize_per_tensor_default_4" [label="(49, 64, 96)", style=solid]; +"161 quantize_per_tensor_default_4" -> "162 dequantize_per_tensor_default_4" [label="(49, 64, 96)", style=solid]; +"162 dequantize_per_tensor_default_4" -> "169 linear_3" [label="(49, 64, 96)", style=solid]; +"163 linear_3_scale_0" -> "166 quantize_per_channel_default_3" [label="(96,)", style=solid]; +"163 linear_3_scale_0" -> "167 dequantize_per_channel_default_3" [label="(96,)", style=solid]; +"164 linear_3_zero_point_0" -> "166 quantize_per_channel_default_3" [label="(96,)", style=solid]; +"164 linear_3_zero_point_0" -> "167 dequantize_per_channel_default_3" [label="(96,)", style=solid]; +"165 compressed_weight_updated_constant2" -> "166 quantize_per_channel_default_3" [label="(96, 96)", style=solid]; +"166 quantize_per_channel_default_3" -> "167 dequantize_per_channel_default_3" [label="(96, 96)", style=solid]; +"167 dequantize_per_channel_default_3" -> "169 linear_3" 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style=solid]; +"368 dropout_8" -> "369 matmul_5" [label="(16, 6, 64, 64)", style=solid]; +"369 matmul_5" -> "370 transpose_5" [label="(16, 6, 64, 32)", style=solid]; +"370 transpose_5" -> "371 reshape_11" [label="(16, 64, 6, 32)", style=solid]; +"371 reshape_11" -> "372 reshape_11_0_0_nncf_smooth_quant_0" [label="(16, 64, 192)", style=solid]; +"372 reshape_11_0_0_nncf_smooth_quant_0" -> "373 quantize_per_tensor_default_17" [label="(16, 64, 192)", style=solid]; +"373 quantize_per_tensor_default_17" -> "374 dequantize_per_tensor_default_17" [label="(16, 64, 192)", style=solid]; +"374 dequantize_per_tensor_default_17" -> "381 linear_16" [label="(16, 64, 192)", style=solid]; +"375 linear_16_scale_0" -> "378 quantize_per_channel_default_14" [label="(192,)", style=solid]; +"375 linear_16_scale_0" -> "379 dequantize_per_channel_default_14" [label="(192,)", style=solid]; +"376 linear_16_zero_point_0" -> "378 quantize_per_channel_default_14" [label="(192,)", style=solid]; +"376 linear_16_zero_point_0" -> "379 dequantize_per_channel_default_14" [label="(192,)", style=solid]; +"377 compressed_weight_updated_constant11" -> "378 quantize_per_channel_default_14" [label="(192, 192)", style=solid]; +"378 quantize_per_channel_default_14" -> "379 dequantize_per_channel_default_14" [label="(192, 192)", style=solid]; +"379 dequantize_per_channel_default_14" -> "381 linear_16" [label="(192, 192)", style=solid]; +"380 features_3_0_attn_proj_bias_0_0" -> "381 linear_16" [label="(192,)", style=solid]; +"381 linear_16" -> "382 dropout_9" [label="(16, 64, 192)", style=solid]; +"382 dropout_9" -> "383 view_14" [label="(16, 64, 192)", style=solid]; +"383 view_14" -> "384 permute_13" [label="(1, 4, 4, 8, 8, 192)", style=solid]; +"384 permute_13" -> "385 reshape_12" [label="(1, 4, 8, 4, 8, 192)", style=solid]; +"385 reshape_12" -> "386 slice_38" [label="(1, 32, 32, 192)", style=solid]; +"386 slice_38" -> "387 slice_39" [label="(1, 32, 32, 192)", style=solid]; +"387 slice_39" -> "388 slice_40" [label="(1, 28, 32, 192)", style=solid]; +"388 slice_40" -> "389 slice_41" [label="(1, 28, 28, 192)", style=solid]; +"389 slice_41" -> "390 contiguous_3" [label="(1, 28, 28, 192)", style=solid]; +"390 contiguous_3" -> "391 layer_norm_6" [label="(1, 28, 28, 192)", style=solid]; +"391 layer_norm_6" -> "392 add_8" [label="(1, 28, 28, 192)", style=solid]; +"392 add_8" -> "393 add_8_0_0_nncf_smooth_quant_0" [label="(1, 28, 28, 192)", style=solid]; +"392 add_8" -> "417 add_9" [label="(1, 28, 28, 192)", style=solid]; +"393 add_8_0_0_nncf_smooth_quant_0" -> "394 quantize_per_tensor_default_18" [label="(1, 28, 28, 192)", style=solid]; +"394 quantize_per_tensor_default_18" -> "395 dequantize_per_tensor_default_18" [label="(1, 28, 28, 192)", style=solid]; +"395 dequantize_per_tensor_default_18" -> "402 linear_17" [label="(1, 28, 28, 192)", style=solid]; +"396 linear_17_scale_0" -> "399 quantize_per_channel_default_15" [label="(768,)", style=solid]; +"396 linear_17_scale_0" -> "400 dequantize_per_channel_default_15" [label="(768,)", style=solid]; +"397 linear_17_zero_point_0" -> "399 quantize_per_channel_default_15" [label="(768,)", style=solid]; +"397 linear_17_zero_point_0" -> "400 dequantize_per_channel_default_15" [label="(768,)", style=solid]; +"398 compressed_weight_updated_constant12" -> "399 quantize_per_channel_default_15" [label="(768, 192)", style=solid]; +"399 quantize_per_channel_default_15" -> "400 dequantize_per_channel_default_15" [label="(768, 192)", style=solid]; +"400 dequantize_per_channel_default_15" -> "402 linear_17" [label="(768, 192)", style=solid]; +"401 features_3_0_mlp_0_bias_0_0" -> "402 linear_17" [label="(768,)", style=solid]; +"402 linear_17" -> "403 gelu_2" [label="(1, 28, 28, 768)", style=solid]; +"403 gelu_2" -> "404 dropout_10" [label="(1, 28, 28, 768)", style=solid]; +"404 dropout_10" -> "405 dropout_10_0_0_nncf_smooth_quant_0" [label="(1, 28, 28, 768)", style=solid]; +"405 dropout_10_0_0_nncf_smooth_quant_0" -> "406 quantize_per_tensor_default_19" [label="(1, 28, 28, 768)", style=solid]; +"406 quantize_per_tensor_default_19" -> "407 dequantize_per_tensor_default_19" [label="(1, 28, 28, 768)", style=solid]; +"407 dequantize_per_tensor_default_19" -> "414 linear_18" [label="(1, 28, 28, 768)", style=solid]; +"408 linear_18_scale_0" -> "411 quantize_per_channel_default_16" [label="(192,)", style=solid]; +"408 linear_18_scale_0" -> "412 dequantize_per_channel_default_16" [label="(192,)", style=solid]; +"409 linear_18_zero_point_0" -> "411 quantize_per_channel_default_16" [label="(192,)", style=solid]; +"409 linear_18_zero_point_0" -> "412 dequantize_per_channel_default_16" [label="(192,)", style=solid]; +"410 compressed_weight_updated_constant13" -> "411 quantize_per_channel_default_16" [label="(192, 768)", style=solid]; +"411 quantize_per_channel_default_16" -> "412 dequantize_per_channel_default_16" [label="(192, 768)", style=solid]; +"412 dequantize_per_channel_default_16" -> "414 linear_18" [label="(192, 768)", style=solid]; +"413 features_3_0_mlp_3_bias_0_0" -> "414 linear_18" [label="(192,)", style=solid]; +"414 linear_18" -> "415 dropout_11" [label="(1, 28, 28, 192)", style=solid]; +"415 dropout_11" -> "416 layer_norm_7" [label="(1, 28, 28, 192)", style=solid]; +"416 layer_norm_7" -> "417 add_9" [label="(1, 28, 28, 192)", style=solid]; +"417 add_9" -> "419 pad_4" [label="(1, 28, 28, 192)", style=solid]; +"417 add_9" -> "498 add_12" [label="(1, 28, 28, 192)", style=solid]; +"418 _frozen_param21" -> "455 add_10" [label="(1, 6, 64, 64)", style=solid]; +"419 pad_4" -> "420 roll_2" [label="(1, 32, 32, 192)", style=solid]; +"420 roll_2" -> "421 view_17" [label="(1, 32, 32, 192)", style=solid]; +"421 view_17" -> "422 permute_15" [label="(1, 4, 8, 4, 8, 192)", style=solid]; +"422 permute_15" -> "423 reshape_13" [label="(1, 4, 4, 8, 8, 192)", style=solid]; +"423 reshape_13" -> "424 reshape_13_0_0_nncf_smooth_quant_0" [label="(16, 64, 192)", style=solid]; +"423 reshape_13" -> "456 new_zeros_1" [label="(16, 64, 192)", style=solid]; +"424 reshape_13_0_0_nncf_smooth_quant_0" -> "425 quantize_per_tensor_default_20" [label="(16, 64, 192)", style=solid]; +"425 quantize_per_tensor_default_20" -> "426 dequantize_per_tensor_default_20" [label="(16, 64, 192)", style=solid]; +"426 dequantize_per_tensor_default_20" -> "433 linear_21" [label="(16, 64, 192)", style=solid]; +"427 linear_21_scale_0" -> "430 quantize_per_channel_default_18" [label="(576,)", style=solid]; +"427 linear_21_scale_0" -> "431 dequantize_per_channel_default_18" [label="(576,)", style=solid]; +"428 linear_21_zero_point_0" -> "430 quantize_per_channel_default_18" [label="(576,)", style=solid]; +"428 linear_21_zero_point_0" -> "431 dequantize_per_channel_default_18" [label="(576,)", style=solid]; +"429 compressed_weight_updated_constant14" -> "430 quantize_per_channel_default_18" [label="(576, 192)", style=solid]; +"430 quantize_per_channel_default_18" -> "431 dequantize_per_channel_default_18" [label="(576, 192)", style=solid]; +"431 dequantize_per_channel_default_18" -> "433 linear_21" [label="(576, 192)", style=solid]; +"432 _frozen_param22_0_0" -> "433 linear_21" [label="(576,)", style=solid]; +"433 linear_21" -> "434 reshape_14" [label="(16, 64, 576)", style=solid]; +"434 reshape_14" -> "435 permute_16" [label="(16, 64, 3, 6, 32)", style=solid]; +"435 permute_16" -> "436 select_9" [label="(3, 16, 6, 64, 32)", style=solid]; +"435 permute_16" -> "437 select_10" [label="(3, 16, 6, 64, 32)", style=solid]; +"435 permute_16" -> "438 select_11" [label="(3, 16, 6, 64, 32)", style=solid]; +"436 select_9" -> "439 linalg_vector_norm_6" [label="(16, 6, 64, 32)", style=solid]; +"436 select_9" -> "441 expand_as_6" [label="(16, 6, 64, 32)", style=solid]; +"436 select_9" -> "442 div_6" [label="(16, 6, 64, 32)", style=solid]; +"437 select_10" -> "445 linalg_vector_norm_7" [label="(16, 6, 64, 32)", style=solid]; +"437 select_10" -> "447 expand_as_7" [label="(16, 6, 64, 32)", style=solid]; +"437 select_10" -> "448 div_7" [label="(16, 6, 64, 32)", style=solid]; +"438 select_11" -> "474 matmul_7" [label="(16, 6, 64, 32)", style=solid]; +"439 linalg_vector_norm_6" -> "440 clamp_min_6" [label="(16, 6, 64, 1)", style=solid]; +"440 clamp_min_6" -> "441 expand_as_6" [label="(16, 6, 64, 1)", style=solid]; +"441 expand_as_6" -> "442 div_6" [label="(16, 6, 64, 32)", style=solid]; +"442 div_6" -> "443 quantize_per_tensor_default_21" [label="(16, 6, 64, 32)", style=solid]; +"443 quantize_per_tensor_default_21" -> "444 dequantize_per_tensor_default_21" [label="(16, 6, 64, 32)", style=solid]; +"444 dequantize_per_tensor_default_21" -> "452 matmul_6" [label="(16, 6, 64, 32)", style=solid]; +"445 linalg_vector_norm_7" -> "446 clamp_min_7" [label="(16, 6, 64, 1)", style=solid]; +"446 clamp_min_7" -> "447 expand_as_7" [label="(16, 6, 64, 1)", style=solid]; +"447 expand_as_7" -> "448 div_7" [label="(16, 6, 64, 32)", style=solid]; +"448 div_7" -> "449 quantize_per_tensor_default_22" [label="(16, 6, 64, 32)", style=solid]; +"449 quantize_per_tensor_default_22" -> "450 dequantize_per_tensor_default_22" [label="(16, 6, 64, 32)", style=solid]; +"450 dequantize_per_tensor_default_22" -> "451 transpose_6" [label="(16, 6, 64, 32)", style=solid]; +"451 transpose_6" -> "452 matmul_6" [label="(16, 6, 32, 64)", style=solid]; +"452 matmul_6" -> "454 mul_7" [label="(16, 6, 64, 64)", style=solid]; +"453 _frozen_param24" -> "454 mul_7" [label="(6, 1, 1)", style=solid]; +"454 mul_7" -> "455 add_10" [label="(16, 6, 64, 64)", style=solid]; +"455 add_10" -> "467 view_19" [label="(16, 6, 64, 64)", style=solid]; +"456 new_zeros_1" -> "457 view_18" [label="(32, 32)", style=solid]; +"457 view_18" -> "458 permute_17" [label="(4, 8, 4, 8)", style=solid]; +"458 permute_17" -> "459 reshape_15" [label="(4, 4, 8, 8)", style=solid]; +"459 reshape_15" -> "460 unsqueeze_8" [label="(16, 64)", style=solid]; +"459 reshape_15" -> "461 unsqueeze_9" [label="(16, 64)", style=solid]; +"460 unsqueeze_8" 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style=solid]; +"472 softmax_3" -> "473 dropout_12" [label="(16, 6, 64, 64)", style=solid]; +"473 dropout_12" -> "474 matmul_7" [label="(16, 6, 64, 64)", style=solid]; +"474 matmul_7" -> "475 transpose_7" [label="(16, 6, 64, 32)", style=solid]; +"475 transpose_7" -> "476 reshape_16" [label="(16, 64, 6, 32)", style=solid]; +"476 reshape_16" -> "477 reshape_16_0_0_nncf_smooth_quant_0" [label="(16, 64, 192)", style=solid]; +"477 reshape_16_0_0_nncf_smooth_quant_0" -> "478 quantize_per_tensor_default_23" [label="(16, 64, 192)", style=solid]; +"478 quantize_per_tensor_default_23" -> "479 dequantize_per_tensor_default_23" [label="(16, 64, 192)", style=solid]; +"479 dequantize_per_tensor_default_23" -> "486 linear_22" [label="(16, 64, 192)", style=solid]; +"480 linear_22_scale_0" -> "483 quantize_per_channel_default_19" [label="(192,)", style=solid]; +"480 linear_22_scale_0" -> "484 dequantize_per_channel_default_19" [label="(192,)", style=solid]; +"481 linear_22_zero_point_0" -> "483 quantize_per_channel_default_19" [label="(192,)", style=solid]; +"481 linear_22_zero_point_0" -> "484 dequantize_per_channel_default_19" [label="(192,)", style=solid]; +"482 compressed_weight_updated_constant15" -> "483 quantize_per_channel_default_19" [label="(192, 192)", style=solid]; +"483 quantize_per_channel_default_19" -> "484 dequantize_per_channel_default_19" [label="(192, 192)", style=solid]; +"484 dequantize_per_channel_default_19" -> "486 linear_22" [label="(192, 192)", style=solid]; +"485 features_3_1_attn_proj_bias_0_0" -> "486 linear_22" [label="(192,)", style=solid]; +"486 linear_22" -> "487 dropout_13" [label="(16, 64, 192)", style=solid]; +"487 dropout_13" -> "488 view_21" [label="(16, 64, 192)", style=solid]; +"488 view_21" -> "489 permute_18" [label="(1, 4, 4, 8, 8, 192)", style=solid]; +"489 permute_18" -> "490 reshape_17" [label="(1, 4, 8, 4, 8, 192)", style=solid]; +"490 reshape_17" -> "491 roll_3" [label="(1, 32, 32, 192)", style=solid]; +"491 roll_3" -> "492 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64, 32)", style=solid]; +"1857 transpose_35" -> "1858 reshape_79" [label="(4, 64, 12, 32)", style=solid]; +"1858 reshape_79" -> "1859 reshape_79_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"1859 reshape_79_0_0_nncf_smooth_quant_0" -> "1860 quantize_per_tensor_default_108" [label="(4, 64, 384)", style=solid]; +"1860 quantize_per_tensor_default_108" -> "1861 dequantize_per_tensor_default_108" [label="(4, 64, 384)", style=solid]; +"1861 dequantize_per_tensor_default_108" -> "1868 linear_107" [label="(4, 64, 384)", style=solid]; +"1862 linear_107_scale_0" -> "1865 quantize_per_channel_default_90" [label="(384,)", style=solid]; +"1862 linear_107_scale_0" -> "1866 dequantize_per_channel_default_90" [label="(384,)", style=solid]; +"1863 linear_107_zero_point_0" -> "1865 quantize_per_channel_default_90" [label="(384,)", style=solid]; +"1863 linear_107_zero_point_0" -> "1866 dequantize_per_channel_default_90" [label="(384,)", style=solid]; +"1864 compressed_weight_updated_constant72" -> "1865 quantize_per_channel_default_90" [label="(384, 384)", style=solid]; +"1865 quantize_per_channel_default_90" -> "1866 dequantize_per_channel_default_90" [label="(384, 384)", style=solid]; +"1866 dequantize_per_channel_default_90" -> "1868 linear_107" [label="(384, 384)", style=solid]; +"1867 features_5_13_attn_proj_bias_0_0" -> "1868 linear_107" [label="(384,)", style=solid]; +"1868 linear_107" -> "1869 dropout_69" [label="(4, 64, 384)", style=solid]; +"1869 dropout_69" -> "1870 view_98" [label="(4, 64, 384)", style=solid]; +"1870 view_98" -> "1871 permute_81" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1871 permute_81" -> "1872 reshape_80" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1872 reshape_80" -> "1873 roll_17" [label="(1, 16, 16, 384)", style=solid]; +"1873 roll_17" -> "1874 slice_269" [label="(1, 16, 16, 384)", style=solid]; +"1874 slice_269" -> "1875 slice_270" [label="(1, 16, 16, 384)", style=solid]; +"1875 slice_270" -> "1876 slice_271" [label="(1, 14, 16, 384)", style=solid]; +"1876 slice_271" -> "1877 slice_272" [label="(1, 14, 14, 384)", style=solid]; +"1877 slice_272" -> "1878 contiguous_33" [label="(1, 14, 14, 384)", style=solid]; +"1878 contiguous_33" -> "1879 layer_norm_37" [label="(1, 14, 14, 384)", style=solid]; +"1879 layer_norm_37" -> "1880 add_61" [label="(1, 14, 14, 384)", style=solid]; +"1880 add_61" -> "1881 add_61_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 384)", style=solid]; +"1880 add_61" -> "1905 add_62" [label="(1, 14, 14, 384)", style=solid]; +"1881 add_61_0_0_nncf_smooth_quant_0" -> "1882 quantize_per_tensor_default_109" [label="(1, 14, 14, 384)", style=solid]; +"1882 quantize_per_tensor_default_109" -> "1883 dequantize_per_tensor_default_109" [label="(1, 14, 14, 384)", style=solid]; +"1883 dequantize_per_tensor_default_109" -> "1890 linear_108" [label="(1, 14, 14, 384)", style=solid]; +"1884 linear_108_scale_0" -> "1887 quantize_per_channel_default_91" [label="(1536,)", style=solid]; +"1884 linear_108_scale_0" -> "1888 dequantize_per_channel_default_91" [label="(1536,)", style=solid]; +"1885 linear_108_zero_point_0" -> "1887 quantize_per_channel_default_91" [label="(1536,)", style=solid]; +"1885 linear_108_zero_point_0" -> "1888 dequantize_per_channel_default_91" [label="(1536,)", style=solid]; +"1886 compressed_weight_updated_constant73" -> "1887 quantize_per_channel_default_91" [label="(1536, 384)", style=solid]; +"1887 quantize_per_channel_default_91" -> "1888 dequantize_per_channel_default_91" [label="(1536, 384)", style=solid]; +"1888 dequantize_per_channel_default_91" -> "1890 linear_108" [label="(1536, 384)", style=solid]; +"1889 features_5_13_mlp_0_bias_0_0" -> "1890 linear_108" [label="(1536,)", style=solid]; +"1890 linear_108" -> "1891 gelu_17" [label="(1, 14, 14, 1536)", style=solid]; +"1891 gelu_17" -> "1892 dropout_70" [label="(1, 14, 14, 1536)", style=solid]; +"1892 dropout_70" -> "1893 dropout_70_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 1536)", style=solid]; +"1893 dropout_70_0_0_nncf_smooth_quant_0" -> "1894 quantize_per_tensor_default_110" [label="(1, 14, 14, 1536)", style=solid]; +"1894 quantize_per_tensor_default_110" -> "1895 dequantize_per_tensor_default_110" [label="(1, 14, 14, 1536)", style=solid]; +"1895 dequantize_per_tensor_default_110" -> "1902 linear_109" [label="(1, 14, 14, 1536)", style=solid]; +"1896 linear_109_scale_0" -> "1899 quantize_per_channel_default_92" [label="(384,)", style=solid]; +"1896 linear_109_scale_0" -> "1900 dequantize_per_channel_default_92" [label="(384,)", style=solid]; +"1897 linear_109_zero_point_0" -> "1899 quantize_per_channel_default_92" [label="(384,)", style=solid]; +"1897 linear_109_zero_point_0" -> "1900 dequantize_per_channel_default_92" [label="(384,)", style=solid]; +"1898 compressed_weight_updated_constant74" -> "1899 quantize_per_channel_default_92" [label="(384, 1536)", style=solid]; +"1899 quantize_per_channel_default_92" -> "1900 dequantize_per_channel_default_92" [label="(384, 1536)", style=solid]; +"1900 dequantize_per_channel_default_92" -> "1902 linear_109" [label="(384, 1536)", style=solid]; +"1901 features_5_13_mlp_3_bias_0_0" -> "1902 linear_109" [label="(384,)", style=solid]; +"1902 linear_109" -> "1903 dropout_71" [label="(1, 14, 14, 384)", style=solid]; +"1903 dropout_71" -> "1904 layer_norm_38" [label="(1, 14, 14, 384)", style=solid]; +"1904 layer_norm_38" -> "1905 add_62" [label="(1, 14, 14, 384)", style=solid]; +"1905 add_62" -> "1907 pad_20" [label="(1, 14, 14, 384)", style=solid]; +"1905 add_62" -> "1968 add_64" [label="(1, 14, 14, 384)", style=solid]; +"1906 _frozen_param153" -> "1942 add_63" [label="(1, 12, 64, 64)", style=solid]; +"1907 pad_20" -> "1908 view_101" [label="(1, 16, 16, 384)", style=solid]; +"1908 view_101" -> "1909 permute_83" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1909 permute_83" -> "1910 reshape_81" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1910 reshape_81" -> "1911 reshape_81_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"1911 reshape_81_0_0_nncf_smooth_quant_0" -> "1912 quantize_per_tensor_default_111" [label="(4, 64, 384)", style=solid]; +"1912 quantize_per_tensor_default_111" -> "1913 dequantize_per_tensor_default_111" [label="(4, 64, 384)", style=solid]; +"1913 dequantize_per_tensor_default_111" -> "1920 linear_112" [label="(4, 64, 384)", style=solid]; +"1914 linear_112_scale_0" -> "1917 quantize_per_channel_default_94" [label="(1152,)", style=solid]; +"1914 linear_112_scale_0" -> "1918 dequantize_per_channel_default_94" [label="(1152,)", style=solid]; +"1915 linear_112_zero_point_0" -> "1917 quantize_per_channel_default_94" [label="(1152,)", style=solid]; +"1915 linear_112_zero_point_0" -> "1918 dequantize_per_channel_default_94" [label="(1152,)", style=solid]; +"1916 compressed_weight_updated_constant75" -> "1917 quantize_per_channel_default_94" [label="(1152, 384)", style=solid]; +"1917 quantize_per_channel_default_94" -> "1918 dequantize_per_channel_default_94" [label="(1152, 384)", style=solid]; +"1918 dequantize_per_channel_default_94" -> "1920 linear_112" [label="(1152, 384)", style=solid]; +"1919 _frozen_param154_0_0" -> "1920 linear_112" [label="(1152,)", style=solid]; +"1920 linear_112" -> "1921 reshape_82" [label="(4, 64, 1152)", style=solid]; +"1921 reshape_82" -> "1922 permute_84" [label="(4, 64, 3, 12, 32)", style=solid]; +"1922 permute_84" -> "1923 select_54" [label="(3, 4, 12, 64, 32)", style=solid]; +"1922 permute_84" -> "1924 select_55" [label="(3, 4, 12, 64, 32)", style=solid]; +"1922 permute_84" -> "1925 select_56" [label="(3, 4, 12, 64, 32)", style=solid]; +"1923 select_54" -> "1926 linalg_vector_norm_36" [label="(4, 12, 64, 32)", style=solid]; +"1923 select_54" -> "1928 expand_as_36" [label="(4, 12, 64, 32)", style=solid]; +"1923 select_54" -> "1929 div_36" [label="(4, 12, 64, 32)", style=solid]; +"1924 select_55" -> "1932 linalg_vector_norm_37" [label="(4, 12, 64, 32)", style=solid]; +"1924 select_55" -> "1934 expand_as_37" [label="(4, 12, 64, 32)", style=solid]; +"1924 select_55" -> "1935 div_37" [label="(4, 12, 64, 32)", style=solid]; +"1925 select_56" -> "1945 matmul_37" [label="(4, 12, 64, 32)", style=solid]; +"1926 linalg_vector_norm_36" -> "1927 clamp_min_36" [label="(4, 12, 64, 1)", style=solid]; +"1927 clamp_min_36" -> "1928 expand_as_36" [label="(4, 12, 64, 1)", style=solid]; +"1928 expand_as_36" -> "1929 div_36" [label="(4, 12, 64, 32)", style=solid]; +"1929 div_36" -> "1930 quantize_per_tensor_default_112" [label="(4, 12, 64, 32)", style=solid]; +"1930 quantize_per_tensor_default_112" -> "1931 dequantize_per_tensor_default_112" [label="(4, 12, 64, 32)", style=solid]; +"1931 dequantize_per_tensor_default_112" -> "1939 matmul_36" [label="(4, 12, 64, 32)", style=solid]; +"1932 linalg_vector_norm_37" -> "1933 clamp_min_37" [label="(4, 12, 64, 1)", style=solid]; +"1933 clamp_min_37" -> "1934 expand_as_37" [label="(4, 12, 64, 1)", style=solid]; +"1934 expand_as_37" -> "1935 div_37" [label="(4, 12, 64, 32)", style=solid]; +"1935 div_37" -> "1936 quantize_per_tensor_default_113" [label="(4, 12, 64, 32)", style=solid]; +"1936 quantize_per_tensor_default_113" -> "1937 dequantize_per_tensor_default_113" [label="(4, 12, 64, 32)", style=solid]; +"1937 dequantize_per_tensor_default_113" -> "1938 transpose_36" [label="(4, 12, 64, 32)", style=solid]; +"1938 transpose_36" -> "1939 matmul_36" [label="(4, 12, 32, 64)", style=solid]; +"1939 matmul_36" -> "1941 mul_37" [label="(4, 12, 64, 64)", style=solid]; +"1940 _frozen_param156" -> "1941 mul_37" [label="(12, 1, 1)", style=solid]; +"1941 mul_37" -> "1942 add_63" [label="(4, 12, 64, 64)", style=solid]; +"1942 add_63" -> "1943 softmax_18" [label="(4, 12, 64, 64)", style=solid]; +"1943 softmax_18" -> "1944 dropout_72" [label="(4, 12, 64, 64)", style=solid]; +"1944 dropout_72" -> "1945 matmul_37" [label="(4, 12, 64, 64)", style=solid]; +"1945 matmul_37" -> "1946 transpose_37" [label="(4, 12, 64, 32)", style=solid]; +"1946 transpose_37" -> "1947 reshape_83" [label="(4, 64, 12, 32)", style=solid]; +"1947 reshape_83" -> "1948 reshape_83_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"1948 reshape_83_0_0_nncf_smooth_quant_0" -> "1949 quantize_per_tensor_default_114" [label="(4, 64, 384)", style=solid]; +"1949 quantize_per_tensor_default_114" -> "1950 dequantize_per_tensor_default_114" [label="(4, 64, 384)", style=solid]; +"1950 dequantize_per_tensor_default_114" -> "1957 linear_113" [label="(4, 64, 384)", style=solid]; +"1951 linear_113_scale_0" -> "1954 quantize_per_channel_default_95" [label="(384,)", style=solid]; +"1951 linear_113_scale_0" -> "1955 dequantize_per_channel_default_95" [label="(384,)", style=solid]; +"1952 linear_113_zero_point_0" -> "1954 quantize_per_channel_default_95" [label="(384,)", style=solid]; +"1952 linear_113_zero_point_0" -> "1955 dequantize_per_channel_default_95" [label="(384,)", style=solid]; +"1953 compressed_weight_updated_constant76" -> "1954 quantize_per_channel_default_95" [label="(384, 384)", style=solid]; +"1954 quantize_per_channel_default_95" -> "1955 dequantize_per_channel_default_95" [label="(384, 384)", style=solid]; +"1955 dequantize_per_channel_default_95" -> "1957 linear_113" [label="(384, 384)", style=solid]; +"1956 features_5_14_attn_proj_bias_0_0" -> "1957 linear_113" [label="(384,)", style=solid]; +"1957 linear_113" -> "1958 dropout_73" [label="(4, 64, 384)", style=solid]; +"1958 dropout_73" -> "1959 view_102" [label="(4, 64, 384)", style=solid]; +"1959 view_102" -> "1960 permute_85" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1960 permute_85" -> "1961 reshape_84" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1961 reshape_84" -> "1962 slice_274" [label="(1, 16, 16, 384)", style=solid]; +"1962 slice_274" -> "1963 slice_275" [label="(1, 16, 16, 384)", style=solid]; +"1963 slice_275" -> "1964 slice_276" [label="(1, 14, 16, 384)", style=solid]; +"1964 slice_276" -> "1965 slice_277" [label="(1, 14, 14, 384)", style=solid]; +"1965 slice_277" -> "1966 contiguous_35" [label="(1, 14, 14, 384)", style=solid]; +"1966 contiguous_35" -> "1967 layer_norm_39" [label="(1, 14, 14, 384)", style=solid]; +"1967 layer_norm_39" -> "1968 add_64" [label="(1, 14, 14, 384)", style=solid]; +"1968 add_64" -> "1969 add_64_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 384)", style=solid]; +"1968 add_64" -> "1993 add_65" [label="(1, 14, 14, 384)", style=solid]; +"1969 add_64_0_0_nncf_smooth_quant_0" -> "1970 quantize_per_tensor_default_115" [label="(1, 14, 14, 384)", style=solid]; +"1970 quantize_per_tensor_default_115" -> "1971 dequantize_per_tensor_default_115" [label="(1, 14, 14, 384)", style=solid]; +"1971 dequantize_per_tensor_default_115" -> "1978 linear_114" [label="(1, 14, 14, 384)", style=solid]; +"1972 linear_114_scale_0" -> "1975 quantize_per_channel_default_96" [label="(1536,)", style=solid]; +"1972 linear_114_scale_0" -> "1976 dequantize_per_channel_default_96" [label="(1536,)", style=solid]; +"1973 linear_114_zero_point_0" -> "1975 quantize_per_channel_default_96" [label="(1536,)", style=solid]; +"1973 linear_114_zero_point_0" -> "1976 dequantize_per_channel_default_96" [label="(1536,)", style=solid]; +"1974 compressed_weight_updated_constant77" -> "1975 quantize_per_channel_default_96" [label="(1536, 384)", style=solid]; +"1975 quantize_per_channel_default_96" -> "1976 dequantize_per_channel_default_96" [label="(1536, 384)", style=solid]; +"1976 dequantize_per_channel_default_96" -> "1978 linear_114" [label="(1536, 384)", style=solid]; +"1977 features_5_14_mlp_0_bias_0_0" -> "1978 linear_114" [label="(1536,)", style=solid]; +"1978 linear_114" -> "1979 gelu_18" [label="(1, 14, 14, 1536)", style=solid]; +"1979 gelu_18" -> "1980 dropout_74" [label="(1, 14, 14, 1536)", style=solid]; +"1980 dropout_74" -> "1981 dropout_74_0_0_nncf_smooth_quant_0" [label="(1, 14, 14, 1536)", style=solid]; +"1981 dropout_74_0_0_nncf_smooth_quant_0" -> "1982 quantize_per_tensor_default_116" [label="(1, 14, 14, 1536)", style=solid]; +"1982 quantize_per_tensor_default_116" -> "1983 dequantize_per_tensor_default_116" [label="(1, 14, 14, 1536)", style=solid]; +"1983 dequantize_per_tensor_default_116" -> "1990 linear_115" [label="(1, 14, 14, 1536)", style=solid]; +"1984 linear_115_scale_0" -> "1987 quantize_per_channel_default_97" [label="(384,)", style=solid]; +"1984 linear_115_scale_0" -> "1988 dequantize_per_channel_default_97" [label="(384,)", style=solid]; +"1985 linear_115_zero_point_0" -> "1987 quantize_per_channel_default_97" [label="(384,)", style=solid]; +"1985 linear_115_zero_point_0" -> "1988 dequantize_per_channel_default_97" [label="(384,)", style=solid]; +"1986 compressed_weight_updated_constant78" -> "1987 quantize_per_channel_default_97" [label="(384, 1536)", style=solid]; +"1987 quantize_per_channel_default_97" -> "1988 dequantize_per_channel_default_97" [label="(384, 1536)", style=solid]; +"1988 dequantize_per_channel_default_97" -> "1990 linear_115" [label="(384, 1536)", style=solid]; +"1989 features_5_14_mlp_3_bias_0_0" -> "1990 linear_115" [label="(384,)", style=solid]; +"1990 linear_115" -> "1991 dropout_75" [label="(1, 14, 14, 384)", style=solid]; +"1991 dropout_75" -> "1992 layer_norm_40" [label="(1, 14, 14, 384)", style=solid]; +"1992 layer_norm_40" -> "1993 add_65" [label="(1, 14, 14, 384)", style=solid]; +"1993 add_65" -> "1995 pad_21" [label="(1, 14, 14, 384)", style=solid]; +"1993 add_65" -> "2074 add_68" [label="(1, 14, 14, 384)", style=solid]; +"1994 _frozen_param157" -> "2031 add_66" [label="(1, 12, 64, 64)", style=solid]; +"1995 pad_21" -> "1996 roll_18" [label="(1, 16, 16, 384)", style=solid]; +"1996 roll_18" -> "1997 view_105" [label="(1, 16, 16, 384)", style=solid]; +"1997 view_105" -> "1998 permute_87" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1998 permute_87" -> "1999 reshape_85" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1999 reshape_85" -> "2000 reshape_85_0_0_nncf_smooth_quant_0" [label="(4, 64, 384)", style=solid]; +"1999 reshape_85" -> "2032 new_zeros_9" [label="(4, 64, 384)", style=solid]; +"2000 reshape_85_0_0_nncf_smooth_quant_0" -> "2001 quantize_per_tensor_default_117" [label="(4, 64, 384)", style=solid]; +"2001 quantize_per_tensor_default_117" -> "2002 dequantize_per_tensor_default_117" [label="(4, 64, 384)", style=solid]; +"2002 dequantize_per_tensor_default_117" -> "2009 linear_118" [label="(4, 64, 384)", style=solid]; +"2003 linear_118_scale_0" -> "2006 quantize_per_channel_default_99" [label="(1152,)", style=solid]; +"2003 linear_118_scale_0" -> "2007 dequantize_per_channel_default_99" [label="(1152,)", style=solid]; +"2004 linear_118_zero_point_0" -> "2006 quantize_per_channel_default_99" [label="(1152,)", style=solid]; +"2004 linear_118_zero_point_0" -> "2007 dequantize_per_channel_default_99" [label="(1152,)", style=solid]; +"2005 compressed_weight_updated_constant79" -> "2006 quantize_per_channel_default_99" [label="(1152, 384)", style=solid]; +"2006 quantize_per_channel_default_99" -> "2007 dequantize_per_channel_default_99" [label="(1152, 384)", style=solid]; +"2007 dequantize_per_channel_default_99" -> "2009 linear_118" [label="(1152, 384)", style=solid]; +"2008 _frozen_param158_0_0" -> "2009 linear_118" [label="(1152,)", style=solid]; +"2009 linear_118" -> "2010 reshape_86" [label="(4, 64, 1152)", style=solid]; +"2010 reshape_86" -> "2011 permute_88" [label="(4, 64, 3, 12, 32)", style=solid]; +"2011 permute_88" -> "2012 select_57" [label="(3, 4, 12, 64, 32)", style=solid]; +"2011 permute_88" -> "2013 select_58" [label="(3, 4, 12, 64, 32)", style=solid]; +"2011 permute_88" -> "2014 select_59" [label="(3, 4, 12, 64, 32)", style=solid]; +"2012 select_57" -> "2015 linalg_vector_norm_38" [label="(4, 12, 64, 32)", style=solid]; +"2012 select_57" -> "2017 expand_as_38" [label="(4, 12, 64, 32)", style=solid]; +"2012 select_57" -> "2018 div_38" [label="(4, 12, 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linear_146_scale_0" -> "2487 quantize_per_channel_default_123" [label="(768,)", style=solid]; +"2484 linear_146_scale_0" -> "2488 dequantize_per_channel_default_123" [label="(768,)", style=solid]; +"2485 linear_146_zero_point_0" -> "2487 quantize_per_channel_default_123" [label="(768,)", style=solid]; +"2485 linear_146_zero_point_0" -> "2488 dequantize_per_channel_default_123" [label="(768,)", style=solid]; +"2486 compressed_weight_updated_constant99" -> "2487 quantize_per_channel_default_123" [label="(768, 3072)", style=solid]; +"2487 quantize_per_channel_default_123" -> "2488 dequantize_per_channel_default_123" [label="(768, 3072)", style=solid]; +"2488 dequantize_per_channel_default_123" -> "2490 linear_146" [label="(768, 3072)", style=solid]; +"2489 features_7_1_mlp_3_bias_0_0" -> "2490 linear_146" [label="(768,)", style=solid]; +"2490 linear_146" -> "2491 dropout_95" [label="(1, 7, 7, 768)", style=solid]; +"2491 dropout_95" -> "2492 layer_norm_51" [label="(1, 7, 7, 768)", style=solid]; +"2492 layer_norm_51" -> "2493 add_82" [label="(1, 7, 7, 768)", style=solid]; +"2493 add_82" -> "2494 layer_norm_52" [label="(1, 7, 7, 768)", style=solid]; +"2494 layer_norm_52" -> "2495 permute_108" [label="(1, 7, 7, 768)", style=solid]; +"2495 permute_108" -> "2496 adaptive_avg_pool2d" [label="(1, 768, 7, 7)", style=solid]; +"2496 adaptive_avg_pool2d" -> "2497 flatten" [label="(1, 768, 1, 1)", style=solid]; +"2497 flatten" -> "2498 flatten_0_0_nncf_smooth_quant_0" [label="(1, 768)", style=solid]; +"2498 flatten_0_0_nncf_smooth_quant_0" -> "2499 quantize_per_tensor_default_148" [label="(1, 768)", style=solid]; +"2499 quantize_per_tensor_default_148" -> "2500 dequantize_per_tensor_default_148" [label="(1, 768)", style=solid]; +"2500 dequantize_per_tensor_default_148" -> "2507 linear_147" [label="(1, 768)", style=solid]; +"2501 linear_147_scale_0" -> "2504 quantize_per_channel_default_124" [label="(1000,)", style=solid]; +"2501 linear_147_scale_0" -> "2505 dequantize_per_channel_default_124" [label="(1000,)", style=solid]; +"2502 linear_147_zero_point_0" -> "2504 quantize_per_channel_default_124" [label="(1000,)", style=solid]; +"2502 linear_147_zero_point_0" -> "2505 dequantize_per_channel_default_124" [label="(1000,)", style=solid]; +"2503 compressed_weight_updated_constant100" -> "2504 quantize_per_channel_default_124" [label="(1000, 768)", style=solid]; +"2504 quantize_per_channel_default_124" -> "2505 dequantize_per_channel_default_124" [label="(1000, 768)", style=solid]; +"2505 dequantize_per_channel_default_124" -> "2507 linear_147" [label="(1000, 768)", style=solid]; +"2506 head_bias_0_0" -> "2507 linear_147" [label="(1000,)", style=solid]; +"2507 linear_147" -> "2508 output" [label="(1, 1000)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/quantized/synthetic_transformer.dot b/tests/torch/data/reference_graphs/fx/quantized/synthetic_transformer.dot new file mode 100644 index 00000000000..3fd5285222d --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/quantized/synthetic_transformer.dot @@ -0,0 +1,53 @@ +strict digraph { +"0 wte_weight" [id=0, type=get_attr]; +"1 input_ids" [id=1, type=input]; +"2 embedding" [id=2, type=embedding]; +"3 embedding_0_0_nncf_smooth_quant_0" [id=3, type=call_module]; +"4 quantize_per_tensor_default" [id=4, type=quantize_per_tensor]; +"5 dequantize_per_tensor_default" [id=5, type=dequantize_per_tensor]; +"6 linear_scale_0" [id=6, type=get_attr]; +"7 linear_zero_point_0" [id=7, type=get_attr]; +"8 compressed_weight_updated_constant0" [id=8, type=get_attr]; +"9 quantize_per_channel_default" [id=9, type=quantize_per_channel]; +"10 dequantize_per_channel_default" [id=10, type=dequantize_per_channel]; +"11 linear_bias_0_0" [id=11, type=get_attr]; +"12 linear" [id=12, type=linear]; +"13 add_tensor_0_0_nncf_smooth_quant_0" [id=13, type=call_module]; +"14 quantize_per_tensor_default_1" [id=14, type=quantize_per_tensor]; +"15 dequantize_per_tensor_default_1" [id=15, type=dequantize_per_tensor]; +"16 linear_1_scale_0" [id=16, type=get_attr]; +"17 linear_1_zero_point_0" [id=17, type=get_attr]; +"18 compressed_weight_updated_constant1" [id=18, type=get_attr]; +"19 quantize_per_channel_default_1" [id=19, type=quantize_per_channel]; +"20 dequantize_per_channel_default_1" [id=20, type=dequantize_per_channel]; +"21 lm_head_bias_0_0" [id=21, type=get_attr]; +"22 linear_1" [id=22, type=linear]; +"23 output" [id=23, type=output]; +"0 wte_weight" -> "2 embedding" [label="(10, 5)", style=solid]; +"1 input_ids" -> "2 embedding" [label="(5,)", style=solid]; +"2 embedding" -> "3 embedding_0_0_nncf_smooth_quant_0" [label="(5, 5)", style=solid]; +"3 embedding_0_0_nncf_smooth_quant_0" -> "4 quantize_per_tensor_default" [label="(5, 5)", style=solid]; +"4 quantize_per_tensor_default" -> "5 dequantize_per_tensor_default" [label="(5, 5)", style=solid]; +"5 dequantize_per_tensor_default" -> "12 linear" [label="(5, 5)", style=solid]; +"6 linear_scale_0" -> "9 quantize_per_channel_default" [label="(5,)", style=solid]; +"6 linear_scale_0" -> "10 dequantize_per_channel_default" [label="(5,)", style=solid]; +"7 linear_zero_point_0" -> "9 quantize_per_channel_default" [label="(5,)", style=solid]; +"7 linear_zero_point_0" -> "10 dequantize_per_channel_default" [label="(5,)", style=solid]; +"8 compressed_weight_updated_constant0" -> "9 quantize_per_channel_default" [label="(5, 5)", style=solid]; +"9 quantize_per_channel_default" -> "10 dequantize_per_channel_default" [label="(5, 5)", style=solid]; +"10 dequantize_per_channel_default" -> "12 linear" [label="(5, 5)", style=solid]; +"11 linear_bias_0_0" -> "12 linear" [label="(5,)", style=solid]; +"12 linear" -> "13 add_tensor_0_0_nncf_smooth_quant_0" [label="(5, 5)", style=solid]; +"13 add_tensor_0_0_nncf_smooth_quant_0" -> "14 quantize_per_tensor_default_1" [label="(5, 5)", style=solid]; +"14 quantize_per_tensor_default_1" -> "15 dequantize_per_tensor_default_1" [label="(5, 5)", style=solid]; +"15 dequantize_per_tensor_default_1" -> "22 linear_1" [label="(5, 5)", style=solid]; +"16 linear_1_scale_0" -> "19 quantize_per_channel_default_1" [label="(10,)", style=solid]; +"16 linear_1_scale_0" -> "20 dequantize_per_channel_default_1" [label="(10,)", style=solid]; +"17 linear_1_zero_point_0" -> "19 quantize_per_channel_default_1" [label="(10,)", style=solid]; +"17 linear_1_zero_point_0" -> "20 dequantize_per_channel_default_1" [label="(10,)", style=solid]; +"18 compressed_weight_updated_constant1" -> "19 quantize_per_channel_default_1" [label="(10, 5)", style=solid]; +"19 quantize_per_channel_default_1" -> "20 dequantize_per_channel_default_1" [label="(10, 5)", style=solid]; +"20 dequantize_per_channel_default_1" -> "22 linear_1" [label="(10, 5)", style=solid]; +"21 lm_head_bias_0_0" -> "22 linear_1" [label="(10,)", style=solid]; +"22 linear_1" -> "23 output" [label="(5, 10)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/quantized/unet.dot b/tests/torch/data/reference_graphs/fx/quantized/unet.dot index bd82cc0c653..b299be70340 100644 --- a/tests/torch/data/reference_graphs/fx/quantized/unet.dot +++ b/tests/torch/data/reference_graphs/fx/quantized/unet.dot @@ -1,121 +1,121 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; +"0 x" [id=0, type=input]; "1 quantize_per_tensor_default_8" [id=1, type=quantize_per_tensor]; "2 dequantize_per_tensor_default_12" [id=2, type=dequantize_per_tensor]; -"3 _param_constant0" [id=3, type=get_attr]; -"4 conv2d_scale_0" [id=4, type=get_attr]; -"5 conv2d_zero_point_0" [id=5, type=get_attr]; +"3 conv2d_scale_0" [id=3, type=get_attr]; +"4 conv2d_zero_point_0" [id=4, type=get_attr]; +"5 compressed_weight_updated_constant0" [id=5, type=get_attr]; "6 quantize_per_channel_default" [id=6, type=quantize_per_channel]; "7 dequantize_per_channel_default" [id=7, type=dequantize_per_channel]; -"8 _param_constant1_0_0" [id=8, type=get_attr]; +"8 down_path_0_block_0_bias_0_0" [id=8, type=get_attr]; "9 conv2d" [id=9, type=conv2d]; "10 relu" [id=10, type=relu]; "11 quantize_per_tensor_default_9" [id=11, type=quantize_per_tensor]; "12 dequantize_per_tensor_default_13" [id=12, type=dequantize_per_tensor]; -"13 _param_constant4" [id=13, type=get_attr]; -"14 conv2d_1_scale_0" [id=14, type=get_attr]; -"15 conv2d_1_zero_point_0" [id=15, type=get_attr]; +"13 conv2d_1_scale_0" [id=13, type=get_attr]; +"14 conv2d_1_zero_point_0" [id=14, type=get_attr]; +"15 compressed_weight_updated_constant1" [id=15, type=get_attr]; "16 quantize_per_channel_default_1" [id=16, type=quantize_per_channel]; "17 dequantize_per_channel_default_1" [id=17, type=dequantize_per_channel]; -"18 _param_constant5_0_0" [id=18, type=get_attr]; +"18 down_path_0_block_3_bias_0_0" [id=18, type=get_attr]; "19 conv2d_1" [id=19, type=conv2d]; "20 relu_1" [id=20, type=relu]; "21 quantize_per_tensor_default" [id=21, type=quantize_per_tensor]; "22 dequantize_per_tensor_default_1" [id=22, type=dequantize_per_tensor]; "23 dequantize_per_tensor_default" [id=23, type=dequantize_per_tensor]; "24 max_pool2d" [id=24, type=max_pool2d]; -"25 _param_constant8" [id=25, type=get_attr]; -"26 conv2d_2_scale_0" [id=26, type=get_attr]; -"27 conv2d_2_zero_point_0" [id=27, type=get_attr]; +"25 conv2d_2_scale_0" [id=25, type=get_attr]; +"26 conv2d_2_zero_point_0" [id=26, type=get_attr]; +"27 compressed_weight_updated_constant2" [id=27, type=get_attr]; "28 quantize_per_channel_default_2" [id=28, type=quantize_per_channel]; "29 dequantize_per_channel_default_2" [id=29, type=dequantize_per_channel]; -"30 _param_constant9_0_0" [id=30, type=get_attr]; +"30 down_path_1_block_0_bias_0_0" [id=30, type=get_attr]; "31 conv2d_2" [id=31, type=conv2d]; "32 relu_2" [id=32, type=relu]; "33 quantize_per_tensor_default_10" [id=33, type=quantize_per_tensor]; "34 dequantize_per_tensor_default_14" [id=34, type=dequantize_per_tensor]; -"35 _param_constant12" [id=35, type=get_attr]; -"36 conv2d_3_scale_0" [id=36, type=get_attr]; -"37 conv2d_3_zero_point_0" [id=37, type=get_attr]; +"35 conv2d_3_scale_0" [id=35, type=get_attr]; +"36 conv2d_3_zero_point_0" [id=36, type=get_attr]; +"37 compressed_weight_updated_constant3" [id=37, type=get_attr]; "38 quantize_per_channel_default_3" [id=38, type=quantize_per_channel]; "39 dequantize_per_channel_default_3" [id=39, type=dequantize_per_channel]; -"40 _param_constant13_0_0" [id=40, type=get_attr]; +"40 down_path_1_block_3_bias_0_0" [id=40, type=get_attr]; "41 conv2d_3" [id=41, type=conv2d]; "42 relu_3" [id=42, type=relu]; "43 quantize_per_tensor_default_3" [id=43, type=quantize_per_tensor]; "44 dequantize_per_tensor_default_5" [id=44, type=dequantize_per_tensor]; "45 dequantize_per_tensor_default_4" [id=45, type=dequantize_per_tensor]; "46 max_pool2d_1" [id=46, type=max_pool2d]; -"47 _param_constant16" [id=47, type=get_attr]; -"48 conv2d_4_scale_0" [id=48, type=get_attr]; -"49 conv2d_4_zero_point_0" [id=49, type=get_attr]; +"47 conv2d_4_scale_0" [id=47, type=get_attr]; +"48 conv2d_4_zero_point_0" [id=48, type=get_attr]; +"49 compressed_weight_updated_constant4" [id=49, type=get_attr]; "50 quantize_per_channel_default_4" [id=50, type=quantize_per_channel]; "51 dequantize_per_channel_default_4" [id=51, type=dequantize_per_channel]; -"52 _param_constant17_0_0" [id=52, type=get_attr]; +"52 down_path_2_block_0_bias_0_0" [id=52, type=get_attr]; "53 conv2d_4" [id=53, type=conv2d]; "54 relu_4" [id=54, type=relu]; "55 quantize_per_tensor_default_11" [id=55, type=quantize_per_tensor]; "56 dequantize_per_tensor_default_15" [id=56, type=dequantize_per_tensor]; -"57 _param_constant20" [id=57, type=get_attr]; -"58 conv2d_5_scale_0" [id=58, type=get_attr]; -"59 conv2d_5_zero_point_0" [id=59, type=get_attr]; +"57 conv2d_5_scale_0" [id=57, type=get_attr]; +"58 conv2d_5_zero_point_0" [id=58, type=get_attr]; +"59 compressed_weight_updated_constant5" [id=59, type=get_attr]; "60 quantize_per_channel_default_5" [id=60, type=quantize_per_channel]; "61 dequantize_per_channel_default_5" [id=61, type=dequantize_per_channel]; -"62 _param_constant21_0_0" [id=62, type=get_attr]; +"62 down_path_2_block_3_bias_0_0" [id=62, type=get_attr]; "63 conv2d_5" [id=63, type=conv2d]; "64 relu_5" [id=64, type=relu]; "65 quantize_per_tensor_default_4" [id=65, type=quantize_per_tensor]; "66 dequantize_per_tensor_default_7" [id=66, type=dequantize_per_tensor]; "67 dequantize_per_tensor_default_6" [id=67, type=dequantize_per_tensor]; "68 max_pool2d_2" [id=68, type=max_pool2d]; -"69 _param_constant24" [id=69, type=get_attr]; -"70 conv2d_6_scale_0" [id=70, type=get_attr]; -"71 conv2d_6_zero_point_0" [id=71, type=get_attr]; +"69 conv2d_6_scale_0" [id=69, type=get_attr]; +"70 conv2d_6_zero_point_0" [id=70, type=get_attr]; +"71 compressed_weight_updated_constant6" [id=71, type=get_attr]; "72 quantize_per_channel_default_6" [id=72, type=quantize_per_channel]; "73 dequantize_per_channel_default_6" [id=73, type=dequantize_per_channel]; -"74 _param_constant25_0_0" [id=74, type=get_attr]; +"74 down_path_3_block_0_bias_0_0" [id=74, type=get_attr]; "75 conv2d_6" [id=75, type=conv2d]; "76 relu_6" [id=76, type=relu]; "77 quantize_per_tensor_default_12" [id=77, type=quantize_per_tensor]; "78 dequantize_per_tensor_default_16" [id=78, type=dequantize_per_tensor]; -"79 _param_constant28" [id=79, type=get_attr]; -"80 conv2d_7_scale_0" [id=80, type=get_attr]; -"81 conv2d_7_zero_point_0" [id=81, type=get_attr]; +"79 conv2d_7_scale_0" [id=79, type=get_attr]; +"80 conv2d_7_zero_point_0" [id=80, type=get_attr]; +"81 compressed_weight_updated_constant7" [id=81, type=get_attr]; "82 quantize_per_channel_default_7" [id=82, type=quantize_per_channel]; "83 dequantize_per_channel_default_7" [id=83, type=dequantize_per_channel]; -"84 _param_constant29_0_0" [id=84, type=get_attr]; +"84 down_path_3_block_3_bias_0_0" [id=84, type=get_attr]; "85 conv2d_7" [id=85, type=conv2d]; "86 relu_7" [id=86, type=relu]; "87 quantize_per_tensor_default_7" [id=87, type=quantize_per_tensor]; "88 dequantize_per_tensor_default_11" [id=88, type=dequantize_per_tensor]; "89 dequantize_per_tensor_default_10" [id=89, type=dequantize_per_tensor]; "90 max_pool2d_3" [id=90, type=max_pool2d]; -"91 _param_constant32" [id=91, type=get_attr]; -"92 conv2d_8_scale_0" [id=92, type=get_attr]; -"93 conv2d_8_zero_point_0" [id=93, type=get_attr]; +"91 conv2d_8_scale_0" [id=91, type=get_attr]; +"92 conv2d_8_zero_point_0" [id=92, type=get_attr]; +"93 compressed_weight_updated_constant8" [id=93, type=get_attr]; "94 quantize_per_channel_default_8" [id=94, type=quantize_per_channel]; "95 dequantize_per_channel_default_8" [id=95, type=dequantize_per_channel]; -"96 _param_constant33_0_0" [id=96, type=get_attr]; +"96 down_path_4_block_0_bias_0_0" [id=96, type=get_attr]; "97 conv2d_8" [id=97, type=conv2d]; "98 relu_8" [id=98, type=relu]; "99 quantize_per_tensor_default_13" [id=99, type=quantize_per_tensor]; "100 dequantize_per_tensor_default_17" [id=100, type=dequantize_per_tensor]; -"101 _param_constant36" [id=101, type=get_attr]; -"102 conv2d_9_scale_0" [id=102, type=get_attr]; -"103 conv2d_9_zero_point_0" [id=103, type=get_attr]; +"101 conv2d_9_scale_0" [id=101, type=get_attr]; +"102 conv2d_9_zero_point_0" [id=102, type=get_attr]; +"103 compressed_weight_updated_constant9" [id=103, type=get_attr]; "104 quantize_per_channel_default_9" [id=104, type=quantize_per_channel]; "105 dequantize_per_channel_default_9" [id=105, type=dequantize_per_channel]; -"106 _param_constant37_0_0" [id=106, type=get_attr]; +"106 down_path_4_block_3_bias_0_0" [id=106, type=get_attr]; "107 conv2d_9" [id=107, type=conv2d]; "108 relu_9" [id=108, type=relu]; "109 quantize_per_tensor_default_14" [id=109, type=quantize_per_tensor]; "110 dequantize_per_tensor_default_18" [id=110, type=dequantize_per_tensor]; -"111 _param_constant40" [id=111, type=get_attr]; -"112 _param_constant41" [id=112, type=get_attr]; -"113 conv_transpose2d_scale_0" [id=113, type=get_attr]; -"114 conv_transpose2d_zero_point_0" [id=114, type=get_attr]; -"115 quantize_per_channel_default_10" [id=115, type=quantize_per_channel]; -"116 dequantize_per_channel_default_10" [id=116, type=dequantize_per_channel]; +"111 conv_transpose2d_scale_0" [id=111, type=get_attr]; +"112 conv_transpose2d_zero_point_0" [id=112, type=get_attr]; +"113 compressed_weight_updated_constant10" [id=113, type=get_attr]; +"114 quantize_per_channel_default_10" [id=114, type=quantize_per_channel]; +"115 dequantize_per_channel_default_10" [id=115, type=dequantize_per_channel]; +"116 up_path_0_up_bias_0_0" [id=116, type=get_attr]; "117 conv_transpose2d" [id=117, type=conv_transpose2d]; "118 quantize_per_tensor_default_6" [id=118, type=quantize_per_tensor]; "119 dequantize_per_tensor_default_9" [id=119, type=dequantize_per_tensor]; @@ -124,32 +124,32 @@ strict digraph { "122 slice_3" [id=122, type=slice]; "123 slice_4" [id=123, type=slice]; "124 cat" [id=124, type=cat]; -"125 _param_constant42" [id=125, type=get_attr]; -"126 conv2d_10_scale_0" [id=126, type=get_attr]; -"127 conv2d_10_zero_point_0" [id=127, type=get_attr]; +"125 conv2d_10_scale_0" [id=125, type=get_attr]; +"126 conv2d_10_zero_point_0" [id=126, type=get_attr]; +"127 compressed_weight_updated_constant11" [id=127, type=get_attr]; "128 quantize_per_channel_default_11" [id=128, type=quantize_per_channel]; "129 dequantize_per_channel_default_11" [id=129, type=dequantize_per_channel]; -"130 _param_constant43_0_0" [id=130, type=get_attr]; +"130 up_path_0_conv_block_block_0_bias_0_0" [id=130, type=get_attr]; "131 conv2d_10" [id=131, type=conv2d]; "132 relu_10" [id=132, type=relu]; "133 quantize_per_tensor_default_15" [id=133, type=quantize_per_tensor]; "134 dequantize_per_tensor_default_19" [id=134, type=dequantize_per_tensor]; -"135 _param_constant46" [id=135, type=get_attr]; -"136 conv2d_11_scale_0" [id=136, type=get_attr]; -"137 conv2d_11_zero_point_0" [id=137, type=get_attr]; +"135 conv2d_11_scale_0" [id=135, type=get_attr]; +"136 conv2d_11_zero_point_0" [id=136, type=get_attr]; +"137 compressed_weight_updated_constant12" [id=137, type=get_attr]; "138 quantize_per_channel_default_12" [id=138, type=quantize_per_channel]; "139 dequantize_per_channel_default_12" [id=139, type=dequantize_per_channel]; -"140 _param_constant47_0_0" [id=140, type=get_attr]; +"140 up_path_0_conv_block_block_3_bias_0_0" [id=140, type=get_attr]; "141 conv2d_11" [id=141, type=conv2d]; "142 relu_11" [id=142, type=relu]; "143 quantize_per_tensor_default_16" [id=143, type=quantize_per_tensor]; "144 dequantize_per_tensor_default_20" [id=144, type=dequantize_per_tensor]; -"145 _param_constant50" [id=145, type=get_attr]; -"146 _param_constant51" [id=146, type=get_attr]; -"147 conv_transpose2d_1_scale_0" [id=147, type=get_attr]; -"148 conv_transpose2d_1_zero_point_0" [id=148, type=get_attr]; -"149 quantize_per_channel_default_13" [id=149, type=quantize_per_channel]; -"150 dequantize_per_channel_default_13" [id=150, type=dequantize_per_channel]; +"145 conv_transpose2d_1_scale_0" [id=145, type=get_attr]; +"146 conv_transpose2d_1_zero_point_0" [id=146, type=get_attr]; +"147 compressed_weight_updated_constant13" [id=147, type=get_attr]; +"148 quantize_per_channel_default_13" [id=148, type=quantize_per_channel]; +"149 dequantize_per_channel_default_13" [id=149, type=dequantize_per_channel]; +"150 up_path_1_up_bias_0_0" [id=150, type=get_attr]; "151 conv_transpose2d_1" [id=151, type=conv_transpose2d]; "152 quantize_per_tensor_default_5" [id=152, type=quantize_per_tensor]; "153 dequantize_per_tensor_default_8" [id=153, type=dequantize_per_tensor]; @@ -158,32 +158,32 @@ strict digraph { "156 slice_7" [id=156, type=slice]; "157 slice_8" [id=157, type=slice]; "158 cat_1" [id=158, type=cat]; -"159 _param_constant52" [id=159, type=get_attr]; -"160 conv2d_12_scale_0" [id=160, type=get_attr]; -"161 conv2d_12_zero_point_0" [id=161, type=get_attr]; +"159 conv2d_12_scale_0" [id=159, type=get_attr]; +"160 conv2d_12_zero_point_0" [id=160, type=get_attr]; +"161 compressed_weight_updated_constant14" [id=161, type=get_attr]; "162 quantize_per_channel_default_14" [id=162, type=quantize_per_channel]; "163 dequantize_per_channel_default_14" [id=163, type=dequantize_per_channel]; -"164 _param_constant53_0_0" [id=164, type=get_attr]; +"164 up_path_1_conv_block_block_0_bias_0_0" [id=164, type=get_attr]; "165 conv2d_12" [id=165, type=conv2d]; "166 relu_12" [id=166, type=relu]; "167 quantize_per_tensor_default_17" [id=167, type=quantize_per_tensor]; "168 dequantize_per_tensor_default_21" [id=168, type=dequantize_per_tensor]; -"169 _param_constant56" [id=169, type=get_attr]; -"170 conv2d_13_scale_0" [id=170, type=get_attr]; -"171 conv2d_13_zero_point_0" [id=171, type=get_attr]; +"169 conv2d_13_scale_0" [id=169, type=get_attr]; +"170 conv2d_13_zero_point_0" [id=170, type=get_attr]; +"171 compressed_weight_updated_constant15" [id=171, type=get_attr]; "172 quantize_per_channel_default_15" [id=172, type=quantize_per_channel]; "173 dequantize_per_channel_default_15" [id=173, type=dequantize_per_channel]; -"174 _param_constant57_0_0" [id=174, type=get_attr]; +"174 up_path_1_conv_block_block_3_bias_0_0" [id=174, type=get_attr]; "175 conv2d_13" [id=175, type=conv2d]; "176 relu_13" [id=176, type=relu]; "177 quantize_per_tensor_default_18" [id=177, type=quantize_per_tensor]; "178 dequantize_per_tensor_default_22" [id=178, type=dequantize_per_tensor]; -"179 _param_constant60" [id=179, type=get_attr]; -"180 _param_constant61" [id=180, type=get_attr]; -"181 conv_transpose2d_2_scale_0" [id=181, type=get_attr]; -"182 conv_transpose2d_2_zero_point_0" [id=182, type=get_attr]; -"183 quantize_per_channel_default_16" [id=183, type=quantize_per_channel]; -"184 dequantize_per_channel_default_16" [id=184, type=dequantize_per_channel]; +"179 conv_transpose2d_2_scale_0" [id=179, type=get_attr]; +"180 conv_transpose2d_2_zero_point_0" [id=180, type=get_attr]; +"181 compressed_weight_updated_constant16" [id=181, type=get_attr]; +"182 quantize_per_channel_default_16" [id=182, type=quantize_per_channel]; +"183 dequantize_per_channel_default_16" [id=183, type=dequantize_per_channel]; +"184 up_path_2_up_bias_0_0" [id=184, type=get_attr]; "185 conv_transpose2d_2" [id=185, type=conv_transpose2d]; "186 quantize_per_tensor_default_2" [id=186, type=quantize_per_tensor]; "187 dequantize_per_tensor_default_3" [id=187, type=dequantize_per_tensor]; @@ -192,32 +192,32 @@ strict digraph { "190 slice_11" [id=190, type=slice]; "191 slice_12" [id=191, type=slice]; "192 cat_2" [id=192, type=cat]; -"193 _param_constant62" [id=193, type=get_attr]; -"194 conv2d_14_scale_0" [id=194, type=get_attr]; -"195 conv2d_14_zero_point_0" [id=195, type=get_attr]; +"193 conv2d_14_scale_0" [id=193, type=get_attr]; +"194 conv2d_14_zero_point_0" [id=194, type=get_attr]; +"195 compressed_weight_updated_constant17" [id=195, type=get_attr]; "196 quantize_per_channel_default_17" [id=196, type=quantize_per_channel]; "197 dequantize_per_channel_default_17" [id=197, type=dequantize_per_channel]; -"198 _param_constant63_0_0" [id=198, type=get_attr]; +"198 up_path_2_conv_block_block_0_bias_0_0" [id=198, type=get_attr]; "199 conv2d_14" [id=199, type=conv2d]; "200 relu_14" [id=200, type=relu]; "201 quantize_per_tensor_default_19" [id=201, type=quantize_per_tensor]; "202 dequantize_per_tensor_default_23" [id=202, type=dequantize_per_tensor]; -"203 _param_constant66" [id=203, type=get_attr]; -"204 conv2d_15_scale_0" [id=204, type=get_attr]; -"205 conv2d_15_zero_point_0" [id=205, type=get_attr]; +"203 conv2d_15_scale_0" [id=203, type=get_attr]; +"204 conv2d_15_zero_point_0" [id=204, type=get_attr]; +"205 compressed_weight_updated_constant18" [id=205, type=get_attr]; "206 quantize_per_channel_default_18" [id=206, type=quantize_per_channel]; "207 dequantize_per_channel_default_18" [id=207, type=dequantize_per_channel]; -"208 _param_constant67_0_0" [id=208, type=get_attr]; +"208 up_path_2_conv_block_block_3_bias_0_0" [id=208, type=get_attr]; "209 conv2d_15" [id=209, type=conv2d]; "210 relu_15" [id=210, type=relu]; "211 quantize_per_tensor_default_20" [id=211, type=quantize_per_tensor]; "212 dequantize_per_tensor_default_24" [id=212, type=dequantize_per_tensor]; -"213 _param_constant70" [id=213, type=get_attr]; -"214 _param_constant71" [id=214, type=get_attr]; -"215 conv_transpose2d_3_scale_0" [id=215, type=get_attr]; -"216 conv_transpose2d_3_zero_point_0" [id=216, type=get_attr]; -"217 quantize_per_channel_default_19" [id=217, type=quantize_per_channel]; -"218 dequantize_per_channel_default_19" [id=218, type=dequantize_per_channel]; +"213 conv_transpose2d_3_scale_0" [id=213, type=get_attr]; +"214 conv_transpose2d_3_zero_point_0" [id=214, type=get_attr]; +"215 compressed_weight_updated_constant19" [id=215, type=get_attr]; +"216 quantize_per_channel_default_19" [id=216, type=quantize_per_channel]; +"217 dequantize_per_channel_default_19" [id=217, type=dequantize_per_channel]; +"218 up_path_3_up_bias_0_0" [id=218, type=get_attr]; "219 conv_transpose2d_3" [id=219, type=conv_transpose2d]; "220 quantize_per_tensor_default_1" [id=220, type=quantize_per_tensor]; "221 dequantize_per_tensor_default_2" [id=221, type=dequantize_per_tensor]; @@ -226,336 +226,336 @@ strict digraph { "224 slice_15" [id=224, type=slice]; "225 slice_16" [id=225, type=slice]; "226 cat_3" [id=226, type=cat]; -"227 _param_constant72" [id=227, type=get_attr]; -"228 conv2d_16_scale_0" [id=228, type=get_attr]; -"229 conv2d_16_zero_point_0" [id=229, type=get_attr]; +"227 conv2d_16_scale_0" [id=227, type=get_attr]; +"228 conv2d_16_zero_point_0" [id=228, type=get_attr]; +"229 compressed_weight_updated_constant20" [id=229, type=get_attr]; "230 quantize_per_channel_default_20" [id=230, type=quantize_per_channel]; "231 dequantize_per_channel_default_20" [id=231, type=dequantize_per_channel]; -"232 _param_constant73_0_0" [id=232, type=get_attr]; +"232 up_path_3_conv_block_block_0_bias_0_0" [id=232, type=get_attr]; "233 conv2d_16" [id=233, type=conv2d]; "234 relu_16" [id=234, type=relu]; "235 quantize_per_tensor_default_21" [id=235, type=quantize_per_tensor]; "236 dequantize_per_tensor_default_25" [id=236, type=dequantize_per_tensor]; -"237 _param_constant76" [id=237, type=get_attr]; -"238 conv2d_17_scale_0" [id=238, type=get_attr]; -"239 conv2d_17_zero_point_0" [id=239, type=get_attr]; +"237 conv2d_17_scale_0" [id=237, type=get_attr]; +"238 conv2d_17_zero_point_0" [id=238, type=get_attr]; +"239 compressed_weight_updated_constant21" [id=239, type=get_attr]; "240 quantize_per_channel_default_21" [id=240, type=quantize_per_channel]; "241 dequantize_per_channel_default_21" [id=241, type=dequantize_per_channel]; -"242 _param_constant77_0_0" [id=242, type=get_attr]; +"242 up_path_3_conv_block_block_3_bias_0_0" [id=242, type=get_attr]; "243 conv2d_17" [id=243, type=conv2d]; "244 relu_17" [id=244, type=relu]; "245 quantize_per_tensor_default_22" [id=245, type=quantize_per_tensor]; "246 dequantize_per_tensor_default_26" [id=246, type=dequantize_per_tensor]; -"247 _param_constant80" [id=247, type=get_attr]; -"248 conv2d_18_scale_0" [id=248, type=get_attr]; -"249 conv2d_18_zero_point_0" [id=249, type=get_attr]; +"247 conv2d_18_scale_0" [id=247, type=get_attr]; +"248 conv2d_18_zero_point_0" [id=248, type=get_attr]; +"249 compressed_weight_updated_constant22" [id=249, type=get_attr]; "250 quantize_per_channel_default_22" [id=250, type=quantize_per_channel]; "251 dequantize_per_channel_default_22" [id=251, type=dequantize_per_channel]; -"252 _param_constant81_0_0" [id=252, type=get_attr]; +"252 last_bias_0_0" [id=252, type=get_attr]; "253 conv2d_18" [id=253, type=conv2d]; "254 output" [id=254, type=output]; -"0 arg0_1" -> "1 quantize_per_tensor_default_8"; -"1 quantize_per_tensor_default_8" -> "2 dequantize_per_tensor_default_12"; -"2 dequantize_per_tensor_default_12" -> "9 conv2d"; -"3 _param_constant0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "7 dequantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "6 quantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "7 dequantize_per_channel_default"; -"6 quantize_per_channel_default" -> "7 dequantize_per_channel_default"; -"7 dequantize_per_channel_default" -> "9 conv2d"; -"8 _param_constant1_0_0" -> "9 conv2d"; -"9 conv2d" -> "10 relu"; -"10 relu" -> "11 quantize_per_tensor_default_9"; -"11 quantize_per_tensor_default_9" -> "12 dequantize_per_tensor_default_13"; -"12 dequantize_per_tensor_default_13" -> "19 conv2d_1"; -"13 _param_constant4" -> "16 quantize_per_channel_default_1"; -"14 conv2d_1_scale_0" -> "16 quantize_per_channel_default_1"; -"14 conv2d_1_scale_0" -> "17 dequantize_per_channel_default_1"; -"15 conv2d_1_zero_point_0" -> "16 quantize_per_channel_default_1"; -"15 conv2d_1_zero_point_0" -> "17 dequantize_per_channel_default_1"; -"16 quantize_per_channel_default_1" -> "17 dequantize_per_channel_default_1"; -"17 dequantize_per_channel_default_1" -> "19 conv2d_1"; -"18 _param_constant5_0_0" -> "19 conv2d_1"; -"19 conv2d_1" -> "20 relu_1"; -"20 relu_1" -> "21 quantize_per_tensor_default"; -"21 quantize_per_tensor_default" -> "22 dequantize_per_tensor_default_1"; -"21 quantize_per_tensor_default" -> "23 dequantize_per_tensor_default"; -"22 dequantize_per_tensor_default_1" -> "222 slice_13"; -"23 dequantize_per_tensor_default" -> "24 max_pool2d"; -"24 max_pool2d" -> "31 conv2d_2"; -"25 _param_constant8" -> "28 quantize_per_channel_default_2"; -"26 conv2d_2_scale_0" -> "28 quantize_per_channel_default_2"; -"26 conv2d_2_scale_0" -> "29 dequantize_per_channel_default_2"; -"27 conv2d_2_zero_point_0" -> "28 quantize_per_channel_default_2"; -"27 conv2d_2_zero_point_0" -> "29 dequantize_per_channel_default_2"; -"28 quantize_per_channel_default_2" -> "29 dequantize_per_channel_default_2"; -"29 dequantize_per_channel_default_2" -> "31 conv2d_2"; -"30 _param_constant9_0_0" -> "31 conv2d_2"; -"31 conv2d_2" -> "32 relu_2"; -"32 relu_2" -> "33 quantize_per_tensor_default_10"; -"33 quantize_per_tensor_default_10" -> "34 dequantize_per_tensor_default_14"; -"34 dequantize_per_tensor_default_14" -> "41 conv2d_3"; -"35 _param_constant12" -> "38 quantize_per_channel_default_3"; -"36 conv2d_3_scale_0" -> "38 quantize_per_channel_default_3"; -"36 conv2d_3_scale_0" -> "39 dequantize_per_channel_default_3"; -"37 conv2d_3_zero_point_0" -> "38 quantize_per_channel_default_3"; -"37 conv2d_3_zero_point_0" -> "39 dequantize_per_channel_default_3"; -"38 quantize_per_channel_default_3" -> "39 dequantize_per_channel_default_3"; -"39 dequantize_per_channel_default_3" -> "41 conv2d_3"; -"40 _param_constant13_0_0" -> "41 conv2d_3"; -"41 conv2d_3" -> "42 relu_3"; -"42 relu_3" -> "43 quantize_per_tensor_default_3"; -"43 quantize_per_tensor_default_3" -> "44 dequantize_per_tensor_default_5"; -"43 quantize_per_tensor_default_3" -> "45 dequantize_per_tensor_default_4"; -"44 dequantize_per_tensor_default_5" -> "188 slice_9"; -"45 dequantize_per_tensor_default_4" -> "46 max_pool2d_1"; -"46 max_pool2d_1" -> "53 conv2d_4"; -"47 _param_constant16" -> "50 quantize_per_channel_default_4"; -"48 conv2d_4_scale_0" -> "50 quantize_per_channel_default_4"; -"48 conv2d_4_scale_0" -> "51 dequantize_per_channel_default_4"; -"49 conv2d_4_zero_point_0" -> "50 quantize_per_channel_default_4"; -"49 conv2d_4_zero_point_0" -> "51 dequantize_per_channel_default_4"; -"50 quantize_per_channel_default_4" -> "51 dequantize_per_channel_default_4"; -"51 dequantize_per_channel_default_4" -> "53 conv2d_4"; -"52 _param_constant17_0_0" -> "53 conv2d_4"; -"53 conv2d_4" -> "54 relu_4"; -"54 relu_4" -> "55 quantize_per_tensor_default_11"; -"55 quantize_per_tensor_default_11" -> "56 dequantize_per_tensor_default_15"; -"56 dequantize_per_tensor_default_15" -> "63 conv2d_5"; -"57 _param_constant20" -> "60 quantize_per_channel_default_5"; -"58 conv2d_5_scale_0" -> "60 quantize_per_channel_default_5"; -"58 conv2d_5_scale_0" -> "61 dequantize_per_channel_default_5"; -"59 conv2d_5_zero_point_0" -> "60 quantize_per_channel_default_5"; -"59 conv2d_5_zero_point_0" -> "61 dequantize_per_channel_default_5"; -"60 quantize_per_channel_default_5" -> "61 dequantize_per_channel_default_5"; -"61 dequantize_per_channel_default_5" -> "63 conv2d_5"; -"62 _param_constant21_0_0" -> "63 conv2d_5"; -"63 conv2d_5" -> "64 relu_5"; -"64 relu_5" -> "65 quantize_per_tensor_default_4"; -"65 quantize_per_tensor_default_4" -> "66 dequantize_per_tensor_default_7"; -"65 quantize_per_tensor_default_4" -> "67 dequantize_per_tensor_default_6"; -"66 dequantize_per_tensor_default_7" -> "154 slice_5"; -"67 dequantize_per_tensor_default_6" -> "68 max_pool2d_2"; -"68 max_pool2d_2" -> "75 conv2d_6"; -"69 _param_constant24" -> "72 quantize_per_channel_default_6"; -"70 conv2d_6_scale_0" -> "72 quantize_per_channel_default_6"; -"70 conv2d_6_scale_0" -> "73 dequantize_per_channel_default_6"; -"71 conv2d_6_zero_point_0" -> "72 quantize_per_channel_default_6"; -"71 conv2d_6_zero_point_0" -> "73 dequantize_per_channel_default_6"; -"72 quantize_per_channel_default_6" -> "73 dequantize_per_channel_default_6"; -"73 dequantize_per_channel_default_6" -> "75 conv2d_6"; -"74 _param_constant25_0_0" -> "75 conv2d_6"; -"75 conv2d_6" -> "76 relu_6"; -"76 relu_6" -> "77 quantize_per_tensor_default_12"; -"77 quantize_per_tensor_default_12" -> "78 dequantize_per_tensor_default_16"; -"78 dequantize_per_tensor_default_16" -> "85 conv2d_7"; -"79 _param_constant28" -> "82 quantize_per_channel_default_7"; -"80 conv2d_7_scale_0" -> "82 quantize_per_channel_default_7"; -"80 conv2d_7_scale_0" -> "83 dequantize_per_channel_default_7"; -"81 conv2d_7_zero_point_0" -> "82 quantize_per_channel_default_7"; -"81 conv2d_7_zero_point_0" -> "83 dequantize_per_channel_default_7"; -"82 quantize_per_channel_default_7" -> "83 dequantize_per_channel_default_7"; -"83 dequantize_per_channel_default_7" -> "85 conv2d_7"; -"84 _param_constant29_0_0" -> "85 conv2d_7"; -"85 conv2d_7" -> "86 relu_7"; -"86 relu_7" -> "87 quantize_per_tensor_default_7"; -"87 quantize_per_tensor_default_7" -> "88 dequantize_per_tensor_default_11"; -"87 quantize_per_tensor_default_7" -> "89 dequantize_per_tensor_default_10"; -"88 dequantize_per_tensor_default_11" -> "120 slice_1"; -"89 dequantize_per_tensor_default_10" -> "90 max_pool2d_3"; -"90 max_pool2d_3" -> "97 conv2d_8"; -"91 _param_constant32" -> "94 quantize_per_channel_default_8"; -"92 conv2d_8_scale_0" -> "94 quantize_per_channel_default_8"; -"92 conv2d_8_scale_0" -> "95 dequantize_per_channel_default_8"; -"93 conv2d_8_zero_point_0" -> "94 quantize_per_channel_default_8"; -"93 conv2d_8_zero_point_0" -> "95 dequantize_per_channel_default_8"; -"94 quantize_per_channel_default_8" -> "95 dequantize_per_channel_default_8"; -"95 dequantize_per_channel_default_8" -> "97 conv2d_8"; -"96 _param_constant33_0_0" -> "97 conv2d_8"; -"97 conv2d_8" -> "98 relu_8"; -"98 relu_8" -> "99 quantize_per_tensor_default_13"; -"99 quantize_per_tensor_default_13" -> "100 dequantize_per_tensor_default_17"; -"100 dequantize_per_tensor_default_17" -> "107 conv2d_9"; -"101 _param_constant36" -> "104 quantize_per_channel_default_9"; -"102 conv2d_9_scale_0" -> "104 quantize_per_channel_default_9"; -"102 conv2d_9_scale_0" -> "105 dequantize_per_channel_default_9"; -"103 conv2d_9_zero_point_0" -> "104 quantize_per_channel_default_9"; -"103 conv2d_9_zero_point_0" -> "105 dequantize_per_channel_default_9"; -"104 quantize_per_channel_default_9" -> "105 dequantize_per_channel_default_9"; -"105 dequantize_per_channel_default_9" -> "107 conv2d_9"; -"106 _param_constant37_0_0" -> "107 conv2d_9"; -"107 conv2d_9" -> "108 relu_9"; -"108 relu_9" -> "109 quantize_per_tensor_default_14"; -"109 quantize_per_tensor_default_14" -> "110 dequantize_per_tensor_default_18"; -"110 dequantize_per_tensor_default_18" -> "117 conv_transpose2d"; -"111 _param_constant40" -> "115 quantize_per_channel_default_10"; -"112 _param_constant41" -> "117 conv_transpose2d"; -"113 conv_transpose2d_scale_0" -> "115 quantize_per_channel_default_10"; -"113 conv_transpose2d_scale_0" -> "116 dequantize_per_channel_default_10"; -"114 conv_transpose2d_zero_point_0" -> "115 quantize_per_channel_default_10"; -"114 conv_transpose2d_zero_point_0" -> "116 dequantize_per_channel_default_10"; -"115 quantize_per_channel_default_10" -> "116 dequantize_per_channel_default_10"; -"116 dequantize_per_channel_default_10" -> "117 conv_transpose2d"; -"117 conv_transpose2d" -> "118 quantize_per_tensor_default_6"; -"118 quantize_per_tensor_default_6" -> "119 dequantize_per_tensor_default_9"; -"119 dequantize_per_tensor_default_9" -> "124 cat"; -"120 slice_1" -> "121 slice_2"; -"121 slice_2" -> "122 slice_3"; -"122 slice_3" -> "123 slice_4"; -"123 slice_4" -> "124 cat"; -"124 cat" -> "131 conv2d_10"; -"125 _param_constant42" -> "128 quantize_per_channel_default_11"; -"126 conv2d_10_scale_0" -> "128 quantize_per_channel_default_11"; -"126 conv2d_10_scale_0" -> "129 dequantize_per_channel_default_11"; -"127 conv2d_10_zero_point_0" -> "128 quantize_per_channel_default_11"; -"127 conv2d_10_zero_point_0" -> "129 dequantize_per_channel_default_11"; -"128 quantize_per_channel_default_11" -> "129 dequantize_per_channel_default_11"; -"129 dequantize_per_channel_default_11" -> "131 conv2d_10"; -"130 _param_constant43_0_0" -> "131 conv2d_10"; -"131 conv2d_10" -> "132 relu_10"; -"132 relu_10" -> "133 quantize_per_tensor_default_15"; -"133 quantize_per_tensor_default_15" -> "134 dequantize_per_tensor_default_19"; -"134 dequantize_per_tensor_default_19" -> "141 conv2d_11"; -"135 _param_constant46" -> "138 quantize_per_channel_default_12"; -"136 conv2d_11_scale_0" -> "138 quantize_per_channel_default_12"; -"136 conv2d_11_scale_0" -> "139 dequantize_per_channel_default_12"; -"137 conv2d_11_zero_point_0" -> "138 quantize_per_channel_default_12"; -"137 conv2d_11_zero_point_0" -> "139 dequantize_per_channel_default_12"; -"138 quantize_per_channel_default_12" -> "139 dequantize_per_channel_default_12"; -"139 dequantize_per_channel_default_12" -> "141 conv2d_11"; -"140 _param_constant47_0_0" -> "141 conv2d_11"; -"141 conv2d_11" -> "142 relu_11"; -"142 relu_11" -> "143 quantize_per_tensor_default_16"; -"143 quantize_per_tensor_default_16" -> "144 dequantize_per_tensor_default_20"; -"144 dequantize_per_tensor_default_20" -> "151 conv_transpose2d_1"; -"145 _param_constant50" -> "149 quantize_per_channel_default_13"; -"146 _param_constant51" -> "151 conv_transpose2d_1"; -"147 conv_transpose2d_1_scale_0" -> "149 quantize_per_channel_default_13"; -"147 conv_transpose2d_1_scale_0" -> "150 dequantize_per_channel_default_13"; -"148 conv_transpose2d_1_zero_point_0" -> "149 quantize_per_channel_default_13"; -"148 conv_transpose2d_1_zero_point_0" -> "150 dequantize_per_channel_default_13"; -"149 quantize_per_channel_default_13" -> "150 dequantize_per_channel_default_13"; -"150 dequantize_per_channel_default_13" -> "151 conv_transpose2d_1"; -"151 conv_transpose2d_1" -> "152 quantize_per_tensor_default_5"; -"152 quantize_per_tensor_default_5" -> "153 dequantize_per_tensor_default_8"; -"153 dequantize_per_tensor_default_8" -> "158 cat_1"; -"154 slice_5" -> "155 slice_6"; -"155 slice_6" -> "156 slice_7"; -"156 slice_7" -> "157 slice_8"; -"157 slice_8" -> "158 cat_1"; -"158 cat_1" -> "165 conv2d_12"; -"159 _param_constant52" -> "162 quantize_per_channel_default_14"; -"160 conv2d_12_scale_0" -> "162 quantize_per_channel_default_14"; -"160 conv2d_12_scale_0" -> "163 dequantize_per_channel_default_14"; -"161 conv2d_12_zero_point_0" -> "162 quantize_per_channel_default_14"; -"161 conv2d_12_zero_point_0" -> "163 dequantize_per_channel_default_14"; -"162 quantize_per_channel_default_14" -> "163 dequantize_per_channel_default_14"; -"163 dequantize_per_channel_default_14" -> "165 conv2d_12"; -"164 _param_constant53_0_0" -> "165 conv2d_12"; -"165 conv2d_12" -> "166 relu_12"; -"166 relu_12" -> "167 quantize_per_tensor_default_17"; -"167 quantize_per_tensor_default_17" -> "168 dequantize_per_tensor_default_21"; -"168 dequantize_per_tensor_default_21" -> "175 conv2d_13"; -"169 _param_constant56" -> "172 quantize_per_channel_default_15"; -"170 conv2d_13_scale_0" -> "172 quantize_per_channel_default_15"; -"170 conv2d_13_scale_0" -> "173 dequantize_per_channel_default_15"; -"171 conv2d_13_zero_point_0" -> "172 quantize_per_channel_default_15"; -"171 conv2d_13_zero_point_0" -> "173 dequantize_per_channel_default_15"; -"172 quantize_per_channel_default_15" -> "173 dequantize_per_channel_default_15"; -"173 dequantize_per_channel_default_15" -> "175 conv2d_13"; -"174 _param_constant57_0_0" -> "175 conv2d_13"; -"175 conv2d_13" -> "176 relu_13"; -"176 relu_13" -> "177 quantize_per_tensor_default_18"; -"177 quantize_per_tensor_default_18" -> "178 dequantize_per_tensor_default_22"; -"178 dequantize_per_tensor_default_22" -> "185 conv_transpose2d_2"; -"179 _param_constant60" -> "183 quantize_per_channel_default_16"; -"180 _param_constant61" -> "185 conv_transpose2d_2"; -"181 conv_transpose2d_2_scale_0" -> "183 quantize_per_channel_default_16"; -"181 conv_transpose2d_2_scale_0" -> "184 dequantize_per_channel_default_16"; -"182 conv_transpose2d_2_zero_point_0" -> "183 quantize_per_channel_default_16"; -"182 conv_transpose2d_2_zero_point_0" -> "184 dequantize_per_channel_default_16"; -"183 quantize_per_channel_default_16" -> "184 dequantize_per_channel_default_16"; -"184 dequantize_per_channel_default_16" -> "185 conv_transpose2d_2"; -"185 conv_transpose2d_2" -> "186 quantize_per_tensor_default_2"; -"186 quantize_per_tensor_default_2" -> "187 dequantize_per_tensor_default_3"; -"187 dequantize_per_tensor_default_3" -> "192 cat_2"; -"188 slice_9" -> "189 slice_10"; -"189 slice_10" -> "190 slice_11"; -"190 slice_11" -> "191 slice_12"; -"191 slice_12" -> "192 cat_2"; -"192 cat_2" -> "199 conv2d_14"; -"193 _param_constant62" -> "196 quantize_per_channel_default_17"; -"194 conv2d_14_scale_0" -> "196 quantize_per_channel_default_17"; -"194 conv2d_14_scale_0" -> "197 dequantize_per_channel_default_17"; -"195 conv2d_14_zero_point_0" -> "196 quantize_per_channel_default_17"; -"195 conv2d_14_zero_point_0" -> "197 dequantize_per_channel_default_17"; -"196 quantize_per_channel_default_17" -> "197 dequantize_per_channel_default_17"; -"197 dequantize_per_channel_default_17" -> "199 conv2d_14"; -"198 _param_constant63_0_0" -> "199 conv2d_14"; -"199 conv2d_14" -> "200 relu_14"; -"200 relu_14" -> "201 quantize_per_tensor_default_19"; -"201 quantize_per_tensor_default_19" -> "202 dequantize_per_tensor_default_23"; -"202 dequantize_per_tensor_default_23" -> "209 conv2d_15"; -"203 _param_constant66" -> "206 quantize_per_channel_default_18"; -"204 conv2d_15_scale_0" -> "206 quantize_per_channel_default_18"; -"204 conv2d_15_scale_0" -> "207 dequantize_per_channel_default_18"; -"205 conv2d_15_zero_point_0" -> "206 quantize_per_channel_default_18"; -"205 conv2d_15_zero_point_0" -> "207 dequantize_per_channel_default_18"; -"206 quantize_per_channel_default_18" -> "207 dequantize_per_channel_default_18"; -"207 dequantize_per_channel_default_18" -> "209 conv2d_15"; -"208 _param_constant67_0_0" -> "209 conv2d_15"; -"209 conv2d_15" -> "210 relu_15"; -"210 relu_15" -> "211 quantize_per_tensor_default_20"; -"211 quantize_per_tensor_default_20" -> "212 dequantize_per_tensor_default_24"; -"212 dequantize_per_tensor_default_24" -> "219 conv_transpose2d_3"; -"213 _param_constant70" -> "217 quantize_per_channel_default_19"; -"214 _param_constant71" -> "219 conv_transpose2d_3"; -"215 conv_transpose2d_3_scale_0" -> "217 quantize_per_channel_default_19"; -"215 conv_transpose2d_3_scale_0" -> "218 dequantize_per_channel_default_19"; -"216 conv_transpose2d_3_zero_point_0" -> "217 quantize_per_channel_default_19"; -"216 conv_transpose2d_3_zero_point_0" -> "218 dequantize_per_channel_default_19"; -"217 quantize_per_channel_default_19" -> "218 dequantize_per_channel_default_19"; -"218 dequantize_per_channel_default_19" -> "219 conv_transpose2d_3"; -"219 conv_transpose2d_3" -> "220 quantize_per_tensor_default_1"; -"220 quantize_per_tensor_default_1" -> "221 dequantize_per_tensor_default_2"; -"221 dequantize_per_tensor_default_2" -> "226 cat_3"; -"222 slice_13" -> "223 slice_14"; -"223 slice_14" -> "224 slice_15"; -"224 slice_15" -> "225 slice_16"; -"225 slice_16" -> "226 cat_3"; -"226 cat_3" -> "233 conv2d_16"; -"227 _param_constant72" -> "230 quantize_per_channel_default_20"; -"228 conv2d_16_scale_0" -> "230 quantize_per_channel_default_20"; -"228 conv2d_16_scale_0" -> "231 dequantize_per_channel_default_20"; -"229 conv2d_16_zero_point_0" -> "230 quantize_per_channel_default_20"; -"229 conv2d_16_zero_point_0" -> "231 dequantize_per_channel_default_20"; -"230 quantize_per_channel_default_20" -> "231 dequantize_per_channel_default_20"; -"231 dequantize_per_channel_default_20" -> "233 conv2d_16"; -"232 _param_constant73_0_0" -> "233 conv2d_16"; -"233 conv2d_16" -> "234 relu_16"; -"234 relu_16" -> "235 quantize_per_tensor_default_21"; -"235 quantize_per_tensor_default_21" -> "236 dequantize_per_tensor_default_25"; -"236 dequantize_per_tensor_default_25" -> "243 conv2d_17"; -"237 _param_constant76" -> "240 quantize_per_channel_default_21"; -"238 conv2d_17_scale_0" -> "240 quantize_per_channel_default_21"; -"238 conv2d_17_scale_0" -> "241 dequantize_per_channel_default_21"; -"239 conv2d_17_zero_point_0" -> "240 quantize_per_channel_default_21"; -"239 conv2d_17_zero_point_0" -> "241 dequantize_per_channel_default_21"; -"240 quantize_per_channel_default_21" -> "241 dequantize_per_channel_default_21"; -"241 dequantize_per_channel_default_21" -> "243 conv2d_17"; -"242 _param_constant77_0_0" -> "243 conv2d_17"; -"243 conv2d_17" -> "244 relu_17"; -"244 relu_17" -> "245 quantize_per_tensor_default_22"; -"245 quantize_per_tensor_default_22" -> "246 dequantize_per_tensor_default_26"; -"246 dequantize_per_tensor_default_26" -> "253 conv2d_18"; -"247 _param_constant80" -> "250 quantize_per_channel_default_22"; -"248 conv2d_18_scale_0" -> "250 quantize_per_channel_default_22"; -"248 conv2d_18_scale_0" -> "251 dequantize_per_channel_default_22"; -"249 conv2d_18_zero_point_0" -> "250 quantize_per_channel_default_22"; -"249 conv2d_18_zero_point_0" -> "251 dequantize_per_channel_default_22"; -"250 quantize_per_channel_default_22" -> "251 dequantize_per_channel_default_22"; -"251 dequantize_per_channel_default_22" -> "253 conv2d_18"; -"252 _param_constant81_0_0" -> "253 conv2d_18"; -"253 conv2d_18" -> "254 output"; +"0 x" -> "1 quantize_per_tensor_default_8" [label="(1, 3, 224, 224)", style=solid]; +"1 quantize_per_tensor_default_8" -> "2 dequantize_per_tensor_default_12" [label="(1, 3, 224, 224)", style=solid]; +"2 dequantize_per_tensor_default_12" -> "9 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"3 conv2d_scale_0" -> "6 quantize_per_channel_default" [label="(64,)", style=solid]; +"3 conv2d_scale_0" -> "7 dequantize_per_channel_default" [label="(64,)", style=solid]; +"4 conv2d_zero_point_0" -> "6 quantize_per_channel_default" [label="(64,)", style=solid]; +"4 conv2d_zero_point_0" -> "7 dequantize_per_channel_default" [label="(64,)", style=solid]; +"5 compressed_weight_updated_constant0" -> "6 quantize_per_channel_default" [label="(64, 3, 3, 3)", style=solid]; +"6 quantize_per_channel_default" -> "7 dequantize_per_channel_default" [label="(64, 3, 3, 3)", style=solid]; +"7 dequantize_per_channel_default" -> "9 conv2d" [label="(64, 3, 3, 3)", style=solid]; +"8 down_path_0_block_0_bias_0_0" -> "9 conv2d" [label="(64,)", style=solid]; +"9 conv2d" -> "10 relu" [label="(1, 64, 222, 222)", style=solid]; +"10 relu" -> "11 quantize_per_tensor_default_9" [label="(1, 64, 222, 222)", style=solid]; +"11 quantize_per_tensor_default_9" -> "12 dequantize_per_tensor_default_13" [label="(1, 64, 222, 222)", style=solid]; +"12 dequantize_per_tensor_default_13" -> "19 conv2d_1" [label="(1, 64, 222, 222)", style=solid]; +"13 conv2d_1_scale_0" -> "16 quantize_per_channel_default_1" [label="(64,)", style=solid]; +"13 conv2d_1_scale_0" -> "17 dequantize_per_channel_default_1" [label="(64,)", style=solid]; +"14 conv2d_1_zero_point_0" -> "16 quantize_per_channel_default_1" [label="(64,)", style=solid]; +"14 conv2d_1_zero_point_0" -> "17 dequantize_per_channel_default_1" [label="(64,)", style=solid]; +"15 compressed_weight_updated_constant1" -> "16 quantize_per_channel_default_1" [label="(64, 64, 3, 3)", style=solid]; +"16 quantize_per_channel_default_1" -> "17 dequantize_per_channel_default_1" [label="(64, 64, 3, 3)", style=solid]; +"17 dequantize_per_channel_default_1" -> "19 conv2d_1" [label="(64, 64, 3, 3)", style=solid]; +"18 down_path_0_block_3_bias_0_0" -> "19 conv2d_1" [label="(64,)", style=solid]; +"19 conv2d_1" -> "20 relu_1" [label="(1, 64, 220, 220)", style=solid]; +"20 relu_1" -> "21 quantize_per_tensor_default" [label="(1, 64, 220, 220)", style=solid]; +"21 quantize_per_tensor_default" -> "22 dequantize_per_tensor_default_1" [label="(1, 64, 220, 220)", style=solid]; +"21 quantize_per_tensor_default" -> "23 dequantize_per_tensor_default" [label="(1, 64, 220, 220)", style=solid]; +"22 dequantize_per_tensor_default_1" -> "222 slice_13" [label="(1, 64, 220, 220)", style=solid]; +"23 dequantize_per_tensor_default" -> "24 max_pool2d" [label="(1, 64, 220, 220)", style=solid]; +"24 max_pool2d" -> "31 conv2d_2" [label="(1, 64, 110, 110)", style=solid]; +"25 conv2d_2_scale_0" -> "28 quantize_per_channel_default_2" [label="(128,)", style=solid]; +"25 conv2d_2_scale_0" -> "29 dequantize_per_channel_default_2" [label="(128,)", style=solid]; +"26 conv2d_2_zero_point_0" -> "28 quantize_per_channel_default_2" [label="(128,)", style=solid]; +"26 conv2d_2_zero_point_0" -> "29 dequantize_per_channel_default_2" [label="(128,)", style=solid]; +"27 compressed_weight_updated_constant2" -> "28 quantize_per_channel_default_2" [label="(128, 64, 3, 3)", style=solid]; +"28 quantize_per_channel_default_2" -> "29 dequantize_per_channel_default_2" [label="(128, 64, 3, 3)", style=solid]; +"29 dequantize_per_channel_default_2" -> "31 conv2d_2" [label="(128, 64, 3, 3)", style=solid]; +"30 down_path_1_block_0_bias_0_0" -> "31 conv2d_2" [label="(128,)", style=solid]; +"31 conv2d_2" -> "32 relu_2" [label="(1, 128, 108, 108)", style=solid]; +"32 relu_2" -> "33 quantize_per_tensor_default_10" [label="(1, 128, 108, 108)", style=solid]; +"33 quantize_per_tensor_default_10" -> "34 dequantize_per_tensor_default_14" [label="(1, 128, 108, 108)", style=solid]; +"34 dequantize_per_tensor_default_14" -> "41 conv2d_3" [label="(1, 128, 108, 108)", style=solid]; +"35 conv2d_3_scale_0" -> "38 quantize_per_channel_default_3" [label="(128,)", style=solid]; +"35 conv2d_3_scale_0" -> "39 dequantize_per_channel_default_3" [label="(128,)", style=solid]; +"36 conv2d_3_zero_point_0" -> "38 quantize_per_channel_default_3" [label="(128,)", style=solid]; +"36 conv2d_3_zero_point_0" -> "39 dequantize_per_channel_default_3" [label="(128,)", style=solid]; +"37 compressed_weight_updated_constant3" -> "38 quantize_per_channel_default_3" [label="(128, 128, 3, 3)", style=solid]; +"38 quantize_per_channel_default_3" -> "39 dequantize_per_channel_default_3" [label="(128, 128, 3, 3)", style=solid]; +"39 dequantize_per_channel_default_3" -> "41 conv2d_3" [label="(128, 128, 3, 3)", style=solid]; +"40 down_path_1_block_3_bias_0_0" -> "41 conv2d_3" [label="(128,)", style=solid]; +"41 conv2d_3" -> "42 relu_3" [label="(1, 128, 106, 106)", style=solid]; +"42 relu_3" -> "43 quantize_per_tensor_default_3" [label="(1, 128, 106, 106)", style=solid]; +"43 quantize_per_tensor_default_3" -> "44 dequantize_per_tensor_default_5" [label="(1, 128, 106, 106)", style=solid]; +"43 quantize_per_tensor_default_3" -> "45 dequantize_per_tensor_default_4" [label="(1, 128, 106, 106)", style=solid]; +"44 dequantize_per_tensor_default_5" -> "188 slice_9" [label="(1, 128, 106, 106)", style=solid]; +"45 dequantize_per_tensor_default_4" -> "46 max_pool2d_1" [label="(1, 128, 106, 106)", style=solid]; +"46 max_pool2d_1" -> "53 conv2d_4" [label="(1, 128, 53, 53)", style=solid]; +"47 conv2d_4_scale_0" -> "50 quantize_per_channel_default_4" [label="(256,)", style=solid]; +"47 conv2d_4_scale_0" -> "51 dequantize_per_channel_default_4" [label="(256,)", style=solid]; +"48 conv2d_4_zero_point_0" -> "50 quantize_per_channel_default_4" [label="(256,)", style=solid]; +"48 conv2d_4_zero_point_0" -> "51 dequantize_per_channel_default_4" [label="(256,)", style=solid]; +"49 compressed_weight_updated_constant4" -> "50 quantize_per_channel_default_4" [label="(256, 128, 3, 3)", style=solid]; +"50 quantize_per_channel_default_4" -> "51 dequantize_per_channel_default_4" [label="(256, 128, 3, 3)", style=solid]; +"51 dequantize_per_channel_default_4" -> "53 conv2d_4" [label="(256, 128, 3, 3)", style=solid]; +"52 down_path_2_block_0_bias_0_0" -> "53 conv2d_4" [label="(256,)", style=solid]; +"53 conv2d_4" -> "54 relu_4" [label="(1, 256, 51, 51)", style=solid]; +"54 relu_4" -> "55 quantize_per_tensor_default_11" [label="(1, 256, 51, 51)", style=solid]; +"55 quantize_per_tensor_default_11" -> "56 dequantize_per_tensor_default_15" [label="(1, 256, 51, 51)", style=solid]; +"56 dequantize_per_tensor_default_15" -> "63 conv2d_5" [label="(1, 256, 51, 51)", style=solid]; +"57 conv2d_5_scale_0" -> "60 quantize_per_channel_default_5" [label="(256,)", style=solid]; +"57 conv2d_5_scale_0" -> "61 dequantize_per_channel_default_5" [label="(256,)", style=solid]; +"58 conv2d_5_zero_point_0" -> "60 quantize_per_channel_default_5" [label="(256,)", style=solid]; +"58 conv2d_5_zero_point_0" -> "61 dequantize_per_channel_default_5" [label="(256,)", style=solid]; +"59 compressed_weight_updated_constant5" -> "60 quantize_per_channel_default_5" [label="(256, 256, 3, 3)", style=solid]; +"60 quantize_per_channel_default_5" -> "61 dequantize_per_channel_default_5" [label="(256, 256, 3, 3)", style=solid]; +"61 dequantize_per_channel_default_5" -> "63 conv2d_5" [label="(256, 256, 3, 3)", style=solid]; +"62 down_path_2_block_3_bias_0_0" -> "63 conv2d_5" [label="(256,)", style=solid]; +"63 conv2d_5" -> "64 relu_5" [label="(1, 256, 49, 49)", style=solid]; +"64 relu_5" -> "65 quantize_per_tensor_default_4" [label="(1, 256, 49, 49)", style=solid]; +"65 quantize_per_tensor_default_4" -> "66 dequantize_per_tensor_default_7" [label="(1, 256, 49, 49)", style=solid]; +"65 quantize_per_tensor_default_4" -> "67 dequantize_per_tensor_default_6" [label="(1, 256, 49, 49)", style=solid]; +"66 dequantize_per_tensor_default_7" -> "154 slice_5" [label="(1, 256, 49, 49)", style=solid]; +"67 dequantize_per_tensor_default_6" -> "68 max_pool2d_2" [label="(1, 256, 49, 49)", style=solid]; +"68 max_pool2d_2" -> "75 conv2d_6" [label="(1, 256, 24, 24)", style=solid]; +"69 conv2d_6_scale_0" -> "72 quantize_per_channel_default_6" [label="(512,)", style=solid]; +"69 conv2d_6_scale_0" -> "73 dequantize_per_channel_default_6" [label="(512,)", style=solid]; +"70 conv2d_6_zero_point_0" -> "72 quantize_per_channel_default_6" [label="(512,)", style=solid]; +"70 conv2d_6_zero_point_0" -> "73 dequantize_per_channel_default_6" [label="(512,)", style=solid]; +"71 compressed_weight_updated_constant6" -> "72 quantize_per_channel_default_6" [label="(512, 256, 3, 3)", style=solid]; +"72 quantize_per_channel_default_6" -> "73 dequantize_per_channel_default_6" [label="(512, 256, 3, 3)", style=solid]; +"73 dequantize_per_channel_default_6" -> "75 conv2d_6" [label="(512, 256, 3, 3)", style=solid]; +"74 down_path_3_block_0_bias_0_0" -> "75 conv2d_6" [label="(512,)", style=solid]; +"75 conv2d_6" -> "76 relu_6" [label="(1, 512, 22, 22)", style=solid]; +"76 relu_6" -> "77 quantize_per_tensor_default_12" [label="(1, 512, 22, 22)", style=solid]; +"77 quantize_per_tensor_default_12" -> "78 dequantize_per_tensor_default_16" [label="(1, 512, 22, 22)", style=solid]; +"78 dequantize_per_tensor_default_16" -> "85 conv2d_7" [label="(1, 512, 22, 22)", style=solid]; +"79 conv2d_7_scale_0" -> "82 quantize_per_channel_default_7" [label="(512,)", style=solid]; +"79 conv2d_7_scale_0" -> "83 dequantize_per_channel_default_7" [label="(512,)", style=solid]; +"80 conv2d_7_zero_point_0" -> "82 quantize_per_channel_default_7" [label="(512,)", style=solid]; +"80 conv2d_7_zero_point_0" -> "83 dequantize_per_channel_default_7" [label="(512,)", style=solid]; +"81 compressed_weight_updated_constant7" -> "82 quantize_per_channel_default_7" [label="(512, 512, 3, 3)", style=solid]; +"82 quantize_per_channel_default_7" -> "83 dequantize_per_channel_default_7" [label="(512, 512, 3, 3)", style=solid]; +"83 dequantize_per_channel_default_7" -> "85 conv2d_7" [label="(512, 512, 3, 3)", style=solid]; +"84 down_path_3_block_3_bias_0_0" -> "85 conv2d_7" [label="(512,)", style=solid]; +"85 conv2d_7" -> "86 relu_7" [label="(1, 512, 20, 20)", style=solid]; +"86 relu_7" -> "87 quantize_per_tensor_default_7" [label="(1, 512, 20, 20)", style=solid]; +"87 quantize_per_tensor_default_7" -> "88 dequantize_per_tensor_default_11" [label="(1, 512, 20, 20)", style=solid]; +"87 quantize_per_tensor_default_7" -> "89 dequantize_per_tensor_default_10" [label="(1, 512, 20, 20)", style=solid]; +"88 dequantize_per_tensor_default_11" -> "120 slice_1" [label="(1, 512, 20, 20)", style=solid]; +"89 dequantize_per_tensor_default_10" -> "90 max_pool2d_3" [label="(1, 512, 20, 20)", style=solid]; +"90 max_pool2d_3" -> "97 conv2d_8" [label="(1, 512, 10, 10)", style=solid]; +"91 conv2d_8_scale_0" -> "94 quantize_per_channel_default_8" [label="(1024,)", style=solid]; +"91 conv2d_8_scale_0" -> "95 dequantize_per_channel_default_8" [label="(1024,)", style=solid]; +"92 conv2d_8_zero_point_0" -> "94 quantize_per_channel_default_8" [label="(1024,)", style=solid]; +"92 conv2d_8_zero_point_0" -> "95 dequantize_per_channel_default_8" [label="(1024,)", style=solid]; +"93 compressed_weight_updated_constant8" -> "94 quantize_per_channel_default_8" [label="(1024, 512, 3, 3)", style=solid]; +"94 quantize_per_channel_default_8" -> "95 dequantize_per_channel_default_8" [label="(1024, 512, 3, 3)", style=solid]; +"95 dequantize_per_channel_default_8" -> "97 conv2d_8" [label="(1024, 512, 3, 3)", style=solid]; +"96 down_path_4_block_0_bias_0_0" -> "97 conv2d_8" [label="(1024,)", style=solid]; +"97 conv2d_8" -> "98 relu_8" [label="(1, 1024, 8, 8)", style=solid]; +"98 relu_8" -> "99 quantize_per_tensor_default_13" [label="(1, 1024, 8, 8)", style=solid]; +"99 quantize_per_tensor_default_13" -> "100 dequantize_per_tensor_default_17" [label="(1, 1024, 8, 8)", style=solid]; +"100 dequantize_per_tensor_default_17" -> "107 conv2d_9" [label="(1, 1024, 8, 8)", style=solid]; +"101 conv2d_9_scale_0" -> "104 quantize_per_channel_default_9" [label="(1024,)", style=solid]; +"101 conv2d_9_scale_0" -> "105 dequantize_per_channel_default_9" [label="(1024,)", style=solid]; +"102 conv2d_9_zero_point_0" -> "104 quantize_per_channel_default_9" [label="(1024,)", style=solid]; +"102 conv2d_9_zero_point_0" -> "105 dequantize_per_channel_default_9" [label="(1024,)", style=solid]; +"103 compressed_weight_updated_constant9" -> "104 quantize_per_channel_default_9" [label="(1024, 1024, 3, 3)", style=solid]; +"104 quantize_per_channel_default_9" -> "105 dequantize_per_channel_default_9" [label="(1024, 1024, 3, 3)", style=solid]; +"105 dequantize_per_channel_default_9" -> "107 conv2d_9" [label="(1024, 1024, 3, 3)", style=solid]; +"106 down_path_4_block_3_bias_0_0" -> "107 conv2d_9" [label="(1024,)", style=solid]; +"107 conv2d_9" -> "108 relu_9" [label="(1, 1024, 6, 6)", style=solid]; +"108 relu_9" -> "109 quantize_per_tensor_default_14" [label="(1, 1024, 6, 6)", style=solid]; +"109 quantize_per_tensor_default_14" -> "110 dequantize_per_tensor_default_18" [label="(1, 1024, 6, 6)", style=solid]; +"110 dequantize_per_tensor_default_18" -> "117 conv_transpose2d" [label="(1, 1024, 6, 6)", style=solid]; +"111 conv_transpose2d_scale_0" -> "114 quantize_per_channel_default_10" [label="(1024,)", style=solid]; +"111 conv_transpose2d_scale_0" -> "115 dequantize_per_channel_default_10" [label="(1024,)", style=solid]; +"112 conv_transpose2d_zero_point_0" -> "114 quantize_per_channel_default_10" [label="(1024,)", style=solid]; +"112 conv_transpose2d_zero_point_0" -> "115 dequantize_per_channel_default_10" [label="(1024,)", style=solid]; +"113 compressed_weight_updated_constant10" -> "114 quantize_per_channel_default_10" [label="(1024, 512, 2, 2)", style=solid]; +"114 quantize_per_channel_default_10" -> "115 dequantize_per_channel_default_10" [label="(1024, 512, 2, 2)", style=solid]; +"115 dequantize_per_channel_default_10" -> "117 conv_transpose2d" [label="(1024, 512, 2, 2)", style=solid]; +"116 up_path_0_up_bias_0_0" -> "117 conv_transpose2d" [label="(512,)", style=solid]; +"117 conv_transpose2d" -> "118 quantize_per_tensor_default_6" [label="(1, 512, 12, 12)", style=solid]; +"118 quantize_per_tensor_default_6" -> "119 dequantize_per_tensor_default_9" [label="(1, 512, 12, 12)", style=solid]; +"119 dequantize_per_tensor_default_9" -> "124 cat" [label="(1, 512, 12, 12)", style=solid]; +"120 slice_1" -> "121 slice_2" [label="(1, 512, 20, 20)", style=solid]; +"121 slice_2" -> "122 slice_3" [label="(1, 512, 20, 20)", style=solid]; +"122 slice_3" -> "123 slice_4" [label="(1, 512, 12, 20)", style=solid]; +"123 slice_4" -> "124 cat" [label="(1, 512, 12, 12)", style=solid]; +"124 cat" -> "131 conv2d_10" [label="(1, 1024, 12, 12)", style=solid]; +"125 conv2d_10_scale_0" -> "128 quantize_per_channel_default_11" [label="(512,)", style=solid]; +"125 conv2d_10_scale_0" -> "129 dequantize_per_channel_default_11" [label="(512,)", style=solid]; +"126 conv2d_10_zero_point_0" -> "128 quantize_per_channel_default_11" [label="(512,)", style=solid]; +"126 conv2d_10_zero_point_0" -> "129 dequantize_per_channel_default_11" [label="(512,)", style=solid]; +"127 compressed_weight_updated_constant11" -> "128 quantize_per_channel_default_11" [label="(512, 1024, 3, 3)", style=solid]; +"128 quantize_per_channel_default_11" -> "129 dequantize_per_channel_default_11" [label="(512, 1024, 3, 3)", style=solid]; +"129 dequantize_per_channel_default_11" -> "131 conv2d_10" [label="(512, 1024, 3, 3)", style=solid]; +"130 up_path_0_conv_block_block_0_bias_0_0" -> "131 conv2d_10" [label="(512,)", style=solid]; +"131 conv2d_10" -> "132 relu_10" [label="(1, 512, 10, 10)", style=solid]; +"132 relu_10" -> "133 quantize_per_tensor_default_15" [label="(1, 512, 10, 10)", style=solid]; +"133 quantize_per_tensor_default_15" -> "134 dequantize_per_tensor_default_19" [label="(1, 512, 10, 10)", style=solid]; +"134 dequantize_per_tensor_default_19" -> "141 conv2d_11" [label="(1, 512, 10, 10)", style=solid]; +"135 conv2d_11_scale_0" -> "138 quantize_per_channel_default_12" [label="(512,)", style=solid]; +"135 conv2d_11_scale_0" -> "139 dequantize_per_channel_default_12" [label="(512,)", style=solid]; +"136 conv2d_11_zero_point_0" -> "138 quantize_per_channel_default_12" [label="(512,)", style=solid]; +"136 conv2d_11_zero_point_0" -> "139 dequantize_per_channel_default_12" [label="(512,)", style=solid]; +"137 compressed_weight_updated_constant12" -> "138 quantize_per_channel_default_12" [label="(512, 512, 3, 3)", style=solid]; +"138 quantize_per_channel_default_12" -> "139 dequantize_per_channel_default_12" [label="(512, 512, 3, 3)", style=solid]; +"139 dequantize_per_channel_default_12" -> "141 conv2d_11" [label="(512, 512, 3, 3)", style=solid]; +"140 up_path_0_conv_block_block_3_bias_0_0" -> "141 conv2d_11" [label="(512,)", style=solid]; +"141 conv2d_11" -> "142 relu_11" [label="(1, 512, 8, 8)", style=solid]; +"142 relu_11" -> "143 quantize_per_tensor_default_16" [label="(1, 512, 8, 8)", style=solid]; +"143 quantize_per_tensor_default_16" -> "144 dequantize_per_tensor_default_20" [label="(1, 512, 8, 8)", style=solid]; +"144 dequantize_per_tensor_default_20" -> "151 conv_transpose2d_1" [label="(1, 512, 8, 8)", style=solid]; +"145 conv_transpose2d_1_scale_0" -> "148 quantize_per_channel_default_13" [label="(512,)", style=solid]; +"145 conv_transpose2d_1_scale_0" -> "149 dequantize_per_channel_default_13" [label="(512,)", style=solid]; +"146 conv_transpose2d_1_zero_point_0" -> "148 quantize_per_channel_default_13" [label="(512,)", style=solid]; +"146 conv_transpose2d_1_zero_point_0" -> "149 dequantize_per_channel_default_13" [label="(512,)", style=solid]; +"147 compressed_weight_updated_constant13" -> "148 quantize_per_channel_default_13" [label="(512, 256, 2, 2)", style=solid]; +"148 quantize_per_channel_default_13" -> "149 dequantize_per_channel_default_13" [label="(512, 256, 2, 2)", style=solid]; +"149 dequantize_per_channel_default_13" -> "151 conv_transpose2d_1" [label="(512, 256, 2, 2)", style=solid]; +"150 up_path_1_up_bias_0_0" -> "151 conv_transpose2d_1" [label="(256,)", style=solid]; +"151 conv_transpose2d_1" -> "152 quantize_per_tensor_default_5" [label="(1, 256, 16, 16)", style=solid]; +"152 quantize_per_tensor_default_5" -> "153 dequantize_per_tensor_default_8" [label="(1, 256, 16, 16)", style=solid]; +"153 dequantize_per_tensor_default_8" -> "158 cat_1" [label="(1, 256, 16, 16)", style=solid]; +"154 slice_5" -> "155 slice_6" [label="(1, 256, 49, 49)", style=solid]; +"155 slice_6" -> "156 slice_7" [label="(1, 256, 49, 49)", style=solid]; +"156 slice_7" -> "157 slice_8" [label="(1, 256, 16, 49)", style=solid]; +"157 slice_8" -> "158 cat_1" [label="(1, 256, 16, 16)", style=solid]; +"158 cat_1" -> "165 conv2d_12" [label="(1, 512, 16, 16)", style=solid]; +"159 conv2d_12_scale_0" -> "162 quantize_per_channel_default_14" [label="(256,)", style=solid]; +"159 conv2d_12_scale_0" -> "163 dequantize_per_channel_default_14" [label="(256,)", style=solid]; +"160 conv2d_12_zero_point_0" -> "162 quantize_per_channel_default_14" [label="(256,)", style=solid]; +"160 conv2d_12_zero_point_0" -> "163 dequantize_per_channel_default_14" [label="(256,)", style=solid]; +"161 compressed_weight_updated_constant14" -> "162 quantize_per_channel_default_14" [label="(256, 512, 3, 3)", style=solid]; +"162 quantize_per_channel_default_14" -> "163 dequantize_per_channel_default_14" [label="(256, 512, 3, 3)", style=solid]; +"163 dequantize_per_channel_default_14" -> "165 conv2d_12" [label="(256, 512, 3, 3)", style=solid]; +"164 up_path_1_conv_block_block_0_bias_0_0" -> "165 conv2d_12" [label="(256,)", style=solid]; +"165 conv2d_12" -> "166 relu_12" [label="(1, 256, 14, 14)", style=solid]; +"166 relu_12" -> "167 quantize_per_tensor_default_17" [label="(1, 256, 14, 14)", style=solid]; +"167 quantize_per_tensor_default_17" -> "168 dequantize_per_tensor_default_21" [label="(1, 256, 14, 14)", style=solid]; +"168 dequantize_per_tensor_default_21" -> "175 conv2d_13" [label="(1, 256, 14, 14)", style=solid]; +"169 conv2d_13_scale_0" -> "172 quantize_per_channel_default_15" [label="(256,)", style=solid]; +"169 conv2d_13_scale_0" -> "173 dequantize_per_channel_default_15" [label="(256,)", style=solid]; +"170 conv2d_13_zero_point_0" -> "172 quantize_per_channel_default_15" [label="(256,)", style=solid]; +"170 conv2d_13_zero_point_0" -> "173 dequantize_per_channel_default_15" [label="(256,)", style=solid]; +"171 compressed_weight_updated_constant15" -> "172 quantize_per_channel_default_15" [label="(256, 256, 3, 3)", style=solid]; +"172 quantize_per_channel_default_15" -> "173 dequantize_per_channel_default_15" [label="(256, 256, 3, 3)", style=solid]; +"173 dequantize_per_channel_default_15" -> "175 conv2d_13" [label="(256, 256, 3, 3)", style=solid]; +"174 up_path_1_conv_block_block_3_bias_0_0" -> "175 conv2d_13" [label="(256,)", style=solid]; +"175 conv2d_13" -> "176 relu_13" [label="(1, 256, 12, 12)", style=solid]; +"176 relu_13" -> "177 quantize_per_tensor_default_18" [label="(1, 256, 12, 12)", style=solid]; +"177 quantize_per_tensor_default_18" -> "178 dequantize_per_tensor_default_22" [label="(1, 256, 12, 12)", style=solid]; +"178 dequantize_per_tensor_default_22" -> "185 conv_transpose2d_2" [label="(1, 256, 12, 12)", style=solid]; +"179 conv_transpose2d_2_scale_0" -> "182 quantize_per_channel_default_16" [label="(256,)", style=solid]; +"179 conv_transpose2d_2_scale_0" -> "183 dequantize_per_channel_default_16" [label="(256,)", style=solid]; +"180 conv_transpose2d_2_zero_point_0" -> "182 quantize_per_channel_default_16" [label="(256,)", style=solid]; +"180 conv_transpose2d_2_zero_point_0" -> "183 dequantize_per_channel_default_16" [label="(256,)", style=solid]; +"181 compressed_weight_updated_constant16" -> "182 quantize_per_channel_default_16" [label="(256, 128, 2, 2)", style=solid]; +"182 quantize_per_channel_default_16" -> "183 dequantize_per_channel_default_16" [label="(256, 128, 2, 2)", style=solid]; +"183 dequantize_per_channel_default_16" -> "185 conv_transpose2d_2" [label="(256, 128, 2, 2)", style=solid]; +"184 up_path_2_up_bias_0_0" -> "185 conv_transpose2d_2" [label="(128,)", style=solid]; +"185 conv_transpose2d_2" -> "186 quantize_per_tensor_default_2" [label="(1, 128, 24, 24)", style=solid]; +"186 quantize_per_tensor_default_2" -> "187 dequantize_per_tensor_default_3" [label="(1, 128, 24, 24)", style=solid]; +"187 dequantize_per_tensor_default_3" -> "192 cat_2" [label="(1, 128, 24, 24)", style=solid]; +"188 slice_9" -> "189 slice_10" [label="(1, 128, 106, 106)", style=solid]; +"189 slice_10" -> "190 slice_11" [label="(1, 128, 106, 106)", style=solid]; +"190 slice_11" -> "191 slice_12" [label="(1, 128, 24, 106)", style=solid]; +"191 slice_12" -> "192 cat_2" [label="(1, 128, 24, 24)", style=solid]; +"192 cat_2" -> "199 conv2d_14" [label="(1, 256, 24, 24)", style=solid]; +"193 conv2d_14_scale_0" -> "196 quantize_per_channel_default_17" [label="(128,)", style=solid]; +"193 conv2d_14_scale_0" -> "197 dequantize_per_channel_default_17" [label="(128,)", style=solid]; +"194 conv2d_14_zero_point_0" -> "196 quantize_per_channel_default_17" [label="(128,)", style=solid]; +"194 conv2d_14_zero_point_0" -> "197 dequantize_per_channel_default_17" [label="(128,)", style=solid]; +"195 compressed_weight_updated_constant17" -> "196 quantize_per_channel_default_17" [label="(128, 256, 3, 3)", style=solid]; +"196 quantize_per_channel_default_17" -> "197 dequantize_per_channel_default_17" [label="(128, 256, 3, 3)", style=solid]; +"197 dequantize_per_channel_default_17" -> "199 conv2d_14" [label="(128, 256, 3, 3)", style=solid]; +"198 up_path_2_conv_block_block_0_bias_0_0" -> "199 conv2d_14" [label="(128,)", style=solid]; +"199 conv2d_14" -> "200 relu_14" [label="(1, 128, 22, 22)", style=solid]; +"200 relu_14" -> "201 quantize_per_tensor_default_19" [label="(1, 128, 22, 22)", style=solid]; +"201 quantize_per_tensor_default_19" -> "202 dequantize_per_tensor_default_23" [label="(1, 128, 22, 22)", style=solid]; +"202 dequantize_per_tensor_default_23" -> "209 conv2d_15" [label="(1, 128, 22, 22)", style=solid]; +"203 conv2d_15_scale_0" -> "206 quantize_per_channel_default_18" [label="(128,)", style=solid]; +"203 conv2d_15_scale_0" -> "207 dequantize_per_channel_default_18" [label="(128,)", style=solid]; +"204 conv2d_15_zero_point_0" -> "206 quantize_per_channel_default_18" [label="(128,)", style=solid]; +"204 conv2d_15_zero_point_0" -> "207 dequantize_per_channel_default_18" [label="(128,)", style=solid]; +"205 compressed_weight_updated_constant18" -> "206 quantize_per_channel_default_18" [label="(128, 128, 3, 3)", style=solid]; +"206 quantize_per_channel_default_18" -> "207 dequantize_per_channel_default_18" [label="(128, 128, 3, 3)", style=solid]; +"207 dequantize_per_channel_default_18" -> "209 conv2d_15" [label="(128, 128, 3, 3)", style=solid]; +"208 up_path_2_conv_block_block_3_bias_0_0" -> "209 conv2d_15" [label="(128,)", style=solid]; +"209 conv2d_15" -> "210 relu_15" [label="(1, 128, 20, 20)", style=solid]; +"210 relu_15" -> "211 quantize_per_tensor_default_20" [label="(1, 128, 20, 20)", style=solid]; +"211 quantize_per_tensor_default_20" -> "212 dequantize_per_tensor_default_24" [label="(1, 128, 20, 20)", style=solid]; +"212 dequantize_per_tensor_default_24" -> "219 conv_transpose2d_3" [label="(1, 128, 20, 20)", style=solid]; +"213 conv_transpose2d_3_scale_0" -> "216 quantize_per_channel_default_19" [label="(128,)", style=solid]; +"213 conv_transpose2d_3_scale_0" -> "217 dequantize_per_channel_default_19" [label="(128,)", style=solid]; +"214 conv_transpose2d_3_zero_point_0" -> "216 quantize_per_channel_default_19" [label="(128,)", style=solid]; +"214 conv_transpose2d_3_zero_point_0" -> "217 dequantize_per_channel_default_19" [label="(128,)", style=solid]; +"215 compressed_weight_updated_constant19" -> "216 quantize_per_channel_default_19" [label="(128, 64, 2, 2)", style=solid]; +"216 quantize_per_channel_default_19" -> "217 dequantize_per_channel_default_19" [label="(128, 64, 2, 2)", style=solid]; +"217 dequantize_per_channel_default_19" -> "219 conv_transpose2d_3" [label="(128, 64, 2, 2)", style=solid]; +"218 up_path_3_up_bias_0_0" -> "219 conv_transpose2d_3" [label="(64,)", style=solid]; +"219 conv_transpose2d_3" -> "220 quantize_per_tensor_default_1" [label="(1, 64, 40, 40)", style=solid]; +"220 quantize_per_tensor_default_1" -> "221 dequantize_per_tensor_default_2" [label="(1, 64, 40, 40)", style=solid]; +"221 dequantize_per_tensor_default_2" -> "226 cat_3" [label="(1, 64, 40, 40)", style=solid]; +"222 slice_13" -> "223 slice_14" [label="(1, 64, 220, 220)", style=solid]; +"223 slice_14" -> "224 slice_15" [label="(1, 64, 220, 220)", style=solid]; +"224 slice_15" -> "225 slice_16" [label="(1, 64, 40, 220)", style=solid]; +"225 slice_16" -> "226 cat_3" [label="(1, 64, 40, 40)", style=solid]; +"226 cat_3" -> "233 conv2d_16" [label="(1, 128, 40, 40)", style=solid]; +"227 conv2d_16_scale_0" -> "230 quantize_per_channel_default_20" [label="(64,)", style=solid]; +"227 conv2d_16_scale_0" -> "231 dequantize_per_channel_default_20" [label="(64,)", style=solid]; +"228 conv2d_16_zero_point_0" -> "230 quantize_per_channel_default_20" [label="(64,)", style=solid]; +"228 conv2d_16_zero_point_0" -> "231 dequantize_per_channel_default_20" [label="(64,)", style=solid]; +"229 compressed_weight_updated_constant20" -> "230 quantize_per_channel_default_20" [label="(64, 128, 3, 3)", style=solid]; +"230 quantize_per_channel_default_20" -> "231 dequantize_per_channel_default_20" [label="(64, 128, 3, 3)", style=solid]; +"231 dequantize_per_channel_default_20" -> "233 conv2d_16" [label="(64, 128, 3, 3)", style=solid]; +"232 up_path_3_conv_block_block_0_bias_0_0" -> "233 conv2d_16" [label="(64,)", style=solid]; +"233 conv2d_16" -> "234 relu_16" [label="(1, 64, 38, 38)", style=solid]; +"234 relu_16" -> "235 quantize_per_tensor_default_21" [label="(1, 64, 38, 38)", style=solid]; +"235 quantize_per_tensor_default_21" -> "236 dequantize_per_tensor_default_25" [label="(1, 64, 38, 38)", style=solid]; +"236 dequantize_per_tensor_default_25" -> "243 conv2d_17" [label="(1, 64, 38, 38)", style=solid]; +"237 conv2d_17_scale_0" -> "240 quantize_per_channel_default_21" [label="(64,)", style=solid]; +"237 conv2d_17_scale_0" -> "241 dequantize_per_channel_default_21" [label="(64,)", style=solid]; +"238 conv2d_17_zero_point_0" -> "240 quantize_per_channel_default_21" [label="(64,)", style=solid]; +"238 conv2d_17_zero_point_0" -> "241 dequantize_per_channel_default_21" [label="(64,)", style=solid]; +"239 compressed_weight_updated_constant21" -> "240 quantize_per_channel_default_21" [label="(64, 64, 3, 3)", style=solid]; +"240 quantize_per_channel_default_21" -> "241 dequantize_per_channel_default_21" [label="(64, 64, 3, 3)", style=solid]; +"241 dequantize_per_channel_default_21" -> "243 conv2d_17" [label="(64, 64, 3, 3)", style=solid]; +"242 up_path_3_conv_block_block_3_bias_0_0" -> "243 conv2d_17" [label="(64,)", style=solid]; +"243 conv2d_17" -> "244 relu_17" [label="(1, 64, 36, 36)", style=solid]; +"244 relu_17" -> "245 quantize_per_tensor_default_22" [label="(1, 64, 36, 36)", style=solid]; +"245 quantize_per_tensor_default_22" -> "246 dequantize_per_tensor_default_26" [label="(1, 64, 36, 36)", style=solid]; +"246 dequantize_per_tensor_default_26" -> "253 conv2d_18" [label="(1, 64, 36, 36)", style=solid]; +"247 conv2d_18_scale_0" -> "250 quantize_per_channel_default_22" [label="(12,)", style=solid]; +"247 conv2d_18_scale_0" -> "251 dequantize_per_channel_default_22" [label="(12,)", style=solid]; +"248 conv2d_18_zero_point_0" -> "250 quantize_per_channel_default_22" [label="(12,)", style=solid]; +"248 conv2d_18_zero_point_0" -> "251 dequantize_per_channel_default_22" [label="(12,)", style=solid]; +"249 compressed_weight_updated_constant22" -> "250 quantize_per_channel_default_22" [label="(12, 64, 1, 1)", style=solid]; +"250 quantize_per_channel_default_22" -> "251 dequantize_per_channel_default_22" [label="(12, 64, 1, 1)", style=solid]; +"251 dequantize_per_channel_default_22" -> "253 conv2d_18" [label="(12, 64, 1, 1)", style=solid]; +"252 last_bias_0_0" -> "253 conv2d_18" [label="(12,)", style=solid]; +"253 conv2d_18" -> "254 output" [label="(1, 12, 36, 36)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/quantized/vit_b_16.dot b/tests/torch/data/reference_graphs/fx/quantized/vit_b_16.dot index 340c0e0714a..51f70602ba7 100644 --- a/tests/torch/data/reference_graphs/fx/quantized/vit_b_16.dot +++ b/tests/torch/data/reference_graphs/fx/quantized/vit_b_16.dot @@ -1,2113 +1,2111 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; -"1 quantize_per_tensor_default" [id=1, type=quantize_per_tensor]; -"2 dequantize_per_tensor_default" [id=2, type=dequantize_per_tensor]; -"3 _param_constant0" [id=3, type=get_attr]; -"4 conv2d_scale_0" [id=4, type=get_attr]; -"5 conv2d_zero_point_0" [id=5, type=get_attr]; -"6 quantize_per_channel_default" [id=6, type=quantize_per_channel]; -"7 dequantize_per_channel_default" [id=7, type=dequantize_per_channel]; -"8 _param_constant1_0_0" [id=8, type=get_attr]; -"9 conv2d" [id=9, type=conv2d]; -"10 reshape" [id=10, type=reshape]; -"11 permute" [id=11, type=permute]; -"12 _param_constant2" [id=12, type=get_attr]; -"13 expand" [id=13, type=expand]; -"14 cat" [id=14, type=cat]; -"15 _param_constant3" [id=15, type=get_attr]; -"16 add" [id=16, type=add]; -"17 dropout" [id=17, type=dropout]; -"18 _param_constant4" [id=18, type=get_attr]; -"19 _param_constant5" [id=19, type=get_attr]; -"20 layer_norm" [id=20, type=layer_norm]; -"21 transpose" [id=21, type=transpose]; -"22 linear_updated_constant0" [id=22, type=get_attr]; -"23 transpose_0_0_nncf_smooth_quant_0" [id=23, type=call_module]; -"24 quantize_per_tensor_default_1" [id=24, type=quantize_per_tensor]; -"25 dequantize_per_tensor_default_1" [id=25, type=dequantize_per_tensor]; -"26 linear_scale_0" [id=26, type=get_attr]; -"27 linear_zero_point_0" [id=27, type=get_attr]; -"28 quantize_per_channel_default_1" [id=28, type=quantize_per_channel]; -"29 dequantize_per_channel_default_1" [id=29, type=dequantize_per_channel]; -"30 _param_constant7_0_0" [id=30, type=get_attr]; -"31 linear" [id=31, type=linear]; -"32 unflatten" [id=32, type=unflatten]; -"33 unsqueeze" [id=33, type=unsqueeze]; -"34 transpose_1" [id=34, type=transpose]; -"35 squeeze" [id=35, type=squeeze]; -"36 contiguous" [id=36, type=contiguous]; -"37 quantize_per_tensor_default_2" [id=37, type=quantize_per_tensor]; -"38 dequantize_per_tensor_default_2" [id=38, type=dequantize_per_tensor]; -"39 select" [id=39, type=select]; -"40 quantize_per_tensor_default_3" [id=40, type=quantize_per_tensor]; -"41 dequantize_per_tensor_default_3" [id=41, type=dequantize_per_tensor]; -"42 select_1" [id=42, type=select]; -"43 select_2" [id=43, type=select]; -"44 view" [id=44, type=view]; -"45 transpose_2" [id=45, type=transpose]; -"46 view_1" [id=46, type=view]; -"47 transpose_3" [id=47, type=transpose]; -"48 view_2" [id=48, type=view]; -"49 transpose_4" [id=49, type=transpose]; -"50 view_3" [id=50, type=view]; -"51 view_4" [id=51, type=view]; -"52 view_5" [id=52, type=view]; -"53 scaled_dot_product_attention" [id=53, type=scaled_dot_product_attention]; -"54 permute_1" [id=54, type=permute]; -"55 view_6" [id=55, type=view]; -"56 linear_1_updated_constant0" [id=56, type=get_attr]; -"57 view_6_0_0_nncf_smooth_quant_0" [id=57, type=call_module]; -"58 quantize_per_tensor_default_4" [id=58, type=quantize_per_tensor]; -"59 dequantize_per_tensor_default_4" [id=59, type=dequantize_per_tensor]; -"60 linear_1_scale_0" [id=60, type=get_attr]; -"61 linear_1_zero_point_0" [id=61, type=get_attr]; -"62 quantize_per_channel_default_2" [id=62, type=quantize_per_channel]; -"63 dequantize_per_channel_default_2" [id=63, type=dequantize_per_channel]; -"64 _param_constant9_0_0" [id=64, type=get_attr]; -"65 linear_1" [id=65, type=linear]; -"66 view_7" [id=66, type=view]; -"67 transpose_5" [id=67, type=transpose]; -"68 dropout_1" [id=68, type=dropout]; -"69 add_1" [id=69, type=add]; -"70 _param_constant10" [id=70, type=get_attr]; -"71 _param_constant11" [id=71, type=get_attr]; -"72 layer_norm_1" [id=72, type=layer_norm]; -"73 linear_2_updated_constant0" [id=73, type=get_attr]; -"74 layer_norm_1_0_0_nncf_smooth_quant_0" [id=74, type=call_module]; -"75 quantize_per_tensor_default_5" [id=75, type=quantize_per_tensor]; -"76 dequantize_per_tensor_default_5" [id=76, type=dequantize_per_tensor]; -"77 linear_2_scale_0" [id=77, type=get_attr]; -"78 linear_2_zero_point_0" [id=78, type=get_attr]; -"79 quantize_per_channel_default_3" [id=79, type=quantize_per_channel]; -"80 dequantize_per_channel_default_3" [id=80, type=dequantize_per_channel]; -"81 _param_constant13_0_0" [id=81, type=get_attr]; -"82 linear_2" [id=82, type=linear]; -"83 gelu" [id=83, type=gelu]; -"84 dropout_2" [id=84, type=dropout]; -"85 linear_3_updated_constant0" [id=85, type=get_attr]; -"86 dropout_2_0_0_nncf_smooth_quant_0" [id=86, type=call_module]; -"87 quantize_per_tensor_default_6" [id=87, type=quantize_per_tensor]; -"88 dequantize_per_tensor_default_6" [id=88, type=dequantize_per_tensor]; -"89 linear_3_scale_0" [id=89, type=get_attr]; -"90 linear_3_zero_point_0" [id=90, type=get_attr]; -"91 quantize_per_channel_default_4" [id=91, type=quantize_per_channel]; -"92 dequantize_per_channel_default_4" [id=92, type=dequantize_per_channel]; -"93 _param_constant15_0_0" [id=93, type=get_attr]; -"94 linear_3" [id=94, type=linear]; -"95 dropout_3" [id=95, type=dropout]; -"96 add_2" [id=96, type=add]; -"97 _param_constant16" [id=97, type=get_attr]; -"98 _param_constant17" [id=98, type=get_attr]; -"99 layer_norm_2" [id=99, type=layer_norm]; -"100 transpose_6" [id=100, type=transpose]; -"101 linear_4_updated_constant0" [id=101, type=get_attr]; -"102 transpose_6_0_0_nncf_smooth_quant_0" [id=102, type=call_module]; -"103 quantize_per_tensor_default_7" [id=103, type=quantize_per_tensor]; -"104 dequantize_per_tensor_default_7" [id=104, type=dequantize_per_tensor]; -"105 linear_4_scale_0" [id=105, type=get_attr]; -"106 linear_4_zero_point_0" [id=106, type=get_attr]; -"107 quantize_per_channel_default_5" [id=107, type=quantize_per_channel]; -"108 dequantize_per_channel_default_5" [id=108, type=dequantize_per_channel]; -"109 _param_constant19_0_0" [id=109, type=get_attr]; -"110 linear_4" [id=110, type=linear]; -"111 unflatten_1" [id=111, type=unflatten]; -"112 unsqueeze_1" [id=112, type=unsqueeze]; -"113 transpose_7" [id=113, type=transpose]; -"114 squeeze_1" [id=114, type=squeeze]; -"115 contiguous_1" [id=115, type=contiguous]; -"116 quantize_per_tensor_default_8" [id=116, type=quantize_per_tensor]; -"117 dequantize_per_tensor_default_8" [id=117, type=dequantize_per_tensor]; -"118 select_3" [id=118, type=select]; -"119 quantize_per_tensor_default_9" [id=119, type=quantize_per_tensor]; -"120 dequantize_per_tensor_default_9" [id=120, type=dequantize_per_tensor]; -"121 select_4" [id=121, type=select]; -"122 select_5" [id=122, type=select]; -"123 view_8" [id=123, type=view]; -"124 transpose_8" [id=124, type=transpose]; -"125 view_9" [id=125, type=view]; -"126 transpose_9" [id=126, type=transpose]; -"127 view_10" [id=127, type=view]; -"128 transpose_10" [id=128, type=transpose]; -"129 view_11" [id=129, type=view]; -"130 view_12" [id=130, type=view]; -"131 view_13" [id=131, type=view]; -"132 scaled_dot_product_attention_1" [id=132, type=scaled_dot_product_attention]; -"133 permute_2" [id=133, type=permute]; -"134 view_14" [id=134, type=view]; -"135 linear_5_updated_constant0" [id=135, type=get_attr]; -"136 view_14_0_0_nncf_smooth_quant_0" [id=136, type=call_module]; -"137 quantize_per_tensor_default_10" [id=137, type=quantize_per_tensor]; -"138 dequantize_per_tensor_default_10" [id=138, type=dequantize_per_tensor]; -"139 linear_5_scale_0" [id=139, type=get_attr]; -"140 linear_5_zero_point_0" [id=140, type=get_attr]; -"141 quantize_per_channel_default_6" [id=141, type=quantize_per_channel]; -"142 dequantize_per_channel_default_6" [id=142, type=dequantize_per_channel]; -"143 _param_constant21_0_0" [id=143, type=get_attr]; -"144 linear_5" [id=144, type=linear]; -"145 view_15" [id=145, type=view]; -"146 transpose_11" [id=146, type=transpose]; -"147 dropout_4" [id=147, type=dropout]; -"148 add_3" [id=148, type=add]; -"149 _param_constant22" [id=149, type=get_attr]; -"150 _param_constant23" [id=150, type=get_attr]; -"151 layer_norm_3" [id=151, type=layer_norm]; -"152 linear_6_updated_constant0" [id=152, type=get_attr]; -"153 layer_norm_3_0_0_nncf_smooth_quant_0" [id=153, type=call_module]; -"154 quantize_per_tensor_default_11" [id=154, type=quantize_per_tensor]; -"155 dequantize_per_tensor_default_11" [id=155, type=dequantize_per_tensor]; -"156 linear_6_scale_0" [id=156, type=get_attr]; -"157 linear_6_zero_point_0" [id=157, type=get_attr]; -"158 quantize_per_channel_default_7" [id=158, type=quantize_per_channel]; -"159 dequantize_per_channel_default_7" [id=159, type=dequantize_per_channel]; -"160 _param_constant25_0_0" [id=160, type=get_attr]; -"161 linear_6" [id=161, type=linear]; -"162 gelu_1" [id=162, type=gelu]; -"163 dropout_5" [id=163, type=dropout]; -"164 linear_7_updated_constant0" [id=164, type=get_attr]; -"165 dropout_5_0_0_nncf_smooth_quant_0" [id=165, type=call_module]; -"166 quantize_per_tensor_default_12" [id=166, type=quantize_per_tensor]; -"167 dequantize_per_tensor_default_12" [id=167, type=dequantize_per_tensor]; -"168 linear_7_scale_0" [id=168, type=get_attr]; -"169 linear_7_zero_point_0" [id=169, type=get_attr]; -"170 quantize_per_channel_default_8" [id=170, type=quantize_per_channel]; -"171 dequantize_per_channel_default_8" [id=171, type=dequantize_per_channel]; -"172 _param_constant27_0_0" [id=172, type=get_attr]; -"173 linear_7" [id=173, type=linear]; -"174 dropout_6" [id=174, type=dropout]; -"175 add_4" [id=175, type=add]; -"176 _param_constant28" [id=176, type=get_attr]; -"177 _param_constant29" [id=177, type=get_attr]; -"178 layer_norm_4" [id=178, type=layer_norm]; -"179 transpose_12" [id=179, type=transpose]; -"180 linear_8_updated_constant0" [id=180, type=get_attr]; -"181 transpose_12_0_0_nncf_smooth_quant_0" [id=181, type=call_module]; -"182 quantize_per_tensor_default_13" [id=182, type=quantize_per_tensor]; -"183 dequantize_per_tensor_default_13" [id=183, type=dequantize_per_tensor]; -"184 linear_8_scale_0" [id=184, type=get_attr]; -"185 linear_8_zero_point_0" [id=185, type=get_attr]; -"186 quantize_per_channel_default_9" [id=186, type=quantize_per_channel]; -"187 dequantize_per_channel_default_9" [id=187, type=dequantize_per_channel]; -"188 _param_constant31_0_0" [id=188, type=get_attr]; -"189 linear_8" [id=189, type=linear]; -"190 unflatten_2" [id=190, type=unflatten]; -"191 unsqueeze_2" [id=191, type=unsqueeze]; -"192 transpose_13" [id=192, type=transpose]; -"193 squeeze_2" [id=193, type=squeeze]; -"194 contiguous_2" [id=194, type=contiguous]; -"195 quantize_per_tensor_default_14" [id=195, type=quantize_per_tensor]; -"196 dequantize_per_tensor_default_14" [id=196, type=dequantize_per_tensor]; -"197 select_6" [id=197, type=select]; -"198 quantize_per_tensor_default_15" [id=198, type=quantize_per_tensor]; -"199 dequantize_per_tensor_default_15" [id=199, type=dequantize_per_tensor]; -"200 select_7" [id=200, type=select]; -"201 select_8" [id=201, type=select]; -"202 view_16" [id=202, type=view]; -"203 transpose_14" [id=203, type=transpose]; -"204 view_17" [id=204, type=view]; -"205 transpose_15" [id=205, type=transpose]; -"206 view_18" [id=206, type=view]; -"207 transpose_16" [id=207, type=transpose]; -"208 view_19" [id=208, type=view]; -"209 view_20" [id=209, type=view]; -"210 view_21" [id=210, type=view]; -"211 scaled_dot_product_attention_2" [id=211, type=scaled_dot_product_attention]; -"212 permute_3" [id=212, type=permute]; -"213 view_22" [id=213, type=view]; -"214 linear_9_updated_constant0" [id=214, type=get_attr]; -"215 view_22_0_0_nncf_smooth_quant_0" [id=215, type=call_module]; -"216 quantize_per_tensor_default_16" [id=216, type=quantize_per_tensor]; -"217 dequantize_per_tensor_default_16" [id=217, type=dequantize_per_tensor]; -"218 linear_9_scale_0" [id=218, type=get_attr]; -"219 linear_9_zero_point_0" [id=219, type=get_attr]; -"220 quantize_per_channel_default_10" [id=220, type=quantize_per_channel]; -"221 dequantize_per_channel_default_10" [id=221, type=dequantize_per_channel]; -"222 _param_constant33_0_0" [id=222, type=get_attr]; -"223 linear_9" [id=223, type=linear]; -"224 view_23" [id=224, type=view]; -"225 transpose_17" [id=225, type=transpose]; -"226 dropout_7" [id=226, type=dropout]; -"227 add_5" [id=227, type=add]; -"228 _param_constant34" [id=228, type=get_attr]; -"229 _param_constant35" [id=229, type=get_attr]; -"230 layer_norm_5" [id=230, type=layer_norm]; -"231 linear_10_updated_constant0" [id=231, type=get_attr]; -"232 layer_norm_5_0_0_nncf_smooth_quant_0" [id=232, type=call_module]; -"233 quantize_per_tensor_default_17" [id=233, type=quantize_per_tensor]; -"234 dequantize_per_tensor_default_17" [id=234, type=dequantize_per_tensor]; -"235 linear_10_scale_0" [id=235, type=get_attr]; -"236 linear_10_zero_point_0" [id=236, type=get_attr]; -"237 quantize_per_channel_default_11" [id=237, type=quantize_per_channel]; -"238 dequantize_per_channel_default_11" [id=238, type=dequantize_per_channel]; -"239 _param_constant37_0_0" [id=239, type=get_attr]; -"240 linear_10" [id=240, type=linear]; -"241 gelu_2" [id=241, type=gelu]; -"242 dropout_8" [id=242, type=dropout]; -"243 linear_11_updated_constant0" [id=243, type=get_attr]; -"244 dropout_8_0_0_nncf_smooth_quant_0" [id=244, type=call_module]; -"245 quantize_per_tensor_default_18" [id=245, type=quantize_per_tensor]; -"246 dequantize_per_tensor_default_18" [id=246, type=dequantize_per_tensor]; -"247 linear_11_scale_0" [id=247, type=get_attr]; -"248 linear_11_zero_point_0" [id=248, type=get_attr]; -"249 quantize_per_channel_default_12" [id=249, type=quantize_per_channel]; -"250 dequantize_per_channel_default_12" [id=250, type=dequantize_per_channel]; -"251 _param_constant39_0_0" [id=251, type=get_attr]; -"252 linear_11" [id=252, type=linear]; -"253 dropout_9" [id=253, type=dropout]; -"254 add_6" [id=254, type=add]; -"255 _param_constant40" [id=255, type=get_attr]; -"256 _param_constant41" [id=256, type=get_attr]; -"257 layer_norm_6" [id=257, type=layer_norm]; -"258 transpose_18" [id=258, type=transpose]; -"259 linear_12_updated_constant0" [id=259, type=get_attr]; -"260 transpose_18_0_0_nncf_smooth_quant_0" [id=260, type=call_module]; -"261 quantize_per_tensor_default_19" [id=261, type=quantize_per_tensor]; -"262 dequantize_per_tensor_default_19" [id=262, type=dequantize_per_tensor]; -"263 linear_12_scale_0" [id=263, type=get_attr]; -"264 linear_12_zero_point_0" [id=264, type=get_attr]; -"265 quantize_per_channel_default_13" [id=265, type=quantize_per_channel]; -"266 dequantize_per_channel_default_13" [id=266, type=dequantize_per_channel]; -"267 _param_constant43_0_0" [id=267, type=get_attr]; -"268 linear_12" [id=268, type=linear]; -"269 unflatten_3" [id=269, type=unflatten]; -"270 unsqueeze_3" [id=270, type=unsqueeze]; -"271 transpose_19" [id=271, type=transpose]; -"272 squeeze_3" [id=272, type=squeeze]; -"273 contiguous_3" [id=273, type=contiguous]; -"274 quantize_per_tensor_default_20" [id=274, type=quantize_per_tensor]; -"275 dequantize_per_tensor_default_20" [id=275, type=dequantize_per_tensor]; -"276 select_9" [id=276, type=select]; -"277 quantize_per_tensor_default_21" [id=277, type=quantize_per_tensor]; -"278 dequantize_per_tensor_default_21" [id=278, type=dequantize_per_tensor]; -"279 select_10" [id=279, type=select]; -"280 select_11" [id=280, type=select]; 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dequantize_per_channel_default_14" [id=300, type=dequantize_per_channel]; -"301 _param_constant45_0_0" [id=301, type=get_attr]; -"302 linear_13" [id=302, type=linear]; -"303 view_31" [id=303, type=view]; -"304 transpose_23" [id=304, type=transpose]; -"305 dropout_10" [id=305, type=dropout]; -"306 add_7" [id=306, type=add]; -"307 _param_constant46" [id=307, type=get_attr]; -"308 _param_constant47" [id=308, type=get_attr]; -"309 layer_norm_7" [id=309, type=layer_norm]; -"310 linear_14_updated_constant0" [id=310, type=get_attr]; -"311 layer_norm_7_0_0_nncf_smooth_quant_0" [id=311, type=call_module]; -"312 quantize_per_tensor_default_23" [id=312, type=quantize_per_tensor]; -"313 dequantize_per_tensor_default_23" [id=313, type=dequantize_per_tensor]; -"314 linear_14_scale_0" [id=314, type=get_attr]; -"315 linear_14_zero_point_0" [id=315, type=get_attr]; -"316 quantize_per_channel_default_15" [id=316, type=quantize_per_channel]; -"317 dequantize_per_channel_default_15" [id=317, type=dequantize_per_channel]; -"318 _param_constant49_0_0" [id=318, type=get_attr]; -"319 linear_14" [id=319, type=linear]; -"320 gelu_3" [id=320, type=gelu]; -"321 dropout_11" [id=321, type=dropout]; -"322 linear_15_updated_constant0" [id=322, type=get_attr]; -"323 dropout_11_0_0_nncf_smooth_quant_0" [id=323, type=call_module]; -"324 quantize_per_tensor_default_24" [id=324, type=quantize_per_tensor]; -"325 dequantize_per_tensor_default_24" [id=325, type=dequantize_per_tensor]; -"326 linear_15_scale_0" [id=326, type=get_attr]; -"327 linear_15_zero_point_0" [id=327, type=get_attr]; -"328 quantize_per_channel_default_16" [id=328, type=quantize_per_channel]; -"329 dequantize_per_channel_default_16" [id=329, type=dequantize_per_channel]; -"330 _param_constant51_0_0" [id=330, type=get_attr]; -"331 linear_15" [id=331, type=linear]; -"332 dropout_12" [id=332, type=dropout]; -"333 add_8" [id=333, type=add]; -"334 _param_constant52" [id=334, type=get_attr]; -"335 _param_constant53" [id=335, type=get_attr]; -"336 layer_norm_8" [id=336, type=layer_norm]; -"337 transpose_24" [id=337, type=transpose]; -"338 linear_16_updated_constant0" [id=338, type=get_attr]; -"339 transpose_24_0_0_nncf_smooth_quant_0" [id=339, type=call_module]; -"340 quantize_per_tensor_default_25" [id=340, type=quantize_per_tensor]; -"341 dequantize_per_tensor_default_25" [id=341, type=dequantize_per_tensor]; -"342 linear_16_scale_0" [id=342, type=get_attr]; -"343 linear_16_zero_point_0" [id=343, type=get_attr]; -"344 quantize_per_channel_default_17" [id=344, type=quantize_per_channel]; -"345 dequantize_per_channel_default_17" [id=345, type=dequantize_per_channel]; -"346 _param_constant55_0_0" [id=346, type=get_attr]; -"347 linear_16" [id=347, type=linear]; -"348 unflatten_4" [id=348, type=unflatten]; -"349 unsqueeze_4" [id=349, type=unsqueeze]; -"350 transpose_25" [id=350, type=transpose]; -"351 squeeze_4" [id=351, type=squeeze]; -"352 contiguous_4" [id=352, type=contiguous]; -"353 quantize_per_tensor_default_26" [id=353, type=quantize_per_tensor]; -"354 dequantize_per_tensor_default_26" [id=354, type=dequantize_per_tensor]; -"355 select_12" [id=355, type=select]; -"356 quantize_per_tensor_default_27" [id=356, type=quantize_per_tensor]; -"357 dequantize_per_tensor_default_27" [id=357, type=dequantize_per_tensor]; -"358 select_13" [id=358, type=select]; -"359 select_14" [id=359, type=select]; -"360 view_32" [id=360, type=view]; -"361 transpose_26" [id=361, type=transpose]; -"362 view_33" [id=362, type=view]; -"363 transpose_27" [id=363, type=transpose]; -"364 view_34" [id=364, type=view]; -"365 transpose_28" [id=365, type=transpose]; -"366 view_35" [id=366, type=view]; -"367 view_36" [id=367, type=view]; -"368 view_37" [id=368, type=view]; -"369 scaled_dot_product_attention_4" [id=369, type=scaled_dot_product_attention]; -"370 permute_5" [id=370, type=permute]; -"371 view_38" [id=371, type=view]; -"372 linear_17_updated_constant0" [id=372, type=get_attr]; -"373 view_38_0_0_nncf_smooth_quant_0" [id=373, type=call_module]; -"374 quantize_per_tensor_default_28" [id=374, type=quantize_per_tensor]; -"375 dequantize_per_tensor_default_28" [id=375, type=dequantize_per_tensor]; -"376 linear_17_scale_0" [id=376, type=get_attr]; -"377 linear_17_zero_point_0" [id=377, type=get_attr]; -"378 quantize_per_channel_default_18" [id=378, type=quantize_per_channel]; -"379 dequantize_per_channel_default_18" [id=379, type=dequantize_per_channel]; -"380 _param_constant57_0_0" [id=380, type=get_attr]; -"381 linear_17" [id=381, type=linear]; -"382 view_39" [id=382, type=view]; -"383 transpose_29" [id=383, type=transpose]; -"384 dropout_13" [id=384, type=dropout]; -"385 add_9" [id=385, type=add]; -"386 _param_constant58" [id=386, type=get_attr]; -"387 _param_constant59" [id=387, type=get_attr]; -"388 layer_norm_9" [id=388, type=layer_norm]; -"389 linear_18_updated_constant0" [id=389, type=get_attr]; -"390 layer_norm_9_0_0_nncf_smooth_quant_0" [id=390, type=call_module]; -"391 quantize_per_tensor_default_29" [id=391, type=quantize_per_tensor]; -"392 dequantize_per_tensor_default_29" [id=392, type=dequantize_per_tensor]; -"393 linear_18_scale_0" [id=393, type=get_attr]; -"394 linear_18_zero_point_0" [id=394, type=get_attr]; -"395 quantize_per_channel_default_19" [id=395, type=quantize_per_channel]; -"396 dequantize_per_channel_default_19" [id=396, type=dequantize_per_channel]; -"397 _param_constant61_0_0" [id=397, type=get_attr]; -"398 linear_18" [id=398, type=linear]; -"399 gelu_4" [id=399, type=gelu]; -"400 dropout_14" [id=400, type=dropout]; -"401 linear_19_updated_constant0" [id=401, type=get_attr]; -"402 dropout_14_0_0_nncf_smooth_quant_0" [id=402, type=call_module]; -"403 quantize_per_tensor_default_30" [id=403, type=quantize_per_tensor]; -"404 dequantize_per_tensor_default_30" [id=404, type=dequantize_per_tensor]; -"405 linear_19_scale_0" [id=405, type=get_attr]; -"406 linear_19_zero_point_0" [id=406, type=get_attr]; -"407 quantize_per_channel_default_20" [id=407, type=quantize_per_channel]; -"408 dequantize_per_channel_default_20" [id=408, type=dequantize_per_channel]; -"409 _param_constant63_0_0" [id=409, type=get_attr]; -"410 linear_19" [id=410, type=linear]; -"411 dropout_15" [id=411, type=dropout]; -"412 add_10" [id=412, type=add]; -"413 _param_constant64" [id=413, type=get_attr]; -"414 _param_constant65" [id=414, type=get_attr]; -"415 layer_norm_10" [id=415, type=layer_norm]; -"416 transpose_30" [id=416, type=transpose]; -"417 linear_20_updated_constant0" [id=417, type=get_attr]; -"418 transpose_30_0_0_nncf_smooth_quant_0" [id=418, type=call_module]; -"419 quantize_per_tensor_default_31" [id=419, type=quantize_per_tensor]; -"420 dequantize_per_tensor_default_31" [id=420, type=dequantize_per_tensor]; -"421 linear_20_scale_0" [id=421, type=get_attr]; -"422 linear_20_zero_point_0" [id=422, type=get_attr]; -"423 quantize_per_channel_default_21" [id=423, type=quantize_per_channel]; -"424 dequantize_per_channel_default_21" [id=424, type=dequantize_per_channel]; -"425 _param_constant67_0_0" [id=425, type=get_attr]; -"426 linear_20" [id=426, type=linear]; -"427 unflatten_5" [id=427, type=unflatten]; -"428 unsqueeze_5" [id=428, type=unsqueeze]; -"429 transpose_31" [id=429, type=transpose]; -"430 squeeze_5" [id=430, type=squeeze]; -"431 contiguous_5" [id=431, type=contiguous]; -"432 quantize_per_tensor_default_32" [id=432, type=quantize_per_tensor]; -"433 dequantize_per_tensor_default_32" [id=433, type=dequantize_per_tensor]; -"434 select_15" [id=434, type=select]; -"435 quantize_per_tensor_default_33" [id=435, type=quantize_per_tensor]; -"436 dequantize_per_tensor_default_33" [id=436, type=dequantize_per_tensor]; -"437 select_16" [id=437, type=select]; -"438 select_17" [id=438, type=select]; -"439 view_40" [id=439, type=view]; -"440 transpose_32" [id=440, type=transpose]; -"441 view_41" [id=441, type=view]; -"442 transpose_33" [id=442, type=transpose]; -"443 view_42" [id=443, type=view]; -"444 transpose_34" [id=444, type=transpose]; -"445 view_43" [id=445, type=view]; -"446 view_44" [id=446, type=view]; -"447 view_45" [id=447, type=view]; -"448 scaled_dot_product_attention_5" [id=448, type=scaled_dot_product_attention]; -"449 permute_6" [id=449, type=permute]; -"450 view_46" [id=450, type=view]; -"451 linear_21_updated_constant0" [id=451, type=get_attr]; -"452 view_46_0_0_nncf_smooth_quant_0" [id=452, type=call_module]; -"453 quantize_per_tensor_default_34" [id=453, type=quantize_per_tensor]; -"454 dequantize_per_tensor_default_34" [id=454, type=dequantize_per_tensor]; -"455 linear_21_scale_0" [id=455, type=get_attr]; -"456 linear_21_zero_point_0" [id=456, type=get_attr]; -"457 quantize_per_channel_default_22" [id=457, type=quantize_per_channel]; -"458 dequantize_per_channel_default_22" [id=458, type=dequantize_per_channel]; -"459 _param_constant69_0_0" [id=459, type=get_attr]; -"460 linear_21" [id=460, type=linear]; -"461 view_47" [id=461, type=view]; -"462 transpose_35" [id=462, type=transpose]; -"463 dropout_16" [id=463, type=dropout]; -"464 add_11" [id=464, type=add]; -"465 _param_constant70" [id=465, type=get_attr]; -"466 _param_constant71" [id=466, type=get_attr]; -"467 layer_norm_11" [id=467, type=layer_norm]; -"468 linear_22_updated_constant0" [id=468, type=get_attr]; -"469 layer_norm_11_0_0_nncf_smooth_quant_0" [id=469, type=call_module]; -"470 quantize_per_tensor_default_35" [id=470, type=quantize_per_tensor]; -"471 dequantize_per_tensor_default_35" [id=471, type=dequantize_per_tensor]; -"472 linear_22_scale_0" [id=472, type=get_attr]; -"473 linear_22_zero_point_0" [id=473, type=get_attr]; -"474 quantize_per_channel_default_23" [id=474, type=quantize_per_channel]; -"475 dequantize_per_channel_default_23" [id=475, type=dequantize_per_channel]; -"476 _param_constant73_0_0" [id=476, type=get_attr]; -"477 linear_22" [id=477, type=linear]; -"478 gelu_5" [id=478, type=gelu]; -"479 dropout_17" [id=479, type=dropout]; -"480 linear_23_updated_constant0" [id=480, type=get_attr]; -"481 dropout_17_0_0_nncf_smooth_quant_0" [id=481, type=call_module]; -"482 quantize_per_tensor_default_36" [id=482, type=quantize_per_tensor]; -"483 dequantize_per_tensor_default_36" [id=483, type=dequantize_per_tensor]; -"484 linear_23_scale_0" [id=484, type=get_attr]; -"485 linear_23_zero_point_0" [id=485, type=get_attr]; -"486 quantize_per_channel_default_24" [id=486, type=quantize_per_channel]; -"487 dequantize_per_channel_default_24" [id=487, type=dequantize_per_channel]; -"488 _param_constant75_0_0" [id=488, type=get_attr]; -"489 linear_23" [id=489, type=linear]; -"490 dropout_18" [id=490, type=dropout]; -"491 add_12" [id=491, type=add]; -"492 _param_constant76" [id=492, type=get_attr]; -"493 _param_constant77" [id=493, type=get_attr]; -"494 layer_norm_12" [id=494, type=layer_norm]; -"495 transpose_36" [id=495, type=transpose]; -"496 linear_24_updated_constant0" [id=496, type=get_attr]; -"497 transpose_36_0_0_nncf_smooth_quant_0" [id=497, type=call_module]; -"498 quantize_per_tensor_default_37" [id=498, type=quantize_per_tensor]; -"499 dequantize_per_tensor_default_37" [id=499, type=dequantize_per_tensor]; -"500 linear_24_scale_0" [id=500, type=get_attr]; -"501 linear_24_zero_point_0" [id=501, type=get_attr]; -"502 quantize_per_channel_default_25" [id=502, type=quantize_per_channel]; -"503 dequantize_per_channel_default_25" [id=503, type=dequantize_per_channel]; -"504 _param_constant79_0_0" [id=504, type=get_attr]; -"505 linear_24" [id=505, type=linear]; -"506 unflatten_6" [id=506, type=unflatten]; -"507 unsqueeze_6" [id=507, type=unsqueeze]; -"508 transpose_37" [id=508, type=transpose]; -"509 squeeze_6" [id=509, type=squeeze]; -"510 contiguous_6" [id=510, type=contiguous]; -"511 quantize_per_tensor_default_38" [id=511, type=quantize_per_tensor]; -"512 dequantize_per_tensor_default_38" [id=512, type=dequantize_per_tensor]; -"513 select_18" [id=513, type=select]; -"514 quantize_per_tensor_default_39" [id=514, type=quantize_per_tensor]; -"515 dequantize_per_tensor_default_39" [id=515, type=dequantize_per_tensor]; -"516 select_19" [id=516, type=select]; -"517 select_20" [id=517, type=select]; -"518 view_48" [id=518, type=view]; -"519 transpose_38" [id=519, type=transpose]; -"520 view_49" [id=520, type=view]; -"521 transpose_39" [id=521, type=transpose]; -"522 view_50" [id=522, type=view]; -"523 transpose_40" [id=523, type=transpose]; -"524 view_51" [id=524, type=view]; -"525 view_52" [id=525, type=view]; -"526 view_53" [id=526, type=view]; -"527 scaled_dot_product_attention_6" [id=527, type=scaled_dot_product_attention]; -"528 permute_7" [id=528, type=permute]; -"529 view_54" [id=529, type=view]; -"530 linear_25_updated_constant0" [id=530, type=get_attr]; -"531 view_54_0_0_nncf_smooth_quant_0" [id=531, type=call_module]; -"532 quantize_per_tensor_default_40" [id=532, type=quantize_per_tensor]; -"533 dequantize_per_tensor_default_40" [id=533, type=dequantize_per_tensor]; -"534 linear_25_scale_0" [id=534, type=get_attr]; -"535 linear_25_zero_point_0" [id=535, type=get_attr]; -"536 quantize_per_channel_default_26" [id=536, type=quantize_per_channel]; -"537 dequantize_per_channel_default_26" [id=537, type=dequantize_per_channel]; -"538 _param_constant81_0_0" [id=538, type=get_attr]; -"539 linear_25" [id=539, type=linear]; -"540 view_55" [id=540, type=view]; -"541 transpose_41" [id=541, type=transpose]; -"542 dropout_19" [id=542, type=dropout]; -"543 add_13" [id=543, type=add]; -"544 _param_constant82" [id=544, type=get_attr]; -"545 _param_constant83" [id=545, type=get_attr]; -"546 layer_norm_13" [id=546, type=layer_norm]; -"547 linear_26_updated_constant0" [id=547, type=get_attr]; -"548 layer_norm_13_0_0_nncf_smooth_quant_0" [id=548, type=call_module]; -"549 quantize_per_tensor_default_41" [id=549, type=quantize_per_tensor]; -"550 dequantize_per_tensor_default_41" [id=550, type=dequantize_per_tensor]; -"551 linear_26_scale_0" 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type=quantize_per_tensor]; +"238 dequantize_per_tensor_default_15" [id=238, type=dequantize_per_tensor]; +"239 select_7" [id=239, type=select]; +"240 select_8" [id=240, type=select]; +"241 view_16" [id=241, type=view]; +"242 transpose_14" [id=242, type=transpose]; +"243 view_17" [id=243, type=view]; +"244 transpose_15" [id=244, type=transpose]; +"245 view_18" [id=245, type=view]; +"246 transpose_16" [id=246, type=transpose]; +"247 view_19" [id=247, type=view]; +"248 view_20" [id=248, type=view]; +"249 view_21" [id=249, type=view]; +"250 scaled_dot_product_attention_2" [id=250, type=scaled_dot_product_attention]; +"251 permute_3" [id=251, type=permute]; +"252 view_22" [id=252, type=view]; +"253 view_22_0_0_nncf_smooth_quant_0" [id=253, type=call_module]; +"254 quantize_per_tensor_default_16" [id=254, type=quantize_per_tensor]; +"255 dequantize_per_tensor_default_16" [id=255, type=dequantize_per_tensor]; +"256 linear_9_scale_0" [id=256, type=get_attr]; +"257 linear_9_zero_point_0" 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quantize_per_channel_default_11" [id=274, type=quantize_per_channel]; +"275 dequantize_per_channel_default_11" [id=275, type=dequantize_per_channel]; +"276 encoder_layers_encoder_layer_2_mlp_0_bias_0_0" [id=276, type=get_attr]; +"277 linear_10" [id=277, type=linear]; +"278 gelu_2" [id=278, type=gelu]; +"279 dropout_8" [id=279, type=dropout]; +"280 dropout_8_0_0_nncf_smooth_quant_0" [id=280, type=call_module]; +"281 quantize_per_tensor_default_18" [id=281, type=quantize_per_tensor]; +"282 dequantize_per_tensor_default_18" [id=282, type=dequantize_per_tensor]; +"283 linear_11_scale_0" [id=283, type=get_attr]; +"284 linear_11_zero_point_0" [id=284, type=get_attr]; +"285 compressed_weight_updated_constant12" [id=285, type=get_attr]; +"286 quantize_per_channel_default_12" [id=286, type=quantize_per_channel]; +"287 dequantize_per_channel_default_12" [id=287, type=dequantize_per_channel]; +"288 encoder_layers_encoder_layer_2_mlp_3_bias_0_0" [id=288, type=get_attr]; +"289 linear_11" [id=289, type=linear]; +"290 dropout_9" [id=290, type=dropout]; +"291 add_6" [id=291, type=add]; +"292 layer_norm_6" [id=292, type=layer_norm]; +"293 transpose_18" [id=293, type=transpose]; +"294 transpose_18_0_0_nncf_smooth_quant_0" [id=294, type=call_module]; +"295 quantize_per_tensor_default_19" [id=295, type=quantize_per_tensor]; +"296 dequantize_per_tensor_default_19" [id=296, type=dequantize_per_tensor]; +"297 linear_12_scale_0" [id=297, type=get_attr]; +"298 linear_12_zero_point_0" [id=298, type=get_attr]; +"299 compressed_weight_updated_constant13" [id=299, type=get_attr]; +"300 quantize_per_channel_default_13" [id=300, type=quantize_per_channel]; +"301 dequantize_per_channel_default_13" [id=301, type=dequantize_per_channel]; +"302 encoder_layers_encoder_layer_3_self_attention_in_proj_bias_0_0" [id=302, type=get_attr]; +"303 linear_12" [id=303, type=linear]; +"304 unflatten_3" [id=304, type=unflatten]; +"305 unsqueeze_3" [id=305, type=unsqueeze]; +"306 transpose_19" [id=306, 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type=permute]; +"327 view_30" [id=327, type=view]; +"328 view_30_0_0_nncf_smooth_quant_0" [id=328, type=call_module]; +"329 quantize_per_tensor_default_22" [id=329, type=quantize_per_tensor]; +"330 dequantize_per_tensor_default_22" [id=330, type=dequantize_per_tensor]; +"331 linear_13_scale_0" [id=331, type=get_attr]; +"332 linear_13_zero_point_0" [id=332, type=get_attr]; +"333 compressed_weight_updated_constant14" [id=333, type=get_attr]; +"334 quantize_per_channel_default_14" [id=334, type=quantize_per_channel]; +"335 dequantize_per_channel_default_14" [id=335, type=dequantize_per_channel]; +"336 encoder_layers_encoder_layer_3_self_attention_out_proj_bias_0_0" [id=336, type=get_attr]; +"337 linear_13" [id=337, type=linear]; +"338 view_31" [id=338, type=view]; +"339 transpose_23" [id=339, type=transpose]; +"340 dropout_10" [id=340, type=dropout]; +"341 add_7" [id=341, type=add]; +"342 layer_norm_7" [id=342, type=layer_norm]; +"343 layer_norm_7_0_0_nncf_smooth_quant_0" [id=343, type=call_module]; +"344 quantize_per_tensor_default_23" [id=344, type=quantize_per_tensor]; +"345 dequantize_per_tensor_default_23" [id=345, type=dequantize_per_tensor]; +"346 linear_14_scale_0" [id=346, type=get_attr]; +"347 linear_14_zero_point_0" [id=347, type=get_attr]; +"348 compressed_weight_updated_constant15" [id=348, type=get_attr]; +"349 quantize_per_channel_default_15" [id=349, type=quantize_per_channel]; +"350 dequantize_per_channel_default_15" [id=350, type=dequantize_per_channel]; +"351 encoder_layers_encoder_layer_3_mlp_0_bias_0_0" [id=351, type=get_attr]; +"352 linear_14" [id=352, type=linear]; +"353 gelu_3" [id=353, type=gelu]; +"354 dropout_11" [id=354, type=dropout]; +"355 dropout_11_0_0_nncf_smooth_quant_0" [id=355, type=call_module]; +"356 quantize_per_tensor_default_24" [id=356, type=quantize_per_tensor]; +"357 dequantize_per_tensor_default_24" [id=357, type=dequantize_per_tensor]; +"358 linear_15_scale_0" [id=358, type=get_attr]; +"359 linear_15_zero_point_0" 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type=quantize_per_channel]; +"376 dequantize_per_channel_default_17" [id=376, type=dequantize_per_channel]; +"377 encoder_layers_encoder_layer_4_self_attention_in_proj_bias_0_0" [id=377, type=get_attr]; +"378 linear_16" [id=378, type=linear]; +"379 unflatten_4" [id=379, type=unflatten]; +"380 unsqueeze_4" [id=380, type=unsqueeze]; +"381 transpose_25" [id=381, type=transpose]; +"382 squeeze_4" [id=382, type=squeeze]; +"383 contiguous_4" [id=383, type=contiguous]; +"384 quantize_per_tensor_default_26" [id=384, type=quantize_per_tensor]; +"385 dequantize_per_tensor_default_26" [id=385, type=dequantize_per_tensor]; +"386 select_12" [id=386, type=select]; +"387 quantize_per_tensor_default_27" [id=387, type=quantize_per_tensor]; +"388 dequantize_per_tensor_default_27" [id=388, type=dequantize_per_tensor]; +"389 select_13" [id=389, type=select]; +"390 select_14" [id=390, type=select]; +"391 view_32" [id=391, type=view]; +"392 transpose_26" [id=392, type=transpose]; +"393 view_33" [id=393, 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type=linear]; +"428 gelu_4" [id=428, type=gelu]; +"429 dropout_14" [id=429, type=dropout]; +"430 dropout_14_0_0_nncf_smooth_quant_0" [id=430, type=call_module]; +"431 quantize_per_tensor_default_30" [id=431, type=quantize_per_tensor]; +"432 dequantize_per_tensor_default_30" [id=432, type=dequantize_per_tensor]; +"433 linear_19_scale_0" [id=433, type=get_attr]; +"434 linear_19_zero_point_0" [id=434, type=get_attr]; +"435 compressed_weight_updated_constant20" [id=435, type=get_attr]; +"436 quantize_per_channel_default_20" [id=436, type=quantize_per_channel]; +"437 dequantize_per_channel_default_20" [id=437, type=dequantize_per_channel]; +"438 encoder_layers_encoder_layer_4_mlp_3_bias_0_0" [id=438, type=get_attr]; +"439 linear_19" [id=439, type=linear]; +"440 dropout_15" [id=440, type=dropout]; +"441 add_10" [id=441, type=add]; +"442 layer_norm_10" [id=442, type=layer_norm]; +"443 transpose_30" [id=443, type=transpose]; +"444 transpose_30_0_0_nncf_smooth_quant_0" [id=444, type=call_module]; +"445 quantize_per_tensor_default_31" [id=445, type=quantize_per_tensor]; +"446 dequantize_per_tensor_default_31" [id=446, type=dequantize_per_tensor]; +"447 linear_20_scale_0" [id=447, type=get_attr]; +"448 linear_20_zero_point_0" [id=448, type=get_attr]; +"449 compressed_weight_updated_constant21" [id=449, type=get_attr]; +"450 quantize_per_channel_default_21" [id=450, type=quantize_per_channel]; +"451 dequantize_per_channel_default_21" [id=451, type=dequantize_per_channel]; +"452 encoder_layers_encoder_layer_5_self_attention_in_proj_bias_0_0" [id=452, type=get_attr]; +"453 linear_20" [id=453, type=linear]; +"454 unflatten_5" [id=454, type=unflatten]; +"455 unsqueeze_5" [id=455, type=unsqueeze]; +"456 transpose_31" [id=456, type=transpose]; +"457 squeeze_5" [id=457, type=squeeze]; +"458 contiguous_5" [id=458, type=contiguous]; +"459 quantize_per_tensor_default_32" [id=459, type=quantize_per_tensor]; +"460 dequantize_per_tensor_default_32" [id=460, type=dequantize_per_tensor]; +"461 select_15" [id=461, type=select]; +"462 quantize_per_tensor_default_33" [id=462, type=quantize_per_tensor]; +"463 dequantize_per_tensor_default_33" [id=463, type=dequantize_per_tensor]; +"464 select_16" [id=464, type=select]; +"465 select_17" [id=465, type=select]; +"466 view_40" [id=466, type=view]; +"467 transpose_32" [id=467, type=transpose]; +"468 view_41" [id=468, type=view]; +"469 transpose_33" [id=469, type=transpose]; +"470 view_42" [id=470, type=view]; +"471 transpose_34" [id=471, type=transpose]; +"472 view_43" [id=472, type=view]; +"473 view_44" [id=473, type=view]; +"474 view_45" [id=474, type=view]; +"475 scaled_dot_product_attention_5" [id=475, type=scaled_dot_product_attention]; +"476 permute_6" [id=476, type=permute]; +"477 view_46" [id=477, type=view]; +"478 view_46_0_0_nncf_smooth_quant_0" [id=478, type=call_module]; +"479 quantize_per_tensor_default_34" [id=479, type=quantize_per_tensor]; +"480 dequantize_per_tensor_default_34" [id=480, type=dequantize_per_tensor]; +"481 linear_21_scale_0" [id=481, type=get_attr]; +"482 linear_21_zero_point_0" [id=482, type=get_attr]; +"483 compressed_weight_updated_constant22" [id=483, type=get_attr]; +"484 quantize_per_channel_default_22" [id=484, type=quantize_per_channel]; +"485 dequantize_per_channel_default_22" [id=485, type=dequantize_per_channel]; +"486 encoder_layers_encoder_layer_5_self_attention_out_proj_bias_0_0" [id=486, type=get_attr]; +"487 linear_21" [id=487, type=linear]; +"488 view_47" [id=488, type=view]; +"489 transpose_35" [id=489, type=transpose]; +"490 dropout_16" [id=490, type=dropout]; +"491 add_11" [id=491, type=add]; +"492 layer_norm_11" [id=492, type=layer_norm]; +"493 layer_norm_11_0_0_nncf_smooth_quant_0" [id=493, type=call_module]; +"494 quantize_per_tensor_default_35" [id=494, type=quantize_per_tensor]; +"495 dequantize_per_tensor_default_35" [id=495, type=dequantize_per_tensor]; +"496 linear_22_scale_0" [id=496, type=get_attr]; +"497 linear_22_zero_point_0" [id=497, type=get_attr]; +"498 compressed_weight_updated_constant23" [id=498, type=get_attr]; +"499 quantize_per_channel_default_23" [id=499, type=quantize_per_channel]; +"500 dequantize_per_channel_default_23" [id=500, type=dequantize_per_channel]; +"501 encoder_layers_encoder_layer_5_mlp_0_bias_0_0" [id=501, type=get_attr]; +"502 linear_22" [id=502, type=linear]; +"503 gelu_5" [id=503, type=gelu]; +"504 dropout_17" [id=504, type=dropout]; +"505 dropout_17_0_0_nncf_smooth_quant_0" [id=505, type=call_module]; +"506 quantize_per_tensor_default_36" [id=506, type=quantize_per_tensor]; +"507 dequantize_per_tensor_default_36" [id=507, type=dequantize_per_tensor]; +"508 linear_23_scale_0" [id=508, type=get_attr]; +"509 linear_23_zero_point_0" [id=509, type=get_attr]; +"510 compressed_weight_updated_constant24" [id=510, type=get_attr]; +"511 quantize_per_channel_default_24" [id=511, type=quantize_per_channel]; +"512 dequantize_per_channel_default_24" [id=512, 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add_13" [id=566, type=add]; +"567 layer_norm_13" [id=567, type=layer_norm]; +"568 layer_norm_13_0_0_nncf_smooth_quant_0" [id=568, type=call_module]; +"569 quantize_per_tensor_default_41" [id=569, type=quantize_per_tensor]; +"570 dequantize_per_tensor_default_41" [id=570, type=dequantize_per_tensor]; +"571 linear_26_scale_0" [id=571, type=get_attr]; +"572 linear_26_zero_point_0" [id=572, type=get_attr]; +"573 compressed_weight_updated_constant27" [id=573, type=get_attr]; +"574 quantize_per_channel_default_27" [id=574, type=quantize_per_channel]; +"575 dequantize_per_channel_default_27" [id=575, type=dequantize_per_channel]; +"576 encoder_layers_encoder_layer_6_mlp_0_bias_0_0" [id=576, type=get_attr]; +"577 linear_26" [id=577, type=linear]; +"578 gelu_6" [id=578, type=gelu]; +"579 dropout_20" [id=579, type=dropout]; +"580 dropout_20_0_0_nncf_smooth_quant_0" [id=580, type=call_module]; +"581 quantize_per_tensor_default_42" [id=581, type=quantize_per_tensor]; +"582 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quantize_per_channel_default_36" [id=736, type=quantize_per_channel]; +"737 dequantize_per_channel_default_36" [id=737, type=dequantize_per_channel]; +"738 encoder_layers_encoder_layer_8_mlp_3_bias_0_0" [id=738, type=get_attr]; +"739 linear_35" [id=739, type=linear]; +"740 dropout_27" [id=740, type=dropout]; +"741 add_18" [id=741, type=add]; +"742 layer_norm_18" [id=742, type=layer_norm]; +"743 transpose_54" [id=743, type=transpose]; +"744 transpose_54_0_0_nncf_smooth_quant_0" [id=744, type=call_module]; +"745 quantize_per_tensor_default_55" [id=745, type=quantize_per_tensor]; +"746 dequantize_per_tensor_default_55" [id=746, type=dequantize_per_tensor]; +"747 linear_36_scale_0" [id=747, type=get_attr]; +"748 linear_36_zero_point_0" [id=748, type=get_attr]; +"749 compressed_weight_updated_constant37" [id=749, type=get_attr]; +"750 quantize_per_channel_default_37" [id=750, type=quantize_per_channel]; +"751 dequantize_per_channel_default_37" [id=751, type=dequantize_per_channel]; +"752 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type=call_module]; +"806 quantize_per_tensor_default_60" [id=806, type=quantize_per_tensor]; +"807 dequantize_per_tensor_default_60" [id=807, type=dequantize_per_tensor]; +"808 linear_39_scale_0" [id=808, type=get_attr]; +"809 linear_39_zero_point_0" [id=809, type=get_attr]; +"810 compressed_weight_updated_constant40" [id=810, type=get_attr]; +"811 quantize_per_channel_default_40" [id=811, type=quantize_per_channel]; +"812 dequantize_per_channel_default_40" [id=812, type=dequantize_per_channel]; +"813 encoder_layers_encoder_layer_9_mlp_3_bias_0_0" [id=813, type=get_attr]; +"814 linear_39" [id=814, type=linear]; +"815 dropout_30" [id=815, type=dropout]; +"816 add_20" [id=816, type=add]; +"817 layer_norm_20" [id=817, type=layer_norm]; +"818 transpose_60" [id=818, type=transpose]; +"819 transpose_60_0_0_nncf_smooth_quant_0" [id=819, type=call_module]; +"820 quantize_per_tensor_default_61" [id=820, type=quantize_per_tensor]; +"821 dequantize_per_tensor_default_61" [id=821, type=dequantize_per_tensor]; +"822 linear_40_scale_0" [id=822, type=get_attr]; +"823 linear_40_zero_point_0" [id=823, type=get_attr]; +"824 compressed_weight_updated_constant41" [id=824, type=get_attr]; +"825 quantize_per_channel_default_41" [id=825, type=quantize_per_channel]; +"826 dequantize_per_channel_default_41" [id=826, type=dequantize_per_channel]; +"827 encoder_layers_encoder_layer_10_self_attention_in_proj_bias_0_0" [id=827, type=get_attr]; +"828 linear_40" [id=828, type=linear]; +"829 unflatten_10" [id=829, type=unflatten]; +"830 unsqueeze_10" [id=830, type=unsqueeze]; +"831 transpose_61" [id=831, type=transpose]; +"832 squeeze_10" [id=832, type=squeeze]; +"833 contiguous_10" [id=833, type=contiguous]; +"834 quantize_per_tensor_default_62" [id=834, type=quantize_per_tensor]; +"835 dequantize_per_tensor_default_62" [id=835, type=dequantize_per_tensor]; +"836 select_30" [id=836, type=select]; +"837 quantize_per_tensor_default_63" [id=837, type=quantize_per_tensor]; +"838 dequantize_per_tensor_default_63" [id=838, type=dequantize_per_tensor]; +"839 select_31" [id=839, type=select]; +"840 select_32" [id=840, type=select]; +"841 view_80" [id=841, type=view]; +"842 transpose_62" [id=842, type=transpose]; +"843 view_81" [id=843, type=view]; +"844 transpose_63" [id=844, type=transpose]; +"845 view_82" [id=845, type=view]; +"846 transpose_64" [id=846, type=transpose]; +"847 view_83" [id=847, type=view]; +"848 view_84" [id=848, type=view]; +"849 view_85" [id=849, type=view]; +"850 scaled_dot_product_attention_10" [id=850, type=scaled_dot_product_attention]; +"851 permute_11" [id=851, type=permute]; +"852 view_86" [id=852, type=view]; +"853 view_86_0_0_nncf_smooth_quant_0" [id=853, type=call_module]; +"854 quantize_per_tensor_default_64" [id=854, type=quantize_per_tensor]; +"855 dequantize_per_tensor_default_64" [id=855, type=dequantize_per_tensor]; +"856 linear_41_scale_0" [id=856, type=get_attr]; +"857 linear_41_zero_point_0" [id=857, type=get_attr]; +"858 compressed_weight_updated_constant42" [id=858, type=get_attr]; +"859 quantize_per_channel_default_42" [id=859, type=quantize_per_channel]; +"860 dequantize_per_channel_default_42" [id=860, type=dequantize_per_channel]; +"861 encoder_layers_encoder_layer_10_self_attention_out_proj_bias_0_0" [id=861, type=get_attr]; +"862 linear_41" [id=862, type=linear]; +"863 view_87" [id=863, type=view]; +"864 transpose_65" [id=864, type=transpose]; +"865 dropout_31" [id=865, type=dropout]; +"866 add_21" [id=866, type=add]; +"867 layer_norm_21" [id=867, type=layer_norm]; +"868 layer_norm_21_0_0_nncf_smooth_quant_0" [id=868, type=call_module]; +"869 quantize_per_tensor_default_65" [id=869, type=quantize_per_tensor]; +"870 dequantize_per_tensor_default_65" [id=870, type=dequantize_per_tensor]; +"871 linear_42_scale_0" [id=871, type=get_attr]; +"872 linear_42_zero_point_0" [id=872, type=get_attr]; +"873 compressed_weight_updated_constant43" [id=873, type=get_attr]; +"874 quantize_per_channel_default_43" [id=874, type=quantize_per_channel]; +"875 dequantize_per_channel_default_43" [id=875, type=dequantize_per_channel]; +"876 encoder_layers_encoder_layer_10_mlp_0_bias_0_0" [id=876, type=get_attr]; +"877 linear_42" [id=877, type=linear]; +"878 gelu_10" [id=878, type=gelu]; +"879 dropout_32" [id=879, type=dropout]; +"880 dropout_32_0_0_nncf_smooth_quant_0" [id=880, type=call_module]; +"881 quantize_per_tensor_default_66" [id=881, type=quantize_per_tensor]; +"882 dequantize_per_tensor_default_66" [id=882, type=dequantize_per_tensor]; +"883 linear_43_scale_0" [id=883, type=get_attr]; +"884 linear_43_zero_point_0" [id=884, type=get_attr]; +"885 compressed_weight_updated_constant44" [id=885, type=get_attr]; +"886 quantize_per_channel_default_44" [id=886, type=quantize_per_channel]; +"887 dequantize_per_channel_default_44" [id=887, type=dequantize_per_channel]; +"888 encoder_layers_encoder_layer_10_mlp_3_bias_0_0" [id=888, type=get_attr]; +"889 linear_43" [id=889, type=linear]; +"890 dropout_33" [id=890, type=dropout]; +"891 add_22" [id=891, type=add]; +"892 layer_norm_22" [id=892, type=layer_norm]; +"893 transpose_66" [id=893, type=transpose]; +"894 transpose_66_0_0_nncf_smooth_quant_0" [id=894, type=call_module]; +"895 quantize_per_tensor_default_67" [id=895, type=quantize_per_tensor]; +"896 dequantize_per_tensor_default_67" [id=896, type=dequantize_per_tensor]; +"897 linear_44_scale_0" [id=897, type=get_attr]; +"898 linear_44_zero_point_0" [id=898, type=get_attr]; +"899 compressed_weight_updated_constant45" [id=899, type=get_attr]; +"900 quantize_per_channel_default_45" [id=900, type=quantize_per_channel]; +"901 dequantize_per_channel_default_45" [id=901, type=dequantize_per_channel]; +"902 encoder_layers_encoder_layer_11_self_attention_in_proj_bias_0_0" [id=902, type=get_attr]; +"903 linear_44" [id=903, type=linear]; +"904 unflatten_11" [id=904, type=unflatten]; +"905 unsqueeze_11" [id=905, type=unsqueeze]; +"906 transpose_67" [id=906, type=transpose]; +"907 squeeze_11" [id=907, type=squeeze]; +"908 contiguous_11" [id=908, type=contiguous]; +"909 quantize_per_tensor_default_68" [id=909, type=quantize_per_tensor]; +"910 dequantize_per_tensor_default_68" [id=910, type=dequantize_per_tensor]; +"911 select_33" [id=911, type=select]; +"912 quantize_per_tensor_default_69" [id=912, type=quantize_per_tensor]; +"913 dequantize_per_tensor_default_69" [id=913, type=dequantize_per_tensor]; +"914 select_34" [id=914, type=select]; +"915 select_35" [id=915, type=select]; +"916 view_88" [id=916, type=view]; +"917 transpose_68" [id=917, type=transpose]; +"918 view_89" [id=918, type=view]; +"919 transpose_69" [id=919, type=transpose]; +"920 view_90" [id=920, type=view]; +"921 transpose_70" [id=921, type=transpose]; +"922 view_91" [id=922, type=view]; +"923 view_92" [id=923, type=view]; +"924 view_93" [id=924, type=view]; +"925 scaled_dot_product_attention_11" [id=925, type=scaled_dot_product_attention]; +"926 permute_12" [id=926, type=permute]; +"927 view_94" [id=927, type=view]; +"928 view_94_0_0_nncf_smooth_quant_0" [id=928, type=call_module]; +"929 quantize_per_tensor_default_70" [id=929, type=quantize_per_tensor]; +"930 dequantize_per_tensor_default_70" [id=930, type=dequantize_per_tensor]; +"931 linear_45_scale_0" [id=931, type=get_attr]; +"932 linear_45_zero_point_0" [id=932, type=get_attr]; +"933 compressed_weight_updated_constant46" [id=933, type=get_attr]; +"934 quantize_per_channel_default_46" [id=934, type=quantize_per_channel]; +"935 dequantize_per_channel_default_46" [id=935, type=dequantize_per_channel]; +"936 encoder_layers_encoder_layer_11_self_attention_out_proj_bias_0_0" [id=936, type=get_attr]; +"937 linear_45" [id=937, type=linear]; +"938 view_95" [id=938, type=view]; +"939 transpose_71" [id=939, type=transpose]; +"940 dropout_34" [id=940, type=dropout]; +"941 add_23" [id=941, type=add]; +"942 layer_norm_23" [id=942, type=layer_norm]; "943 layer_norm_23_0_0_nncf_smooth_quant_0" [id=943, type=call_module]; "944 quantize_per_tensor_default_71" [id=944, type=quantize_per_tensor]; "945 dequantize_per_tensor_default_71" [id=945, type=dequantize_per_tensor]; "946 linear_46_scale_0" [id=946, type=get_attr]; "947 linear_46_zero_point_0" [id=947, type=get_attr]; -"948 quantize_per_channel_default_47" [id=948, type=quantize_per_channel]; -"949 dequantize_per_channel_default_47" [id=949, type=dequantize_per_channel]; -"950 _param_constant145_0_0" [id=950, type=get_attr]; -"951 linear_46" [id=951, type=linear]; -"952 gelu_11" [id=952, type=gelu]; -"953 dropout_35" [id=953, type=dropout]; -"954 linear_47_updated_constant0" [id=954, type=get_attr]; +"948 compressed_weight_updated_constant47" [id=948, type=get_attr]; +"949 quantize_per_channel_default_47" [id=949, type=quantize_per_channel]; +"950 dequantize_per_channel_default_47" [id=950, type=dequantize_per_channel]; +"951 encoder_layers_encoder_layer_11_mlp_0_bias_0_0" [id=951, type=get_attr]; +"952 linear_46" [id=952, type=linear]; +"953 gelu_11" [id=953, type=gelu]; +"954 dropout_35" [id=954, type=dropout]; "955 dropout_35_0_0_nncf_smooth_quant_0" [id=955, type=call_module]; "956 quantize_per_tensor_default_72" [id=956, type=quantize_per_tensor]; "957 dequantize_per_tensor_default_72" [id=957, type=dequantize_per_tensor]; "958 linear_47_scale_0" [id=958, type=get_attr]; "959 linear_47_zero_point_0" [id=959, type=get_attr]; -"960 quantize_per_channel_default_48" [id=960, type=quantize_per_channel]; -"961 dequantize_per_channel_default_48" [id=961, type=dequantize_per_channel]; -"962 _param_constant147_0_0" [id=962, type=get_attr]; -"963 linear_47" [id=963, type=linear]; -"964 dropout_36" [id=964, type=dropout]; -"965 add_24" [id=965, type=add]; -"966 _param_constant148" [id=966, type=get_attr]; -"967 _param_constant149" [id=967, type=get_attr]; -"968 layer_norm_24" [id=968, type=layer_norm]; -"969 slice_1" [id=969, type=slice]; -"970 select_36" [id=970, type=select]; -"971 linear_48_updated_constant0" [id=971, type=get_attr]; -"972 select_36_0_0_nncf_smooth_quant_0" [id=972, type=call_module]; -"973 quantize_per_tensor_default_73" [id=973, type=quantize_per_tensor]; -"974 dequantize_per_tensor_default_73" [id=974, type=dequantize_per_tensor]; -"975 linear_48_scale_0" [id=975, type=get_attr]; -"976 linear_48_zero_point_0" [id=976, type=get_attr]; -"977 quantize_per_channel_default_49" [id=977, type=quantize_per_channel]; -"978 dequantize_per_channel_default_49" [id=978, type=dequantize_per_channel]; -"979 _param_constant151_0_0" [id=979, type=get_attr]; -"980 linear_48" [id=980, type=linear]; -"981 output" [id=981, type=output]; -"0 arg0_1" -> "1 quantize_per_tensor_default"; -"1 quantize_per_tensor_default" -> "2 dequantize_per_tensor_default"; -"2 dequantize_per_tensor_default" -> "9 conv2d"; -"3 _param_constant0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "6 quantize_per_channel_default"; -"4 conv2d_scale_0" -> "7 dequantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "6 quantize_per_channel_default"; -"5 conv2d_zero_point_0" -> "7 dequantize_per_channel_default"; -"6 quantize_per_channel_default" -> "7 dequantize_per_channel_default"; -"7 dequantize_per_channel_default" -> "9 conv2d"; -"8 _param_constant1_0_0" -> "9 conv2d"; -"9 conv2d" -> "10 reshape"; -"10 reshape" -> "11 permute"; -"11 permute" -> "14 cat"; -"12 _param_constant2" -> "13 expand"; -"13 expand" -> "14 cat"; -"14 cat" -> "16 add"; -"15 _param_constant3" -> "16 add"; -"16 add" -> "17 dropout"; -"17 dropout" -> "20 layer_norm"; -"17 dropout" -> "69 add_1"; -"18 _param_constant4" -> "20 layer_norm"; -"19 _param_constant5" -> "20 layer_norm"; -"20 layer_norm" -> "21 transpose"; -"21 transpose" -> "23 transpose_0_0_nncf_smooth_quant_0"; -"22 linear_updated_constant0" -> "28 quantize_per_channel_default_1"; -"23 transpose_0_0_nncf_smooth_quant_0" -> "24 quantize_per_tensor_default_1"; -"24 quantize_per_tensor_default_1" -> "25 dequantize_per_tensor_default_1"; -"25 dequantize_per_tensor_default_1" -> "31 linear"; -"26 linear_scale_0" -> "28 quantize_per_channel_default_1"; -"26 linear_scale_0" -> "29 dequantize_per_channel_default_1"; -"27 linear_zero_point_0" -> "28 quantize_per_channel_default_1"; -"27 linear_zero_point_0" -> "29 dequantize_per_channel_default_1"; -"28 quantize_per_channel_default_1" -> "29 dequantize_per_channel_default_1"; -"29 dequantize_per_channel_default_1" -> "31 linear"; -"30 _param_constant7_0_0" -> "31 linear"; -"31 linear" -> "32 unflatten"; -"32 unflatten" -> "33 unsqueeze"; -"33 unsqueeze" -> "34 transpose_1"; -"34 transpose_1" -> "35 squeeze"; -"35 squeeze" -> "36 contiguous"; -"36 contiguous" -> "37 quantize_per_tensor_default_2"; -"36 contiguous" -> "40 quantize_per_tensor_default_3"; -"36 contiguous" -> "43 select_2"; -"37 quantize_per_tensor_default_2" -> "38 dequantize_per_tensor_default_2"; -"38 dequantize_per_tensor_default_2" -> "39 select"; -"39 select" -> "44 view"; -"40 quantize_per_tensor_default_3" -> "41 dequantize_per_tensor_default_3"; -"41 dequantize_per_tensor_default_3" -> "42 select_1"; -"42 select_1" -> "46 view_1"; -"43 select_2" -> "48 view_2"; -"44 view" -> "45 transpose_2"; -"45 transpose_2" -> "50 view_3"; -"46 view_1" -> "47 transpose_3"; -"47 transpose_3" -> "51 view_4"; -"48 view_2" -> "49 transpose_4"; -"49 transpose_4" -> "52 view_5"; -"50 view_3" -> "53 scaled_dot_product_attention"; -"51 view_4" -> "53 scaled_dot_product_attention"; -"52 view_5" -> "53 scaled_dot_product_attention"; -"53 scaled_dot_product_attention" -> "54 permute_1"; -"54 permute_1" -> "55 view_6"; -"55 view_6" -> "57 view_6_0_0_nncf_smooth_quant_0"; -"56 linear_1_updated_constant0" -> "62 quantize_per_channel_default_2"; -"57 view_6_0_0_nncf_smooth_quant_0" -> "58 quantize_per_tensor_default_4"; -"58 quantize_per_tensor_default_4" -> "59 dequantize_per_tensor_default_4"; -"59 dequantize_per_tensor_default_4" -> "65 linear_1"; -"60 linear_1_scale_0" -> "62 quantize_per_channel_default_2"; -"60 linear_1_scale_0" -> "63 dequantize_per_channel_default_2"; -"61 linear_1_zero_point_0" -> "62 quantize_per_channel_default_2"; -"61 linear_1_zero_point_0" -> "63 dequantize_per_channel_default_2"; -"62 quantize_per_channel_default_2" -> "63 dequantize_per_channel_default_2"; -"63 dequantize_per_channel_default_2" -> "65 linear_1"; -"64 _param_constant9_0_0" -> "65 linear_1"; -"65 linear_1" -> "66 view_7"; -"66 view_7" -> "67 transpose_5"; -"67 transpose_5" -> "68 dropout_1"; -"68 dropout_1" -> "69 add_1"; -"69 add_1" -> "72 layer_norm_1"; -"69 add_1" -> "96 add_2"; -"70 _param_constant10" -> "72 layer_norm_1"; -"71 _param_constant11" -> "72 layer_norm_1"; -"72 layer_norm_1" -> "74 layer_norm_1_0_0_nncf_smooth_quant_0"; -"73 linear_2_updated_constant0" -> "79 quantize_per_channel_default_3"; -"74 layer_norm_1_0_0_nncf_smooth_quant_0" -> "75 quantize_per_tensor_default_5"; -"75 quantize_per_tensor_default_5" -> "76 dequantize_per_tensor_default_5"; -"76 dequantize_per_tensor_default_5" -> "82 linear_2"; -"77 linear_2_scale_0" -> "79 quantize_per_channel_default_3"; -"77 linear_2_scale_0" -> "80 dequantize_per_channel_default_3"; -"78 linear_2_zero_point_0" -> "79 quantize_per_channel_default_3"; -"78 linear_2_zero_point_0" -> "80 dequantize_per_channel_default_3"; -"79 quantize_per_channel_default_3" -> "80 dequantize_per_channel_default_3"; -"80 dequantize_per_channel_default_3" -> "82 linear_2"; -"81 _param_constant13_0_0" -> "82 linear_2"; -"82 linear_2" -> "83 gelu"; -"83 gelu" -> "84 dropout_2"; -"84 dropout_2" -> "86 dropout_2_0_0_nncf_smooth_quant_0"; -"85 linear_3_updated_constant0" -> "91 quantize_per_channel_default_4"; -"86 dropout_2_0_0_nncf_smooth_quant_0" -> "87 quantize_per_tensor_default_6"; -"87 quantize_per_tensor_default_6" -> "88 dequantize_per_tensor_default_6"; -"88 dequantize_per_tensor_default_6" -> "94 linear_3"; -"89 linear_3_scale_0" -> "91 quantize_per_channel_default_4"; -"89 linear_3_scale_0" -> "92 dequantize_per_channel_default_4"; -"90 linear_3_zero_point_0" -> "91 quantize_per_channel_default_4"; -"90 linear_3_zero_point_0" -> "92 dequantize_per_channel_default_4"; -"91 quantize_per_channel_default_4" -> "92 dequantize_per_channel_default_4"; -"92 dequantize_per_channel_default_4" -> "94 linear_3"; -"93 _param_constant15_0_0" -> "94 linear_3"; -"94 linear_3" -> "95 dropout_3"; -"95 dropout_3" -> "96 add_2"; -"96 add_2" -> "99 layer_norm_2"; -"96 add_2" -> "148 add_3"; -"97 _param_constant16" -> "99 layer_norm_2"; -"98 _param_constant17" -> "99 layer_norm_2"; -"99 layer_norm_2" -> "100 transpose_6"; -"100 transpose_6" -> "102 transpose_6_0_0_nncf_smooth_quant_0"; -"101 linear_4_updated_constant0" -> "107 quantize_per_channel_default_5"; -"102 transpose_6_0_0_nncf_smooth_quant_0" -> "103 quantize_per_tensor_default_7"; -"103 quantize_per_tensor_default_7" -> "104 dequantize_per_tensor_default_7"; -"104 dequantize_per_tensor_default_7" -> "110 linear_4"; -"105 linear_4_scale_0" -> "107 quantize_per_channel_default_5"; -"105 linear_4_scale_0" -> "108 dequantize_per_channel_default_5"; -"106 linear_4_zero_point_0" -> "107 quantize_per_channel_default_5"; -"106 linear_4_zero_point_0" -> "108 dequantize_per_channel_default_5"; -"107 quantize_per_channel_default_5" -> "108 dequantize_per_channel_default_5"; -"108 dequantize_per_channel_default_5" -> "110 linear_4"; -"109 _param_constant19_0_0" -> "110 linear_4"; -"110 linear_4" -> "111 unflatten_1"; -"111 unflatten_1" -> "112 unsqueeze_1"; -"112 unsqueeze_1" -> "113 transpose_7"; -"113 transpose_7" -> "114 squeeze_1"; -"114 squeeze_1" -> "115 contiguous_1"; -"115 contiguous_1" -> "116 quantize_per_tensor_default_8"; -"115 contiguous_1" -> "119 quantize_per_tensor_default_9"; -"115 contiguous_1" -> "122 select_5"; -"116 quantize_per_tensor_default_8" -> "117 dequantize_per_tensor_default_8"; -"117 dequantize_per_tensor_default_8" -> "118 select_3"; -"118 select_3" -> "123 view_8"; -"119 quantize_per_tensor_default_9" -> "120 dequantize_per_tensor_default_9"; -"120 dequantize_per_tensor_default_9" -> "121 select_4"; -"121 select_4" -> "125 view_9"; -"122 select_5" -> "127 view_10"; -"123 view_8" -> "124 transpose_8"; -"124 transpose_8" -> "129 view_11"; -"125 view_9" -> "126 transpose_9"; -"126 transpose_9" -> "130 view_12"; -"127 view_10" -> "128 transpose_10"; -"128 transpose_10" -> "131 view_13"; -"129 view_11" -> "132 scaled_dot_product_attention_1"; -"130 view_12" -> "132 scaled_dot_product_attention_1"; -"131 view_13" -> "132 scaled_dot_product_attention_1"; -"132 scaled_dot_product_attention_1" -> "133 permute_2"; -"133 permute_2" -> "134 view_14"; -"134 view_14" -> "136 view_14_0_0_nncf_smooth_quant_0"; -"135 linear_5_updated_constant0" -> "141 quantize_per_channel_default_6"; -"136 view_14_0_0_nncf_smooth_quant_0" -> "137 quantize_per_tensor_default_10"; -"137 quantize_per_tensor_default_10" -> "138 dequantize_per_tensor_default_10"; -"138 dequantize_per_tensor_default_10" -> "144 linear_5"; -"139 linear_5_scale_0" -> "141 quantize_per_channel_default_6"; -"139 linear_5_scale_0" -> "142 dequantize_per_channel_default_6"; -"140 linear_5_zero_point_0" -> "141 quantize_per_channel_default_6"; -"140 linear_5_zero_point_0" -> "142 dequantize_per_channel_default_6"; -"141 quantize_per_channel_default_6" -> "142 dequantize_per_channel_default_6"; -"142 dequantize_per_channel_default_6" -> "144 linear_5"; -"143 _param_constant21_0_0" -> "144 linear_5"; -"144 linear_5" -> "145 view_15"; -"145 view_15" -> "146 transpose_11"; -"146 transpose_11" -> "147 dropout_4"; -"147 dropout_4" -> "148 add_3"; -"148 add_3" -> "151 layer_norm_3"; -"148 add_3" -> "175 add_4"; -"149 _param_constant22" -> "151 layer_norm_3"; -"150 _param_constant23" -> "151 layer_norm_3"; -"151 layer_norm_3" -> "153 layer_norm_3_0_0_nncf_smooth_quant_0"; -"152 linear_6_updated_constant0" -> "158 quantize_per_channel_default_7"; -"153 layer_norm_3_0_0_nncf_smooth_quant_0" -> "154 quantize_per_tensor_default_11"; -"154 quantize_per_tensor_default_11" -> "155 dequantize_per_tensor_default_11"; -"155 dequantize_per_tensor_default_11" -> "161 linear_6"; -"156 linear_6_scale_0" -> "158 quantize_per_channel_default_7"; -"156 linear_6_scale_0" -> "159 dequantize_per_channel_default_7"; -"157 linear_6_zero_point_0" -> "158 quantize_per_channel_default_7"; -"157 linear_6_zero_point_0" -> "159 dequantize_per_channel_default_7"; -"158 quantize_per_channel_default_7" -> "159 dequantize_per_channel_default_7"; -"159 dequantize_per_channel_default_7" -> "161 linear_6"; -"160 _param_constant25_0_0" -> "161 linear_6"; -"161 linear_6" -> "162 gelu_1"; -"162 gelu_1" -> "163 dropout_5"; -"163 dropout_5" -> "165 dropout_5_0_0_nncf_smooth_quant_0"; -"164 linear_7_updated_constant0" -> "170 quantize_per_channel_default_8"; -"165 dropout_5_0_0_nncf_smooth_quant_0" -> "166 quantize_per_tensor_default_12"; -"166 quantize_per_tensor_default_12" -> "167 dequantize_per_tensor_default_12"; -"167 dequantize_per_tensor_default_12" -> "173 linear_7"; -"168 linear_7_scale_0" -> "170 quantize_per_channel_default_8"; -"168 linear_7_scale_0" -> "171 dequantize_per_channel_default_8"; -"169 linear_7_zero_point_0" -> "170 quantize_per_channel_default_8"; -"169 linear_7_zero_point_0" -> "171 dequantize_per_channel_default_8"; -"170 quantize_per_channel_default_8" -> "171 dequantize_per_channel_default_8"; -"171 dequantize_per_channel_default_8" -> "173 linear_7"; -"172 _param_constant27_0_0" -> "173 linear_7"; -"173 linear_7" -> "174 dropout_6"; -"174 dropout_6" -> "175 add_4"; -"175 add_4" -> "178 layer_norm_4"; -"175 add_4" -> "227 add_5"; -"176 _param_constant28" -> "178 layer_norm_4"; -"177 _param_constant29" -> "178 layer_norm_4"; -"178 layer_norm_4" -> "179 transpose_12"; -"179 transpose_12" -> "181 transpose_12_0_0_nncf_smooth_quant_0"; -"180 linear_8_updated_constant0" -> "186 quantize_per_channel_default_9"; -"181 transpose_12_0_0_nncf_smooth_quant_0" -> "182 quantize_per_tensor_default_13"; -"182 quantize_per_tensor_default_13" -> "183 dequantize_per_tensor_default_13"; -"183 dequantize_per_tensor_default_13" -> "189 linear_8"; -"184 linear_8_scale_0" -> "186 quantize_per_channel_default_9"; -"184 linear_8_scale_0" -> "187 dequantize_per_channel_default_9"; -"185 linear_8_zero_point_0" -> "186 quantize_per_channel_default_9"; -"185 linear_8_zero_point_0" -> "187 dequantize_per_channel_default_9"; -"186 quantize_per_channel_default_9" -> "187 dequantize_per_channel_default_9"; -"187 dequantize_per_channel_default_9" -> "189 linear_8"; -"188 _param_constant31_0_0" -> "189 linear_8"; -"189 linear_8" -> "190 unflatten_2"; -"190 unflatten_2" -> "191 unsqueeze_2"; -"191 unsqueeze_2" -> "192 transpose_13"; -"192 transpose_13" -> "193 squeeze_2"; -"193 squeeze_2" -> "194 contiguous_2"; -"194 contiguous_2" -> "195 quantize_per_tensor_default_14"; -"194 contiguous_2" -> "198 quantize_per_tensor_default_15"; -"194 contiguous_2" -> "201 select_8"; -"195 quantize_per_tensor_default_14" -> "196 dequantize_per_tensor_default_14"; -"196 dequantize_per_tensor_default_14" -> "197 select_6"; -"197 select_6" -> "202 view_16"; -"198 quantize_per_tensor_default_15" -> "199 dequantize_per_tensor_default_15"; -"199 dequantize_per_tensor_default_15" -> "200 select_7"; -"200 select_7" -> "204 view_17"; -"201 select_8" -> "206 view_18"; -"202 view_16" -> "203 transpose_14"; -"203 transpose_14" -> "208 view_19"; -"204 view_17" -> "205 transpose_15"; -"205 transpose_15" -> "209 view_20"; -"206 view_18" -> "207 transpose_16"; -"207 transpose_16" -> "210 view_21"; -"208 view_19" -> "211 scaled_dot_product_attention_2"; -"209 view_20" -> "211 scaled_dot_product_attention_2"; -"210 view_21" -> "211 scaled_dot_product_attention_2"; -"211 scaled_dot_product_attention_2" -> "212 permute_3"; -"212 permute_3" -> "213 view_22"; -"213 view_22" -> "215 view_22_0_0_nncf_smooth_quant_0"; -"214 linear_9_updated_constant0" -> "220 quantize_per_channel_default_10"; -"215 view_22_0_0_nncf_smooth_quant_0" -> "216 quantize_per_tensor_default_16"; -"216 quantize_per_tensor_default_16" -> "217 dequantize_per_tensor_default_16"; -"217 dequantize_per_tensor_default_16" -> "223 linear_9"; -"218 linear_9_scale_0" -> "220 quantize_per_channel_default_10"; -"218 linear_9_scale_0" -> "221 dequantize_per_channel_default_10"; -"219 linear_9_zero_point_0" -> "220 quantize_per_channel_default_10"; -"219 linear_9_zero_point_0" -> "221 dequantize_per_channel_default_10"; -"220 quantize_per_channel_default_10" -> "221 dequantize_per_channel_default_10"; -"221 dequantize_per_channel_default_10" -> "223 linear_9"; -"222 _param_constant33_0_0" -> "223 linear_9"; -"223 linear_9" -> "224 view_23"; -"224 view_23" -> "225 transpose_17"; -"225 transpose_17" -> "226 dropout_7"; -"226 dropout_7" -> "227 add_5"; -"227 add_5" -> "230 layer_norm_5"; -"227 add_5" -> "254 add_6"; -"228 _param_constant34" -> "230 layer_norm_5"; -"229 _param_constant35" -> "230 layer_norm_5"; -"230 layer_norm_5" -> "232 layer_norm_5_0_0_nncf_smooth_quant_0"; -"231 linear_10_updated_constant0" -> "237 quantize_per_channel_default_11"; -"232 layer_norm_5_0_0_nncf_smooth_quant_0" -> "233 quantize_per_tensor_default_17"; -"233 quantize_per_tensor_default_17" -> "234 dequantize_per_tensor_default_17"; -"234 dequantize_per_tensor_default_17" -> "240 linear_10"; -"235 linear_10_scale_0" -> "237 quantize_per_channel_default_11"; -"235 linear_10_scale_0" -> "238 dequantize_per_channel_default_11"; -"236 linear_10_zero_point_0" -> "237 quantize_per_channel_default_11"; -"236 linear_10_zero_point_0" -> "238 dequantize_per_channel_default_11"; -"237 quantize_per_channel_default_11" -> "238 dequantize_per_channel_default_11"; -"238 dequantize_per_channel_default_11" -> "240 linear_10"; -"239 _param_constant37_0_0" -> "240 linear_10"; -"240 linear_10" -> "241 gelu_2"; -"241 gelu_2" -> "242 dropout_8"; -"242 dropout_8" -> "244 dropout_8_0_0_nncf_smooth_quant_0"; -"243 linear_11_updated_constant0" -> "249 quantize_per_channel_default_12"; -"244 dropout_8_0_0_nncf_smooth_quant_0" -> "245 quantize_per_tensor_default_18"; -"245 quantize_per_tensor_default_18" -> "246 dequantize_per_tensor_default_18"; -"246 dequantize_per_tensor_default_18" -> "252 linear_11"; -"247 linear_11_scale_0" -> "249 quantize_per_channel_default_12"; -"247 linear_11_scale_0" -> "250 dequantize_per_channel_default_12"; -"248 linear_11_zero_point_0" -> "249 quantize_per_channel_default_12"; -"248 linear_11_zero_point_0" -> "250 dequantize_per_channel_default_12"; -"249 quantize_per_channel_default_12" -> "250 dequantize_per_channel_default_12"; -"250 dequantize_per_channel_default_12" -> "252 linear_11"; -"251 _param_constant39_0_0" -> "252 linear_11"; -"252 linear_11" -> "253 dropout_9"; -"253 dropout_9" -> "254 add_6"; -"254 add_6" -> "257 layer_norm_6"; -"254 add_6" -> "306 add_7"; -"255 _param_constant40" -> "257 layer_norm_6"; -"256 _param_constant41" -> "257 layer_norm_6"; -"257 layer_norm_6" -> "258 transpose_18"; -"258 transpose_18" -> "260 transpose_18_0_0_nncf_smooth_quant_0"; -"259 linear_12_updated_constant0" -> "265 quantize_per_channel_default_13"; -"260 transpose_18_0_0_nncf_smooth_quant_0" -> "261 quantize_per_tensor_default_19"; -"261 quantize_per_tensor_default_19" -> "262 dequantize_per_tensor_default_19"; -"262 dequantize_per_tensor_default_19" -> "268 linear_12"; -"263 linear_12_scale_0" -> "265 quantize_per_channel_default_13"; -"263 linear_12_scale_0" -> "266 dequantize_per_channel_default_13"; -"264 linear_12_zero_point_0" -> "265 quantize_per_channel_default_13"; -"264 linear_12_zero_point_0" -> "266 dequantize_per_channel_default_13"; -"265 quantize_per_channel_default_13" -> "266 dequantize_per_channel_default_13"; -"266 dequantize_per_channel_default_13" -> "268 linear_12"; -"267 _param_constant43_0_0" -> "268 linear_12"; -"268 linear_12" -> "269 unflatten_3"; -"269 unflatten_3" -> "270 unsqueeze_3"; -"270 unsqueeze_3" -> "271 transpose_19"; -"271 transpose_19" -> "272 squeeze_3"; -"272 squeeze_3" -> "273 contiguous_3"; -"273 contiguous_3" -> "274 quantize_per_tensor_default_20"; -"273 contiguous_3" -> "277 quantize_per_tensor_default_21"; -"273 contiguous_3" -> "280 select_11"; -"274 quantize_per_tensor_default_20" -> "275 dequantize_per_tensor_default_20"; -"275 dequantize_per_tensor_default_20" -> "276 select_9"; -"276 select_9" -> "281 view_24"; -"277 quantize_per_tensor_default_21" -> "278 dequantize_per_tensor_default_21"; -"278 dequantize_per_tensor_default_21" -> "279 select_10"; -"279 select_10" -> "283 view_25"; -"280 select_11" -> "285 view_26"; -"281 view_24" -> "282 transpose_20"; -"282 transpose_20" -> "287 view_27"; -"283 view_25" -> "284 transpose_21"; -"284 transpose_21" -> "288 view_28"; -"285 view_26" -> "286 transpose_22"; -"286 transpose_22" -> "289 view_29"; -"287 view_27" -> "290 scaled_dot_product_attention_3"; -"288 view_28" -> "290 scaled_dot_product_attention_3"; -"289 view_29" -> "290 scaled_dot_product_attention_3"; -"290 scaled_dot_product_attention_3" -> "291 permute_4"; -"291 permute_4" -> "292 view_30"; -"292 view_30" -> "294 view_30_0_0_nncf_smooth_quant_0"; -"293 linear_13_updated_constant0" -> "299 quantize_per_channel_default_14"; -"294 view_30_0_0_nncf_smooth_quant_0" -> "295 quantize_per_tensor_default_22"; -"295 quantize_per_tensor_default_22" -> "296 dequantize_per_tensor_default_22"; -"296 dequantize_per_tensor_default_22" -> "302 linear_13"; -"297 linear_13_scale_0" -> "299 quantize_per_channel_default_14"; -"297 linear_13_scale_0" -> "300 dequantize_per_channel_default_14"; -"298 linear_13_zero_point_0" -> "299 quantize_per_channel_default_14"; -"298 linear_13_zero_point_0" -> "300 dequantize_per_channel_default_14"; -"299 quantize_per_channel_default_14" -> "300 dequantize_per_channel_default_14"; -"300 dequantize_per_channel_default_14" -> "302 linear_13"; -"301 _param_constant45_0_0" -> "302 linear_13"; -"302 linear_13" -> "303 view_31"; -"303 view_31" -> "304 transpose_23"; -"304 transpose_23" -> "305 dropout_10"; -"305 dropout_10" -> "306 add_7"; -"306 add_7" -> "309 layer_norm_7"; -"306 add_7" -> "333 add_8"; -"307 _param_constant46" -> "309 layer_norm_7"; -"308 _param_constant47" -> "309 layer_norm_7"; -"309 layer_norm_7" -> "311 layer_norm_7_0_0_nncf_smooth_quant_0"; -"310 linear_14_updated_constant0" -> "316 quantize_per_channel_default_15"; -"311 layer_norm_7_0_0_nncf_smooth_quant_0" -> "312 quantize_per_tensor_default_23"; -"312 quantize_per_tensor_default_23" -> "313 dequantize_per_tensor_default_23"; -"313 dequantize_per_tensor_default_23" -> "319 linear_14"; -"314 linear_14_scale_0" -> "316 quantize_per_channel_default_15"; -"314 linear_14_scale_0" -> "317 dequantize_per_channel_default_15"; -"315 linear_14_zero_point_0" -> "316 quantize_per_channel_default_15"; -"315 linear_14_zero_point_0" -> "317 dequantize_per_channel_default_15"; -"316 quantize_per_channel_default_15" -> "317 dequantize_per_channel_default_15"; -"317 dequantize_per_channel_default_15" -> "319 linear_14"; -"318 _param_constant49_0_0" -> "319 linear_14"; -"319 linear_14" -> "320 gelu_3"; -"320 gelu_3" -> "321 dropout_11"; -"321 dropout_11" -> "323 dropout_11_0_0_nncf_smooth_quant_0"; -"322 linear_15_updated_constant0" -> "328 quantize_per_channel_default_16"; -"323 dropout_11_0_0_nncf_smooth_quant_0" -> "324 quantize_per_tensor_default_24"; -"324 quantize_per_tensor_default_24" -> "325 dequantize_per_tensor_default_24"; -"325 dequantize_per_tensor_default_24" -> "331 linear_15"; -"326 linear_15_scale_0" -> "328 quantize_per_channel_default_16"; -"326 linear_15_scale_0" -> "329 dequantize_per_channel_default_16"; -"327 linear_15_zero_point_0" -> "328 quantize_per_channel_default_16"; -"327 linear_15_zero_point_0" -> "329 dequantize_per_channel_default_16"; -"328 quantize_per_channel_default_16" -> "329 dequantize_per_channel_default_16"; -"329 dequantize_per_channel_default_16" -> "331 linear_15"; -"330 _param_constant51_0_0" -> "331 linear_15"; -"331 linear_15" -> "332 dropout_12"; -"332 dropout_12" -> "333 add_8"; -"333 add_8" -> "336 layer_norm_8"; -"333 add_8" -> "385 add_9"; -"334 _param_constant52" -> "336 layer_norm_8"; -"335 _param_constant53" -> "336 layer_norm_8"; -"336 layer_norm_8" -> "337 transpose_24"; -"337 transpose_24" -> "339 transpose_24_0_0_nncf_smooth_quant_0"; -"338 linear_16_updated_constant0" -> "344 quantize_per_channel_default_17"; -"339 transpose_24_0_0_nncf_smooth_quant_0" -> "340 quantize_per_tensor_default_25"; -"340 quantize_per_tensor_default_25" -> "341 dequantize_per_tensor_default_25"; -"341 dequantize_per_tensor_default_25" -> "347 linear_16"; -"342 linear_16_scale_0" -> "344 quantize_per_channel_default_17"; -"342 linear_16_scale_0" -> "345 dequantize_per_channel_default_17"; -"343 linear_16_zero_point_0" -> "344 quantize_per_channel_default_17"; -"343 linear_16_zero_point_0" -> "345 dequantize_per_channel_default_17"; -"344 quantize_per_channel_default_17" -> "345 dequantize_per_channel_default_17"; -"345 dequantize_per_channel_default_17" -> "347 linear_16"; -"346 _param_constant55_0_0" -> "347 linear_16"; -"347 linear_16" -> "348 unflatten_4"; -"348 unflatten_4" -> "349 unsqueeze_4"; -"349 unsqueeze_4" -> "350 transpose_25"; -"350 transpose_25" -> "351 squeeze_4"; -"351 squeeze_4" -> "352 contiguous_4"; -"352 contiguous_4" -> "353 quantize_per_tensor_default_26"; -"352 contiguous_4" -> "356 quantize_per_tensor_default_27"; -"352 contiguous_4" -> "359 select_14"; -"353 quantize_per_tensor_default_26" -> "354 dequantize_per_tensor_default_26"; -"354 dequantize_per_tensor_default_26" -> "355 select_12"; -"355 select_12" -> "360 view_32"; -"356 quantize_per_tensor_default_27" -> "357 dequantize_per_tensor_default_27"; -"357 dequantize_per_tensor_default_27" -> "358 select_13"; -"358 select_13" -> "362 view_33"; -"359 select_14" -> "364 view_34"; -"360 view_32" -> "361 transpose_26"; -"361 transpose_26" -> "366 view_35"; -"362 view_33" -> "363 transpose_27"; -"363 transpose_27" -> "367 view_36"; -"364 view_34" -> "365 transpose_28"; -"365 transpose_28" -> "368 view_37"; -"366 view_35" -> "369 scaled_dot_product_attention_4"; -"367 view_36" -> "369 scaled_dot_product_attention_4"; -"368 view_37" -> "369 scaled_dot_product_attention_4"; -"369 scaled_dot_product_attention_4" -> "370 permute_5"; -"370 permute_5" -> "371 view_38"; -"371 view_38" -> "373 view_38_0_0_nncf_smooth_quant_0"; -"372 linear_17_updated_constant0" -> "378 quantize_per_channel_default_18"; -"373 view_38_0_0_nncf_smooth_quant_0" -> "374 quantize_per_tensor_default_28"; -"374 quantize_per_tensor_default_28" -> "375 dequantize_per_tensor_default_28"; -"375 dequantize_per_tensor_default_28" -> "381 linear_17"; -"376 linear_17_scale_0" -> "378 quantize_per_channel_default_18"; -"376 linear_17_scale_0" -> "379 dequantize_per_channel_default_18"; -"377 linear_17_zero_point_0" -> "378 quantize_per_channel_default_18"; -"377 linear_17_zero_point_0" -> "379 dequantize_per_channel_default_18"; -"378 quantize_per_channel_default_18" -> "379 dequantize_per_channel_default_18"; -"379 dequantize_per_channel_default_18" -> "381 linear_17"; -"380 _param_constant57_0_0" -> "381 linear_17"; -"381 linear_17" -> "382 view_39"; -"382 view_39" -> "383 transpose_29"; -"383 transpose_29" -> "384 dropout_13"; -"384 dropout_13" -> "385 add_9"; -"385 add_9" -> "388 layer_norm_9"; -"385 add_9" -> "412 add_10"; -"386 _param_constant58" -> "388 layer_norm_9"; -"387 _param_constant59" -> "388 layer_norm_9"; -"388 layer_norm_9" -> "390 layer_norm_9_0_0_nncf_smooth_quant_0"; -"389 linear_18_updated_constant0" -> "395 quantize_per_channel_default_19"; -"390 layer_norm_9_0_0_nncf_smooth_quant_0" -> "391 quantize_per_tensor_default_29"; -"391 quantize_per_tensor_default_29" -> "392 dequantize_per_tensor_default_29"; -"392 dequantize_per_tensor_default_29" -> "398 linear_18"; -"393 linear_18_scale_0" -> "395 quantize_per_channel_default_19"; -"393 linear_18_scale_0" -> "396 dequantize_per_channel_default_19"; -"394 linear_18_zero_point_0" -> "395 quantize_per_channel_default_19"; -"394 linear_18_zero_point_0" -> "396 dequantize_per_channel_default_19"; -"395 quantize_per_channel_default_19" -> "396 dequantize_per_channel_default_19"; -"396 dequantize_per_channel_default_19" -> "398 linear_18"; -"397 _param_constant61_0_0" -> "398 linear_18"; -"398 linear_18" -> "399 gelu_4"; -"399 gelu_4" -> "400 dropout_14"; -"400 dropout_14" -> "402 dropout_14_0_0_nncf_smooth_quant_0"; -"401 linear_19_updated_constant0" -> "407 quantize_per_channel_default_20"; -"402 dropout_14_0_0_nncf_smooth_quant_0" -> "403 quantize_per_tensor_default_30"; -"403 quantize_per_tensor_default_30" -> "404 dequantize_per_tensor_default_30"; -"404 dequantize_per_tensor_default_30" -> "410 linear_19"; -"405 linear_19_scale_0" -> "407 quantize_per_channel_default_20"; -"405 linear_19_scale_0" -> "408 dequantize_per_channel_default_20"; -"406 linear_19_zero_point_0" -> "407 quantize_per_channel_default_20"; -"406 linear_19_zero_point_0" -> "408 dequantize_per_channel_default_20"; -"407 quantize_per_channel_default_20" -> "408 dequantize_per_channel_default_20"; -"408 dequantize_per_channel_default_20" -> "410 linear_19"; -"409 _param_constant63_0_0" -> "410 linear_19"; -"410 linear_19" -> "411 dropout_15"; -"411 dropout_15" -> "412 add_10"; -"412 add_10" -> "415 layer_norm_10"; -"412 add_10" -> "464 add_11"; -"413 _param_constant64" -> "415 layer_norm_10"; -"414 _param_constant65" -> "415 layer_norm_10"; -"415 layer_norm_10" -> "416 transpose_30"; -"416 transpose_30" -> "418 transpose_30_0_0_nncf_smooth_quant_0"; -"417 linear_20_updated_constant0" -> "423 quantize_per_channel_default_21"; -"418 transpose_30_0_0_nncf_smooth_quant_0" -> "419 quantize_per_tensor_default_31"; -"419 quantize_per_tensor_default_31" -> "420 dequantize_per_tensor_default_31"; -"420 dequantize_per_tensor_default_31" -> "426 linear_20"; -"421 linear_20_scale_0" -> "423 quantize_per_channel_default_21"; -"421 linear_20_scale_0" -> "424 dequantize_per_channel_default_21"; -"422 linear_20_zero_point_0" -> "423 quantize_per_channel_default_21"; -"422 linear_20_zero_point_0" -> "424 dequantize_per_channel_default_21"; -"423 quantize_per_channel_default_21" -> "424 dequantize_per_channel_default_21"; -"424 dequantize_per_channel_default_21" -> "426 linear_20"; -"425 _param_constant67_0_0" -> "426 linear_20"; -"426 linear_20" -> "427 unflatten_5"; -"427 unflatten_5" -> "428 unsqueeze_5"; -"428 unsqueeze_5" -> "429 transpose_31"; -"429 transpose_31" -> "430 squeeze_5"; -"430 squeeze_5" -> "431 contiguous_5"; -"431 contiguous_5" -> "432 quantize_per_tensor_default_32"; -"431 contiguous_5" -> "435 quantize_per_tensor_default_33"; -"431 contiguous_5" -> "438 select_17"; -"432 quantize_per_tensor_default_32" -> "433 dequantize_per_tensor_default_32"; -"433 dequantize_per_tensor_default_32" -> "434 select_15"; -"434 select_15" -> "439 view_40"; -"435 quantize_per_tensor_default_33" -> "436 dequantize_per_tensor_default_33"; -"436 dequantize_per_tensor_default_33" -> "437 select_16"; -"437 select_16" -> "441 view_41"; -"438 select_17" -> "443 view_42"; -"439 view_40" -> "440 transpose_32"; -"440 transpose_32" -> "445 view_43"; -"441 view_41" -> "442 transpose_33"; -"442 transpose_33" -> "446 view_44"; -"443 view_42" -> "444 transpose_34"; -"444 transpose_34" -> "447 view_45"; -"445 view_43" -> "448 scaled_dot_product_attention_5"; -"446 view_44" -> "448 scaled_dot_product_attention_5"; -"447 view_45" -> "448 scaled_dot_product_attention_5"; -"448 scaled_dot_product_attention_5" -> "449 permute_6"; -"449 permute_6" -> "450 view_46"; -"450 view_46" -> "452 view_46_0_0_nncf_smooth_quant_0"; -"451 linear_21_updated_constant0" -> "457 quantize_per_channel_default_22"; -"452 view_46_0_0_nncf_smooth_quant_0" -> "453 quantize_per_tensor_default_34"; -"453 quantize_per_tensor_default_34" -> "454 dequantize_per_tensor_default_34"; -"454 dequantize_per_tensor_default_34" -> "460 linear_21"; -"455 linear_21_scale_0" -> "457 quantize_per_channel_default_22"; -"455 linear_21_scale_0" -> "458 dequantize_per_channel_default_22"; -"456 linear_21_zero_point_0" -> "457 quantize_per_channel_default_22"; -"456 linear_21_zero_point_0" -> "458 dequantize_per_channel_default_22"; -"457 quantize_per_channel_default_22" -> "458 dequantize_per_channel_default_22"; -"458 dequantize_per_channel_default_22" -> "460 linear_21"; -"459 _param_constant69_0_0" -> "460 linear_21"; -"460 linear_21" -> "461 view_47"; -"461 view_47" -> "462 transpose_35"; -"462 transpose_35" -> "463 dropout_16"; -"463 dropout_16" -> "464 add_11"; -"464 add_11" -> "467 layer_norm_11"; -"464 add_11" -> "491 add_12"; -"465 _param_constant70" -> "467 layer_norm_11"; -"466 _param_constant71" -> "467 layer_norm_11"; -"467 layer_norm_11" -> "469 layer_norm_11_0_0_nncf_smooth_quant_0"; -"468 linear_22_updated_constant0" -> "474 quantize_per_channel_default_23"; -"469 layer_norm_11_0_0_nncf_smooth_quant_0" -> "470 quantize_per_tensor_default_35"; -"470 quantize_per_tensor_default_35" -> "471 dequantize_per_tensor_default_35"; -"471 dequantize_per_tensor_default_35" -> "477 linear_22"; -"472 linear_22_scale_0" -> "474 quantize_per_channel_default_23"; -"472 linear_22_scale_0" -> "475 dequantize_per_channel_default_23"; -"473 linear_22_zero_point_0" -> "474 quantize_per_channel_default_23"; -"473 linear_22_zero_point_0" -> "475 dequantize_per_channel_default_23"; -"474 quantize_per_channel_default_23" -> "475 dequantize_per_channel_default_23"; -"475 dequantize_per_channel_default_23" -> "477 linear_22"; -"476 _param_constant73_0_0" -> "477 linear_22"; -"477 linear_22" -> "478 gelu_5"; -"478 gelu_5" -> "479 dropout_17"; -"479 dropout_17" -> "481 dropout_17_0_0_nncf_smooth_quant_0"; -"480 linear_23_updated_constant0" -> "486 quantize_per_channel_default_24"; -"481 dropout_17_0_0_nncf_smooth_quant_0" -> "482 quantize_per_tensor_default_36"; -"482 quantize_per_tensor_default_36" -> "483 dequantize_per_tensor_default_36"; -"483 dequantize_per_tensor_default_36" -> "489 linear_23"; -"484 linear_23_scale_0" -> "486 quantize_per_channel_default_24"; -"484 linear_23_scale_0" -> "487 dequantize_per_channel_default_24"; -"485 linear_23_zero_point_0" -> "486 quantize_per_channel_default_24"; -"485 linear_23_zero_point_0" -> "487 dequantize_per_channel_default_24"; -"486 quantize_per_channel_default_24" -> "487 dequantize_per_channel_default_24"; -"487 dequantize_per_channel_default_24" -> "489 linear_23"; -"488 _param_constant75_0_0" -> "489 linear_23"; -"489 linear_23" -> "490 dropout_18"; -"490 dropout_18" -> "491 add_12"; -"491 add_12" -> "494 layer_norm_12"; -"491 add_12" -> "543 add_13"; -"492 _param_constant76" -> "494 layer_norm_12"; -"493 _param_constant77" -> "494 layer_norm_12"; -"494 layer_norm_12" -> "495 transpose_36"; -"495 transpose_36" -> "497 transpose_36_0_0_nncf_smooth_quant_0"; -"496 linear_24_updated_constant0" -> "502 quantize_per_channel_default_25"; -"497 transpose_36_0_0_nncf_smooth_quant_0" -> "498 quantize_per_tensor_default_37"; -"498 quantize_per_tensor_default_37" -> "499 dequantize_per_tensor_default_37"; -"499 dequantize_per_tensor_default_37" -> "505 linear_24"; -"500 linear_24_scale_0" -> "502 quantize_per_channel_default_25"; -"500 linear_24_scale_0" -> "503 dequantize_per_channel_default_25"; -"501 linear_24_zero_point_0" -> "502 quantize_per_channel_default_25"; -"501 linear_24_zero_point_0" -> "503 dequantize_per_channel_default_25"; -"502 quantize_per_channel_default_25" -> "503 dequantize_per_channel_default_25"; -"503 dequantize_per_channel_default_25" -> "505 linear_24"; -"504 _param_constant79_0_0" -> "505 linear_24"; -"505 linear_24" -> "506 unflatten_6"; -"506 unflatten_6" -> "507 unsqueeze_6"; -"507 unsqueeze_6" -> "508 transpose_37"; -"508 transpose_37" -> "509 squeeze_6"; -"509 squeeze_6" -> "510 contiguous_6"; -"510 contiguous_6" -> "511 quantize_per_tensor_default_38"; -"510 contiguous_6" -> "514 quantize_per_tensor_default_39"; -"510 contiguous_6" -> "517 select_20"; -"511 quantize_per_tensor_default_38" -> "512 dequantize_per_tensor_default_38"; -"512 dequantize_per_tensor_default_38" -> "513 select_18"; -"513 select_18" -> "518 view_48"; -"514 quantize_per_tensor_default_39" -> "515 dequantize_per_tensor_default_39"; -"515 dequantize_per_tensor_default_39" -> "516 select_19"; -"516 select_19" -> "520 view_49"; -"517 select_20" -> "522 view_50"; -"518 view_48" -> "519 transpose_38"; -"519 transpose_38" -> "524 view_51"; -"520 view_49" -> "521 transpose_39"; -"521 transpose_39" -> "525 view_52"; -"522 view_50" -> "523 transpose_40"; -"523 transpose_40" -> "526 view_53"; -"524 view_51" -> "527 scaled_dot_product_attention_6"; -"525 view_52" -> "527 scaled_dot_product_attention_6"; -"526 view_53" -> "527 scaled_dot_product_attention_6"; -"527 scaled_dot_product_attention_6" -> "528 permute_7"; -"528 permute_7" -> "529 view_54"; -"529 view_54" -> "531 view_54_0_0_nncf_smooth_quant_0"; -"530 linear_25_updated_constant0" -> "536 quantize_per_channel_default_26"; -"531 view_54_0_0_nncf_smooth_quant_0" -> "532 quantize_per_tensor_default_40"; -"532 quantize_per_tensor_default_40" -> "533 dequantize_per_tensor_default_40"; -"533 dequantize_per_tensor_default_40" -> "539 linear_25"; -"534 linear_25_scale_0" -> "536 quantize_per_channel_default_26"; -"534 linear_25_scale_0" -> "537 dequantize_per_channel_default_26"; -"535 linear_25_zero_point_0" -> "536 quantize_per_channel_default_26"; -"535 linear_25_zero_point_0" -> "537 dequantize_per_channel_default_26"; -"536 quantize_per_channel_default_26" -> "537 dequantize_per_channel_default_26"; -"537 dequantize_per_channel_default_26" -> "539 linear_25"; -"538 _param_constant81_0_0" -> "539 linear_25"; -"539 linear_25" -> "540 view_55"; -"540 view_55" -> "541 transpose_41"; -"541 transpose_41" -> "542 dropout_19"; -"542 dropout_19" -> "543 add_13"; -"543 add_13" -> "546 layer_norm_13"; -"543 add_13" -> "570 add_14"; -"544 _param_constant82" -> "546 layer_norm_13"; -"545 _param_constant83" -> "546 layer_norm_13"; -"546 layer_norm_13" -> "548 layer_norm_13_0_0_nncf_smooth_quant_0"; -"547 linear_26_updated_constant0" -> "553 quantize_per_channel_default_27"; -"548 layer_norm_13_0_0_nncf_smooth_quant_0" -> "549 quantize_per_tensor_default_41"; -"549 quantize_per_tensor_default_41" -> "550 dequantize_per_tensor_default_41"; -"550 dequantize_per_tensor_default_41" -> "556 linear_26"; -"551 linear_26_scale_0" -> "553 quantize_per_channel_default_27"; -"551 linear_26_scale_0" -> "554 dequantize_per_channel_default_27"; -"552 linear_26_zero_point_0" -> "553 quantize_per_channel_default_27"; -"552 linear_26_zero_point_0" -> "554 dequantize_per_channel_default_27"; -"553 quantize_per_channel_default_27" -> "554 dequantize_per_channel_default_27"; -"554 dequantize_per_channel_default_27" -> "556 linear_26"; -"555 _param_constant85_0_0" -> "556 linear_26"; -"556 linear_26" -> "557 gelu_6"; -"557 gelu_6" -> "558 dropout_20"; -"558 dropout_20" -> "560 dropout_20_0_0_nncf_smooth_quant_0"; -"559 linear_27_updated_constant0" -> "565 quantize_per_channel_default_28"; -"560 dropout_20_0_0_nncf_smooth_quant_0" -> "561 quantize_per_tensor_default_42"; -"561 quantize_per_tensor_default_42" -> "562 dequantize_per_tensor_default_42"; -"562 dequantize_per_tensor_default_42" -> "568 linear_27"; -"563 linear_27_scale_0" -> "565 quantize_per_channel_default_28"; -"563 linear_27_scale_0" -> "566 dequantize_per_channel_default_28"; -"564 linear_27_zero_point_0" -> "565 quantize_per_channel_default_28"; -"564 linear_27_zero_point_0" -> "566 dequantize_per_channel_default_28"; -"565 quantize_per_channel_default_28" -> "566 dequantize_per_channel_default_28"; -"566 dequantize_per_channel_default_28" -> "568 linear_27"; -"567 _param_constant87_0_0" -> "568 linear_27"; -"568 linear_27" -> "569 dropout_21"; -"569 dropout_21" -> "570 add_14"; -"570 add_14" -> "573 layer_norm_14"; -"570 add_14" -> "622 add_15"; -"571 _param_constant88" -> "573 layer_norm_14"; -"572 _param_constant89" -> "573 layer_norm_14"; -"573 layer_norm_14" -> "574 transpose_42"; -"574 transpose_42" -> "576 transpose_42_0_0_nncf_smooth_quant_0"; -"575 linear_28_updated_constant0" -> "581 quantize_per_channel_default_29"; -"576 transpose_42_0_0_nncf_smooth_quant_0" -> "577 quantize_per_tensor_default_43"; -"577 quantize_per_tensor_default_43" -> "578 dequantize_per_tensor_default_43"; -"578 dequantize_per_tensor_default_43" -> "584 linear_28"; -"579 linear_28_scale_0" -> "581 quantize_per_channel_default_29"; -"579 linear_28_scale_0" -> "582 dequantize_per_channel_default_29"; -"580 linear_28_zero_point_0" -> "581 quantize_per_channel_default_29"; -"580 linear_28_zero_point_0" -> "582 dequantize_per_channel_default_29"; -"581 quantize_per_channel_default_29" -> "582 dequantize_per_channel_default_29"; -"582 dequantize_per_channel_default_29" -> "584 linear_28"; -"583 _param_constant91_0_0" -> "584 linear_28"; -"584 linear_28" -> "585 unflatten_7"; -"585 unflatten_7" -> "586 unsqueeze_7"; -"586 unsqueeze_7" -> "587 transpose_43"; -"587 transpose_43" -> "588 squeeze_7"; -"588 squeeze_7" -> "589 contiguous_7"; -"589 contiguous_7" -> "590 quantize_per_tensor_default_44"; -"589 contiguous_7" -> "593 quantize_per_tensor_default_45"; -"589 contiguous_7" -> "596 select_23"; -"590 quantize_per_tensor_default_44" -> "591 dequantize_per_tensor_default_44"; -"591 dequantize_per_tensor_default_44" -> "592 select_21"; -"592 select_21" -> "597 view_56"; -"593 quantize_per_tensor_default_45" -> "594 dequantize_per_tensor_default_45"; -"594 dequantize_per_tensor_default_45" -> "595 select_22"; -"595 select_22" -> "599 view_57"; -"596 select_23" -> "601 view_58"; -"597 view_56" -> "598 transpose_44"; -"598 transpose_44" -> "603 view_59"; -"599 view_57" -> "600 transpose_45"; -"600 transpose_45" -> "604 view_60"; -"601 view_58" -> "602 transpose_46"; -"602 transpose_46" -> "605 view_61"; -"603 view_59" -> "606 scaled_dot_product_attention_7"; -"604 view_60" -> "606 scaled_dot_product_attention_7"; -"605 view_61" -> "606 scaled_dot_product_attention_7"; -"606 scaled_dot_product_attention_7" -> "607 permute_8"; -"607 permute_8" -> "608 view_62"; -"608 view_62" -> "610 view_62_0_0_nncf_smooth_quant_0"; -"609 linear_29_updated_constant0" -> "615 quantize_per_channel_default_30"; -"610 view_62_0_0_nncf_smooth_quant_0" -> "611 quantize_per_tensor_default_46"; -"611 quantize_per_tensor_default_46" -> "612 dequantize_per_tensor_default_46"; -"612 dequantize_per_tensor_default_46" -> "618 linear_29"; -"613 linear_29_scale_0" -> "615 quantize_per_channel_default_30"; -"613 linear_29_scale_0" -> "616 dequantize_per_channel_default_30"; -"614 linear_29_zero_point_0" -> "615 quantize_per_channel_default_30"; -"614 linear_29_zero_point_0" -> "616 dequantize_per_channel_default_30"; -"615 quantize_per_channel_default_30" -> "616 dequantize_per_channel_default_30"; -"616 dequantize_per_channel_default_30" -> "618 linear_29"; -"617 _param_constant93_0_0" -> "618 linear_29"; -"618 linear_29" -> "619 view_63"; -"619 view_63" -> "620 transpose_47"; -"620 transpose_47" -> "621 dropout_22"; -"621 dropout_22" -> "622 add_15"; -"622 add_15" -> "625 layer_norm_15"; -"622 add_15" -> "649 add_16"; -"623 _param_constant94" -> "625 layer_norm_15"; -"624 _param_constant95" -> "625 layer_norm_15"; -"625 layer_norm_15" -> "627 layer_norm_15_0_0_nncf_smooth_quant_0"; -"626 linear_30_updated_constant0" -> "632 quantize_per_channel_default_31"; -"627 layer_norm_15_0_0_nncf_smooth_quant_0" -> "628 quantize_per_tensor_default_47"; -"628 quantize_per_tensor_default_47" -> "629 dequantize_per_tensor_default_47"; -"629 dequantize_per_tensor_default_47" -> "635 linear_30"; -"630 linear_30_scale_0" -> "632 quantize_per_channel_default_31"; -"630 linear_30_scale_0" -> "633 dequantize_per_channel_default_31"; -"631 linear_30_zero_point_0" -> "632 quantize_per_channel_default_31"; -"631 linear_30_zero_point_0" -> "633 dequantize_per_channel_default_31"; -"632 quantize_per_channel_default_31" -> "633 dequantize_per_channel_default_31"; -"633 dequantize_per_channel_default_31" -> "635 linear_30"; -"634 _param_constant97_0_0" -> "635 linear_30"; -"635 linear_30" -> "636 gelu_7"; -"636 gelu_7" -> "637 dropout_23"; -"637 dropout_23" -> "639 dropout_23_0_0_nncf_smooth_quant_0"; -"638 linear_31_updated_constant0" -> "644 quantize_per_channel_default_32"; -"639 dropout_23_0_0_nncf_smooth_quant_0" -> "640 quantize_per_tensor_default_48"; -"640 quantize_per_tensor_default_48" -> "641 dequantize_per_tensor_default_48"; -"641 dequantize_per_tensor_default_48" -> "647 linear_31"; -"642 linear_31_scale_0" -> "644 quantize_per_channel_default_32"; -"642 linear_31_scale_0" -> "645 dequantize_per_channel_default_32"; -"643 linear_31_zero_point_0" -> "644 quantize_per_channel_default_32"; -"643 linear_31_zero_point_0" -> "645 dequantize_per_channel_default_32"; -"644 quantize_per_channel_default_32" -> "645 dequantize_per_channel_default_32"; -"645 dequantize_per_channel_default_32" -> "647 linear_31"; -"646 _param_constant99_0_0" -> "647 linear_31"; -"647 linear_31" -> "648 dropout_24"; -"648 dropout_24" -> "649 add_16"; -"649 add_16" -> "652 layer_norm_16"; -"649 add_16" -> "701 add_17"; -"650 _param_constant100" -> "652 layer_norm_16"; -"651 _param_constant101" -> "652 layer_norm_16"; -"652 layer_norm_16" -> "653 transpose_48"; -"653 transpose_48" -> "655 transpose_48_0_0_nncf_smooth_quant_0"; -"654 linear_32_updated_constant0" -> "660 quantize_per_channel_default_33"; -"655 transpose_48_0_0_nncf_smooth_quant_0" -> "656 quantize_per_tensor_default_49"; -"656 quantize_per_tensor_default_49" -> "657 dequantize_per_tensor_default_49"; -"657 dequantize_per_tensor_default_49" -> "663 linear_32"; -"658 linear_32_scale_0" -> "660 quantize_per_channel_default_33"; -"658 linear_32_scale_0" -> "661 dequantize_per_channel_default_33"; -"659 linear_32_zero_point_0" -> "660 quantize_per_channel_default_33"; -"659 linear_32_zero_point_0" -> "661 dequantize_per_channel_default_33"; -"660 quantize_per_channel_default_33" -> "661 dequantize_per_channel_default_33"; -"661 dequantize_per_channel_default_33" -> "663 linear_32"; -"662 _param_constant103_0_0" -> "663 linear_32"; -"663 linear_32" -> "664 unflatten_8"; -"664 unflatten_8" -> "665 unsqueeze_8"; -"665 unsqueeze_8" -> "666 transpose_49"; -"666 transpose_49" -> "667 squeeze_8"; -"667 squeeze_8" -> "668 contiguous_8"; -"668 contiguous_8" -> "669 quantize_per_tensor_default_50"; -"668 contiguous_8" -> "672 quantize_per_tensor_default_51"; -"668 contiguous_8" -> "675 select_26"; -"669 quantize_per_tensor_default_50" -> "670 dequantize_per_tensor_default_50"; -"670 dequantize_per_tensor_default_50" -> "671 select_24"; -"671 select_24" -> "676 view_64"; -"672 quantize_per_tensor_default_51" -> "673 dequantize_per_tensor_default_51"; -"673 dequantize_per_tensor_default_51" -> "674 select_25"; -"674 select_25" -> "678 view_65"; -"675 select_26" -> "680 view_66"; -"676 view_64" -> "677 transpose_50"; -"677 transpose_50" -> "682 view_67"; -"678 view_65" -> "679 transpose_51"; -"679 transpose_51" -> "683 view_68"; -"680 view_66" -> "681 transpose_52"; -"681 transpose_52" -> "684 view_69"; -"682 view_67" -> "685 scaled_dot_product_attention_8"; -"683 view_68" -> "685 scaled_dot_product_attention_8"; -"684 view_69" -> "685 scaled_dot_product_attention_8"; -"685 scaled_dot_product_attention_8" -> "686 permute_9"; -"686 permute_9" -> "687 view_70"; -"687 view_70" -> "689 view_70_0_0_nncf_smooth_quant_0"; -"688 linear_33_updated_constant0" -> "694 quantize_per_channel_default_34"; -"689 view_70_0_0_nncf_smooth_quant_0" -> "690 quantize_per_tensor_default_52"; -"690 quantize_per_tensor_default_52" -> "691 dequantize_per_tensor_default_52"; -"691 dequantize_per_tensor_default_52" -> "697 linear_33"; -"692 linear_33_scale_0" -> "694 quantize_per_channel_default_34"; -"692 linear_33_scale_0" -> "695 dequantize_per_channel_default_34"; -"693 linear_33_zero_point_0" -> "694 quantize_per_channel_default_34"; -"693 linear_33_zero_point_0" -> "695 dequantize_per_channel_default_34"; -"694 quantize_per_channel_default_34" -> "695 dequantize_per_channel_default_34"; -"695 dequantize_per_channel_default_34" -> "697 linear_33"; -"696 _param_constant105_0_0" -> "697 linear_33"; -"697 linear_33" -> "698 view_71"; -"698 view_71" -> "699 transpose_53"; -"699 transpose_53" -> "700 dropout_25"; -"700 dropout_25" -> "701 add_17"; -"701 add_17" -> "704 layer_norm_17"; -"701 add_17" -> "728 add_18"; -"702 _param_constant106" -> "704 layer_norm_17"; -"703 _param_constant107" -> "704 layer_norm_17"; -"704 layer_norm_17" -> "706 layer_norm_17_0_0_nncf_smooth_quant_0"; -"705 linear_34_updated_constant0" -> "711 quantize_per_channel_default_35"; -"706 layer_norm_17_0_0_nncf_smooth_quant_0" -> "707 quantize_per_tensor_default_53"; -"707 quantize_per_tensor_default_53" -> "708 dequantize_per_tensor_default_53"; -"708 dequantize_per_tensor_default_53" -> "714 linear_34"; -"709 linear_34_scale_0" -> "711 quantize_per_channel_default_35"; -"709 linear_34_scale_0" -> "712 dequantize_per_channel_default_35"; -"710 linear_34_zero_point_0" -> "711 quantize_per_channel_default_35"; -"710 linear_34_zero_point_0" -> "712 dequantize_per_channel_default_35"; -"711 quantize_per_channel_default_35" -> "712 dequantize_per_channel_default_35"; -"712 dequantize_per_channel_default_35" -> "714 linear_34"; -"713 _param_constant109_0_0" -> "714 linear_34"; -"714 linear_34" -> "715 gelu_8"; -"715 gelu_8" -> "716 dropout_26"; -"716 dropout_26" -> "718 dropout_26_0_0_nncf_smooth_quant_0"; -"717 linear_35_updated_constant0" -> "723 quantize_per_channel_default_36"; -"718 dropout_26_0_0_nncf_smooth_quant_0" -> "719 quantize_per_tensor_default_54"; -"719 quantize_per_tensor_default_54" -> "720 dequantize_per_tensor_default_54"; -"720 dequantize_per_tensor_default_54" -> "726 linear_35"; -"721 linear_35_scale_0" -> "723 quantize_per_channel_default_36"; -"721 linear_35_scale_0" -> "724 dequantize_per_channel_default_36"; -"722 linear_35_zero_point_0" -> "723 quantize_per_channel_default_36"; -"722 linear_35_zero_point_0" -> "724 dequantize_per_channel_default_36"; -"723 quantize_per_channel_default_36" -> "724 dequantize_per_channel_default_36"; -"724 dequantize_per_channel_default_36" -> "726 linear_35"; -"725 _param_constant111_0_0" -> "726 linear_35"; -"726 linear_35" -> "727 dropout_27"; -"727 dropout_27" -> "728 add_18"; -"728 add_18" -> "731 layer_norm_18"; -"728 add_18" -> "780 add_19"; -"729 _param_constant112" -> "731 layer_norm_18"; -"730 _param_constant113" -> "731 layer_norm_18"; -"731 layer_norm_18" -> "732 transpose_54"; -"732 transpose_54" -> "734 transpose_54_0_0_nncf_smooth_quant_0"; -"733 linear_36_updated_constant0" -> "739 quantize_per_channel_default_37"; -"734 transpose_54_0_0_nncf_smooth_quant_0" -> "735 quantize_per_tensor_default_55"; -"735 quantize_per_tensor_default_55" -> "736 dequantize_per_tensor_default_55"; -"736 dequantize_per_tensor_default_55" -> "742 linear_36"; -"737 linear_36_scale_0" -> "739 quantize_per_channel_default_37"; -"737 linear_36_scale_0" -> "740 dequantize_per_channel_default_37"; -"738 linear_36_zero_point_0" -> "739 quantize_per_channel_default_37"; -"738 linear_36_zero_point_0" -> "740 dequantize_per_channel_default_37"; -"739 quantize_per_channel_default_37" -> "740 dequantize_per_channel_default_37"; -"740 dequantize_per_channel_default_37" -> "742 linear_36"; -"741 _param_constant115_0_0" -> "742 linear_36"; -"742 linear_36" -> "743 unflatten_9"; -"743 unflatten_9" -> "744 unsqueeze_9"; -"744 unsqueeze_9" -> "745 transpose_55"; -"745 transpose_55" -> "746 squeeze_9"; -"746 squeeze_9" -> "747 contiguous_9"; -"747 contiguous_9" -> "748 quantize_per_tensor_default_56"; -"747 contiguous_9" -> "751 quantize_per_tensor_default_57"; -"747 contiguous_9" -> "754 select_29"; -"748 quantize_per_tensor_default_56" -> "749 dequantize_per_tensor_default_56"; -"749 dequantize_per_tensor_default_56" -> "750 select_27"; -"750 select_27" -> "755 view_72"; -"751 quantize_per_tensor_default_57" -> "752 dequantize_per_tensor_default_57"; -"752 dequantize_per_tensor_default_57" -> "753 select_28"; -"753 select_28" -> "757 view_73"; -"754 select_29" -> "759 view_74"; -"755 view_72" -> "756 transpose_56"; -"756 transpose_56" -> "761 view_75"; -"757 view_73" -> "758 transpose_57"; -"758 transpose_57" -> "762 view_76"; -"759 view_74" -> "760 transpose_58"; -"760 transpose_58" -> "763 view_77"; -"761 view_75" -> "764 scaled_dot_product_attention_9"; -"762 view_76" -> "764 scaled_dot_product_attention_9"; -"763 view_77" -> "764 scaled_dot_product_attention_9"; -"764 scaled_dot_product_attention_9" -> "765 permute_10"; -"765 permute_10" -> "766 view_78"; -"766 view_78" -> "768 view_78_0_0_nncf_smooth_quant_0"; -"767 linear_37_updated_constant0" -> "773 quantize_per_channel_default_38"; -"768 view_78_0_0_nncf_smooth_quant_0" -> "769 quantize_per_tensor_default_58"; -"769 quantize_per_tensor_default_58" -> "770 dequantize_per_tensor_default_58"; -"770 dequantize_per_tensor_default_58" -> "776 linear_37"; -"771 linear_37_scale_0" -> "773 quantize_per_channel_default_38"; -"771 linear_37_scale_0" -> "774 dequantize_per_channel_default_38"; -"772 linear_37_zero_point_0" -> "773 quantize_per_channel_default_38"; -"772 linear_37_zero_point_0" -> "774 dequantize_per_channel_default_38"; -"773 quantize_per_channel_default_38" -> "774 dequantize_per_channel_default_38"; -"774 dequantize_per_channel_default_38" -> "776 linear_37"; -"775 _param_constant117_0_0" -> "776 linear_37"; -"776 linear_37" -> "777 view_79"; -"777 view_79" -> "778 transpose_59"; -"778 transpose_59" -> "779 dropout_28"; -"779 dropout_28" -> "780 add_19"; -"780 add_19" -> "783 layer_norm_19"; -"780 add_19" -> "807 add_20"; -"781 _param_constant118" -> "783 layer_norm_19"; -"782 _param_constant119" -> "783 layer_norm_19"; -"783 layer_norm_19" -> "785 layer_norm_19_0_0_nncf_smooth_quant_0"; -"784 linear_38_updated_constant0" -> "790 quantize_per_channel_default_39"; -"785 layer_norm_19_0_0_nncf_smooth_quant_0" -> "786 quantize_per_tensor_default_59"; -"786 quantize_per_tensor_default_59" -> "787 dequantize_per_tensor_default_59"; -"787 dequantize_per_tensor_default_59" -> "793 linear_38"; -"788 linear_38_scale_0" -> "790 quantize_per_channel_default_39"; -"788 linear_38_scale_0" -> "791 dequantize_per_channel_default_39"; -"789 linear_38_zero_point_0" -> "790 quantize_per_channel_default_39"; -"789 linear_38_zero_point_0" -> "791 dequantize_per_channel_default_39"; -"790 quantize_per_channel_default_39" -> "791 dequantize_per_channel_default_39"; -"791 dequantize_per_channel_default_39" -> "793 linear_38"; -"792 _param_constant121_0_0" -> "793 linear_38"; -"793 linear_38" -> "794 gelu_9"; -"794 gelu_9" -> "795 dropout_29"; -"795 dropout_29" -> "797 dropout_29_0_0_nncf_smooth_quant_0"; -"796 linear_39_updated_constant0" -> "802 quantize_per_channel_default_40"; -"797 dropout_29_0_0_nncf_smooth_quant_0" -> "798 quantize_per_tensor_default_60"; -"798 quantize_per_tensor_default_60" -> "799 dequantize_per_tensor_default_60"; -"799 dequantize_per_tensor_default_60" -> "805 linear_39"; -"800 linear_39_scale_0" -> "802 quantize_per_channel_default_40"; -"800 linear_39_scale_0" -> "803 dequantize_per_channel_default_40"; -"801 linear_39_zero_point_0" -> "802 quantize_per_channel_default_40"; -"801 linear_39_zero_point_0" -> "803 dequantize_per_channel_default_40"; -"802 quantize_per_channel_default_40" -> "803 dequantize_per_channel_default_40"; -"803 dequantize_per_channel_default_40" -> "805 linear_39"; -"804 _param_constant123_0_0" -> "805 linear_39"; -"805 linear_39" -> "806 dropout_30"; -"806 dropout_30" -> "807 add_20"; -"807 add_20" -> "810 layer_norm_20"; -"807 add_20" -> "859 add_21"; -"808 _param_constant124" -> "810 layer_norm_20"; -"809 _param_constant125" -> "810 layer_norm_20"; -"810 layer_norm_20" -> "811 transpose_60"; -"811 transpose_60" -> "813 transpose_60_0_0_nncf_smooth_quant_0"; -"812 linear_40_updated_constant0" -> "818 quantize_per_channel_default_41"; -"813 transpose_60_0_0_nncf_smooth_quant_0" -> "814 quantize_per_tensor_default_61"; -"814 quantize_per_tensor_default_61" -> "815 dequantize_per_tensor_default_61"; -"815 dequantize_per_tensor_default_61" -> "821 linear_40"; -"816 linear_40_scale_0" -> "818 quantize_per_channel_default_41"; -"816 linear_40_scale_0" -> "819 dequantize_per_channel_default_41"; -"817 linear_40_zero_point_0" -> "818 quantize_per_channel_default_41"; -"817 linear_40_zero_point_0" -> "819 dequantize_per_channel_default_41"; -"818 quantize_per_channel_default_41" -> "819 dequantize_per_channel_default_41"; -"819 dequantize_per_channel_default_41" -> "821 linear_40"; -"820 _param_constant127_0_0" -> "821 linear_40"; -"821 linear_40" -> "822 unflatten_10"; -"822 unflatten_10" -> "823 unsqueeze_10"; -"823 unsqueeze_10" -> "824 transpose_61"; -"824 transpose_61" -> "825 squeeze_10"; -"825 squeeze_10" -> "826 contiguous_10"; -"826 contiguous_10" -> "827 quantize_per_tensor_default_62"; -"826 contiguous_10" -> "830 quantize_per_tensor_default_63"; -"826 contiguous_10" -> "833 select_32"; -"827 quantize_per_tensor_default_62" -> "828 dequantize_per_tensor_default_62"; -"828 dequantize_per_tensor_default_62" -> "829 select_30"; -"829 select_30" -> "834 view_80"; -"830 quantize_per_tensor_default_63" -> "831 dequantize_per_tensor_default_63"; -"831 dequantize_per_tensor_default_63" -> "832 select_31"; -"832 select_31" -> "836 view_81"; -"833 select_32" -> "838 view_82"; -"834 view_80" -> "835 transpose_62"; -"835 transpose_62" -> "840 view_83"; -"836 view_81" -> "837 transpose_63"; -"837 transpose_63" -> "841 view_84"; -"838 view_82" -> "839 transpose_64"; -"839 transpose_64" -> "842 view_85"; -"840 view_83" -> "843 scaled_dot_product_attention_10"; -"841 view_84" -> "843 scaled_dot_product_attention_10"; -"842 view_85" -> "843 scaled_dot_product_attention_10"; -"843 scaled_dot_product_attention_10" -> "844 permute_11"; -"844 permute_11" -> "845 view_86"; -"845 view_86" -> "847 view_86_0_0_nncf_smooth_quant_0"; -"846 linear_41_updated_constant0" -> "852 quantize_per_channel_default_42"; -"847 view_86_0_0_nncf_smooth_quant_0" -> "848 quantize_per_tensor_default_64"; -"848 quantize_per_tensor_default_64" -> "849 dequantize_per_tensor_default_64"; -"849 dequantize_per_tensor_default_64" -> "855 linear_41"; -"850 linear_41_scale_0" -> "852 quantize_per_channel_default_42"; -"850 linear_41_scale_0" -> "853 dequantize_per_channel_default_42"; -"851 linear_41_zero_point_0" -> "852 quantize_per_channel_default_42"; -"851 linear_41_zero_point_0" -> "853 dequantize_per_channel_default_42"; -"852 quantize_per_channel_default_42" -> "853 dequantize_per_channel_default_42"; -"853 dequantize_per_channel_default_42" -> "855 linear_41"; -"854 _param_constant129_0_0" -> "855 linear_41"; -"855 linear_41" -> "856 view_87"; -"856 view_87" -> "857 transpose_65"; -"857 transpose_65" -> "858 dropout_31"; -"858 dropout_31" -> "859 add_21"; -"859 add_21" -> "862 layer_norm_21"; -"859 add_21" -> "886 add_22"; -"860 _param_constant130" -> "862 layer_norm_21"; -"861 _param_constant131" -> "862 layer_norm_21"; -"862 layer_norm_21" -> "864 layer_norm_21_0_0_nncf_smooth_quant_0"; -"863 linear_42_updated_constant0" -> "869 quantize_per_channel_default_43"; -"864 layer_norm_21_0_0_nncf_smooth_quant_0" -> "865 quantize_per_tensor_default_65"; -"865 quantize_per_tensor_default_65" -> "866 dequantize_per_tensor_default_65"; -"866 dequantize_per_tensor_default_65" -> "872 linear_42"; -"867 linear_42_scale_0" -> "869 quantize_per_channel_default_43"; -"867 linear_42_scale_0" -> "870 dequantize_per_channel_default_43"; -"868 linear_42_zero_point_0" -> "869 quantize_per_channel_default_43"; -"868 linear_42_zero_point_0" -> "870 dequantize_per_channel_default_43"; -"869 quantize_per_channel_default_43" -> "870 dequantize_per_channel_default_43"; -"870 dequantize_per_channel_default_43" -> "872 linear_42"; -"871 _param_constant133_0_0" -> "872 linear_42"; -"872 linear_42" -> "873 gelu_10"; -"873 gelu_10" -> "874 dropout_32"; -"874 dropout_32" -> "876 dropout_32_0_0_nncf_smooth_quant_0"; -"875 linear_43_updated_constant0" -> "881 quantize_per_channel_default_44"; -"876 dropout_32_0_0_nncf_smooth_quant_0" -> "877 quantize_per_tensor_default_66"; -"877 quantize_per_tensor_default_66" -> "878 dequantize_per_tensor_default_66"; -"878 dequantize_per_tensor_default_66" -> "884 linear_43"; -"879 linear_43_scale_0" -> "881 quantize_per_channel_default_44"; -"879 linear_43_scale_0" -> "882 dequantize_per_channel_default_44"; -"880 linear_43_zero_point_0" -> "881 quantize_per_channel_default_44"; -"880 linear_43_zero_point_0" -> "882 dequantize_per_channel_default_44"; -"881 quantize_per_channel_default_44" -> "882 dequantize_per_channel_default_44"; -"882 dequantize_per_channel_default_44" -> "884 linear_43"; -"883 _param_constant135_0_0" -> "884 linear_43"; -"884 linear_43" -> "885 dropout_33"; -"885 dropout_33" -> "886 add_22"; -"886 add_22" -> "889 layer_norm_22"; -"886 add_22" -> "938 add_23"; -"887 _param_constant136" -> "889 layer_norm_22"; -"888 _param_constant137" -> "889 layer_norm_22"; -"889 layer_norm_22" -> "890 transpose_66"; -"890 transpose_66" -> "892 transpose_66_0_0_nncf_smooth_quant_0"; -"891 linear_44_updated_constant0" -> "897 quantize_per_channel_default_45"; -"892 transpose_66_0_0_nncf_smooth_quant_0" -> "893 quantize_per_tensor_default_67"; -"893 quantize_per_tensor_default_67" -> "894 dequantize_per_tensor_default_67"; -"894 dequantize_per_tensor_default_67" -> "900 linear_44"; -"895 linear_44_scale_0" -> "897 quantize_per_channel_default_45"; -"895 linear_44_scale_0" -> "898 dequantize_per_channel_default_45"; -"896 linear_44_zero_point_0" -> "897 quantize_per_channel_default_45"; -"896 linear_44_zero_point_0" -> "898 dequantize_per_channel_default_45"; -"897 quantize_per_channel_default_45" -> "898 dequantize_per_channel_default_45"; -"898 dequantize_per_channel_default_45" -> "900 linear_44"; -"899 _param_constant139_0_0" -> "900 linear_44"; -"900 linear_44" -> "901 unflatten_11"; -"901 unflatten_11" -> "902 unsqueeze_11"; -"902 unsqueeze_11" -> "903 transpose_67"; -"903 transpose_67" -> "904 squeeze_11"; -"904 squeeze_11" -> "905 contiguous_11"; -"905 contiguous_11" -> "906 quantize_per_tensor_default_68"; -"905 contiguous_11" -> "909 quantize_per_tensor_default_69"; -"905 contiguous_11" -> "912 select_35"; -"906 quantize_per_tensor_default_68" -> "907 dequantize_per_tensor_default_68"; -"907 dequantize_per_tensor_default_68" -> "908 select_33"; -"908 select_33" -> "913 view_88"; -"909 quantize_per_tensor_default_69" -> "910 dequantize_per_tensor_default_69"; -"910 dequantize_per_tensor_default_69" -> "911 select_34"; -"911 select_34" -> "915 view_89"; -"912 select_35" -> "917 view_90"; -"913 view_88" -> "914 transpose_68"; -"914 transpose_68" -> "919 view_91"; -"915 view_89" -> "916 transpose_69"; -"916 transpose_69" -> "920 view_92"; -"917 view_90" -> "918 transpose_70"; -"918 transpose_70" -> "921 view_93"; -"919 view_91" -> "922 scaled_dot_product_attention_11"; -"920 view_92" -> "922 scaled_dot_product_attention_11"; -"921 view_93" -> "922 scaled_dot_product_attention_11"; -"922 scaled_dot_product_attention_11" -> "923 permute_12"; -"923 permute_12" -> "924 view_94"; -"924 view_94" -> "926 view_94_0_0_nncf_smooth_quant_0"; -"925 linear_45_updated_constant0" -> "931 quantize_per_channel_default_46"; -"926 view_94_0_0_nncf_smooth_quant_0" -> "927 quantize_per_tensor_default_70"; -"927 quantize_per_tensor_default_70" -> "928 dequantize_per_tensor_default_70"; -"928 dequantize_per_tensor_default_70" -> "934 linear_45"; -"929 linear_45_scale_0" -> "931 quantize_per_channel_default_46"; -"929 linear_45_scale_0" -> "932 dequantize_per_channel_default_46"; -"930 linear_45_zero_point_0" -> "931 quantize_per_channel_default_46"; -"930 linear_45_zero_point_0" -> "932 dequantize_per_channel_default_46"; -"931 quantize_per_channel_default_46" -> "932 dequantize_per_channel_default_46"; -"932 dequantize_per_channel_default_46" -> "934 linear_45"; -"933 _param_constant141_0_0" -> "934 linear_45"; -"934 linear_45" -> "935 view_95"; -"935 view_95" -> "936 transpose_71"; -"936 transpose_71" -> "937 dropout_34"; -"937 dropout_34" -> "938 add_23"; -"938 add_23" -> "941 layer_norm_23"; -"938 add_23" -> "965 add_24"; -"939 _param_constant142" -> "941 layer_norm_23"; -"940 _param_constant143" -> "941 layer_norm_23"; -"941 layer_norm_23" -> "943 layer_norm_23_0_0_nncf_smooth_quant_0"; -"942 linear_46_updated_constant0" -> "948 quantize_per_channel_default_47"; -"943 layer_norm_23_0_0_nncf_smooth_quant_0" -> "944 quantize_per_tensor_default_71"; -"944 quantize_per_tensor_default_71" -> "945 dequantize_per_tensor_default_71"; -"945 dequantize_per_tensor_default_71" -> "951 linear_46"; -"946 linear_46_scale_0" -> "948 quantize_per_channel_default_47"; -"946 linear_46_scale_0" -> "949 dequantize_per_channel_default_47"; -"947 linear_46_zero_point_0" -> "948 quantize_per_channel_default_47"; -"947 linear_46_zero_point_0" -> "949 dequantize_per_channel_default_47"; -"948 quantize_per_channel_default_47" -> "949 dequantize_per_channel_default_47"; -"949 dequantize_per_channel_default_47" -> "951 linear_46"; -"950 _param_constant145_0_0" -> "951 linear_46"; -"951 linear_46" -> "952 gelu_11"; -"952 gelu_11" -> "953 dropout_35"; -"953 dropout_35" -> "955 dropout_35_0_0_nncf_smooth_quant_0"; -"954 linear_47_updated_constant0" -> "960 quantize_per_channel_default_48"; -"955 dropout_35_0_0_nncf_smooth_quant_0" -> "956 quantize_per_tensor_default_72"; -"956 quantize_per_tensor_default_72" -> "957 dequantize_per_tensor_default_72"; -"957 dequantize_per_tensor_default_72" -> "963 linear_47"; -"958 linear_47_scale_0" -> "960 quantize_per_channel_default_48"; -"958 linear_47_scale_0" -> "961 dequantize_per_channel_default_48"; -"959 linear_47_zero_point_0" -> "960 quantize_per_channel_default_48"; -"959 linear_47_zero_point_0" -> "961 dequantize_per_channel_default_48"; -"960 quantize_per_channel_default_48" -> "961 dequantize_per_channel_default_48"; -"961 dequantize_per_channel_default_48" -> "963 linear_47"; -"962 _param_constant147_0_0" -> "963 linear_47"; -"963 linear_47" -> "964 dropout_36"; -"964 dropout_36" -> "965 add_24"; -"965 add_24" -> "968 layer_norm_24"; -"966 _param_constant148" -> "968 layer_norm_24"; -"967 _param_constant149" -> "968 layer_norm_24"; -"968 layer_norm_24" -> "969 slice_1"; -"969 slice_1" -> "970 select_36"; -"970 select_36" -> "972 select_36_0_0_nncf_smooth_quant_0"; -"971 linear_48_updated_constant0" -> "977 quantize_per_channel_default_49"; -"972 select_36_0_0_nncf_smooth_quant_0" -> "973 quantize_per_tensor_default_73"; -"973 quantize_per_tensor_default_73" -> "974 dequantize_per_tensor_default_73"; -"974 dequantize_per_tensor_default_73" -> "980 linear_48"; -"975 linear_48_scale_0" -> "977 quantize_per_channel_default_49"; -"975 linear_48_scale_0" -> "978 dequantize_per_channel_default_49"; -"976 linear_48_zero_point_0" -> "977 quantize_per_channel_default_49"; -"976 linear_48_zero_point_0" -> "978 dequantize_per_channel_default_49"; -"977 quantize_per_channel_default_49" -> "978 dequantize_per_channel_default_49"; -"978 dequantize_per_channel_default_49" -> "980 linear_48"; -"979 _param_constant151_0_0" -> "980 linear_48"; -"980 linear_48" -> "981 output"; +"960 compressed_weight_updated_constant48" [id=960, type=get_attr]; +"961 quantize_per_channel_default_48" [id=961, type=quantize_per_channel]; +"962 dequantize_per_channel_default_48" [id=962, type=dequantize_per_channel]; +"963 encoder_layers_encoder_layer_11_mlp_3_bias_0_0" [id=963, type=get_attr]; +"964 linear_47" [id=964, type=linear]; +"965 dropout_36" [id=965, type=dropout]; +"966 add_24" [id=966, type=add]; +"967 layer_norm_24" [id=967, type=layer_norm]; +"968 slice_1" [id=968, type=slice]; +"969 select_36" [id=969, type=select]; +"970 select_36_0_0_nncf_smooth_quant_0" [id=970, type=call_module]; +"971 quantize_per_tensor_default_73" [id=971, type=quantize_per_tensor]; +"972 dequantize_per_tensor_default_73" [id=972, type=dequantize_per_tensor]; +"973 linear_48_scale_0" [id=973, type=get_attr]; +"974 linear_48_zero_point_0" [id=974, type=get_attr]; +"975 compressed_weight_updated_constant49" [id=975, type=get_attr]; +"976 quantize_per_channel_default_49" [id=976, type=quantize_per_channel]; +"977 dequantize_per_channel_default_49" [id=977, type=dequantize_per_channel]; +"978 heads_head_bias_0_0" [id=978, type=get_attr]; +"979 linear_48" [id=979, type=linear]; +"980 output" [id=980, type=output]; +"0 encoder_pos_embedding" -> "65 add" [label="(1, 197, 768)", style=solid]; +"1 encoder_layers_encoder_layer_0_ln_1_weight" -> "67 layer_norm" [label="(768,)", style=solid]; +"2 encoder_layers_encoder_layer_0_ln_1_bias" -> "67 layer_norm" [label="(768,)", style=solid]; +"3 encoder_layers_encoder_layer_0_ln_2_weight" -> "117 layer_norm_1" [label="(768,)", style=solid]; +"4 encoder_layers_encoder_layer_0_ln_2_bias" -> "117 layer_norm_1" [label="(768,)", style=solid]; +"5 encoder_layers_encoder_layer_1_ln_1_weight" -> "142 layer_norm_2" [label="(768,)", style=solid]; +"6 encoder_layers_encoder_layer_1_ln_1_bias" -> "142 layer_norm_2" [label="(768,)", style=solid]; +"7 encoder_layers_encoder_layer_1_ln_2_weight" -> "192 layer_norm_3" [label="(768,)", style=solid]; +"8 encoder_layers_encoder_layer_1_ln_2_bias" -> "192 layer_norm_3" [label="(768,)", style=solid]; +"9 encoder_layers_encoder_layer_2_ln_1_weight" -> "217 layer_norm_4" [label="(768,)", style=solid]; +"10 encoder_layers_encoder_layer_2_ln_1_bias" -> "217 layer_norm_4" [label="(768,)", style=solid]; +"11 encoder_layers_encoder_layer_2_ln_2_weight" -> "267 layer_norm_5" [label="(768,)", style=solid]; +"12 encoder_layers_encoder_layer_2_ln_2_bias" -> "267 layer_norm_5" [label="(768,)", style=solid]; +"13 encoder_layers_encoder_layer_3_ln_1_weight" -> "292 layer_norm_6" [label="(768,)", style=solid]; +"14 encoder_layers_encoder_layer_3_ln_1_bias" -> "292 layer_norm_6" [label="(768,)", style=solid]; +"15 encoder_layers_encoder_layer_3_ln_2_weight" -> "342 layer_norm_7" [label="(768,)", style=solid]; +"16 encoder_layers_encoder_layer_3_ln_2_bias" -> "342 layer_norm_7" [label="(768,)", style=solid]; +"17 encoder_layers_encoder_layer_4_ln_1_weight" -> "367 layer_norm_8" [label="(768,)", style=solid]; +"18 encoder_layers_encoder_layer_4_ln_1_bias" -> "367 layer_norm_8" [label="(768,)", style=solid]; +"19 encoder_layers_encoder_layer_4_ln_2_weight" -> "417 layer_norm_9" [label="(768,)", style=solid]; +"20 encoder_layers_encoder_layer_4_ln_2_bias" -> "417 layer_norm_9" [label="(768,)", style=solid]; +"21 encoder_layers_encoder_layer_5_ln_1_weight" -> "442 layer_norm_10" [label="(768,)", style=solid]; +"22 encoder_layers_encoder_layer_5_ln_1_bias" -> "442 layer_norm_10" [label="(768,)", style=solid]; +"23 encoder_layers_encoder_layer_5_ln_2_weight" -> "492 layer_norm_11" [label="(768,)", style=solid]; +"24 encoder_layers_encoder_layer_5_ln_2_bias" -> "492 layer_norm_11" [label="(768,)", style=solid]; +"25 encoder_layers_encoder_layer_6_ln_1_weight" -> "517 layer_norm_12" [label="(768,)", style=solid]; +"26 encoder_layers_encoder_layer_6_ln_1_bias" -> "517 layer_norm_12" [label="(768,)", style=solid]; +"27 encoder_layers_encoder_layer_6_ln_2_weight" -> "567 layer_norm_13" [label="(768,)", style=solid]; +"28 encoder_layers_encoder_layer_6_ln_2_bias" -> "567 layer_norm_13" [label="(768,)", style=solid]; +"29 encoder_layers_encoder_layer_7_ln_1_weight" -> "592 layer_norm_14" [label="(768,)", style=solid]; +"30 encoder_layers_encoder_layer_7_ln_1_bias" -> "592 layer_norm_14" [label="(768,)", style=solid]; +"31 encoder_layers_encoder_layer_7_ln_2_weight" -> "642 layer_norm_15" [label="(768,)", style=solid]; +"32 encoder_layers_encoder_layer_7_ln_2_bias" -> "642 layer_norm_15" [label="(768,)", style=solid]; +"33 encoder_layers_encoder_layer_8_ln_1_weight" -> "667 layer_norm_16" [label="(768,)", style=solid]; +"34 encoder_layers_encoder_layer_8_ln_1_bias" -> "667 layer_norm_16" [label="(768,)", style=solid]; +"35 encoder_layers_encoder_layer_8_ln_2_weight" -> "717 layer_norm_17" [label="(768,)", style=solid]; +"36 encoder_layers_encoder_layer_8_ln_2_bias" -> "717 layer_norm_17" [label="(768,)", style=solid]; +"37 encoder_layers_encoder_layer_9_ln_1_weight" -> "742 layer_norm_18" [label="(768,)", style=solid]; +"38 encoder_layers_encoder_layer_9_ln_1_bias" -> "742 layer_norm_18" [label="(768,)", style=solid]; +"39 encoder_layers_encoder_layer_9_ln_2_weight" -> "792 layer_norm_19" [label="(768,)", style=solid]; +"40 encoder_layers_encoder_layer_9_ln_2_bias" -> "792 layer_norm_19" [label="(768,)", style=solid]; +"41 encoder_layers_encoder_layer_10_ln_1_weight" -> "817 layer_norm_20" [label="(768,)", style=solid]; +"42 encoder_layers_encoder_layer_10_ln_1_bias" -> "817 layer_norm_20" [label="(768,)", style=solid]; +"43 encoder_layers_encoder_layer_10_ln_2_weight" -> "867 layer_norm_21" [label="(768,)", style=solid]; +"44 encoder_layers_encoder_layer_10_ln_2_bias" -> "867 layer_norm_21" [label="(768,)", style=solid]; +"45 encoder_layers_encoder_layer_11_ln_1_weight" -> "892 layer_norm_22" [label="(768,)", style=solid]; +"46 encoder_layers_encoder_layer_11_ln_1_bias" -> "892 layer_norm_22" [label="(768,)", style=solid]; +"47 encoder_layers_encoder_layer_11_ln_2_weight" -> "942 layer_norm_23" [label="(768,)", style=solid]; +"48 encoder_layers_encoder_layer_11_ln_2_bias" -> "942 layer_norm_23" [label="(768,)", style=solid]; +"49 encoder_ln_weight" -> "967 layer_norm_24" [label="(768,)", style=solid]; +"50 encoder_ln_bias" -> "967 layer_norm_24" [label="(768,)", style=solid]; +"51 x" -> "52 quantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"52 quantize_per_tensor_default" -> "53 dequantize_per_tensor_default" [label="(1, 3, 224, 224)", style=solid]; +"53 dequantize_per_tensor_default" -> "60 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"54 conv2d_scale_0" -> "57 quantize_per_channel_default" [label="(768,)", style=solid]; +"54 conv2d_scale_0" -> "58 dequantize_per_channel_default" [label="(768,)", style=solid]; +"55 conv2d_zero_point_0" -> "57 quantize_per_channel_default" [label="(768,)", style=solid]; +"55 conv2d_zero_point_0" -> "58 dequantize_per_channel_default" [label="(768,)", style=solid]; +"56 compressed_weight_updated_constant0" -> "57 quantize_per_channel_default" [label="(768, 3, 16, 16)", style=solid]; +"57 quantize_per_channel_default" -> "58 dequantize_per_channel_default" [label="(768, 3, 16, 16)", style=solid]; +"58 dequantize_per_channel_default" -> "60 conv2d" [label="(768, 3, 16, 16)", style=solid]; +"59 conv_proj_bias_0_0" -> "60 conv2d" [label="(768,)", style=solid]; +"60 conv2d" -> "61 reshape" [label="(1, 768, 14, 14)", style=solid]; +"61 reshape" -> "62 permute" [label="(1, 768, 196)", style=solid]; +"62 permute" -> "64 cat" [label="(1, 196, 768)", style=solid]; +"63 _frozen_param0" -> "64 cat" [label="(1, 1, 768)", style=solid]; +"64 cat" -> "65 add" [label="(1, 197, 768)", style=solid]; +"65 add" -> "66 dropout" [label="(1, 197, 768)", style=solid]; +"66 dropout" -> "67 layer_norm" [label="(1, 197, 768)", style=solid]; +"66 dropout" -> "116 add_1" [label="(1, 197, 768)", style=solid]; +"67 layer_norm" -> "68 transpose" [label="(1, 197, 768)", style=solid]; +"68 transpose" -> "69 transpose_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"69 transpose_0_0_nncf_smooth_quant_0" -> "70 quantize_per_tensor_default_1" [label="(197, 1, 768)", style=solid]; +"70 quantize_per_tensor_default_1" -> "71 dequantize_per_tensor_default_1" [label="(197, 1, 768)", style=solid]; +"71 dequantize_per_tensor_default_1" -> "78 linear" [label="(197, 1, 768)", style=solid]; +"72 linear_scale_0" -> "75 quantize_per_channel_default_1" [label="(2304,)", style=solid]; +"72 linear_scale_0" -> "76 dequantize_per_channel_default_1" [label="(2304,)", style=solid]; +"73 linear_zero_point_0" -> "75 quantize_per_channel_default_1" [label="(2304,)", style=solid]; +"73 linear_zero_point_0" -> "76 dequantize_per_channel_default_1" [label="(2304,)", style=solid]; +"74 compressed_weight_updated_constant1" -> "75 quantize_per_channel_default_1" [label="(2304, 768)", style=solid]; +"75 quantize_per_channel_default_1" -> "76 dequantize_per_channel_default_1" [label="(2304, 768)", style=solid]; +"76 dequantize_per_channel_default_1" -> "78 linear" [label="(2304, 768)", style=solid]; +"77 encoder_layers_encoder_layer_0_self_attention_in_proj_bias_0_0" -> "78 linear" [label="(2304,)", style=solid]; +"78 linear" -> "79 unflatten" [label="(197, 1, 2304)", style=solid]; +"79 unflatten" -> "80 unsqueeze" [label="(197, 1, 3, 768)", style=solid]; +"80 unsqueeze" -> "81 transpose_1" [label="(1, 197, 1, 3, 768)", style=solid]; +"81 transpose_1" -> "82 squeeze" [label="(3, 197, 1, 1, 768)", style=solid]; +"82 squeeze" -> "83 contiguous" [label="(3, 197, 1, 768)", style=solid]; +"83 contiguous" -> "84 quantize_per_tensor_default_2" [label="(3, 197, 1, 768)", style=solid]; +"83 contiguous" -> "87 quantize_per_tensor_default_3" [label="(3, 197, 1, 768)", style=solid]; +"83 contiguous" -> "90 select_2" [label="(3, 197, 1, 768)", style=solid]; +"84 quantize_per_tensor_default_2" -> "85 dequantize_per_tensor_default_2" [label="(3, 197, 1, 768)", style=solid]; +"85 dequantize_per_tensor_default_2" -> "86 select" [label="(3, 197, 1, 768)", style=solid]; +"86 select" -> "91 view" [label="(197, 1, 768)", style=solid]; +"87 quantize_per_tensor_default_3" -> "88 dequantize_per_tensor_default_3" [label="(3, 197, 1, 768)", style=solid]; +"88 dequantize_per_tensor_default_3" -> "89 select_1" [label="(3, 197, 1, 768)", style=solid]; +"89 select_1" -> "93 view_1" [label="(197, 1, 768)", style=solid]; +"90 select_2" -> "95 view_2" [label="(197, 1, 768)", style=solid]; +"91 view" -> "92 transpose_2" [label="(197, 12, 64)", style=solid]; +"92 transpose_2" -> "97 view_3" [label="(12, 197, 64)", style=solid]; +"93 view_1" -> "94 transpose_3" [label="(197, 12, 64)", style=solid]; +"94 transpose_3" -> "98 view_4" [label="(12, 197, 64)", style=solid]; +"95 view_2" -> "96 transpose_4" [label="(197, 12, 64)", style=solid]; +"96 transpose_4" -> "99 view_5" [label="(12, 197, 64)", style=solid]; +"97 view_3" -> "100 scaled_dot_product_attention" [label="(1, 12, 197, 64)", style=solid]; +"98 view_4" -> "100 scaled_dot_product_attention" [label="(1, 12, 197, 64)", style=solid]; +"99 view_5" -> "100 scaled_dot_product_attention" [label="(1, 12, 197, 64)", style=solid]; +"100 scaled_dot_product_attention" -> "101 permute_1" [label="(1, 12, 197, 64)", style=solid]; +"101 permute_1" -> "102 view_6" [label="(197, 1, 12, 64)", style=solid]; +"102 view_6" -> "103 view_6_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"103 view_6_0_0_nncf_smooth_quant_0" -> "104 quantize_per_tensor_default_4" [label="(197, 768)", style=solid]; +"104 quantize_per_tensor_default_4" -> "105 dequantize_per_tensor_default_4" [label="(197, 768)", style=solid]; +"105 dequantize_per_tensor_default_4" -> "112 linear_1" [label="(197, 768)", style=solid]; +"106 linear_1_scale_0" -> "109 quantize_per_channel_default_2" [label="(768,)", style=solid]; +"106 linear_1_scale_0" -> "110 dequantize_per_channel_default_2" [label="(768,)", style=solid]; +"107 linear_1_zero_point_0" -> "109 quantize_per_channel_default_2" [label="(768,)", style=solid]; +"107 linear_1_zero_point_0" -> "110 dequantize_per_channel_default_2" [label="(768,)", style=solid]; +"108 compressed_weight_updated_constant2" -> "109 quantize_per_channel_default_2" [label="(768, 768)", style=solid]; +"109 quantize_per_channel_default_2" -> "110 dequantize_per_channel_default_2" [label="(768, 768)", style=solid]; +"110 dequantize_per_channel_default_2" -> "112 linear_1" [label="(768, 768)", style=solid]; +"111 encoder_layers_encoder_layer_0_self_attention_out_proj_bias_0_0" -> "112 linear_1" [label="(768,)", style=solid]; +"112 linear_1" -> "113 view_7" [label="(197, 768)", style=solid]; +"113 view_7" -> "114 transpose_5" [label="(197, 1, 768)", style=solid]; +"114 transpose_5" -> "115 dropout_1" [label="(1, 197, 768)", style=solid]; +"115 dropout_1" -> "116 add_1" [label="(1, 197, 768)", style=solid]; +"116 add_1" -> "117 layer_norm_1" [label="(1, 197, 768)", style=solid]; +"116 add_1" -> "141 add_2" [label="(1, 197, 768)", style=solid]; +"117 layer_norm_1" -> "118 layer_norm_1_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"118 layer_norm_1_0_0_nncf_smooth_quant_0" -> "119 quantize_per_tensor_default_5" [label="(1, 197, 768)", style=solid]; +"119 quantize_per_tensor_default_5" -> "120 dequantize_per_tensor_default_5" [label="(1, 197, 768)", style=solid]; +"120 dequantize_per_tensor_default_5" -> "127 linear_2" [label="(1, 197, 768)", style=solid]; +"121 linear_2_scale_0" -> "124 quantize_per_channel_default_3" [label="(3072,)", style=solid]; +"121 linear_2_scale_0" -> "125 dequantize_per_channel_default_3" [label="(3072,)", style=solid]; +"122 linear_2_zero_point_0" -> "124 quantize_per_channel_default_3" [label="(3072,)", style=solid]; +"122 linear_2_zero_point_0" -> "125 dequantize_per_channel_default_3" [label="(3072,)", style=solid]; +"123 compressed_weight_updated_constant3" -> "124 quantize_per_channel_default_3" [label="(3072, 768)", style=solid]; +"124 quantize_per_channel_default_3" -> "125 dequantize_per_channel_default_3" [label="(3072, 768)", style=solid]; +"125 dequantize_per_channel_default_3" -> "127 linear_2" [label="(3072, 768)", style=solid]; +"126 encoder_layers_encoder_layer_0_mlp_0_bias_0_0" -> "127 linear_2" [label="(3072,)", style=solid]; +"127 linear_2" -> "128 gelu" [label="(1, 197, 3072)", style=solid]; +"128 gelu" -> "129 dropout_2" [label="(1, 197, 3072)", style=solid]; +"129 dropout_2" -> "130 dropout_2_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"130 dropout_2_0_0_nncf_smooth_quant_0" -> "131 quantize_per_tensor_default_6" [label="(1, 197, 3072)", style=solid]; +"131 quantize_per_tensor_default_6" -> "132 dequantize_per_tensor_default_6" [label="(1, 197, 3072)", style=solid]; +"132 dequantize_per_tensor_default_6" -> "139 linear_3" [label="(1, 197, 3072)", style=solid]; +"133 linear_3_scale_0" -> "136 quantize_per_channel_default_4" [label="(768,)", style=solid]; +"133 linear_3_scale_0" -> "137 dequantize_per_channel_default_4" [label="(768,)", style=solid]; +"134 linear_3_zero_point_0" -> "136 quantize_per_channel_default_4" [label="(768,)", style=solid]; +"134 linear_3_zero_point_0" -> "137 dequantize_per_channel_default_4" [label="(768,)", style=solid]; +"135 compressed_weight_updated_constant4" -> "136 quantize_per_channel_default_4" [label="(768, 3072)", style=solid]; +"136 quantize_per_channel_default_4" -> "137 dequantize_per_channel_default_4" [label="(768, 3072)", style=solid]; +"137 dequantize_per_channel_default_4" -> "139 linear_3" [label="(768, 3072)", style=solid]; +"138 encoder_layers_encoder_layer_0_mlp_3_bias_0_0" -> "139 linear_3" [label="(768,)", style=solid]; +"139 linear_3" -> "140 dropout_3" [label="(1, 197, 768)", style=solid]; +"140 dropout_3" -> "141 add_2" [label="(1, 197, 768)", style=solid]; +"141 add_2" -> "142 layer_norm_2" [label="(1, 197, 768)", style=solid]; +"141 add_2" -> "191 add_3" [label="(1, 197, 768)", style=solid]; +"142 layer_norm_2" -> "143 transpose_6" [label="(1, 197, 768)", style=solid]; +"143 transpose_6" -> "144 transpose_6_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"144 transpose_6_0_0_nncf_smooth_quant_0" -> "145 quantize_per_tensor_default_7" [label="(197, 1, 768)", style=solid]; +"145 quantize_per_tensor_default_7" -> "146 dequantize_per_tensor_default_7" [label="(197, 1, 768)", style=solid]; +"146 dequantize_per_tensor_default_7" -> "153 linear_4" [label="(197, 1, 768)", style=solid]; +"147 linear_4_scale_0" -> "150 quantize_per_channel_default_5" [label="(2304,)", style=solid]; +"147 linear_4_scale_0" -> "151 dequantize_per_channel_default_5" [label="(2304,)", style=solid]; +"148 linear_4_zero_point_0" -> "150 quantize_per_channel_default_5" [label="(2304,)", style=solid]; +"148 linear_4_zero_point_0" -> "151 dequantize_per_channel_default_5" [label="(2304,)", style=solid]; +"149 compressed_weight_updated_constant5" -> "150 quantize_per_channel_default_5" [label="(2304, 768)", style=solid]; +"150 quantize_per_channel_default_5" -> "151 dequantize_per_channel_default_5" [label="(2304, 768)", style=solid]; +"151 dequantize_per_channel_default_5" -> "153 linear_4" [label="(2304, 768)", style=solid]; +"152 encoder_layers_encoder_layer_1_self_attention_in_proj_bias_0_0" -> "153 linear_4" [label="(2304,)", style=solid]; +"153 linear_4" -> "154 unflatten_1" [label="(197, 1, 2304)", style=solid]; +"154 unflatten_1" -> "155 unsqueeze_1" [label="(197, 1, 3, 768)", style=solid]; +"155 unsqueeze_1" -> "156 transpose_7" [label="(1, 197, 1, 3, 768)", style=solid]; +"156 transpose_7" -> "157 squeeze_1" [label="(3, 197, 1, 1, 768)", style=solid]; +"157 squeeze_1" -> "158 contiguous_1" [label="(3, 197, 1, 768)", style=solid]; +"158 contiguous_1" -> "159 quantize_per_tensor_default_8" [label="(3, 197, 1, 768)", style=solid]; +"158 contiguous_1" -> "162 quantize_per_tensor_default_9" [label="(3, 197, 1, 768)", style=solid]; +"158 contiguous_1" -> "165 select_5" [label="(3, 197, 1, 768)", style=solid]; +"159 quantize_per_tensor_default_8" -> "160 dequantize_per_tensor_default_8" [label="(3, 197, 1, 768)", style=solid]; +"160 dequantize_per_tensor_default_8" -> "161 select_3" [label="(3, 197, 1, 768)", style=solid]; +"161 select_3" -> "166 view_8" [label="(197, 1, 768)", style=solid]; +"162 quantize_per_tensor_default_9" -> "163 dequantize_per_tensor_default_9" [label="(3, 197, 1, 768)", style=solid]; +"163 dequantize_per_tensor_default_9" -> "164 select_4" [label="(3, 197, 1, 768)", style=solid]; +"164 select_4" -> "168 view_9" [label="(197, 1, 768)", style=solid]; +"165 select_5" -> "170 view_10" [label="(197, 1, 768)", style=solid]; +"166 view_8" -> "167 transpose_8" [label="(197, 12, 64)", style=solid]; +"167 transpose_8" -> "172 view_11" [label="(12, 197, 64)", style=solid]; +"168 view_9" -> "169 transpose_9" [label="(197, 12, 64)", style=solid]; +"169 transpose_9" -> "173 view_12" [label="(12, 197, 64)", style=solid]; +"170 view_10" -> "171 transpose_10" [label="(197, 12, 64)", style=solid]; +"171 transpose_10" -> "174 view_13" [label="(12, 197, 64)", style=solid]; +"172 view_11" -> "175 scaled_dot_product_attention_1" [label="(1, 12, 197, 64)", style=solid]; +"173 view_12" -> "175 scaled_dot_product_attention_1" [label="(1, 12, 197, 64)", style=solid]; +"174 view_13" -> "175 scaled_dot_product_attention_1" [label="(1, 12, 197, 64)", style=solid]; +"175 scaled_dot_product_attention_1" -> "176 permute_2" [label="(1, 12, 197, 64)", style=solid]; +"176 permute_2" -> "177 view_14" [label="(197, 1, 12, 64)", style=solid]; +"177 view_14" -> "178 view_14_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"178 view_14_0_0_nncf_smooth_quant_0" -> "179 quantize_per_tensor_default_10" [label="(197, 768)", style=solid]; +"179 quantize_per_tensor_default_10" -> "180 dequantize_per_tensor_default_10" [label="(197, 768)", style=solid]; +"180 dequantize_per_tensor_default_10" -> "187 linear_5" [label="(197, 768)", style=solid]; +"181 linear_5_scale_0" -> "184 quantize_per_channel_default_6" [label="(768,)", style=solid]; +"181 linear_5_scale_0" -> "185 dequantize_per_channel_default_6" [label="(768,)", style=solid]; +"182 linear_5_zero_point_0" -> "184 quantize_per_channel_default_6" [label="(768,)", style=solid]; +"182 linear_5_zero_point_0" -> "185 dequantize_per_channel_default_6" [label="(768,)", style=solid]; +"183 compressed_weight_updated_constant6" -> "184 quantize_per_channel_default_6" [label="(768, 768)", style=solid]; +"184 quantize_per_channel_default_6" -> "185 dequantize_per_channel_default_6" [label="(768, 768)", style=solid]; +"185 dequantize_per_channel_default_6" -> "187 linear_5" [label="(768, 768)", style=solid]; +"186 encoder_layers_encoder_layer_1_self_attention_out_proj_bias_0_0" -> "187 linear_5" [label="(768,)", style=solid]; +"187 linear_5" -> "188 view_15" [label="(197, 768)", style=solid]; +"188 view_15" -> "189 transpose_11" [label="(197, 1, 768)", style=solid]; +"189 transpose_11" -> "190 dropout_4" [label="(1, 197, 768)", style=solid]; +"190 dropout_4" -> "191 add_3" [label="(1, 197, 768)", style=solid]; +"191 add_3" -> "192 layer_norm_3" [label="(1, 197, 768)", style=solid]; +"191 add_3" -> "216 add_4" [label="(1, 197, 768)", style=solid]; +"192 layer_norm_3" -> "193 layer_norm_3_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"193 layer_norm_3_0_0_nncf_smooth_quant_0" -> "194 quantize_per_tensor_default_11" [label="(1, 197, 768)", style=solid]; +"194 quantize_per_tensor_default_11" -> "195 dequantize_per_tensor_default_11" [label="(1, 197, 768)", style=solid]; +"195 dequantize_per_tensor_default_11" -> "202 linear_6" [label="(1, 197, 768)", style=solid]; +"196 linear_6_scale_0" -> "199 quantize_per_channel_default_7" [label="(3072,)", style=solid]; +"196 linear_6_scale_0" -> "200 dequantize_per_channel_default_7" [label="(3072,)", style=solid]; +"197 linear_6_zero_point_0" -> "199 quantize_per_channel_default_7" [label="(3072,)", style=solid]; +"197 linear_6_zero_point_0" -> "200 dequantize_per_channel_default_7" [label="(3072,)", style=solid]; +"198 compressed_weight_updated_constant7" -> "199 quantize_per_channel_default_7" [label="(3072, 768)", style=solid]; +"199 quantize_per_channel_default_7" -> "200 dequantize_per_channel_default_7" [label="(3072, 768)", style=solid]; +"200 dequantize_per_channel_default_7" -> "202 linear_6" [label="(3072, 768)", style=solid]; +"201 encoder_layers_encoder_layer_1_mlp_0_bias_0_0" -> "202 linear_6" [label="(3072,)", style=solid]; +"202 linear_6" -> "203 gelu_1" [label="(1, 197, 3072)", style=solid]; +"203 gelu_1" -> "204 dropout_5" [label="(1, 197, 3072)", style=solid]; +"204 dropout_5" -> "205 dropout_5_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"205 dropout_5_0_0_nncf_smooth_quant_0" -> "206 quantize_per_tensor_default_12" [label="(1, 197, 3072)", style=solid]; +"206 quantize_per_tensor_default_12" -> "207 dequantize_per_tensor_default_12" [label="(1, 197, 3072)", style=solid]; +"207 dequantize_per_tensor_default_12" -> "214 linear_7" [label="(1, 197, 3072)", style=solid]; +"208 linear_7_scale_0" -> "211 quantize_per_channel_default_8" [label="(768,)", style=solid]; +"208 linear_7_scale_0" -> "212 dequantize_per_channel_default_8" [label="(768,)", style=solid]; +"209 linear_7_zero_point_0" -> "211 quantize_per_channel_default_8" [label="(768,)", style=solid]; +"209 linear_7_zero_point_0" -> "212 dequantize_per_channel_default_8" [label="(768,)", style=solid]; +"210 compressed_weight_updated_constant8" -> "211 quantize_per_channel_default_8" [label="(768, 3072)", style=solid]; +"211 quantize_per_channel_default_8" -> "212 dequantize_per_channel_default_8" [label="(768, 3072)", style=solid]; +"212 dequantize_per_channel_default_8" -> "214 linear_7" [label="(768, 3072)", style=solid]; +"213 encoder_layers_encoder_layer_1_mlp_3_bias_0_0" -> "214 linear_7" [label="(768,)", style=solid]; +"214 linear_7" -> "215 dropout_6" [label="(1, 197, 768)", style=solid]; +"215 dropout_6" -> "216 add_4" [label="(1, 197, 768)", style=solid]; +"216 add_4" -> "217 layer_norm_4" [label="(1, 197, 768)", style=solid]; +"216 add_4" -> "266 add_5" [label="(1, 197, 768)", style=solid]; +"217 layer_norm_4" -> "218 transpose_12" [label="(1, 197, 768)", style=solid]; +"218 transpose_12" -> "219 transpose_12_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"219 transpose_12_0_0_nncf_smooth_quant_0" -> "220 quantize_per_tensor_default_13" [label="(197, 1, 768)", style=solid]; +"220 quantize_per_tensor_default_13" -> "221 dequantize_per_tensor_default_13" [label="(197, 1, 768)", style=solid]; +"221 dequantize_per_tensor_default_13" -> "228 linear_8" [label="(197, 1, 768)", style=solid]; +"222 linear_8_scale_0" -> "225 quantize_per_channel_default_9" [label="(2304,)", style=solid]; +"222 linear_8_scale_0" -> "226 dequantize_per_channel_default_9" [label="(2304,)", style=solid]; +"223 linear_8_zero_point_0" -> "225 quantize_per_channel_default_9" [label="(2304,)", style=solid]; +"223 linear_8_zero_point_0" -> "226 dequantize_per_channel_default_9" [label="(2304,)", style=solid]; +"224 compressed_weight_updated_constant9" -> "225 quantize_per_channel_default_9" [label="(2304, 768)", style=solid]; +"225 quantize_per_channel_default_9" -> "226 dequantize_per_channel_default_9" [label="(2304, 768)", style=solid]; +"226 dequantize_per_channel_default_9" -> "228 linear_8" [label="(2304, 768)", style=solid]; +"227 encoder_layers_encoder_layer_2_self_attention_in_proj_bias_0_0" -> "228 linear_8" [label="(2304,)", style=solid]; +"228 linear_8" -> "229 unflatten_2" [label="(197, 1, 2304)", style=solid]; +"229 unflatten_2" -> "230 unsqueeze_2" [label="(197, 1, 3, 768)", style=solid]; +"230 unsqueeze_2" -> "231 transpose_13" [label="(1, 197, 1, 3, 768)", style=solid]; +"231 transpose_13" -> "232 squeeze_2" [label="(3, 197, 1, 1, 768)", style=solid]; +"232 squeeze_2" -> "233 contiguous_2" [label="(3, 197, 1, 768)", style=solid]; +"233 contiguous_2" -> "234 quantize_per_tensor_default_14" [label="(3, 197, 1, 768)", style=solid]; +"233 contiguous_2" -> "237 quantize_per_tensor_default_15" [label="(3, 197, 1, 768)", style=solid]; +"233 contiguous_2" -> "240 select_8" [label="(3, 197, 1, 768)", style=solid]; +"234 quantize_per_tensor_default_14" -> "235 dequantize_per_tensor_default_14" [label="(3, 197, 1, 768)", style=solid]; +"235 dequantize_per_tensor_default_14" -> "236 select_6" [label="(3, 197, 1, 768)", style=solid]; +"236 select_6" -> "241 view_16" [label="(197, 1, 768)", style=solid]; +"237 quantize_per_tensor_default_15" -> "238 dequantize_per_tensor_default_15" [label="(3, 197, 1, 768)", style=solid]; +"238 dequantize_per_tensor_default_15" -> "239 select_7" [label="(3, 197, 1, 768)", style=solid]; +"239 select_7" -> "243 view_17" [label="(197, 1, 768)", style=solid]; +"240 select_8" -> "245 view_18" [label="(197, 1, 768)", style=solid]; +"241 view_16" -> "242 transpose_14" [label="(197, 12, 64)", style=solid]; +"242 transpose_14" -> "247 view_19" [label="(12, 197, 64)", style=solid]; +"243 view_17" -> "244 transpose_15" [label="(197, 12, 64)", style=solid]; +"244 transpose_15" -> "248 view_20" [label="(12, 197, 64)", style=solid]; +"245 view_18" -> "246 transpose_16" [label="(197, 12, 64)", style=solid]; +"246 transpose_16" -> "249 view_21" [label="(12, 197, 64)", style=solid]; +"247 view_19" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"248 view_20" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"249 view_21" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"250 scaled_dot_product_attention_2" -> "251 permute_3" [label="(1, 12, 197, 64)", style=solid]; +"251 permute_3" -> "252 view_22" [label="(197, 1, 12, 64)", style=solid]; +"252 view_22" -> "253 view_22_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"253 view_22_0_0_nncf_smooth_quant_0" -> "254 quantize_per_tensor_default_16" [label="(197, 768)", style=solid]; +"254 quantize_per_tensor_default_16" -> "255 dequantize_per_tensor_default_16" [label="(197, 768)", style=solid]; +"255 dequantize_per_tensor_default_16" -> "262 linear_9" [label="(197, 768)", style=solid]; +"256 linear_9_scale_0" -> "259 quantize_per_channel_default_10" [label="(768,)", style=solid]; +"256 linear_9_scale_0" -> "260 dequantize_per_channel_default_10" [label="(768,)", style=solid]; +"257 linear_9_zero_point_0" -> "259 quantize_per_channel_default_10" [label="(768,)", style=solid]; +"257 linear_9_zero_point_0" -> "260 dequantize_per_channel_default_10" [label="(768,)", style=solid]; +"258 compressed_weight_updated_constant10" -> "259 quantize_per_channel_default_10" [label="(768, 768)", style=solid]; +"259 quantize_per_channel_default_10" -> "260 dequantize_per_channel_default_10" [label="(768, 768)", style=solid]; +"260 dequantize_per_channel_default_10" -> "262 linear_9" [label="(768, 768)", style=solid]; +"261 encoder_layers_encoder_layer_2_self_attention_out_proj_bias_0_0" -> "262 linear_9" [label="(768,)", style=solid]; +"262 linear_9" -> "263 view_23" [label="(197, 768)", style=solid]; +"263 view_23" -> "264 transpose_17" [label="(197, 1, 768)", style=solid]; +"264 transpose_17" -> "265 dropout_7" [label="(1, 197, 768)", style=solid]; +"265 dropout_7" -> "266 add_5" [label="(1, 197, 768)", style=solid]; +"266 add_5" -> "267 layer_norm_5" [label="(1, 197, 768)", style=solid]; +"266 add_5" -> "291 add_6" [label="(1, 197, 768)", style=solid]; +"267 layer_norm_5" -> "268 layer_norm_5_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"268 layer_norm_5_0_0_nncf_smooth_quant_0" -> "269 quantize_per_tensor_default_17" [label="(1, 197, 768)", style=solid]; +"269 quantize_per_tensor_default_17" -> "270 dequantize_per_tensor_default_17" [label="(1, 197, 768)", style=solid]; +"270 dequantize_per_tensor_default_17" -> "277 linear_10" [label="(1, 197, 768)", style=solid]; +"271 linear_10_scale_0" -> "274 quantize_per_channel_default_11" [label="(3072,)", style=solid]; +"271 linear_10_scale_0" -> "275 dequantize_per_channel_default_11" [label="(3072,)", style=solid]; +"272 linear_10_zero_point_0" -> "274 quantize_per_channel_default_11" [label="(3072,)", style=solid]; +"272 linear_10_zero_point_0" -> "275 dequantize_per_channel_default_11" [label="(3072,)", style=solid]; +"273 compressed_weight_updated_constant11" -> "274 quantize_per_channel_default_11" [label="(3072, 768)", style=solid]; +"274 quantize_per_channel_default_11" -> "275 dequantize_per_channel_default_11" [label="(3072, 768)", style=solid]; +"275 dequantize_per_channel_default_11" -> "277 linear_10" [label="(3072, 768)", style=solid]; +"276 encoder_layers_encoder_layer_2_mlp_0_bias_0_0" -> "277 linear_10" [label="(3072,)", style=solid]; +"277 linear_10" -> "278 gelu_2" [label="(1, 197, 3072)", style=solid]; +"278 gelu_2" -> "279 dropout_8" [label="(1, 197, 3072)", style=solid]; +"279 dropout_8" -> "280 dropout_8_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"280 dropout_8_0_0_nncf_smooth_quant_0" -> "281 quantize_per_tensor_default_18" [label="(1, 197, 3072)", style=solid]; +"281 quantize_per_tensor_default_18" -> "282 dequantize_per_tensor_default_18" [label="(1, 197, 3072)", style=solid]; +"282 dequantize_per_tensor_default_18" -> "289 linear_11" [label="(1, 197, 3072)", style=solid]; +"283 linear_11_scale_0" -> "286 quantize_per_channel_default_12" [label="(768,)", style=solid]; +"283 linear_11_scale_0" -> "287 dequantize_per_channel_default_12" [label="(768,)", style=solid]; +"284 linear_11_zero_point_0" -> "286 quantize_per_channel_default_12" [label="(768,)", style=solid]; +"284 linear_11_zero_point_0" -> "287 dequantize_per_channel_default_12" [label="(768,)", style=solid]; +"285 compressed_weight_updated_constant12" -> "286 quantize_per_channel_default_12" [label="(768, 3072)", style=solid]; +"286 quantize_per_channel_default_12" -> "287 dequantize_per_channel_default_12" [label="(768, 3072)", style=solid]; +"287 dequantize_per_channel_default_12" -> "289 linear_11" [label="(768, 3072)", style=solid]; +"288 encoder_layers_encoder_layer_2_mlp_3_bias_0_0" -> "289 linear_11" [label="(768,)", style=solid]; +"289 linear_11" -> "290 dropout_9" [label="(1, 197, 768)", style=solid]; +"290 dropout_9" -> "291 add_6" [label="(1, 197, 768)", style=solid]; +"291 add_6" -> "292 layer_norm_6" [label="(1, 197, 768)", style=solid]; +"291 add_6" -> "341 add_7" [label="(1, 197, 768)", style=solid]; +"292 layer_norm_6" -> "293 transpose_18" [label="(1, 197, 768)", style=solid]; +"293 transpose_18" -> "294 transpose_18_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"294 transpose_18_0_0_nncf_smooth_quant_0" -> "295 quantize_per_tensor_default_19" [label="(197, 1, 768)", style=solid]; +"295 quantize_per_tensor_default_19" -> "296 dequantize_per_tensor_default_19" [label="(197, 1, 768)", style=solid]; +"296 dequantize_per_tensor_default_19" -> "303 linear_12" [label="(197, 1, 768)", style=solid]; +"297 linear_12_scale_0" -> "300 quantize_per_channel_default_13" [label="(2304,)", style=solid]; +"297 linear_12_scale_0" -> "301 dequantize_per_channel_default_13" [label="(2304,)", style=solid]; +"298 linear_12_zero_point_0" -> "300 quantize_per_channel_default_13" [label="(2304,)", style=solid]; +"298 linear_12_zero_point_0" -> "301 dequantize_per_channel_default_13" [label="(2304,)", style=solid]; +"299 compressed_weight_updated_constant13" -> "300 quantize_per_channel_default_13" [label="(2304, 768)", style=solid]; +"300 quantize_per_channel_default_13" -> "301 dequantize_per_channel_default_13" [label="(2304, 768)", style=solid]; +"301 dequantize_per_channel_default_13" -> "303 linear_12" [label="(2304, 768)", style=solid]; +"302 encoder_layers_encoder_layer_3_self_attention_in_proj_bias_0_0" -> "303 linear_12" [label="(2304,)", style=solid]; +"303 linear_12" -> "304 unflatten_3" [label="(197, 1, 2304)", style=solid]; +"304 unflatten_3" -> "305 unsqueeze_3" [label="(197, 1, 3, 768)", style=solid]; +"305 unsqueeze_3" -> "306 transpose_19" [label="(1, 197, 1, 3, 768)", style=solid]; +"306 transpose_19" -> "307 squeeze_3" [label="(3, 197, 1, 1, 768)", style=solid]; +"307 squeeze_3" -> "308 contiguous_3" [label="(3, 197, 1, 768)", style=solid]; +"308 contiguous_3" -> "309 quantize_per_tensor_default_20" [label="(3, 197, 1, 768)", style=solid]; +"308 contiguous_3" -> "312 quantize_per_tensor_default_21" [label="(3, 197, 1, 768)", style=solid]; +"308 contiguous_3" -> "315 select_11" [label="(3, 197, 1, 768)", style=solid]; +"309 quantize_per_tensor_default_20" -> "310 dequantize_per_tensor_default_20" [label="(3, 197, 1, 768)", style=solid]; +"310 dequantize_per_tensor_default_20" -> "311 select_9" [label="(3, 197, 1, 768)", style=solid]; +"311 select_9" -> "316 view_24" [label="(197, 1, 768)", style=solid]; +"312 quantize_per_tensor_default_21" -> "313 dequantize_per_tensor_default_21" [label="(3, 197, 1, 768)", style=solid]; +"313 dequantize_per_tensor_default_21" -> "314 select_10" [label="(3, 197, 1, 768)", style=solid]; +"314 select_10" -> "318 view_25" [label="(197, 1, 768)", style=solid]; +"315 select_11" -> "320 view_26" [label="(197, 1, 768)", style=solid]; +"316 view_24" -> "317 transpose_20" [label="(197, 12, 64)", style=solid]; +"317 transpose_20" -> "322 view_27" [label="(12, 197, 64)", style=solid]; +"318 view_25" -> "319 transpose_21" [label="(197, 12, 64)", style=solid]; +"319 transpose_21" -> "323 view_28" [label="(12, 197, 64)", style=solid]; +"320 view_26" -> "321 transpose_22" [label="(197, 12, 64)", style=solid]; +"321 transpose_22" -> "324 view_29" [label="(12, 197, 64)", style=solid]; +"322 view_27" -> "325 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"323 view_28" -> "325 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"324 view_29" -> "325 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"325 scaled_dot_product_attention_3" -> "326 permute_4" [label="(1, 12, 197, 64)", style=solid]; +"326 permute_4" -> "327 view_30" [label="(197, 1, 12, 64)", style=solid]; +"327 view_30" -> "328 view_30_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"328 view_30_0_0_nncf_smooth_quant_0" -> "329 quantize_per_tensor_default_22" [label="(197, 768)", style=solid]; +"329 quantize_per_tensor_default_22" -> "330 dequantize_per_tensor_default_22" [label="(197, 768)", style=solid]; +"330 dequantize_per_tensor_default_22" -> "337 linear_13" [label="(197, 768)", style=solid]; +"331 linear_13_scale_0" -> "334 quantize_per_channel_default_14" [label="(768,)", style=solid]; +"331 linear_13_scale_0" -> "335 dequantize_per_channel_default_14" [label="(768,)", style=solid]; +"332 linear_13_zero_point_0" -> "334 quantize_per_channel_default_14" [label="(768,)", style=solid]; +"332 linear_13_zero_point_0" -> "335 dequantize_per_channel_default_14" [label="(768,)", style=solid]; +"333 compressed_weight_updated_constant14" -> "334 quantize_per_channel_default_14" [label="(768, 768)", style=solid]; +"334 quantize_per_channel_default_14" -> "335 dequantize_per_channel_default_14" [label="(768, 768)", style=solid]; +"335 dequantize_per_channel_default_14" -> "337 linear_13" [label="(768, 768)", style=solid]; +"336 encoder_layers_encoder_layer_3_self_attention_out_proj_bias_0_0" -> "337 linear_13" [label="(768,)", style=solid]; +"337 linear_13" -> "338 view_31" [label="(197, 768)", style=solid]; +"338 view_31" -> "339 transpose_23" [label="(197, 1, 768)", style=solid]; +"339 transpose_23" -> "340 dropout_10" [label="(1, 197, 768)", style=solid]; +"340 dropout_10" -> "341 add_7" [label="(1, 197, 768)", style=solid]; +"341 add_7" -> "342 layer_norm_7" [label="(1, 197, 768)", style=solid]; +"341 add_7" -> "366 add_8" [label="(1, 197, 768)", style=solid]; +"342 layer_norm_7" -> "343 layer_norm_7_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"343 layer_norm_7_0_0_nncf_smooth_quant_0" -> "344 quantize_per_tensor_default_23" [label="(1, 197, 768)", style=solid]; +"344 quantize_per_tensor_default_23" -> "345 dequantize_per_tensor_default_23" [label="(1, 197, 768)", style=solid]; +"345 dequantize_per_tensor_default_23" -> "352 linear_14" [label="(1, 197, 768)", style=solid]; +"346 linear_14_scale_0" -> "349 quantize_per_channel_default_15" [label="(3072,)", style=solid]; +"346 linear_14_scale_0" -> "350 dequantize_per_channel_default_15" [label="(3072,)", style=solid]; +"347 linear_14_zero_point_0" -> "349 quantize_per_channel_default_15" [label="(3072,)", style=solid]; +"347 linear_14_zero_point_0" -> "350 dequantize_per_channel_default_15" [label="(3072,)", style=solid]; +"348 compressed_weight_updated_constant15" -> "349 quantize_per_channel_default_15" [label="(3072, 768)", style=solid]; +"349 quantize_per_channel_default_15" -> "350 dequantize_per_channel_default_15" [label="(3072, 768)", style=solid]; +"350 dequantize_per_channel_default_15" -> "352 linear_14" [label="(3072, 768)", style=solid]; +"351 encoder_layers_encoder_layer_3_mlp_0_bias_0_0" -> "352 linear_14" [label="(3072,)", style=solid]; +"352 linear_14" -> "353 gelu_3" [label="(1, 197, 3072)", style=solid]; +"353 gelu_3" -> "354 dropout_11" [label="(1, 197, 3072)", style=solid]; +"354 dropout_11" -> "355 dropout_11_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"355 dropout_11_0_0_nncf_smooth_quant_0" -> "356 quantize_per_tensor_default_24" [label="(1, 197, 3072)", style=solid]; +"356 quantize_per_tensor_default_24" -> "357 dequantize_per_tensor_default_24" [label="(1, 197, 3072)", style=solid]; +"357 dequantize_per_tensor_default_24" -> "364 linear_15" [label="(1, 197, 3072)", style=solid]; +"358 linear_15_scale_0" -> "361 quantize_per_channel_default_16" [label="(768,)", style=solid]; +"358 linear_15_scale_0" -> "362 dequantize_per_channel_default_16" [label="(768,)", style=solid]; +"359 linear_15_zero_point_0" -> "361 quantize_per_channel_default_16" [label="(768,)", style=solid]; +"359 linear_15_zero_point_0" -> "362 dequantize_per_channel_default_16" [label="(768,)", style=solid]; +"360 compressed_weight_updated_constant16" -> "361 quantize_per_channel_default_16" [label="(768, 3072)", style=solid]; +"361 quantize_per_channel_default_16" -> "362 dequantize_per_channel_default_16" [label="(768, 3072)", style=solid]; +"362 dequantize_per_channel_default_16" -> "364 linear_15" [label="(768, 3072)", style=solid]; +"363 encoder_layers_encoder_layer_3_mlp_3_bias_0_0" -> "364 linear_15" [label="(768,)", style=solid]; +"364 linear_15" -> "365 dropout_12" [label="(1, 197, 768)", style=solid]; +"365 dropout_12" -> "366 add_8" [label="(1, 197, 768)", style=solid]; +"366 add_8" -> "367 layer_norm_8" [label="(1, 197, 768)", style=solid]; +"366 add_8" -> "416 add_9" [label="(1, 197, 768)", style=solid]; +"367 layer_norm_8" -> "368 transpose_24" [label="(1, 197, 768)", style=solid]; +"368 transpose_24" -> "369 transpose_24_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"369 transpose_24_0_0_nncf_smooth_quant_0" -> "370 quantize_per_tensor_default_25" [label="(197, 1, 768)", style=solid]; +"370 quantize_per_tensor_default_25" -> "371 dequantize_per_tensor_default_25" [label="(197, 1, 768)", style=solid]; +"371 dequantize_per_tensor_default_25" -> "378 linear_16" [label="(197, 1, 768)", style=solid]; +"372 linear_16_scale_0" -> "375 quantize_per_channel_default_17" [label="(2304,)", style=solid]; +"372 linear_16_scale_0" -> "376 dequantize_per_channel_default_17" [label="(2304,)", style=solid]; +"373 linear_16_zero_point_0" -> "375 quantize_per_channel_default_17" [label="(2304,)", style=solid]; +"373 linear_16_zero_point_0" -> "376 dequantize_per_channel_default_17" [label="(2304,)", style=solid]; +"374 compressed_weight_updated_constant17" -> "375 quantize_per_channel_default_17" [label="(2304, 768)", style=solid]; +"375 quantize_per_channel_default_17" -> "376 dequantize_per_channel_default_17" [label="(2304, 768)", style=solid]; +"376 dequantize_per_channel_default_17" -> "378 linear_16" [label="(2304, 768)", style=solid]; +"377 encoder_layers_encoder_layer_4_self_attention_in_proj_bias_0_0" -> "378 linear_16" [label="(2304,)", style=solid]; +"378 linear_16" -> "379 unflatten_4" [label="(197, 1, 2304)", style=solid]; +"379 unflatten_4" -> "380 unsqueeze_4" [label="(197, 1, 3, 768)", style=solid]; +"380 unsqueeze_4" -> "381 transpose_25" [label="(1, 197, 1, 3, 768)", style=solid]; +"381 transpose_25" -> "382 squeeze_4" [label="(3, 197, 1, 1, 768)", style=solid]; +"382 squeeze_4" -> "383 contiguous_4" [label="(3, 197, 1, 768)", style=solid]; +"383 contiguous_4" -> "384 quantize_per_tensor_default_26" [label="(3, 197, 1, 768)", style=solid]; +"383 contiguous_4" -> "387 quantize_per_tensor_default_27" [label="(3, 197, 1, 768)", style=solid]; +"383 contiguous_4" -> "390 select_14" [label="(3, 197, 1, 768)", style=solid]; +"384 quantize_per_tensor_default_26" -> "385 dequantize_per_tensor_default_26" [label="(3, 197, 1, 768)", style=solid]; +"385 dequantize_per_tensor_default_26" -> "386 select_12" [label="(3, 197, 1, 768)", style=solid]; +"386 select_12" -> "391 view_32" [label="(197, 1, 768)", style=solid]; +"387 quantize_per_tensor_default_27" -> "388 dequantize_per_tensor_default_27" [label="(3, 197, 1, 768)", style=solid]; +"388 dequantize_per_tensor_default_27" -> "389 select_13" [label="(3, 197, 1, 768)", style=solid]; +"389 select_13" -> "393 view_33" [label="(197, 1, 768)", style=solid]; +"390 select_14" -> "395 view_34" [label="(197, 1, 768)", style=solid]; +"391 view_32" -> "392 transpose_26" [label="(197, 12, 64)", style=solid]; +"392 transpose_26" -> "397 view_35" [label="(12, 197, 64)", style=solid]; +"393 view_33" -> "394 transpose_27" [label="(197, 12, 64)", style=solid]; +"394 transpose_27" -> "398 view_36" [label="(12, 197, 64)", style=solid]; +"395 view_34" -> "396 transpose_28" [label="(197, 12, 64)", style=solid]; +"396 transpose_28" -> "399 view_37" [label="(12, 197, 64)", style=solid]; +"397 view_35" -> "400 scaled_dot_product_attention_4" [label="(1, 12, 197, 64)", style=solid]; +"398 view_36" -> "400 scaled_dot_product_attention_4" [label="(1, 12, 197, 64)", style=solid]; +"399 view_37" -> "400 scaled_dot_product_attention_4" [label="(1, 12, 197, 64)", style=solid]; +"400 scaled_dot_product_attention_4" -> "401 permute_5" [label="(1, 12, 197, 64)", style=solid]; +"401 permute_5" -> "402 view_38" [label="(197, 1, 12, 64)", style=solid]; +"402 view_38" -> "403 view_38_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"403 view_38_0_0_nncf_smooth_quant_0" -> "404 quantize_per_tensor_default_28" [label="(197, 768)", style=solid]; +"404 quantize_per_tensor_default_28" -> "405 dequantize_per_tensor_default_28" [label="(197, 768)", style=solid]; +"405 dequantize_per_tensor_default_28" -> "412 linear_17" [label="(197, 768)", style=solid]; +"406 linear_17_scale_0" -> "409 quantize_per_channel_default_18" [label="(768,)", style=solid]; +"406 linear_17_scale_0" -> "410 dequantize_per_channel_default_18" [label="(768,)", style=solid]; +"407 linear_17_zero_point_0" -> "409 quantize_per_channel_default_18" [label="(768,)", style=solid]; +"407 linear_17_zero_point_0" -> "410 dequantize_per_channel_default_18" [label="(768,)", style=solid]; +"408 compressed_weight_updated_constant18" -> "409 quantize_per_channel_default_18" [label="(768, 768)", style=solid]; +"409 quantize_per_channel_default_18" -> "410 dequantize_per_channel_default_18" [label="(768, 768)", style=solid]; +"410 dequantize_per_channel_default_18" -> "412 linear_17" [label="(768, 768)", style=solid]; +"411 encoder_layers_encoder_layer_4_self_attention_out_proj_bias_0_0" -> "412 linear_17" [label="(768,)", style=solid]; +"412 linear_17" -> "413 view_39" [label="(197, 768)", style=solid]; +"413 view_39" -> "414 transpose_29" [label="(197, 1, 768)", style=solid]; +"414 transpose_29" -> "415 dropout_13" [label="(1, 197, 768)", style=solid]; +"415 dropout_13" -> "416 add_9" [label="(1, 197, 768)", style=solid]; +"416 add_9" -> "417 layer_norm_9" [label="(1, 197, 768)", style=solid]; +"416 add_9" -> "441 add_10" [label="(1, 197, 768)", style=solid]; +"417 layer_norm_9" -> "418 layer_norm_9_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"418 layer_norm_9_0_0_nncf_smooth_quant_0" -> "419 quantize_per_tensor_default_29" [label="(1, 197, 768)", style=solid]; +"419 quantize_per_tensor_default_29" -> "420 dequantize_per_tensor_default_29" [label="(1, 197, 768)", style=solid]; +"420 dequantize_per_tensor_default_29" -> "427 linear_18" [label="(1, 197, 768)", style=solid]; +"421 linear_18_scale_0" -> "424 quantize_per_channel_default_19" [label="(3072,)", style=solid]; +"421 linear_18_scale_0" -> "425 dequantize_per_channel_default_19" [label="(3072,)", style=solid]; +"422 linear_18_zero_point_0" -> "424 quantize_per_channel_default_19" [label="(3072,)", style=solid]; +"422 linear_18_zero_point_0" -> "425 dequantize_per_channel_default_19" [label="(3072,)", style=solid]; +"423 compressed_weight_updated_constant19" -> "424 quantize_per_channel_default_19" [label="(3072, 768)", style=solid]; +"424 quantize_per_channel_default_19" -> "425 dequantize_per_channel_default_19" [label="(3072, 768)", style=solid]; +"425 dequantize_per_channel_default_19" -> "427 linear_18" [label="(3072, 768)", style=solid]; +"426 encoder_layers_encoder_layer_4_mlp_0_bias_0_0" -> "427 linear_18" [label="(3072,)", style=solid]; +"427 linear_18" -> "428 gelu_4" [label="(1, 197, 3072)", style=solid]; +"428 gelu_4" -> "429 dropout_14" [label="(1, 197, 3072)", style=solid]; +"429 dropout_14" -> "430 dropout_14_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"430 dropout_14_0_0_nncf_smooth_quant_0" -> "431 quantize_per_tensor_default_30" [label="(1, 197, 3072)", style=solid]; +"431 quantize_per_tensor_default_30" -> "432 dequantize_per_tensor_default_30" [label="(1, 197, 3072)", style=solid]; +"432 dequantize_per_tensor_default_30" -> "439 linear_19" [label="(1, 197, 3072)", style=solid]; +"433 linear_19_scale_0" -> "436 quantize_per_channel_default_20" [label="(768,)", style=solid]; +"433 linear_19_scale_0" -> "437 dequantize_per_channel_default_20" [label="(768,)", style=solid]; +"434 linear_19_zero_point_0" -> "436 quantize_per_channel_default_20" [label="(768,)", style=solid]; +"434 linear_19_zero_point_0" -> "437 dequantize_per_channel_default_20" [label="(768,)", style=solid]; +"435 compressed_weight_updated_constant20" -> "436 quantize_per_channel_default_20" [label="(768, 3072)", style=solid]; +"436 quantize_per_channel_default_20" -> "437 dequantize_per_channel_default_20" [label="(768, 3072)", style=solid]; +"437 dequantize_per_channel_default_20" -> "439 linear_19" [label="(768, 3072)", style=solid]; +"438 encoder_layers_encoder_layer_4_mlp_3_bias_0_0" -> "439 linear_19" [label="(768,)", style=solid]; +"439 linear_19" -> "440 dropout_15" [label="(1, 197, 768)", style=solid]; +"440 dropout_15" -> "441 add_10" [label="(1, 197, 768)", style=solid]; +"441 add_10" -> "442 layer_norm_10" [label="(1, 197, 768)", style=solid]; +"441 add_10" -> "491 add_11" [label="(1, 197, 768)", style=solid]; +"442 layer_norm_10" -> "443 transpose_30" [label="(1, 197, 768)", style=solid]; +"443 transpose_30" -> "444 transpose_30_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"444 transpose_30_0_0_nncf_smooth_quant_0" -> "445 quantize_per_tensor_default_31" [label="(197, 1, 768)", style=solid]; +"445 quantize_per_tensor_default_31" -> "446 dequantize_per_tensor_default_31" [label="(197, 1, 768)", style=solid]; +"446 dequantize_per_tensor_default_31" -> "453 linear_20" [label="(197, 1, 768)", style=solid]; +"447 linear_20_scale_0" -> "450 quantize_per_channel_default_21" [label="(2304,)", style=solid]; +"447 linear_20_scale_0" -> "451 dequantize_per_channel_default_21" [label="(2304,)", style=solid]; +"448 linear_20_zero_point_0" -> "450 quantize_per_channel_default_21" [label="(2304,)", style=solid]; +"448 linear_20_zero_point_0" -> "451 dequantize_per_channel_default_21" [label="(2304,)", style=solid]; +"449 compressed_weight_updated_constant21" -> "450 quantize_per_channel_default_21" [label="(2304, 768)", style=solid]; +"450 quantize_per_channel_default_21" -> "451 dequantize_per_channel_default_21" [label="(2304, 768)", style=solid]; +"451 dequantize_per_channel_default_21" -> "453 linear_20" [label="(2304, 768)", style=solid]; +"452 encoder_layers_encoder_layer_5_self_attention_in_proj_bias_0_0" -> "453 linear_20" [label="(2304,)", style=solid]; +"453 linear_20" -> "454 unflatten_5" [label="(197, 1, 2304)", style=solid]; +"454 unflatten_5" -> "455 unsqueeze_5" [label="(197, 1, 3, 768)", style=solid]; +"455 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[label="(3, 197, 1, 768)", style=solid]; +"464 select_16" -> "468 view_41" [label="(197, 1, 768)", style=solid]; +"465 select_17" -> "470 view_42" [label="(197, 1, 768)", style=solid]; +"466 view_40" -> "467 transpose_32" [label="(197, 12, 64)", style=solid]; +"467 transpose_32" -> "472 view_43" [label="(12, 197, 64)", style=solid]; +"468 view_41" -> "469 transpose_33" [label="(197, 12, 64)", style=solid]; +"469 transpose_33" -> "473 view_44" [label="(12, 197, 64)", style=solid]; +"470 view_42" -> "471 transpose_34" [label="(197, 12, 64)", style=solid]; +"471 transpose_34" -> "474 view_45" [label="(12, 197, 64)", style=solid]; +"472 view_43" -> "475 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"473 view_44" -> "475 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"474 view_45" -> "475 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"475 scaled_dot_product_attention_5" -> "476 permute_6" [label="(1, 12, 197, 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[label="(768, 768)", style=solid]; +"484 quantize_per_channel_default_22" -> "485 dequantize_per_channel_default_22" [label="(768, 768)", style=solid]; +"485 dequantize_per_channel_default_22" -> "487 linear_21" [label="(768, 768)", style=solid]; +"486 encoder_layers_encoder_layer_5_self_attention_out_proj_bias_0_0" -> "487 linear_21" [label="(768,)", style=solid]; +"487 linear_21" -> "488 view_47" [label="(197, 768)", style=solid]; +"488 view_47" -> "489 transpose_35" [label="(197, 1, 768)", style=solid]; +"489 transpose_35" -> "490 dropout_16" [label="(1, 197, 768)", style=solid]; +"490 dropout_16" -> "491 add_11" [label="(1, 197, 768)", style=solid]; +"491 add_11" -> "492 layer_norm_11" [label="(1, 197, 768)", style=solid]; +"491 add_11" -> "516 add_12" [label="(1, 197, 768)", style=solid]; +"492 layer_norm_11" -> "493 layer_norm_11_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"493 layer_norm_11_0_0_nncf_smooth_quant_0" -> "494 quantize_per_tensor_default_35" 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encoder_layers_encoder_layer_5_mlp_0_bias_0_0" -> "502 linear_22" [label="(3072,)", style=solid]; +"502 linear_22" -> "503 gelu_5" [label="(1, 197, 3072)", style=solid]; +"503 gelu_5" -> "504 dropout_17" [label="(1, 197, 3072)", style=solid]; +"504 dropout_17" -> "505 dropout_17_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"505 dropout_17_0_0_nncf_smooth_quant_0" -> "506 quantize_per_tensor_default_36" [label="(1, 197, 3072)", style=solid]; +"506 quantize_per_tensor_default_36" -> "507 dequantize_per_tensor_default_36" [label="(1, 197, 3072)", style=solid]; +"507 dequantize_per_tensor_default_36" -> "514 linear_23" [label="(1, 197, 3072)", style=solid]; +"508 linear_23_scale_0" -> "511 quantize_per_channel_default_24" [label="(768,)", style=solid]; +"508 linear_23_scale_0" -> "512 dequantize_per_channel_default_24" [label="(768,)", style=solid]; +"509 linear_23_zero_point_0" -> "511 quantize_per_channel_default_24" [label="(768,)", style=solid]; +"509 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style=solid]; +"519 transpose_36_0_0_nncf_smooth_quant_0" -> "520 quantize_per_tensor_default_37" [label="(197, 1, 768)", style=solid]; +"520 quantize_per_tensor_default_37" -> "521 dequantize_per_tensor_default_37" [label="(197, 1, 768)", style=solid]; +"521 dequantize_per_tensor_default_37" -> "528 linear_24" [label="(197, 1, 768)", style=solid]; +"522 linear_24_scale_0" -> "525 quantize_per_channel_default_25" [label="(2304,)", style=solid]; +"522 linear_24_scale_0" -> "526 dequantize_per_channel_default_25" [label="(2304,)", style=solid]; +"523 linear_24_zero_point_0" -> "525 quantize_per_channel_default_25" [label="(2304,)", style=solid]; +"523 linear_24_zero_point_0" -> "526 dequantize_per_channel_default_25" [label="(2304,)", style=solid]; +"524 compressed_weight_updated_constant25" -> "525 quantize_per_channel_default_25" [label="(2304, 768)", style=solid]; +"525 quantize_per_channel_default_25" -> "526 dequantize_per_channel_default_25" [label="(2304, 768)", style=solid]; 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linear_25_scale_0" -> "560 dequantize_per_channel_default_26" [label="(768,)", style=solid]; +"557 linear_25_zero_point_0" -> "559 quantize_per_channel_default_26" [label="(768,)", style=solid]; +"557 linear_25_zero_point_0" -> "560 dequantize_per_channel_default_26" [label="(768,)", style=solid]; +"558 compressed_weight_updated_constant26" -> "559 quantize_per_channel_default_26" [label="(768, 768)", style=solid]; +"559 quantize_per_channel_default_26" -> "560 dequantize_per_channel_default_26" [label="(768, 768)", style=solid]; +"560 dequantize_per_channel_default_26" -> "562 linear_25" [label="(768, 768)", style=solid]; +"561 encoder_layers_encoder_layer_6_self_attention_out_proj_bias_0_0" -> "562 linear_25" [label="(768,)", style=solid]; +"562 linear_25" -> "563 view_55" [label="(197, 768)", style=solid]; +"563 view_55" -> "564 transpose_41" [label="(197, 1, 768)", style=solid]; +"564 transpose_41" -> "565 dropout_19" [label="(1, 197, 768)", style=solid]; +"565 dropout_19" -> "566 add_13" [label="(1, 197, 768)", style=solid]; +"566 add_13" -> "567 layer_norm_13" [label="(1, 197, 768)", style=solid]; +"566 add_13" -> "591 add_14" [label="(1, 197, 768)", style=solid]; +"567 layer_norm_13" -> "568 layer_norm_13_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"568 layer_norm_13_0_0_nncf_smooth_quant_0" -> "569 quantize_per_tensor_default_41" [label="(1, 197, 768)", style=solid]; +"569 quantize_per_tensor_default_41" -> "570 dequantize_per_tensor_default_41" [label="(1, 197, 768)", style=solid]; +"570 dequantize_per_tensor_default_41" -> "577 linear_26" [label="(1, 197, 768)", style=solid]; +"571 linear_26_scale_0" -> "574 quantize_per_channel_default_27" [label="(3072,)", style=solid]; +"571 linear_26_scale_0" -> "575 dequantize_per_channel_default_27" [label="(3072,)", style=solid]; +"572 linear_26_zero_point_0" -> "574 quantize_per_channel_default_27" [label="(3072,)", style=solid]; +"572 linear_26_zero_point_0" -> "575 dequantize_per_channel_default_27" [label="(3072,)", style=solid]; +"573 compressed_weight_updated_constant27" -> "574 quantize_per_channel_default_27" [label="(3072, 768)", style=solid]; +"574 quantize_per_channel_default_27" -> "575 dequantize_per_channel_default_27" [label="(3072, 768)", style=solid]; +"575 dequantize_per_channel_default_27" -> "577 linear_26" [label="(3072, 768)", style=solid]; +"576 encoder_layers_encoder_layer_6_mlp_0_bias_0_0" -> "577 linear_26" [label="(3072,)", style=solid]; +"577 linear_26" -> "578 gelu_6" [label="(1, 197, 3072)", style=solid]; +"578 gelu_6" -> "579 dropout_20" [label="(1, 197, 3072)", style=solid]; +"579 dropout_20" -> "580 dropout_20_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"580 dropout_20_0_0_nncf_smooth_quant_0" -> "581 quantize_per_tensor_default_42" [label="(1, 197, 3072)", style=solid]; +"581 quantize_per_tensor_default_42" -> "582 dequantize_per_tensor_default_42" [label="(1, 197, 3072)", style=solid]; +"582 dequantize_per_tensor_default_42" -> "589 linear_27" [label="(1, 197, 3072)", style=solid]; +"583 linear_27_scale_0" -> "586 quantize_per_channel_default_28" [label="(768,)", style=solid]; +"583 linear_27_scale_0" -> "587 dequantize_per_channel_default_28" [label="(768,)", style=solid]; +"584 linear_27_zero_point_0" -> "586 quantize_per_channel_default_28" [label="(768,)", style=solid]; +"584 linear_27_zero_point_0" -> "587 dequantize_per_channel_default_28" [label="(768,)", style=solid]; +"585 compressed_weight_updated_constant28" -> "586 quantize_per_channel_default_28" [label="(768, 3072)", style=solid]; +"586 quantize_per_channel_default_28" -> "587 dequantize_per_channel_default_28" [label="(768, 3072)", style=solid]; +"587 dequantize_per_channel_default_28" -> "589 linear_27" [label="(768, 3072)", style=solid]; +"588 encoder_layers_encoder_layer_6_mlp_3_bias_0_0" -> "589 linear_27" [label="(768,)", style=solid]; +"589 linear_27" -> "590 dropout_21" [label="(1, 197, 768)", 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view_62_0_0_nncf_smooth_quant_0" -> "629 quantize_per_tensor_default_46" [label="(197, 768)", style=solid]; +"629 quantize_per_tensor_default_46" -> "630 dequantize_per_tensor_default_46" [label="(197, 768)", style=solid]; +"630 dequantize_per_tensor_default_46" -> "637 linear_29" [label="(197, 768)", style=solid]; +"631 linear_29_scale_0" -> "634 quantize_per_channel_default_30" [label="(768,)", style=solid]; +"631 linear_29_scale_0" -> "635 dequantize_per_channel_default_30" [label="(768,)", style=solid]; +"632 linear_29_zero_point_0" -> "634 quantize_per_channel_default_30" [label="(768,)", style=solid]; +"632 linear_29_zero_point_0" -> "635 dequantize_per_channel_default_30" [label="(768,)", style=solid]; +"633 compressed_weight_updated_constant30" -> "634 quantize_per_channel_default_30" [label="(768, 768)", style=solid]; +"634 quantize_per_channel_default_30" -> "635 dequantize_per_channel_default_30" [label="(768, 768)", style=solid]; +"635 dequantize_per_channel_default_30" -> 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scaled_dot_product_attention_10" -> "851 permute_11" [label="(1, 12, 197, 64)", style=solid]; +"851 permute_11" -> "852 view_86" [label="(197, 1, 12, 64)", style=solid]; +"852 view_86" -> "853 view_86_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"853 view_86_0_0_nncf_smooth_quant_0" -> "854 quantize_per_tensor_default_64" [label="(197, 768)", style=solid]; +"854 quantize_per_tensor_default_64" -> "855 dequantize_per_tensor_default_64" [label="(197, 768)", style=solid]; +"855 dequantize_per_tensor_default_64" -> "862 linear_41" [label="(197, 768)", style=solid]; +"856 linear_41_scale_0" -> "859 quantize_per_channel_default_42" [label="(768,)", style=solid]; +"856 linear_41_scale_0" -> "860 dequantize_per_channel_default_42" [label="(768,)", style=solid]; +"857 linear_41_zero_point_0" -> "859 quantize_per_channel_default_42" [label="(768,)", style=solid]; +"857 linear_41_zero_point_0" -> "860 dequantize_per_channel_default_42" [label="(768,)", style=solid]; +"858 compressed_weight_updated_constant42" -> "859 quantize_per_channel_default_42" [label="(768, 768)", style=solid]; +"859 quantize_per_channel_default_42" -> "860 dequantize_per_channel_default_42" [label="(768, 768)", style=solid]; +"860 dequantize_per_channel_default_42" -> "862 linear_41" [label="(768, 768)", style=solid]; +"861 encoder_layers_encoder_layer_10_self_attention_out_proj_bias_0_0" -> "862 linear_41" [label="(768,)", style=solid]; +"862 linear_41" -> "863 view_87" [label="(197, 768)", style=solid]; +"863 view_87" -> "864 transpose_65" [label="(197, 1, 768)", style=solid]; +"864 transpose_65" -> "865 dropout_31" [label="(1, 197, 768)", style=solid]; +"865 dropout_31" -> "866 add_21" [label="(1, 197, 768)", style=solid]; +"866 add_21" -> "867 layer_norm_21" [label="(1, 197, 768)", style=solid]; +"866 add_21" -> "891 add_22" [label="(1, 197, 768)", style=solid]; +"867 layer_norm_21" -> "868 layer_norm_21_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"868 layer_norm_21_0_0_nncf_smooth_quant_0" -> "869 quantize_per_tensor_default_65" [label="(1, 197, 768)", style=solid]; +"869 quantize_per_tensor_default_65" -> "870 dequantize_per_tensor_default_65" [label="(1, 197, 768)", style=solid]; +"870 dequantize_per_tensor_default_65" -> "877 linear_42" [label="(1, 197, 768)", style=solid]; +"871 linear_42_scale_0" -> "874 quantize_per_channel_default_43" [label="(3072,)", style=solid]; +"871 linear_42_scale_0" -> "875 dequantize_per_channel_default_43" [label="(3072,)", style=solid]; +"872 linear_42_zero_point_0" -> "874 quantize_per_channel_default_43" [label="(3072,)", style=solid]; +"872 linear_42_zero_point_0" -> "875 dequantize_per_channel_default_43" [label="(3072,)", style=solid]; +"873 compressed_weight_updated_constant43" -> "874 quantize_per_channel_default_43" [label="(3072, 768)", style=solid]; +"874 quantize_per_channel_default_43" -> "875 dequantize_per_channel_default_43" [label="(3072, 768)", style=solid]; +"875 dequantize_per_channel_default_43" -> "877 linear_42" [label="(3072, 768)", style=solid]; +"876 encoder_layers_encoder_layer_10_mlp_0_bias_0_0" -> "877 linear_42" [label="(3072,)", style=solid]; +"877 linear_42" -> "878 gelu_10" [label="(1, 197, 3072)", style=solid]; +"878 gelu_10" -> "879 dropout_32" [label="(1, 197, 3072)", style=solid]; +"879 dropout_32" -> "880 dropout_32_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"880 dropout_32_0_0_nncf_smooth_quant_0" -> "881 quantize_per_tensor_default_66" [label="(1, 197, 3072)", style=solid]; +"881 quantize_per_tensor_default_66" -> "882 dequantize_per_tensor_default_66" [label="(1, 197, 3072)", style=solid]; +"882 dequantize_per_tensor_default_66" -> "889 linear_43" [label="(1, 197, 3072)", style=solid]; +"883 linear_43_scale_0" -> "886 quantize_per_channel_default_44" [label="(768,)", style=solid]; +"883 linear_43_scale_0" -> "887 dequantize_per_channel_default_44" [label="(768,)", style=solid]; +"884 linear_43_zero_point_0" -> "886 quantize_per_channel_default_44" [label="(768,)", style=solid]; +"884 linear_43_zero_point_0" -> "887 dequantize_per_channel_default_44" [label="(768,)", style=solid]; +"885 compressed_weight_updated_constant44" -> "886 quantize_per_channel_default_44" [label="(768, 3072)", style=solid]; +"886 quantize_per_channel_default_44" -> "887 dequantize_per_channel_default_44" [label="(768, 3072)", style=solid]; +"887 dequantize_per_channel_default_44" -> "889 linear_43" [label="(768, 3072)", style=solid]; +"888 encoder_layers_encoder_layer_10_mlp_3_bias_0_0" -> "889 linear_43" [label="(768,)", style=solid]; +"889 linear_43" -> "890 dropout_33" [label="(1, 197, 768)", style=solid]; +"890 dropout_33" -> "891 add_22" [label="(1, 197, 768)", style=solid]; +"891 add_22" -> "892 layer_norm_22" [label="(1, 197, 768)", style=solid]; +"891 add_22" -> "941 add_23" [label="(1, 197, 768)", style=solid]; +"892 layer_norm_22" -> "893 transpose_66" [label="(1, 197, 768)", style=solid]; +"893 transpose_66" -> "894 transpose_66_0_0_nncf_smooth_quant_0" [label="(197, 1, 768)", style=solid]; +"894 transpose_66_0_0_nncf_smooth_quant_0" -> "895 quantize_per_tensor_default_67" [label="(197, 1, 768)", style=solid]; +"895 quantize_per_tensor_default_67" -> "896 dequantize_per_tensor_default_67" [label="(197, 1, 768)", style=solid]; +"896 dequantize_per_tensor_default_67" -> "903 linear_44" [label="(197, 1, 768)", style=solid]; +"897 linear_44_scale_0" -> "900 quantize_per_channel_default_45" [label="(2304,)", style=solid]; +"897 linear_44_scale_0" -> "901 dequantize_per_channel_default_45" [label="(2304,)", style=solid]; +"898 linear_44_zero_point_0" -> "900 quantize_per_channel_default_45" [label="(2304,)", style=solid]; +"898 linear_44_zero_point_0" -> "901 dequantize_per_channel_default_45" [label="(2304,)", style=solid]; +"899 compressed_weight_updated_constant45" -> "900 quantize_per_channel_default_45" [label="(2304, 768)", style=solid]; +"900 quantize_per_channel_default_45" -> "901 dequantize_per_channel_default_45" [label="(2304, 768)", style=solid]; +"901 dequantize_per_channel_default_45" -> "903 linear_44" [label="(2304, 768)", style=solid]; +"902 encoder_layers_encoder_layer_11_self_attention_in_proj_bias_0_0" -> "903 linear_44" [label="(2304,)", style=solid]; +"903 linear_44" -> "904 unflatten_11" [label="(197, 1, 2304)", style=solid]; +"904 unflatten_11" -> "905 unsqueeze_11" [label="(197, 1, 3, 768)", style=solid]; +"905 unsqueeze_11" -> "906 transpose_67" [label="(1, 197, 1, 3, 768)", style=solid]; +"906 transpose_67" -> "907 squeeze_11" [label="(3, 197, 1, 1, 768)", style=solid]; +"907 squeeze_11" -> "908 contiguous_11" [label="(3, 197, 1, 768)", style=solid]; +"908 contiguous_11" -> "909 quantize_per_tensor_default_68" [label="(3, 197, 1, 768)", style=solid]; +"908 contiguous_11" -> "912 quantize_per_tensor_default_69" [label="(3, 197, 1, 768)", style=solid]; +"908 contiguous_11" -> "915 select_35" [label="(3, 197, 1, 768)", style=solid]; +"909 quantize_per_tensor_default_68" -> "910 dequantize_per_tensor_default_68" [label="(3, 197, 1, 768)", style=solid]; +"910 dequantize_per_tensor_default_68" -> "911 select_33" [label="(3, 197, 1, 768)", style=solid]; +"911 select_33" -> "916 view_88" [label="(197, 1, 768)", style=solid]; +"912 quantize_per_tensor_default_69" -> "913 dequantize_per_tensor_default_69" [label="(3, 197, 1, 768)", style=solid]; +"913 dequantize_per_tensor_default_69" -> "914 select_34" [label="(3, 197, 1, 768)", style=solid]; +"914 select_34" -> "918 view_89" [label="(197, 1, 768)", style=solid]; +"915 select_35" -> "920 view_90" [label="(197, 1, 768)", style=solid]; +"916 view_88" -> "917 transpose_68" [label="(197, 12, 64)", style=solid]; +"917 transpose_68" -> "922 view_91" [label="(12, 197, 64)", style=solid]; +"918 view_89" -> "919 transpose_69" [label="(197, 12, 64)", style=solid]; +"919 transpose_69" -> "923 view_92" [label="(12, 197, 64)", style=solid]; +"920 view_90" -> "921 transpose_70" [label="(197, 12, 64)", style=solid]; +"921 transpose_70" -> "924 view_93" [label="(12, 197, 64)", style=solid]; +"922 view_91" -> "925 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"923 view_92" -> "925 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"924 view_93" -> "925 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"925 scaled_dot_product_attention_11" -> "926 permute_12" [label="(1, 12, 197, 64)", style=solid]; +"926 permute_12" -> "927 view_94" [label="(197, 1, 12, 64)", style=solid]; +"927 view_94" -> "928 view_94_0_0_nncf_smooth_quant_0" [label="(197, 768)", style=solid]; +"928 view_94_0_0_nncf_smooth_quant_0" -> "929 quantize_per_tensor_default_70" [label="(197, 768)", style=solid]; +"929 quantize_per_tensor_default_70" -> "930 dequantize_per_tensor_default_70" [label="(197, 768)", style=solid]; +"930 dequantize_per_tensor_default_70" -> "937 linear_45" [label="(197, 768)", style=solid]; +"931 linear_45_scale_0" -> "934 quantize_per_channel_default_46" [label="(768,)", style=solid]; +"931 linear_45_scale_0" -> "935 dequantize_per_channel_default_46" [label="(768,)", style=solid]; +"932 linear_45_zero_point_0" -> "934 quantize_per_channel_default_46" [label="(768,)", style=solid]; +"932 linear_45_zero_point_0" -> "935 dequantize_per_channel_default_46" [label="(768,)", style=solid]; +"933 compressed_weight_updated_constant46" -> "934 quantize_per_channel_default_46" [label="(768, 768)", style=solid]; +"934 quantize_per_channel_default_46" -> "935 dequantize_per_channel_default_46" [label="(768, 768)", style=solid]; +"935 dequantize_per_channel_default_46" -> "937 linear_45" [label="(768, 768)", style=solid]; +"936 encoder_layers_encoder_layer_11_self_attention_out_proj_bias_0_0" -> "937 linear_45" [label="(768,)", style=solid]; +"937 linear_45" -> "938 view_95" [label="(197, 768)", style=solid]; +"938 view_95" -> "939 transpose_71" [label="(197, 1, 768)", style=solid]; +"939 transpose_71" -> "940 dropout_34" [label="(1, 197, 768)", style=solid]; +"940 dropout_34" -> "941 add_23" [label="(1, 197, 768)", style=solid]; +"941 add_23" -> "942 layer_norm_23" [label="(1, 197, 768)", style=solid]; +"941 add_23" -> "966 add_24" [label="(1, 197, 768)", style=solid]; +"942 layer_norm_23" -> "943 layer_norm_23_0_0_nncf_smooth_quant_0" [label="(1, 197, 768)", style=solid]; +"943 layer_norm_23_0_0_nncf_smooth_quant_0" -> "944 quantize_per_tensor_default_71" [label="(1, 197, 768)", style=solid]; +"944 quantize_per_tensor_default_71" -> "945 dequantize_per_tensor_default_71" [label="(1, 197, 768)", style=solid]; +"945 dequantize_per_tensor_default_71" -> "952 linear_46" [label="(1, 197, 768)", style=solid]; +"946 linear_46_scale_0" -> "949 quantize_per_channel_default_47" [label="(3072,)", style=solid]; +"946 linear_46_scale_0" -> "950 dequantize_per_channel_default_47" [label="(3072,)", style=solid]; +"947 linear_46_zero_point_0" -> "949 quantize_per_channel_default_47" [label="(3072,)", style=solid]; +"947 linear_46_zero_point_0" -> "950 dequantize_per_channel_default_47" [label="(3072,)", style=solid]; +"948 compressed_weight_updated_constant47" -> "949 quantize_per_channel_default_47" [label="(3072, 768)", style=solid]; +"949 quantize_per_channel_default_47" -> "950 dequantize_per_channel_default_47" [label="(3072, 768)", style=solid]; +"950 dequantize_per_channel_default_47" -> "952 linear_46" [label="(3072, 768)", style=solid]; +"951 encoder_layers_encoder_layer_11_mlp_0_bias_0_0" -> "952 linear_46" [label="(3072,)", style=solid]; +"952 linear_46" -> "953 gelu_11" [label="(1, 197, 3072)", style=solid]; +"953 gelu_11" -> "954 dropout_35" [label="(1, 197, 3072)", style=solid]; +"954 dropout_35" -> "955 dropout_35_0_0_nncf_smooth_quant_0" [label="(1, 197, 3072)", style=solid]; +"955 dropout_35_0_0_nncf_smooth_quant_0" -> "956 quantize_per_tensor_default_72" [label="(1, 197, 3072)", style=solid]; +"956 quantize_per_tensor_default_72" -> "957 dequantize_per_tensor_default_72" [label="(1, 197, 3072)", style=solid]; +"957 dequantize_per_tensor_default_72" -> "964 linear_47" [label="(1, 197, 3072)", style=solid]; +"958 linear_47_scale_0" -> "961 quantize_per_channel_default_48" [label="(768,)", style=solid]; +"958 linear_47_scale_0" -> "962 dequantize_per_channel_default_48" [label="(768,)", style=solid]; +"959 linear_47_zero_point_0" -> "961 quantize_per_channel_default_48" [label="(768,)", style=solid]; +"959 linear_47_zero_point_0" -> "962 dequantize_per_channel_default_48" [label="(768,)", style=solid]; +"960 compressed_weight_updated_constant48" -> "961 quantize_per_channel_default_48" [label="(768, 3072)", style=solid]; +"961 quantize_per_channel_default_48" -> "962 dequantize_per_channel_default_48" [label="(768, 3072)", style=solid]; +"962 dequantize_per_channel_default_48" -> "964 linear_47" [label="(768, 3072)", style=solid]; +"963 encoder_layers_encoder_layer_11_mlp_3_bias_0_0" -> "964 linear_47" [label="(768,)", style=solid]; +"964 linear_47" -> "965 dropout_36" [label="(1, 197, 768)", style=solid]; +"965 dropout_36" -> "966 add_24" [label="(1, 197, 768)", style=solid]; +"966 add_24" -> "967 layer_norm_24" [label="(1, 197, 768)", style=solid]; +"967 layer_norm_24" -> "968 slice_1" [label="(1, 197, 768)", style=solid]; +"968 slice_1" -> "969 select_36" [label="(1, 197, 768)", style=solid]; +"969 select_36" -> "970 select_36_0_0_nncf_smooth_quant_0" [label="(1, 768)", style=solid]; +"970 select_36_0_0_nncf_smooth_quant_0" -> "971 quantize_per_tensor_default_73" [label="(1, 768)", style=solid]; +"971 quantize_per_tensor_default_73" -> "972 dequantize_per_tensor_default_73" [label="(1, 768)", style=solid]; +"972 dequantize_per_tensor_default_73" -> "979 linear_48" [label="(1, 768)", style=solid]; +"973 linear_48_scale_0" -> "976 quantize_per_channel_default_49" [label="(1000,)", style=solid]; +"973 linear_48_scale_0" -> "977 dequantize_per_channel_default_49" [label="(1000,)", style=solid]; +"974 linear_48_zero_point_0" -> "976 quantize_per_channel_default_49" [label="(1000,)", style=solid]; +"974 linear_48_zero_point_0" -> "977 dequantize_per_channel_default_49" [label="(1000,)", style=solid]; +"975 compressed_weight_updated_constant49" -> "976 quantize_per_channel_default_49" [label="(1000, 768)", style=solid]; +"976 quantize_per_channel_default_49" -> "977 dequantize_per_channel_default_49" [label="(1000, 768)", style=solid]; +"977 dequantize_per_channel_default_49" -> "979 linear_48" [label="(1000, 768)", style=solid]; +"978 heads_head_bias_0_0" -> "979 linear_48" [label="(1000,)", style=solid]; +"979 linear_48" -> "980 output" [label="(1, 1000)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/reference_attributes/default_shared_attribute_test_model.json b/tests/torch/data/reference_graphs/fx/reference_attributes/default_shared_attribute_test_model.json new file mode 100644 index 00000000000..0ea3c861ab7 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/reference_attributes/default_shared_attribute_test_model.json @@ -0,0 +1,18 @@ +{ + "conv_a_weight": false, + "conv_a_bias": false, + "conv_b_weight": false, + "conv_b_bias": false, + "conv_c_weight": false, + "conv_c_bias": false, + "bias": true, + "x": false, + "conv2d": false, + "conv2d_1": false, + "add_": false, + "add__1": false, + "add": false, + "conv2d_2": false, + "add_1": false, + "output_1": false +} \ No newline at end of file diff --git a/tests/torch/data/reference_graphs/fx/reference_metatypes/mobilenet_v3_small.json b/tests/torch/data/reference_graphs/fx/reference_metatypes/mobilenet_v3_small.json index 64c7d3b0e66..f5112693c40 100644 --- a/tests/torch/data/reference_graphs/fx/reference_metatypes/mobilenet_v3_small.json +++ b/tests/torch/data/reference_graphs/fx/reference_metatypes/mobilenet_v3_small.json @@ -1,507 +1,405 @@ { - "arg0_1": "PTInputNoopMetatype", - 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"empty_12": "UnknownMetatype", - "_param_constant45": "PTConstNoopMetatype", - "_param_constant46": "PTConstNoopMetatype", - "_tensor_constant24": "PTConstNoopMetatype", - "_tensor_constant25": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_12": "PTBatchNormMetatype", - "getitem_36": "PTGatherMetatype", - "getitem_37": "PTGatherMetatype", - "getitem_38": "PTGatherMetatype", + "batch_norm_12": "PTBatchNormMetatype", "hardswish__3": "PTHardSwishMetatype", - "_param_constant47": "PTConstNoopMetatype", "conv2d_17": "PTDepthwiseConv2dSubtype", - "empty_13": "UnknownMetatype", - "_param_constant48": "PTConstNoopMetatype", - "_param_constant49": "PTConstNoopMetatype", - "_tensor_constant26": "PTConstNoopMetatype", - "_tensor_constant27": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_13": "PTBatchNormMetatype", - "getitem_39": "PTGatherMetatype", - "getitem_40": "PTGatherMetatype", - "getitem_41": "PTGatherMetatype", + "batch_norm_13": "PTBatchNormMetatype", "hardswish__4": "PTHardSwishMetatype", "adaptive_avg_pool2d_2": "PTAvgPool2dMetatype", - "_param_constant50": "PTConstNoopMetatype", - "_param_constant51": "PTConstNoopMetatype", "conv2d_18": "PTConv2dMetatype", "relu_2": "PTRELUMetatype", - "_param_constant52": "PTConstNoopMetatype", - "_param_constant53": "PTConstNoopMetatype", "conv2d_19": "PTConv2dMetatype", "hardsigmoid_2": "PTHardSigmoidMetatype", "mul_2": "PTMulMetatype", - "_param_constant54": "PTConstNoopMetatype", "conv2d_20": "PTConv2dMetatype", - "empty_14": "UnknownMetatype", - "_param_constant55": "PTConstNoopMetatype", - "_param_constant56": "PTConstNoopMetatype", - "_tensor_constant28": "PTConstNoopMetatype", - "_tensor_constant29": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_14": "PTBatchNormMetatype", - "getitem_42": "PTGatherMetatype", - "getitem_43": "PTGatherMetatype", - "getitem_44": "PTGatherMetatype", + "batch_norm_14": "PTBatchNormMetatype", "add__1": "PTAddMetatype", - "_param_constant57": "PTConstNoopMetatype", "conv2d_21": "PTConv2dMetatype", - "empty_15": "UnknownMetatype", - "_param_constant58": "PTConstNoopMetatype", - "_param_constant59": "PTConstNoopMetatype", - "_tensor_constant30": "PTConstNoopMetatype", - "_tensor_constant31": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_15": "PTBatchNormMetatype", - "getitem_45": "PTGatherMetatype", - "getitem_46": "PTGatherMetatype", - "getitem_47": "PTGatherMetatype", + "batch_norm_15": "PTBatchNormMetatype", "hardswish__5": "PTHardSwishMetatype", - "_param_constant60": "PTConstNoopMetatype", "conv2d_22": "PTDepthwiseConv2dSubtype", - "empty_16": "UnknownMetatype", - "_param_constant61": "PTConstNoopMetatype", - "_param_constant62": "PTConstNoopMetatype", - "_tensor_constant32": "PTConstNoopMetatype", - "_tensor_constant33": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_16": "PTBatchNormMetatype", - "getitem_48": "PTGatherMetatype", - "getitem_49": "PTGatherMetatype", - "getitem_50": "PTGatherMetatype", + "batch_norm_16": "PTBatchNormMetatype", "hardswish__6": "PTHardSwishMetatype", "adaptive_avg_pool2d_3": "PTAvgPool2dMetatype", - "_param_constant63": "PTConstNoopMetatype", - "_param_constant64": "PTConstNoopMetatype", "conv2d_23": "PTConv2dMetatype", "relu_3": "PTRELUMetatype", - "_param_constant65": "PTConstNoopMetatype", - "_param_constant66": "PTConstNoopMetatype", "conv2d_24": "PTConv2dMetatype", "hardsigmoid_3": "PTHardSigmoidMetatype", "mul_3": "PTMulMetatype", - "_param_constant67": "PTConstNoopMetatype", "conv2d_25": "PTConv2dMetatype", - "empty_17": "UnknownMetatype", - "_param_constant68": "PTConstNoopMetatype", - "_param_constant69": "PTConstNoopMetatype", - "_tensor_constant34": "PTConstNoopMetatype", - "_tensor_constant35": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_17": "PTBatchNormMetatype", - "getitem_51": "PTGatherMetatype", - "getitem_52": "PTGatherMetatype", - "getitem_53": "PTGatherMetatype", + "batch_norm_17": "PTBatchNormMetatype", "add__2": "PTAddMetatype", - "_param_constant70": "PTConstNoopMetatype", "conv2d_26": "PTConv2dMetatype", - "empty_18": "UnknownMetatype", - "_param_constant71": "PTConstNoopMetatype", - "_param_constant72": "PTConstNoopMetatype", - "_tensor_constant36": "PTConstNoopMetatype", - "_tensor_constant37": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_18": "PTBatchNormMetatype", - "getitem_54": "PTGatherMetatype", - "getitem_55": "PTGatherMetatype", - "getitem_56": "PTGatherMetatype", + "batch_norm_18": "PTBatchNormMetatype", "hardswish__7": "PTHardSwishMetatype", - "_param_constant73": "PTConstNoopMetatype", "conv2d_27": "PTDepthwiseConv2dSubtype", - "empty_19": "UnknownMetatype", - "_param_constant74": "PTConstNoopMetatype", - "_param_constant75": "PTConstNoopMetatype", - "_tensor_constant38": "PTConstNoopMetatype", - "_tensor_constant39": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_19": "PTBatchNormMetatype", - "getitem_57": "PTGatherMetatype", - "getitem_58": "PTGatherMetatype", - "getitem_59": "PTGatherMetatype", + "batch_norm_19": "PTBatchNormMetatype", "hardswish__8": "PTHardSwishMetatype", "adaptive_avg_pool2d_4": "PTAvgPool2dMetatype", - "_param_constant76": "PTConstNoopMetatype", - "_param_constant77": "PTConstNoopMetatype", "conv2d_28": "PTConv2dMetatype", "relu_4": "PTRELUMetatype", - "_param_constant78": "PTConstNoopMetatype", - "_param_constant79": "PTConstNoopMetatype", "conv2d_29": "PTConv2dMetatype", "hardsigmoid_4": "PTHardSigmoidMetatype", "mul_4": "PTMulMetatype", - "_param_constant80": "PTConstNoopMetatype", "conv2d_30": "PTConv2dMetatype", - "empty_20": "UnknownMetatype", - "_param_constant81": "PTConstNoopMetatype", - "_param_constant82": "PTConstNoopMetatype", - "_tensor_constant40": "PTConstNoopMetatype", - "_tensor_constant41": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_20": "PTBatchNormMetatype", - "getitem_60": "PTGatherMetatype", - "getitem_61": "PTGatherMetatype", - "getitem_62": "PTGatherMetatype", - "_param_constant83": "PTConstNoopMetatype", + "batch_norm_20": "PTBatchNormMetatype", "conv2d_31": "PTConv2dMetatype", - "empty_21": "UnknownMetatype", - "_param_constant84": "PTConstNoopMetatype", - "_param_constant85": "PTConstNoopMetatype", - "_tensor_constant42": "PTConstNoopMetatype", - "_tensor_constant43": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_21": "PTBatchNormMetatype", - "getitem_63": "PTGatherMetatype", - "getitem_64": "PTGatherMetatype", - "getitem_65": "PTGatherMetatype", + "batch_norm_21": "PTBatchNormMetatype", "hardswish__9": "PTHardSwishMetatype", - "_param_constant86": "PTConstNoopMetatype", "conv2d_32": "PTDepthwiseConv2dSubtype", - "empty_22": "UnknownMetatype", - "_param_constant87": "PTConstNoopMetatype", - "_param_constant88": "PTConstNoopMetatype", - "_tensor_constant44": "PTConstNoopMetatype", - "_tensor_constant45": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_22": "PTBatchNormMetatype", - "getitem_66": "PTGatherMetatype", - "getitem_67": "PTGatherMetatype", - "getitem_68": "PTGatherMetatype", + "batch_norm_22": "PTBatchNormMetatype", "hardswish__10": "PTHardSwishMetatype", "adaptive_avg_pool2d_5": "PTAvgPool2dMetatype", - "_param_constant89": "PTConstNoopMetatype", - "_param_constant90": "PTConstNoopMetatype", "conv2d_33": "PTConv2dMetatype", "relu_5": "PTRELUMetatype", - "_param_constant91": "PTConstNoopMetatype", - "_param_constant92": "PTConstNoopMetatype", "conv2d_34": "PTConv2dMetatype", "hardsigmoid_5": "PTHardSigmoidMetatype", "mul_5": "PTMulMetatype", - "_param_constant93": "PTConstNoopMetatype", "conv2d_35": "PTConv2dMetatype", - "empty_23": "UnknownMetatype", - "_param_constant94": "PTConstNoopMetatype", - "_param_constant95": "PTConstNoopMetatype", - "_tensor_constant46": "PTConstNoopMetatype", - "_tensor_constant47": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_23": "PTBatchNormMetatype", - "getitem_69": "PTGatherMetatype", - "getitem_70": "PTGatherMetatype", - "getitem_71": "PTGatherMetatype", + "batch_norm_23": "PTBatchNormMetatype", "add__3": "PTAddMetatype", - "_param_constant96": "PTConstNoopMetatype", "conv2d_36": "PTConv2dMetatype", - "empty_24": "UnknownMetatype", - "_param_constant97": "PTConstNoopMetatype", - "_param_constant98": "PTConstNoopMetatype", - "_tensor_constant48": "PTConstNoopMetatype", - "_tensor_constant49": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_24": "PTBatchNormMetatype", - "getitem_72": "PTGatherMetatype", - "getitem_73": "PTGatherMetatype", - "getitem_74": "PTGatherMetatype", + "batch_norm_24": "PTBatchNormMetatype", "hardswish__11": "PTHardSwishMetatype", - "_param_constant99": "PTConstNoopMetatype", "conv2d_37": "PTDepthwiseConv2dSubtype", - "empty_25": "UnknownMetatype", - "_param_constant100": "PTConstNoopMetatype", - "_param_constant101": "PTConstNoopMetatype", - "_tensor_constant50": "PTConstNoopMetatype", - "_tensor_constant51": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_25": "PTBatchNormMetatype", - "getitem_75": "PTGatherMetatype", - "getitem_76": "PTGatherMetatype", - "getitem_77": "PTGatherMetatype", + "batch_norm_25": "PTBatchNormMetatype", "hardswish__12": "PTHardSwishMetatype", "adaptive_avg_pool2d_6": "PTAvgPool2dMetatype", - "_param_constant102": "PTConstNoopMetatype", - "_param_constant103": "PTConstNoopMetatype", "conv2d_38": "PTConv2dMetatype", "relu_6": "PTRELUMetatype", - "_param_constant104": "PTConstNoopMetatype", - "_param_constant105": "PTConstNoopMetatype", "conv2d_39": "PTConv2dMetatype", "hardsigmoid_6": "PTHardSigmoidMetatype", "mul_6": "PTMulMetatype", - "_param_constant106": "PTConstNoopMetatype", "conv2d_40": "PTConv2dMetatype", - "empty_26": "UnknownMetatype", - "_param_constant107": "PTConstNoopMetatype", - "_param_constant108": "PTConstNoopMetatype", - "_tensor_constant52": "PTConstNoopMetatype", - "_tensor_constant53": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_26": "PTBatchNormMetatype", - "getitem_78": "PTGatherMetatype", - "getitem_79": "PTGatherMetatype", - "getitem_80": "PTGatherMetatype", - "_param_constant109": "PTConstNoopMetatype", + "batch_norm_26": "PTBatchNormMetatype", "conv2d_41": "PTConv2dMetatype", - "empty_27": "UnknownMetatype", - "_param_constant110": "PTConstNoopMetatype", - "_param_constant111": "PTConstNoopMetatype", - "_tensor_constant54": "PTConstNoopMetatype", - "_tensor_constant55": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_27": "PTBatchNormMetatype", - "getitem_81": "PTGatherMetatype", - "getitem_82": "PTGatherMetatype", - "getitem_83": "PTGatherMetatype", + "batch_norm_27": "PTBatchNormMetatype", "hardswish__13": "PTHardSwishMetatype", - "_param_constant112": "PTConstNoopMetatype", "conv2d_42": "PTDepthwiseConv2dSubtype", - "empty_28": "UnknownMetatype", - "_param_constant113": "PTConstNoopMetatype", - "_param_constant114": "PTConstNoopMetatype", - "_tensor_constant56": "PTConstNoopMetatype", - "_tensor_constant57": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_28": "PTBatchNormMetatype", - "getitem_84": "PTGatherMetatype", - "getitem_85": "PTGatherMetatype", - "getitem_86": "PTGatherMetatype", + "batch_norm_28": "PTBatchNormMetatype", "hardswish__14": "PTHardSwishMetatype", "adaptive_avg_pool2d_7": "PTAvgPool2dMetatype", - "_param_constant115": "PTConstNoopMetatype", - "_param_constant116": "PTConstNoopMetatype", "conv2d_43": "PTConv2dMetatype", "relu_7": "PTRELUMetatype", - "_param_constant117": "PTConstNoopMetatype", - "_param_constant118": "PTConstNoopMetatype", "conv2d_44": "PTConv2dMetatype", "hardsigmoid_7": "PTHardSigmoidMetatype", "mul_7": "PTMulMetatype", - "_param_constant119": "PTConstNoopMetatype", "conv2d_45": "PTConv2dMetatype", - "empty_29": "UnknownMetatype", - "_param_constant120": "PTConstNoopMetatype", - "_param_constant121": "PTConstNoopMetatype", - "_tensor_constant58": "PTConstNoopMetatype", - "_tensor_constant59": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_29": "PTBatchNormMetatype", - "getitem_87": "PTGatherMetatype", - "getitem_88": "PTGatherMetatype", - "getitem_89": "PTGatherMetatype", + "batch_norm_29": "PTBatchNormMetatype", "add__4": "PTAddMetatype", - "_param_constant122": "PTConstNoopMetatype", "conv2d_46": "PTConv2dMetatype", - "empty_30": "UnknownMetatype", - "_param_constant123": "PTConstNoopMetatype", - "_param_constant124": "PTConstNoopMetatype", - "_tensor_constant60": "PTConstNoopMetatype", - "_tensor_constant61": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_30": "PTBatchNormMetatype", - "getitem_90": "PTGatherMetatype", - "getitem_91": "PTGatherMetatype", - "getitem_92": "PTGatherMetatype", + "batch_norm_30": "PTBatchNormMetatype", "hardswish__15": "PTHardSwishMetatype", - "_param_constant125": "PTConstNoopMetatype", "conv2d_47": "PTDepthwiseConv2dSubtype", - "empty_31": "UnknownMetatype", - "_param_constant126": "PTConstNoopMetatype", - "_param_constant127": "PTConstNoopMetatype", - "_tensor_constant62": "PTConstNoopMetatype", - "_tensor_constant63": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_31": "PTBatchNormMetatype", - "getitem_93": "PTGatherMetatype", - "getitem_94": "PTGatherMetatype", - "getitem_95": "PTGatherMetatype", + "batch_norm_31": "PTBatchNormMetatype", "hardswish__16": "PTHardSwishMetatype", "adaptive_avg_pool2d_8": "PTAvgPool2dMetatype", - "_param_constant128": "PTConstNoopMetatype", - "_param_constant129": "PTConstNoopMetatype", "conv2d_48": "PTConv2dMetatype", "relu_8": "PTRELUMetatype", - "_param_constant130": "PTConstNoopMetatype", - "_param_constant131": "PTConstNoopMetatype", "conv2d_49": "PTConv2dMetatype", "hardsigmoid_8": "PTHardSigmoidMetatype", "mul_8": "PTMulMetatype", - "_param_constant132": "PTConstNoopMetatype", "conv2d_50": "PTConv2dMetatype", - "empty_32": "UnknownMetatype", - "_param_constant133": "PTConstNoopMetatype", - "_param_constant134": "PTConstNoopMetatype", - "_tensor_constant64": "PTConstNoopMetatype", - "_tensor_constant65": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_32": "PTBatchNormMetatype", - "getitem_96": "PTGatherMetatype", - "getitem_97": "PTGatherMetatype", - "getitem_98": "PTGatherMetatype", + "batch_norm_32": "PTBatchNormMetatype", "add__5": "PTAddMetatype", - "_param_constant135": "PTConstNoopMetatype", "conv2d_51": "PTConv2dMetatype", - "empty_33": "UnknownMetatype", - "_param_constant136": "PTConstNoopMetatype", - "_param_constant137": "PTConstNoopMetatype", - "_tensor_constant66": "PTConstNoopMetatype", - "_tensor_constant67": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_33": "PTBatchNormMetatype", - "getitem_99": "PTGatherMetatype", - "getitem_100": "PTGatherMetatype", - "getitem_101": "PTGatherMetatype", + "batch_norm_33": "PTBatchNormMetatype", "hardswish__17": "PTHardSwishMetatype", "adaptive_avg_pool2d_9": "PTAvgPool2dMetatype", "flatten": "PTReshapeMetatype", - "_param_constant138": "PTConstNoopMetatype", - "_param_constant139": "PTConstNoopMetatype", "linear": "PTLinearMetatype", "hardswish__18": "PTHardSwishMetatype", "dropout_": "PTDropoutMetatype", - "_param_constant140": "PTConstNoopMetatype", - "_param_constant141": "PTConstNoopMetatype", "linear_1": "PTLinearMetatype", - "output": "PTOutputNoopMetatype" + "output_1": "PTOutputNoopMetatype" } \ No newline at end of file diff --git a/tests/torch/data/reference_graphs/fx/reference_metatypes/resnet18.json b/tests/torch/data/reference_graphs/fx/reference_metatypes/resnet18.json index 1ae38c3acae..9544050ce71 100644 --- a/tests/torch/data/reference_graphs/fx/reference_metatypes/resnet18.json +++ b/tests/torch/data/reference_graphs/fx/reference_metatypes/resnet18.json @@ -1,255 +1,195 @@ { - "arg0_1": "PTInputNoopMetatype", - "_param_constant0": "PTConstNoopMetatype", + "conv1_weight": "PTConstNoopMetatype", + "bn1_weight": "PTConstNoopMetatype", + "bn1_bias": "PTConstNoopMetatype", + "layer1_0_conv1_weight": "PTConstNoopMetatype", + "layer1_0_bn1_weight": "PTConstNoopMetatype", + "layer1_0_bn1_bias": "PTConstNoopMetatype", + "layer1_0_conv2_weight": "PTConstNoopMetatype", + "layer1_0_bn2_weight": "PTConstNoopMetatype", + "layer1_0_bn2_bias": "PTConstNoopMetatype", + "layer1_1_conv1_weight": "PTConstNoopMetatype", + "layer1_1_bn1_weight": "PTConstNoopMetatype", + "layer1_1_bn1_bias": "PTConstNoopMetatype", + "layer1_1_conv2_weight": "PTConstNoopMetatype", + "layer1_1_bn2_weight": "PTConstNoopMetatype", + "layer1_1_bn2_bias": "PTConstNoopMetatype", + "layer2_0_conv1_weight": "PTConstNoopMetatype", + "layer2_0_bn1_weight": "PTConstNoopMetatype", + "layer2_0_bn1_bias": "PTConstNoopMetatype", + "layer2_0_conv2_weight": "PTConstNoopMetatype", + "layer2_0_bn2_weight": "PTConstNoopMetatype", + "layer2_0_bn2_bias": "PTConstNoopMetatype", + "layer2_0_downsample_0_weight": "PTConstNoopMetatype", + "layer2_0_downsample_1_weight": "PTConstNoopMetatype", + "layer2_0_downsample_1_bias": "PTConstNoopMetatype", + "layer2_1_conv1_weight": "PTConstNoopMetatype", + "layer2_1_bn1_weight": "PTConstNoopMetatype", + "layer2_1_bn1_bias": "PTConstNoopMetatype", + "layer2_1_conv2_weight": "PTConstNoopMetatype", + "layer2_1_bn2_weight": "PTConstNoopMetatype", + "layer2_1_bn2_bias": "PTConstNoopMetatype", + "layer3_0_conv1_weight": "PTConstNoopMetatype", + "layer3_0_bn1_weight": "PTConstNoopMetatype", + "layer3_0_bn1_bias": "PTConstNoopMetatype", + "layer3_0_conv2_weight": "PTConstNoopMetatype", + "layer3_0_bn2_weight": "PTConstNoopMetatype", + "layer3_0_bn2_bias": "PTConstNoopMetatype", + "layer3_0_downsample_0_weight": "PTConstNoopMetatype", + "layer3_0_downsample_1_weight": "PTConstNoopMetatype", + "layer3_0_downsample_1_bias": "PTConstNoopMetatype", + "layer3_1_conv1_weight": "PTConstNoopMetatype", + "layer3_1_bn1_weight": "PTConstNoopMetatype", + "layer3_1_bn1_bias": "PTConstNoopMetatype", + "layer3_1_conv2_weight": "PTConstNoopMetatype", + "layer3_1_bn2_weight": "PTConstNoopMetatype", + "layer3_1_bn2_bias": "PTConstNoopMetatype", + "layer4_0_conv1_weight": "PTConstNoopMetatype", + "layer4_0_bn1_weight": "PTConstNoopMetatype", + "layer4_0_bn1_bias": "PTConstNoopMetatype", + "layer4_0_conv2_weight": "PTConstNoopMetatype", + "layer4_0_bn2_weight": "PTConstNoopMetatype", + "layer4_0_bn2_bias": "PTConstNoopMetatype", + "layer4_0_downsample_0_weight": "PTConstNoopMetatype", + "layer4_0_downsample_1_weight": "PTConstNoopMetatype", + "layer4_0_downsample_1_bias": "PTConstNoopMetatype", + "layer4_1_conv1_weight": "PTConstNoopMetatype", + "layer4_1_bn1_weight": "PTConstNoopMetatype", + "layer4_1_bn1_bias": "PTConstNoopMetatype", + "layer4_1_conv2_weight": "PTConstNoopMetatype", + "layer4_1_bn2_weight": "PTConstNoopMetatype", + "layer4_1_bn2_bias": "PTConstNoopMetatype", + "fc_weight": "PTConstNoopMetatype", + "fc_bias": "PTConstNoopMetatype", + 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"PTConstNoopMetatype", - "_param_constant26": "PTConstNoopMetatype", - "_tensor_constant16": "PTConstNoopMetatype", - "_tensor_constant17": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_8": "PTBatchNormMetatype", - "getitem_24": "PTGatherMetatype", - "getitem_25": "PTGatherMetatype", - "getitem_26": "PTGatherMetatype", + "batch_norm_8": "PTBatchNormMetatype", "relu__7": "PTRELUMetatype", - "_param_constant27": "PTConstNoopMetatype", "conv2d_9": "PTConv2dMetatype", - "empty_9": "UnknownMetatype", - "_param_constant28": "PTConstNoopMetatype", - "_param_constant29": "PTConstNoopMetatype", - "_tensor_constant18": "PTConstNoopMetatype", - "_tensor_constant19": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_9": "PTBatchNormMetatype", - "getitem_27": "PTGatherMetatype", - "getitem_28": "PTGatherMetatype", - "getitem_29": "PTGatherMetatype", + "batch_norm_9": "PTBatchNormMetatype", "add__3": "PTAddMetatype", "relu__8": "PTRELUMetatype", - "_param_constant30": "PTConstNoopMetatype", "conv2d_10": "PTConv2dMetatype", - "empty_10": "UnknownMetatype", - "_param_constant31": "PTConstNoopMetatype", - "_param_constant32": "PTConstNoopMetatype", - "_tensor_constant20": "PTConstNoopMetatype", - "_tensor_constant21": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_10": "PTBatchNormMetatype", - "getitem_30": "PTGatherMetatype", - "getitem_31": "PTGatherMetatype", - "getitem_32": "PTGatherMetatype", + "batch_norm_10": "PTBatchNormMetatype", "relu__9": "PTRELUMetatype", - "_param_constant33": "PTConstNoopMetatype", "conv2d_11": "PTConv2dMetatype", - "empty_11": "UnknownMetatype", - "_param_constant34": "PTConstNoopMetatype", - "_param_constant35": "PTConstNoopMetatype", - "_tensor_constant22": "PTConstNoopMetatype", - "_tensor_constant23": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_11": "PTBatchNormMetatype", - "getitem_33": "PTGatherMetatype", - "getitem_34": "PTGatherMetatype", - "getitem_35": "PTGatherMetatype", - "_param_constant36": "PTConstNoopMetatype", + "batch_norm_11": "PTBatchNormMetatype", "conv2d_12": "PTConv2dMetatype", - "empty_12": "UnknownMetatype", - "_param_constant37": "PTConstNoopMetatype", - "_param_constant38": "PTConstNoopMetatype", - "_tensor_constant24": "PTConstNoopMetatype", - "_tensor_constant25": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_12": "PTBatchNormMetatype", - "getitem_36": "PTGatherMetatype", - "getitem_37": "PTGatherMetatype", - "getitem_38": "PTGatherMetatype", + "batch_norm_12": "PTBatchNormMetatype", "add__4": "PTAddMetatype", "relu__10": "PTRELUMetatype", - "_param_constant39": "PTConstNoopMetatype", "conv2d_13": "PTConv2dMetatype", - "empty_13": "UnknownMetatype", - "_param_constant40": "PTConstNoopMetatype", - "_param_constant41": "PTConstNoopMetatype", - "_tensor_constant26": "PTConstNoopMetatype", - "_tensor_constant27": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_13": "PTBatchNormMetatype", - "getitem_39": 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"_tensor_constant31": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_15": "PTBatchNormMetatype", - "getitem_45": "PTGatherMetatype", - "getitem_46": "PTGatherMetatype", - "getitem_47": "PTGatherMetatype", + "batch_norm_15": "PTBatchNormMetatype", "relu__13": "PTRELUMetatype", - "_param_constant48": "PTConstNoopMetatype", "conv2d_16": "PTConv2dMetatype", - "empty_16": "UnknownMetatype", - "_param_constant49": "PTConstNoopMetatype", - "_param_constant50": "PTConstNoopMetatype", - "_tensor_constant32": "PTConstNoopMetatype", - "_tensor_constant33": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_16": "PTBatchNormMetatype", - "getitem_48": "PTGatherMetatype", - "getitem_49": "PTGatherMetatype", - "getitem_50": "PTGatherMetatype", - "_param_constant51": "PTConstNoopMetatype", + "batch_norm_16": "PTBatchNormMetatype", "conv2d_17": "PTConv2dMetatype", - "empty_17": "UnknownMetatype", - "_param_constant52": "PTConstNoopMetatype", - "_param_constant53": "PTConstNoopMetatype", - "_tensor_constant34": "PTConstNoopMetatype", - "_tensor_constant35": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_17": "PTBatchNormMetatype", - "getitem_51": "PTGatherMetatype", - "getitem_52": "PTGatherMetatype", - "getitem_53": "PTGatherMetatype", + "batch_norm_17": "PTBatchNormMetatype", "add__6": "PTAddMetatype", "relu__14": "PTRELUMetatype", - "_param_constant54": "PTConstNoopMetatype", "conv2d_18": "PTConv2dMetatype", - "empty_18": "UnknownMetatype", - "_param_constant55": "PTConstNoopMetatype", - "_param_constant56": "PTConstNoopMetatype", - "_tensor_constant36": "PTConstNoopMetatype", - "_tensor_constant37": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_18": "PTBatchNormMetatype", - "getitem_54": "PTGatherMetatype", - "getitem_55": "PTGatherMetatype", - "getitem_56": "PTGatherMetatype", + "batch_norm_18": "PTBatchNormMetatype", "relu__15": "PTRELUMetatype", - "_param_constant57": "PTConstNoopMetatype", "conv2d_19": "PTConv2dMetatype", - "empty_19": "UnknownMetatype", - "_param_constant58": "PTConstNoopMetatype", - "_param_constant59": "PTConstNoopMetatype", - "_tensor_constant38": "PTConstNoopMetatype", - "_tensor_constant39": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_19": "PTBatchNormMetatype", - "getitem_57": "PTGatherMetatype", - "getitem_58": "PTGatherMetatype", - "getitem_59": "PTGatherMetatype", + "batch_norm_19": "PTBatchNormMetatype", "add__7": "PTAddMetatype", "relu__16": "PTRELUMetatype", "adaptive_avg_pool2d": "PTAvgPool2dMetatype", "flatten": "PTReshapeMetatype", - "_param_constant60": "PTConstNoopMetatype", - "_param_constant61": "PTConstNoopMetatype", "linear": "PTLinearMetatype", - "output": "PTOutputNoopMetatype" + "output_1": "PTOutputNoopMetatype" } \ No newline at end of file diff --git a/tests/torch/data/reference_graphs/fx/reference_metatypes/swin_v2_s.json b/tests/torch/data/reference_graphs/fx/reference_metatypes/swin_v2_s.json index 6f038197c8e..b4788d60229 100644 --- a/tests/torch/data/reference_graphs/fx/reference_metatypes/swin_v2_s.json +++ b/tests/torch/data/reference_graphs/fx/reference_metatypes/swin_v2_s.json @@ -1,21 +1,560 @@ { - "arg0_1": "PTInputNoopMetatype", - "_param_constant0": "PTConstNoopMetatype", - "_param_constant1": "PTConstNoopMetatype", + "features_1_0_attn_qkv_weight": "PTConstNoopMetatype", + "features_1_0_attn_proj_weight": "PTConstNoopMetatype", + "features_1_0_attn_qkv_bias": "PTConstNoopMetatype", + "features_1_0_attn_proj_bias": "PTConstNoopMetatype", + "features_1_0_attn_logit_scale": "PTConstNoopMetatype", + "features_1_1_attn_qkv_weight": "PTConstNoopMetatype", + "features_1_1_attn_proj_weight": "PTConstNoopMetatype", + "features_1_1_attn_qkv_bias": "PTConstNoopMetatype", + "features_1_1_attn_proj_bias": "PTConstNoopMetatype", + "features_1_1_attn_logit_scale": "PTConstNoopMetatype", + "features_3_0_attn_qkv_weight": "PTConstNoopMetatype", + "features_3_0_attn_proj_weight": "PTConstNoopMetatype", + "features_3_0_attn_qkv_bias": "PTConstNoopMetatype", + "features_3_0_attn_proj_bias": "PTConstNoopMetatype", + "features_3_0_attn_logit_scale": "PTConstNoopMetatype", + "features_3_1_attn_qkv_weight": "PTConstNoopMetatype", + "features_3_1_attn_proj_weight": "PTConstNoopMetatype", + "features_3_1_attn_qkv_bias": "PTConstNoopMetatype", + "features_3_1_attn_proj_bias": "PTConstNoopMetatype", + "features_3_1_attn_logit_scale": "PTConstNoopMetatype", + "features_5_0_attn_qkv_weight": "PTConstNoopMetatype", + "features_5_0_attn_proj_weight": "PTConstNoopMetatype", + "features_5_0_attn_qkv_bias": "PTConstNoopMetatype", + "features_5_0_attn_proj_bias": "PTConstNoopMetatype", + "features_5_0_attn_logit_scale": "PTConstNoopMetatype", + "features_5_1_attn_qkv_weight": "PTConstNoopMetatype", + "features_5_1_attn_proj_weight": "PTConstNoopMetatype", + "features_5_1_attn_qkv_bias": "PTConstNoopMetatype", + "features_5_1_attn_proj_bias": "PTConstNoopMetatype", + 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"features_5_11_attn_lifted_tensor_71": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_72": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_73": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_74": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_75": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_76": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_77": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_78": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_79": "PTConstNoopMetatype", + "features_5_13_attn_lifted_tensor_80": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_81": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_82": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_83": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_84": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_85": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_86": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_87": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_88": "PTConstNoopMetatype", + "features_5_15_attn_lifted_tensor_89": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_90": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_91": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_92": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_93": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_94": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_95": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_96": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_97": "PTConstNoopMetatype", + "features_5_17_attn_lifted_tensor_98": "PTConstNoopMetatype", + "x": "PTInputNoopMetatype", "conv2d": "PTConv2dMetatype", "permute": "PTTransposeMetatype", - "_param_constant2": "PTConstNoopMetatype", - "_param_constant3": "PTConstNoopMetatype", "layer_norm": "PTLayerNormMetatype", - "_tensor_constant0": "PTConstNoopMetatype", - "_param_constant4": "PTConstNoopMetatype", - "_param_constant5": "PTConstNoopMetatype", "linear": "PTLinearMetatype", "relu_": "PTRELUMetatype", - "_param_constant6": "PTConstNoopMetatype", "linear_1": "PTLinearMetatype", "view": "PTReshapeMetatype", - "_tensor_constant1": "PTConstNoopMetatype", "index": "UnknownMetatype", "view_1": "PTReshapeMetatype", "permute_1": "PTTransposeMetatype", @@ -27,11 +566,9 @@ "view_2": "PTReshapeMetatype", "permute_2": "PTTransposeMetatype", "reshape": "PTReshapeMetatype", - "_param_constant7": "PTConstNoopMetatype", "clone": "PTNoopMetatype", "slice_1": "PTGatherMetatype", "zero_": "UnknownMetatype", - "_param_constant8": "PTConstNoopMetatype", "linear_2": "PTLinearMetatype", "reshape_1": "PTReshapeMetatype", "permute_3": "PTTransposeMetatype", @@ -48,7 +585,6 @@ "div_1": "PTDivMetatype", "transpose": "PTTransposeMetatype", "matmul": "PTMatMulMetatype", - "_param_constant9": "PTConstNoopMetatype", "clamp": "UnknownMetatype", "exp": "PTExpMetatype", "mul_1": "PTMulMetatype", @@ -58,8 +594,6 @@ "matmul_1": "PTMatMulMetatype", "transpose_1": "PTTransposeMetatype", "reshape_2": "PTReshapeMetatype", - "_param_constant10": "PTConstNoopMetatype", - "_param_constant11": "PTConstNoopMetatype", "linear_3": "PTLinearMetatype", "dropout_1": "PTDropoutMetatype", "view_3": "PTReshapeMetatype", @@ -67,32 +601,19 @@ "reshape_3": "PTReshapeMetatype", "slice_2": "PTGatherMetatype", "slice_3": "PTGatherMetatype", - "_param_constant12": "PTConstNoopMetatype", - "_param_constant13": "PTConstNoopMetatype", "layer_norm_1": "PTLayerNormMetatype", "add_1": "PTAddMetatype", - "_param_constant14": "PTConstNoopMetatype", - "_param_constant15": "PTConstNoopMetatype", "linear_4": "PTLinearMetatype", "gelu": "PTGELUMetatype", "dropout_2": "PTDropoutMetatype", - "_param_constant16": "PTConstNoopMetatype", - "_param_constant17": "PTConstNoopMetatype", "linear_5": "PTLinearMetatype", "dropout_3": "PTDropoutMetatype", - "_param_constant18": "PTConstNoopMetatype", - "_param_constant19": "PTConstNoopMetatype", "layer_norm_2": "PTLayerNormMetatype", "add_2": "PTAddMetatype", - "_tensor_constant2": "PTConstNoopMetatype", - "_param_constant20": "PTConstNoopMetatype", - "_param_constant21": "PTConstNoopMetatype", "linear_6": "PTLinearMetatype", "relu__1": "PTRELUMetatype", - "_param_constant22": "PTConstNoopMetatype", "linear_7": "PTLinearMetatype", "view_4": "PTReshapeMetatype", - "_tensor_constant3": "PTConstNoopMetatype", "index_1": "UnknownMetatype", "view_5": "PTReshapeMetatype", "permute_5": "PTTransposeMetatype", @@ -105,11 +626,9 @@ "view_6": "PTReshapeMetatype", "permute_6": "PTTransposeMetatype", "reshape_4": "PTReshapeMetatype", - "_param_constant23": "PTConstNoopMetatype", "clone_1": "PTNoopMetatype", "slice_4": "PTGatherMetatype", "zero__1": "UnknownMetatype", - "_param_constant24": "PTConstNoopMetatype", "linear_8": "PTLinearMetatype", "reshape_5": "PTReshapeMetatype", "permute_7": "PTTransposeMetatype", @@ -126,53 +645,43 @@ "div_3": "PTDivMetatype", "transpose_2": "PTTransposeMetatype", "matmul_2": "PTMatMulMetatype", - "_param_constant25": "PTConstNoopMetatype", "clamp_1": "UnknownMetatype", "exp_1": "PTExpMetatype", "mul_3": "PTMulMetatype", "add_3": "PTAddMetatype", "new_zeros": "UnknownMetatype", - "_tensor_constant4": "PTConstNoopMetatype", "lift_fresh_copy": "UnknownMetatype", "slice_5": "PTGatherMetatype", "slice_6": "PTGatherMetatype", "fill_": "UnknownMetatype", - "_tensor_constant5": "PTConstNoopMetatype", "lift_fresh_copy_1": "UnknownMetatype", "slice_7": "PTGatherMetatype", "slice_8": "PTGatherMetatype", "fill__1": "UnknownMetatype", - "_tensor_constant6": "PTConstNoopMetatype", "lift_fresh_copy_2": "UnknownMetatype", "slice_9": "PTGatherMetatype", "slice_10": "PTGatherMetatype", "fill__2": "UnknownMetatype", - "_tensor_constant7": "PTConstNoopMetatype", "lift_fresh_copy_3": "UnknownMetatype", "slice_11": "PTGatherMetatype", "slice_12": "PTGatherMetatype", "fill__3": "UnknownMetatype", - "_tensor_constant8": "PTConstNoopMetatype", "lift_fresh_copy_4": "UnknownMetatype", "slice_13": "PTGatherMetatype", "slice_14": "PTGatherMetatype", "fill__4": "UnknownMetatype", - "_tensor_constant9": "PTConstNoopMetatype", "lift_fresh_copy_5": "UnknownMetatype", "slice_15": "PTGatherMetatype", "slice_16": "PTGatherMetatype", "fill__5": "UnknownMetatype", - "_tensor_constant10": "PTConstNoopMetatype", "lift_fresh_copy_6": "UnknownMetatype", "slice_17": "PTGatherMetatype", "slice_18": "PTGatherMetatype", "fill__6": "UnknownMetatype", - "_tensor_constant11": "PTConstNoopMetatype", "lift_fresh_copy_7": "UnknownMetatype", "slice_19": "PTGatherMetatype", "slice_20": "PTGatherMetatype", "fill__7": "UnknownMetatype", - "_tensor_constant12": "PTConstNoopMetatype", "lift_fresh_copy_8": "UnknownMetatype", "slice_21": "PTGatherMetatype", "slice_22": "PTGatherMetatype", @@ -197,8 +706,6 @@ "matmul_3": "PTMatMulMetatype", "transpose_3": "PTTransposeMetatype", "reshape_7": "PTReshapeMetatype", - "_param_constant26": "PTConstNoopMetatype", - "_param_constant27": "PTConstNoopMetatype", "linear_9": "PTLinearMetatype", "dropout_5": "PTDropoutMetatype", "view_10": "PTReshapeMetatype", @@ -207,21 +714,13 @@ "roll_1": "UnknownMetatype", "slice_23": "PTGatherMetatype", "slice_24": "PTGatherMetatype", - "_param_constant28": "PTConstNoopMetatype", - "_param_constant29": "PTConstNoopMetatype", "layer_norm_3": "PTLayerNormMetatype", "add_5": "PTAddMetatype", - "_param_constant30": "PTConstNoopMetatype", - "_param_constant31": "PTConstNoopMetatype", "linear_10": "PTLinearMetatype", "gelu_1": "PTGELUMetatype", "dropout_6": "PTDropoutMetatype", - "_param_constant32": "PTConstNoopMetatype", - "_param_constant33": "PTConstNoopMetatype", "linear_11": "PTLinearMetatype", "dropout_7": "PTDropoutMetatype", - "_param_constant34": "PTConstNoopMetatype", - "_param_constant35": "PTConstNoopMetatype", "layer_norm_4": "PTLayerNormMetatype", "add_6": "PTAddMetatype", "pad_2": "PTPadMetatype", @@ -238,20 +737,12 @@ "slice_35": "PTGatherMetatype", "slice_36": "PTGatherMetatype", "cat": "PTCatMetatype", - "_param_constant36": "PTConstNoopMetatype", "linear_12": "PTLinearMetatype", - "_param_constant37": "PTConstNoopMetatype", - "_param_constant38": "PTConstNoopMetatype", "layer_norm_5": "PTLayerNormMetatype", - "_tensor_constant13": "PTConstNoopMetatype", - "_param_constant39": "PTConstNoopMetatype", - "_param_constant40": "PTConstNoopMetatype", "linear_13": "PTLinearMetatype", "relu__2": "PTRELUMetatype", - "_param_constant41": "PTConstNoopMetatype", "linear_14": "PTLinearMetatype", "view_11": "PTReshapeMetatype", - "_tensor_constant14": "PTConstNoopMetatype", "index_2": "UnknownMetatype", "view_12": "PTReshapeMetatype", "permute_10": "PTTransposeMetatype", @@ -263,11 +754,9 @@ "view_13": "PTReshapeMetatype", "permute_11": "PTTransposeMetatype", "reshape_9": "PTReshapeMetatype", - "_param_constant42": "PTConstNoopMetatype", "clone_2": "PTNoopMetatype", "slice_37": "PTGatherMetatype", "zero__2": "UnknownMetatype", - "_param_constant43": "PTConstNoopMetatype", "linear_15": "PTLinearMetatype", "reshape_10": "PTReshapeMetatype", "permute_12": "PTTransposeMetatype", @@ -284,7 +773,6 @@ "div_5": "PTDivMetatype", "transpose_4": "PTTransposeMetatype", "matmul_4": "PTMatMulMetatype", - "_param_constant44": "PTConstNoopMetatype", "clamp_2": "UnknownMetatype", "exp_2": "PTExpMetatype", "mul_5": "PTMulMetatype", @@ -294,8 +782,6 @@ "matmul_5": "PTMatMulMetatype", "transpose_5": "PTTransposeMetatype", "reshape_11": "PTReshapeMetatype", - "_param_constant45": "PTConstNoopMetatype", - "_param_constant46": "PTConstNoopMetatype", "linear_16": "PTLinearMetatype", "dropout_9": "PTDropoutMetatype", "view_14": "PTReshapeMetatype", @@ -306,32 +792,19 @@ "slice_40": "PTGatherMetatype", "slice_41": "PTGatherMetatype", "contiguous_3": "PTNoopMetatype", - "_param_constant47": "PTConstNoopMetatype", - "_param_constant48": "PTConstNoopMetatype", "layer_norm_6": "PTLayerNormMetatype", "add_8": "PTAddMetatype", - "_param_constant49": "PTConstNoopMetatype", - "_param_constant50": "PTConstNoopMetatype", "linear_17": "PTLinearMetatype", "gelu_2": "PTGELUMetatype", "dropout_10": "PTDropoutMetatype", - "_param_constant51": "PTConstNoopMetatype", - "_param_constant52": "PTConstNoopMetatype", "linear_18": "PTLinearMetatype", "dropout_11": "PTDropoutMetatype", - "_param_constant53": "PTConstNoopMetatype", - "_param_constant54": "PTConstNoopMetatype", "layer_norm_7": "PTLayerNormMetatype", "add_9": "PTAddMetatype", - "_tensor_constant15": "PTConstNoopMetatype", - "_param_constant55": "PTConstNoopMetatype", - "_param_constant56": "PTConstNoopMetatype", "linear_19": "PTLinearMetatype", "relu__3": "PTRELUMetatype", - "_param_constant57": "PTConstNoopMetatype", "linear_20": "PTLinearMetatype", "view_15": "PTReshapeMetatype", - "_tensor_constant16": "PTConstNoopMetatype", "index_3": "UnknownMetatype", "view_16": "PTReshapeMetatype", "permute_14": "PTTransposeMetatype", @@ -344,11 +817,9 @@ "view_17": "PTReshapeMetatype", "permute_15": "PTTransposeMetatype", "reshape_13": "PTReshapeMetatype", - "_param_constant58": "PTConstNoopMetatype", "clone_3": "PTNoopMetatype", "slice_42": "PTGatherMetatype", "zero__3": "UnknownMetatype", - "_param_constant59": "PTConstNoopMetatype", "linear_21": "PTLinearMetatype", "reshape_14": "PTReshapeMetatype", "permute_16": "PTTransposeMetatype", @@ -365,53 +836,43 @@ "div_7": "PTDivMetatype", "transpose_6": "PTTransposeMetatype", "matmul_6": "PTMatMulMetatype", - "_param_constant60": "PTConstNoopMetatype", "clamp_3": "UnknownMetatype", "exp_3": "PTExpMetatype", "mul_7": "PTMulMetatype", "add_10": "PTAddMetatype", "new_zeros_1": "UnknownMetatype", - "_tensor_constant17": "PTConstNoopMetatype", "lift_fresh_copy_9": "UnknownMetatype", "slice_43": "PTGatherMetatype", "slice_44": "PTGatherMetatype", "fill__9": "UnknownMetatype", - "_tensor_constant18": "PTConstNoopMetatype", "lift_fresh_copy_10": "UnknownMetatype", "slice_45": "PTGatherMetatype", "slice_46": "PTGatherMetatype", "fill__10": "UnknownMetatype", - "_tensor_constant19": "PTConstNoopMetatype", "lift_fresh_copy_11": "UnknownMetatype", "slice_47": "PTGatherMetatype", "slice_48": "PTGatherMetatype", "fill__11": "UnknownMetatype", - "_tensor_constant20": "PTConstNoopMetatype", "lift_fresh_copy_12": "UnknownMetatype", "slice_49": "PTGatherMetatype", "slice_50": "PTGatherMetatype", "fill__12": "UnknownMetatype", - "_tensor_constant21": "PTConstNoopMetatype", "lift_fresh_copy_13": "UnknownMetatype", "slice_51": "PTGatherMetatype", "slice_52": "PTGatherMetatype", "fill__13": "UnknownMetatype", - "_tensor_constant22": "PTConstNoopMetatype", "lift_fresh_copy_14": "UnknownMetatype", "slice_53": "PTGatherMetatype", "slice_54": "PTGatherMetatype", "fill__14": "UnknownMetatype", - "_tensor_constant23": "PTConstNoopMetatype", "lift_fresh_copy_15": "UnknownMetatype", "slice_55": "PTGatherMetatype", "slice_56": "PTGatherMetatype", "fill__15": "UnknownMetatype", - "_tensor_constant24": "PTConstNoopMetatype", "lift_fresh_copy_16": "UnknownMetatype", "slice_57": "PTGatherMetatype", "slice_58": "PTGatherMetatype", "fill__16": "UnknownMetatype", - "_tensor_constant25": "PTConstNoopMetatype", "lift_fresh_copy_17": "UnknownMetatype", "slice_59": "PTGatherMetatype", "slice_60": "PTGatherMetatype", @@ -436,8 +897,6 @@ "matmul_7": "PTMatMulMetatype", "transpose_7": "PTTransposeMetatype", "reshape_16": "PTReshapeMetatype", - "_param_constant61": "PTConstNoopMetatype", - "_param_constant62": "PTConstNoopMetatype", "linear_22": "PTLinearMetatype", "dropout_13": "PTDropoutMetatype", "view_21": "PTReshapeMetatype", @@ -449,21 +908,13 @@ "slice_63": "PTGatherMetatype", "slice_64": "PTGatherMetatype", "contiguous_5": "PTNoopMetatype", - "_param_constant63": "PTConstNoopMetatype", - "_param_constant64": "PTConstNoopMetatype", "layer_norm_8": "PTLayerNormMetatype", "add_12": "PTAddMetatype", - "_param_constant65": "PTConstNoopMetatype", - "_param_constant66": "PTConstNoopMetatype", "linear_23": "PTLinearMetatype", "gelu_3": "PTGELUMetatype", "dropout_14": "PTDropoutMetatype", - "_param_constant67": "PTConstNoopMetatype", - "_param_constant68": "PTConstNoopMetatype", "linear_24": "PTLinearMetatype", "dropout_15": "PTDropoutMetatype", - "_param_constant69": "PTConstNoopMetatype", - "_param_constant70": "PTConstNoopMetatype", "layer_norm_9": "PTLayerNormMetatype", "add_13": "PTAddMetatype", "pad_5": "PTPadMetatype", @@ -480,20 +931,12 @@ "slice_75": "PTGatherMetatype", "slice_76": "PTGatherMetatype", "cat_1": "PTCatMetatype", - "_param_constant71": "PTConstNoopMetatype", "linear_25": "PTLinearMetatype", - "_param_constant72": "PTConstNoopMetatype", - "_param_constant73": "PTConstNoopMetatype", "layer_norm_10": "PTLayerNormMetatype", - "_tensor_constant26": "PTConstNoopMetatype", - "_param_constant74": "PTConstNoopMetatype", - "_param_constant75": "PTConstNoopMetatype", "linear_26": "PTLinearMetatype", "relu__4": "PTRELUMetatype", - "_param_constant76": "PTConstNoopMetatype", "linear_27": "PTLinearMetatype", "view_22": "PTReshapeMetatype", - "_tensor_constant27": "PTConstNoopMetatype", "index_4": "UnknownMetatype", "view_23": "PTReshapeMetatype", "permute_19": "PTTransposeMetatype", @@ -505,11 +948,9 @@ "view_24": "PTReshapeMetatype", "permute_20": "PTTransposeMetatype", "reshape_18": "PTReshapeMetatype", - "_param_constant77": "PTConstNoopMetatype", "clone_4": "PTNoopMetatype", "slice_77": "PTGatherMetatype", "zero__4": "UnknownMetatype", - "_param_constant78": "PTConstNoopMetatype", "linear_28": "PTLinearMetatype", "reshape_19": "PTReshapeMetatype", "permute_21": "PTTransposeMetatype", @@ -526,7 +967,6 @@ "div_9": "PTDivMetatype", "transpose_8": "PTTransposeMetatype", "matmul_8": "PTMatMulMetatype", - "_param_constant79": "PTConstNoopMetatype", "clamp_4": "UnknownMetatype", "exp_4": "PTExpMetatype", "mul_9": "PTMulMetatype", @@ -536,8 +976,6 @@ "matmul_9": "PTMatMulMetatype", "transpose_9": "PTTransposeMetatype", "reshape_20": "PTReshapeMetatype", - "_param_constant80": "PTConstNoopMetatype", - "_param_constant81": "PTConstNoopMetatype", "linear_29": "PTLinearMetatype", "dropout_17": "PTDropoutMetatype", "view_25": "PTReshapeMetatype", @@ -548,32 +986,19 @@ "slice_80": "PTGatherMetatype", "slice_81": "PTGatherMetatype", "contiguous_7": "PTNoopMetatype", - "_param_constant82": "PTConstNoopMetatype", - "_param_constant83": "PTConstNoopMetatype", "layer_norm_11": "PTLayerNormMetatype", "add_15": "PTAddMetatype", - "_param_constant84": "PTConstNoopMetatype", - "_param_constant85": "PTConstNoopMetatype", "linear_30": "PTLinearMetatype", "gelu_4": "PTGELUMetatype", "dropout_18": "PTDropoutMetatype", - "_param_constant86": "PTConstNoopMetatype", - "_param_constant87": "PTConstNoopMetatype", "linear_31": "PTLinearMetatype", "dropout_19": "PTDropoutMetatype", - "_param_constant88": "PTConstNoopMetatype", - "_param_constant89": "PTConstNoopMetatype", "layer_norm_12": "PTLayerNormMetatype", "add_16": "PTAddMetatype", - "_tensor_constant28": "PTConstNoopMetatype", - "_param_constant90": "PTConstNoopMetatype", - "_param_constant91": "PTConstNoopMetatype", "linear_32": "PTLinearMetatype", "relu__5": "PTRELUMetatype", - "_param_constant92": "PTConstNoopMetatype", "linear_33": "PTLinearMetatype", "view_26": "PTReshapeMetatype", - "_tensor_constant29": "PTConstNoopMetatype", "index_5": "UnknownMetatype", "view_27": "PTReshapeMetatype", "permute_23": "PTTransposeMetatype", @@ -586,11 +1011,9 @@ "view_28": "PTReshapeMetatype", "permute_24": "PTTransposeMetatype", "reshape_22": "PTReshapeMetatype", - "_param_constant93": "PTConstNoopMetatype", "clone_5": "PTNoopMetatype", "slice_82": "PTGatherMetatype", "zero__5": "UnknownMetatype", - "_param_constant94": "PTConstNoopMetatype", "linear_34": "PTLinearMetatype", "reshape_23": "PTReshapeMetatype", "permute_25": "PTTransposeMetatype", @@ -607,53 +1030,43 @@ "div_11": "PTDivMetatype", "transpose_10": "PTTransposeMetatype", "matmul_10": "PTMatMulMetatype", - "_param_constant95": "PTConstNoopMetatype", "clamp_5": "UnknownMetatype", "exp_5": "PTExpMetatype", "mul_11": "PTMulMetatype", "add_17": "PTAddMetatype", "new_zeros_2": "UnknownMetatype", - "_tensor_constant30": "PTConstNoopMetatype", "lift_fresh_copy_18": "UnknownMetatype", "slice_83": "PTGatherMetatype", "slice_84": "PTGatherMetatype", "fill__18": "UnknownMetatype", - "_tensor_constant31": "PTConstNoopMetatype", "lift_fresh_copy_19": "UnknownMetatype", "slice_85": "PTGatherMetatype", "slice_86": "PTGatherMetatype", "fill__19": "UnknownMetatype", - "_tensor_constant32": "PTConstNoopMetatype", "lift_fresh_copy_20": "UnknownMetatype", "slice_87": "PTGatherMetatype", "slice_88": "PTGatherMetatype", "fill__20": "UnknownMetatype", - "_tensor_constant33": "PTConstNoopMetatype", "lift_fresh_copy_21": "UnknownMetatype", "slice_89": "PTGatherMetatype", "slice_90": "PTGatherMetatype", "fill__21": "UnknownMetatype", - "_tensor_constant34": "PTConstNoopMetatype", "lift_fresh_copy_22": "UnknownMetatype", "slice_91": "PTGatherMetatype", "slice_92": "PTGatherMetatype", "fill__22": "UnknownMetatype", - "_tensor_constant35": "PTConstNoopMetatype", "lift_fresh_copy_23": "UnknownMetatype", "slice_93": "PTGatherMetatype", "slice_94": "PTGatherMetatype", "fill__23": "UnknownMetatype", - "_tensor_constant36": "PTConstNoopMetatype", "lift_fresh_copy_24": "UnknownMetatype", "slice_95": "PTGatherMetatype", "slice_96": "PTGatherMetatype", "fill__24": "UnknownMetatype", - "_tensor_constant37": "PTConstNoopMetatype", "lift_fresh_copy_25": "UnknownMetatype", "slice_97": "PTGatherMetatype", "slice_98": "PTGatherMetatype", "fill__25": "UnknownMetatype", - "_tensor_constant38": "PTConstNoopMetatype", "lift_fresh_copy_26": "UnknownMetatype", "slice_99": "PTGatherMetatype", "slice_100": "PTGatherMetatype", @@ -678,8 +1091,6 @@ "matmul_11": "PTMatMulMetatype", "transpose_11": "PTTransposeMetatype", "reshape_25": "PTReshapeMetatype", - "_param_constant96": "PTConstNoopMetatype", - "_param_constant97": "PTConstNoopMetatype", "linear_35": "PTLinearMetatype", "dropout_21": "PTDropoutMetatype", "view_32": "PTReshapeMetatype", @@ -691,32 +1102,19 @@ "slice_103": "PTGatherMetatype", "slice_104": "PTGatherMetatype", "contiguous_9": "PTNoopMetatype", - "_param_constant98": "PTConstNoopMetatype", - "_param_constant99": "PTConstNoopMetatype", "layer_norm_13": "PTLayerNormMetatype", "add_19": "PTAddMetatype", - "_param_constant100": "PTConstNoopMetatype", - "_param_constant101": "PTConstNoopMetatype", "linear_36": "PTLinearMetatype", "gelu_5": "PTGELUMetatype", "dropout_22": "PTDropoutMetatype", - "_param_constant102": "PTConstNoopMetatype", - "_param_constant103": "PTConstNoopMetatype", "linear_37": "PTLinearMetatype", "dropout_23": "PTDropoutMetatype", - "_param_constant104": "PTConstNoopMetatype", - "_param_constant105": "PTConstNoopMetatype", "layer_norm_14": "PTLayerNormMetatype", "add_20": "PTAddMetatype", - "_tensor_constant39": "PTConstNoopMetatype", - "_param_constant106": "PTConstNoopMetatype", - "_param_constant107": "PTConstNoopMetatype", "linear_38": "PTLinearMetatype", "relu__6": "PTRELUMetatype", - "_param_constant108": "PTConstNoopMetatype", "linear_39": "PTLinearMetatype", "view_33": "PTReshapeMetatype", - "_tensor_constant40": "PTConstNoopMetatype", "index_6": "UnknownMetatype", "view_34": "PTReshapeMetatype", "permute_28": "PTTransposeMetatype", @@ -728,11 +1126,9 @@ "view_35": "PTReshapeMetatype", "permute_29": "PTTransposeMetatype", "reshape_27": "PTReshapeMetatype", - "_param_constant109": "PTConstNoopMetatype", "clone_6": "PTNoopMetatype", "slice_105": "PTGatherMetatype", "zero__6": "UnknownMetatype", - "_param_constant110": "PTConstNoopMetatype", "linear_40": "PTLinearMetatype", "reshape_28": "PTReshapeMetatype", "permute_30": "PTTransposeMetatype", @@ -749,7 +1145,6 @@ "div_13": "PTDivMetatype", "transpose_12": "PTTransposeMetatype", "matmul_12": "PTMatMulMetatype", - "_param_constant111": "PTConstNoopMetatype", "clamp_6": "UnknownMetatype", "exp_6": "PTExpMetatype", "mul_13": "PTMulMetatype", @@ -759,8 +1154,6 @@ "matmul_13": "PTMatMulMetatype", "transpose_13": "PTTransposeMetatype", "reshape_29": "PTReshapeMetatype", - "_param_constant112": "PTConstNoopMetatype", - "_param_constant113": "PTConstNoopMetatype", "linear_41": "PTLinearMetatype", "dropout_25": "PTDropoutMetatype", "view_36": "PTReshapeMetatype", @@ -771,32 +1164,19 @@ "slice_108": "PTGatherMetatype", "slice_109": "PTGatherMetatype", "contiguous_11": "PTNoopMetatype", - "_param_constant114": "PTConstNoopMetatype", - "_param_constant115": "PTConstNoopMetatype", "layer_norm_15": "PTLayerNormMetatype", "add_22": "PTAddMetatype", - "_param_constant116": "PTConstNoopMetatype", - "_param_constant117": "PTConstNoopMetatype", "linear_42": "PTLinearMetatype", "gelu_6": "PTGELUMetatype", "dropout_26": "PTDropoutMetatype", - "_param_constant118": "PTConstNoopMetatype", - "_param_constant119": "PTConstNoopMetatype", "linear_43": "PTLinearMetatype", "dropout_27": "PTDropoutMetatype", - "_param_constant120": "PTConstNoopMetatype", - "_param_constant121": "PTConstNoopMetatype", "layer_norm_16": "PTLayerNormMetatype", "add_23": "PTAddMetatype", - "_tensor_constant41": "PTConstNoopMetatype", - "_param_constant122": "PTConstNoopMetatype", - "_param_constant123": "PTConstNoopMetatype", "linear_44": "PTLinearMetatype", "relu__7": "PTRELUMetatype", - "_param_constant124": "PTConstNoopMetatype", "linear_45": "PTLinearMetatype", "view_37": "PTReshapeMetatype", - "_tensor_constant42": "PTConstNoopMetatype", "index_7": "UnknownMetatype", "view_38": "PTReshapeMetatype", "permute_32": "PTTransposeMetatype", @@ -809,11 +1189,9 @@ "view_39": "PTReshapeMetatype", "permute_33": "PTTransposeMetatype", "reshape_31": "PTReshapeMetatype", - "_param_constant125": "PTConstNoopMetatype", "clone_7": "PTNoopMetatype", "slice_110": "PTGatherMetatype", "zero__7": "UnknownMetatype", - "_param_constant126": "PTConstNoopMetatype", "linear_46": "PTLinearMetatype", "reshape_32": "PTReshapeMetatype", "permute_34": "PTTransposeMetatype", @@ -830,53 +1208,43 @@ "div_15": "PTDivMetatype", "transpose_14": "PTTransposeMetatype", "matmul_14": "PTMatMulMetatype", - "_param_constant127": "PTConstNoopMetatype", "clamp_7": "UnknownMetatype", "exp_7": "PTExpMetatype", "mul_15": "PTMulMetatype", "add_24": "PTAddMetatype", "new_zeros_3": "UnknownMetatype", - "_tensor_constant43": "PTConstNoopMetatype", "lift_fresh_copy_27": "UnknownMetatype", "slice_111": "PTGatherMetatype", "slice_112": "PTGatherMetatype", "fill__27": "UnknownMetatype", - "_tensor_constant44": "PTConstNoopMetatype", "lift_fresh_copy_28": "UnknownMetatype", "slice_113": "PTGatherMetatype", "slice_114": "PTGatherMetatype", "fill__28": "UnknownMetatype", - "_tensor_constant45": "PTConstNoopMetatype", "lift_fresh_copy_29": "UnknownMetatype", "slice_115": "PTGatherMetatype", "slice_116": "PTGatherMetatype", "fill__29": "UnknownMetatype", - "_tensor_constant46": "PTConstNoopMetatype", "lift_fresh_copy_30": "UnknownMetatype", "slice_117": "PTGatherMetatype", "slice_118": "PTGatherMetatype", "fill__30": "UnknownMetatype", - "_tensor_constant47": "PTConstNoopMetatype", "lift_fresh_copy_31": "UnknownMetatype", "slice_119": "PTGatherMetatype", "slice_120": "PTGatherMetatype", "fill__31": "UnknownMetatype", - "_tensor_constant48": "PTConstNoopMetatype", "lift_fresh_copy_32": "UnknownMetatype", "slice_121": "PTGatherMetatype", "slice_122": "PTGatherMetatype", "fill__32": "UnknownMetatype", - "_tensor_constant49": "PTConstNoopMetatype", "lift_fresh_copy_33": "UnknownMetatype", "slice_123": "PTGatherMetatype", "slice_124": "PTGatherMetatype", "fill__33": "UnknownMetatype", - "_tensor_constant50": "PTConstNoopMetatype", "lift_fresh_copy_34": "UnknownMetatype", "slice_125": "PTGatherMetatype", "slice_126": "PTGatherMetatype", "fill__34": "UnknownMetatype", - "_tensor_constant51": "PTConstNoopMetatype", "lift_fresh_copy_35": "UnknownMetatype", "slice_127": "PTGatherMetatype", "slice_128": "PTGatherMetatype", @@ -901,8 +1269,6 @@ "matmul_15": "PTMatMulMetatype", "transpose_15": "PTTransposeMetatype", "reshape_34": "PTReshapeMetatype", - "_param_constant128": "PTConstNoopMetatype", - "_param_constant129": "PTConstNoopMetatype", "linear_47": "PTLinearMetatype", "dropout_29": "PTDropoutMetatype", "view_43": "PTReshapeMetatype", @@ -914,32 +1280,19 @@ "slice_131": "PTGatherMetatype", "slice_132": "PTGatherMetatype", "contiguous_13": "PTNoopMetatype", - "_param_constant130": "PTConstNoopMetatype", - "_param_constant131": "PTConstNoopMetatype", "layer_norm_17": "PTLayerNormMetatype", "add_26": "PTAddMetatype", - "_param_constant132": "PTConstNoopMetatype", - "_param_constant133": "PTConstNoopMetatype", "linear_48": "PTLinearMetatype", "gelu_7": "PTGELUMetatype", "dropout_30": "PTDropoutMetatype", - "_param_constant134": "PTConstNoopMetatype", - "_param_constant135": "PTConstNoopMetatype", "linear_49": "PTLinearMetatype", "dropout_31": "PTDropoutMetatype", - "_param_constant136": "PTConstNoopMetatype", - "_param_constant137": "PTConstNoopMetatype", "layer_norm_18": "PTLayerNormMetatype", "add_27": "PTAddMetatype", - "_tensor_constant52": "PTConstNoopMetatype", - "_param_constant138": "PTConstNoopMetatype", - "_param_constant139": "PTConstNoopMetatype", "linear_50": "PTLinearMetatype", "relu__8": "PTRELUMetatype", - "_param_constant140": "PTConstNoopMetatype", "linear_51": "PTLinearMetatype", "view_44": "PTReshapeMetatype", - "_tensor_constant53": "PTConstNoopMetatype", "index_8": "UnknownMetatype", "view_45": "PTReshapeMetatype", "permute_37": "PTTransposeMetatype", @@ -951,11 +1304,9 @@ "view_46": "PTReshapeMetatype", "permute_38": "PTTransposeMetatype", "reshape_36": "PTReshapeMetatype", - "_param_constant141": "PTConstNoopMetatype", "clone_8": "PTNoopMetatype", "slice_133": "PTGatherMetatype", "zero__8": "UnknownMetatype", - "_param_constant142": "PTConstNoopMetatype", "linear_52": "PTLinearMetatype", "reshape_37": "PTReshapeMetatype", "permute_39": "PTTransposeMetatype", @@ -972,7 +1323,6 @@ "div_17": "PTDivMetatype", "transpose_16": "PTTransposeMetatype", "matmul_16": "PTMatMulMetatype", - "_param_constant143": "PTConstNoopMetatype", "clamp_8": "UnknownMetatype", "exp_8": "PTExpMetatype", "mul_17": "PTMulMetatype", @@ -982,8 +1332,6 @@ "matmul_17": "PTMatMulMetatype", "transpose_17": "PTTransposeMetatype", "reshape_38": "PTReshapeMetatype", - "_param_constant144": "PTConstNoopMetatype", - "_param_constant145": "PTConstNoopMetatype", "linear_53": "PTLinearMetatype", "dropout_33": "PTDropoutMetatype", "view_47": "PTReshapeMetatype", @@ -994,32 +1342,19 @@ "slice_136": "PTGatherMetatype", "slice_137": "PTGatherMetatype", "contiguous_15": "PTNoopMetatype", - "_param_constant146": "PTConstNoopMetatype", - "_param_constant147": "PTConstNoopMetatype", "layer_norm_19": "PTLayerNormMetatype", "add_29": "PTAddMetatype", - "_param_constant148": "PTConstNoopMetatype", - "_param_constant149": "PTConstNoopMetatype", "linear_54": "PTLinearMetatype", "gelu_8": "PTGELUMetatype", "dropout_34": "PTDropoutMetatype", - "_param_constant150": "PTConstNoopMetatype", - "_param_constant151": "PTConstNoopMetatype", "linear_55": "PTLinearMetatype", "dropout_35": "PTDropoutMetatype", - "_param_constant152": "PTConstNoopMetatype", - "_param_constant153": "PTConstNoopMetatype", "layer_norm_20": "PTLayerNormMetatype", "add_30": "PTAddMetatype", - "_tensor_constant54": "PTConstNoopMetatype", - "_param_constant154": "PTConstNoopMetatype", - "_param_constant155": "PTConstNoopMetatype", "linear_56": "PTLinearMetatype", "relu__9": "PTRELUMetatype", - "_param_constant156": "PTConstNoopMetatype", "linear_57": "PTLinearMetatype", "view_48": "PTReshapeMetatype", - "_tensor_constant55": "PTConstNoopMetatype", "index_9": "UnknownMetatype", "view_49": "PTReshapeMetatype", "permute_41": "PTTransposeMetatype", @@ -1032,11 +1367,9 @@ "view_50": "PTReshapeMetatype", "permute_42": "PTTransposeMetatype", "reshape_40": "PTReshapeMetatype", - "_param_constant157": "PTConstNoopMetatype", "clone_9": "PTNoopMetatype", "slice_138": "PTGatherMetatype", "zero__9": "UnknownMetatype", - "_param_constant158": "PTConstNoopMetatype", "linear_58": "PTLinearMetatype", "reshape_41": "PTReshapeMetatype", "permute_43": "PTTransposeMetatype", @@ -1053,53 +1386,43 @@ "div_19": "PTDivMetatype", "transpose_18": "PTTransposeMetatype", "matmul_18": "PTMatMulMetatype", - "_param_constant159": "PTConstNoopMetatype", "clamp_9": "UnknownMetatype", "exp_9": "PTExpMetatype", "mul_19": "PTMulMetatype", "add_31": "PTAddMetatype", "new_zeros_4": "UnknownMetatype", - "_tensor_constant56": "PTConstNoopMetatype", "lift_fresh_copy_36": "UnknownMetatype", "slice_139": "PTGatherMetatype", "slice_140": "PTGatherMetatype", "fill__36": "UnknownMetatype", - "_tensor_constant57": "PTConstNoopMetatype", "lift_fresh_copy_37": "UnknownMetatype", "slice_141": "PTGatherMetatype", "slice_142": "PTGatherMetatype", "fill__37": "UnknownMetatype", - "_tensor_constant58": "PTConstNoopMetatype", "lift_fresh_copy_38": "UnknownMetatype", "slice_143": "PTGatherMetatype", "slice_144": "PTGatherMetatype", "fill__38": "UnknownMetatype", - "_tensor_constant59": "PTConstNoopMetatype", "lift_fresh_copy_39": "UnknownMetatype", "slice_145": "PTGatherMetatype", "slice_146": "PTGatherMetatype", "fill__39": "UnknownMetatype", - "_tensor_constant60": "PTConstNoopMetatype", "lift_fresh_copy_40": "UnknownMetatype", "slice_147": "PTGatherMetatype", "slice_148": "PTGatherMetatype", "fill__40": "UnknownMetatype", - "_tensor_constant61": "PTConstNoopMetatype", "lift_fresh_copy_41": "UnknownMetatype", "slice_149": "PTGatherMetatype", "slice_150": "PTGatherMetatype", "fill__41": "UnknownMetatype", - "_tensor_constant62": "PTConstNoopMetatype", "lift_fresh_copy_42": "UnknownMetatype", "slice_151": "PTGatherMetatype", "slice_152": "PTGatherMetatype", "fill__42": "UnknownMetatype", - "_tensor_constant63": "PTConstNoopMetatype", "lift_fresh_copy_43": "UnknownMetatype", "slice_153": "PTGatherMetatype", "slice_154": "PTGatherMetatype", "fill__43": "UnknownMetatype", - "_tensor_constant64": "PTConstNoopMetatype", "lift_fresh_copy_44": "UnknownMetatype", "slice_155": "PTGatherMetatype", "slice_156": "PTGatherMetatype", @@ -1124,8 +1447,6 @@ "matmul_19": "PTMatMulMetatype", "transpose_19": "PTTransposeMetatype", "reshape_43": "PTReshapeMetatype", - "_param_constant160": "PTConstNoopMetatype", - "_param_constant161": "PTConstNoopMetatype", "linear_59": "PTLinearMetatype", "dropout_37": "PTDropoutMetatype", "view_54": "PTReshapeMetatype", @@ -1137,32 +1458,19 @@ "slice_159": "PTGatherMetatype", "slice_160": "PTGatherMetatype", "contiguous_17": "PTNoopMetatype", - "_param_constant162": "PTConstNoopMetatype", - "_param_constant163": "PTConstNoopMetatype", "layer_norm_21": "PTLayerNormMetatype", "add_33": "PTAddMetatype", - "_param_constant164": "PTConstNoopMetatype", - "_param_constant165": "PTConstNoopMetatype", "linear_60": "PTLinearMetatype", "gelu_9": "PTGELUMetatype", "dropout_38": "PTDropoutMetatype", - "_param_constant166": "PTConstNoopMetatype", - "_param_constant167": "PTConstNoopMetatype", "linear_61": "PTLinearMetatype", "dropout_39": "PTDropoutMetatype", - "_param_constant168": "PTConstNoopMetatype", - "_param_constant169": "PTConstNoopMetatype", "layer_norm_22": "PTLayerNormMetatype", "add_34": "PTAddMetatype", - "_tensor_constant65": "PTConstNoopMetatype", - "_param_constant170": "PTConstNoopMetatype", - "_param_constant171": "PTConstNoopMetatype", "linear_62": "PTLinearMetatype", "relu__10": "PTRELUMetatype", - "_param_constant172": "PTConstNoopMetatype", "linear_63": "PTLinearMetatype", "view_55": "PTReshapeMetatype", - "_tensor_constant66": "PTConstNoopMetatype", "index_10": "UnknownMetatype", "view_56": "PTReshapeMetatype", "permute_46": "PTTransposeMetatype", @@ -1174,11 +1482,9 @@ "view_57": "PTReshapeMetatype", "permute_47": "PTTransposeMetatype", "reshape_45": "PTReshapeMetatype", - "_param_constant173": "PTConstNoopMetatype", "clone_10": "PTNoopMetatype", "slice_161": "PTGatherMetatype", "zero__10": "UnknownMetatype", - "_param_constant174": "PTConstNoopMetatype", "linear_64": "PTLinearMetatype", "reshape_46": "PTReshapeMetatype", "permute_48": "PTTransposeMetatype", @@ -1195,7 +1501,6 @@ "div_21": "PTDivMetatype", "transpose_20": "PTTransposeMetatype", "matmul_20": "PTMatMulMetatype", - "_param_constant175": "PTConstNoopMetatype", "clamp_10": "UnknownMetatype", "exp_10": "PTExpMetatype", "mul_21": "PTMulMetatype", @@ -1205,8 +1510,6 @@ "matmul_21": "PTMatMulMetatype", "transpose_21": "PTTransposeMetatype", "reshape_47": "PTReshapeMetatype", - "_param_constant176": "PTConstNoopMetatype", - "_param_constant177": "PTConstNoopMetatype", "linear_65": "PTLinearMetatype", "dropout_41": "PTDropoutMetatype", "view_58": "PTReshapeMetatype", @@ -1217,32 +1520,19 @@ "slice_164": "PTGatherMetatype", "slice_165": "PTGatherMetatype", "contiguous_19": "PTNoopMetatype", - "_param_constant178": "PTConstNoopMetatype", - "_param_constant179": "PTConstNoopMetatype", "layer_norm_23": "PTLayerNormMetatype", "add_36": "PTAddMetatype", - "_param_constant180": "PTConstNoopMetatype", - "_param_constant181": "PTConstNoopMetatype", "linear_66": "PTLinearMetatype", "gelu_10": "PTGELUMetatype", "dropout_42": "PTDropoutMetatype", - "_param_constant182": "PTConstNoopMetatype", - "_param_constant183": "PTConstNoopMetatype", "linear_67": "PTLinearMetatype", "dropout_43": "PTDropoutMetatype", - "_param_constant184": "PTConstNoopMetatype", - "_param_constant185": "PTConstNoopMetatype", "layer_norm_24": "PTLayerNormMetatype", "add_37": "PTAddMetatype", - "_tensor_constant67": "PTConstNoopMetatype", - "_param_constant186": "PTConstNoopMetatype", - "_param_constant187": "PTConstNoopMetatype", "linear_68": "PTLinearMetatype", "relu__11": "PTRELUMetatype", - "_param_constant188": "PTConstNoopMetatype", "linear_69": "PTLinearMetatype", "view_59": "PTReshapeMetatype", - "_tensor_constant68": "PTConstNoopMetatype", "index_11": "UnknownMetatype", "view_60": "PTReshapeMetatype", "permute_50": "PTTransposeMetatype", @@ -1255,11 +1545,9 @@ "view_61": "PTReshapeMetatype", "permute_51": "PTTransposeMetatype", "reshape_49": "PTReshapeMetatype", - "_param_constant189": "PTConstNoopMetatype", "clone_11": "PTNoopMetatype", "slice_166": "PTGatherMetatype", "zero__11": "UnknownMetatype", - "_param_constant190": "PTConstNoopMetatype", "linear_70": "PTLinearMetatype", "reshape_50": "PTReshapeMetatype", "permute_52": "PTTransposeMetatype", @@ -1276,53 +1564,43 @@ "div_23": "PTDivMetatype", "transpose_22": "PTTransposeMetatype", "matmul_22": "PTMatMulMetatype", - "_param_constant191": "PTConstNoopMetatype", "clamp_11": "UnknownMetatype", "exp_11": "PTExpMetatype", "mul_23": "PTMulMetatype", "add_38": "PTAddMetatype", "new_zeros_5": "UnknownMetatype", - "_tensor_constant69": "PTConstNoopMetatype", "lift_fresh_copy_45": "UnknownMetatype", "slice_167": "PTGatherMetatype", "slice_168": "PTGatherMetatype", "fill__45": "UnknownMetatype", - "_tensor_constant70": "PTConstNoopMetatype", "lift_fresh_copy_46": "UnknownMetatype", "slice_169": "PTGatherMetatype", "slice_170": "PTGatherMetatype", "fill__46": "UnknownMetatype", - "_tensor_constant71": "PTConstNoopMetatype", "lift_fresh_copy_47": "UnknownMetatype", "slice_171": "PTGatherMetatype", "slice_172": "PTGatherMetatype", "fill__47": "UnknownMetatype", - "_tensor_constant72": "PTConstNoopMetatype", "lift_fresh_copy_48": "UnknownMetatype", "slice_173": "PTGatherMetatype", "slice_174": "PTGatherMetatype", "fill__48": "UnknownMetatype", - "_tensor_constant73": "PTConstNoopMetatype", "lift_fresh_copy_49": "UnknownMetatype", "slice_175": "PTGatherMetatype", "slice_176": "PTGatherMetatype", "fill__49": "UnknownMetatype", - "_tensor_constant74": "PTConstNoopMetatype", "lift_fresh_copy_50": "UnknownMetatype", "slice_177": "PTGatherMetatype", "slice_178": "PTGatherMetatype", "fill__50": "UnknownMetatype", - "_tensor_constant75": "PTConstNoopMetatype", "lift_fresh_copy_51": "UnknownMetatype", "slice_179": "PTGatherMetatype", "slice_180": "PTGatherMetatype", "fill__51": "UnknownMetatype", - "_tensor_constant76": "PTConstNoopMetatype", "lift_fresh_copy_52": "UnknownMetatype", "slice_181": "PTGatherMetatype", "slice_182": "PTGatherMetatype", "fill__52": "UnknownMetatype", - "_tensor_constant77": "PTConstNoopMetatype", "lift_fresh_copy_53": "UnknownMetatype", "slice_183": "PTGatherMetatype", "slice_184": "PTGatherMetatype", @@ -1347,8 +1625,6 @@ "matmul_23": "PTMatMulMetatype", "transpose_23": "PTTransposeMetatype", "reshape_52": "PTReshapeMetatype", - "_param_constant192": "PTConstNoopMetatype", - "_param_constant193": "PTConstNoopMetatype", "linear_71": "PTLinearMetatype", "dropout_45": "PTDropoutMetatype", "view_65": "PTReshapeMetatype", @@ -1360,32 +1636,19 @@ "slice_187": "PTGatherMetatype", "slice_188": "PTGatherMetatype", "contiguous_21": "PTNoopMetatype", - "_param_constant194": "PTConstNoopMetatype", - "_param_constant195": "PTConstNoopMetatype", "layer_norm_25": "PTLayerNormMetatype", "add_40": "PTAddMetatype", - "_param_constant196": "PTConstNoopMetatype", - "_param_constant197": "PTConstNoopMetatype", "linear_72": "PTLinearMetatype", "gelu_11": "PTGELUMetatype", "dropout_46": "PTDropoutMetatype", - "_param_constant198": "PTConstNoopMetatype", - "_param_constant199": "PTConstNoopMetatype", "linear_73": "PTLinearMetatype", "dropout_47": "PTDropoutMetatype", - "_param_constant200": "PTConstNoopMetatype", - "_param_constant201": "PTConstNoopMetatype", "layer_norm_26": "PTLayerNormMetatype", "add_41": "PTAddMetatype", - "_tensor_constant78": "PTConstNoopMetatype", - "_param_constant202": "PTConstNoopMetatype", - "_param_constant203": "PTConstNoopMetatype", "linear_74": "PTLinearMetatype", "relu__12": "PTRELUMetatype", - "_param_constant204": "PTConstNoopMetatype", "linear_75": "PTLinearMetatype", "view_66": "PTReshapeMetatype", - "_tensor_constant79": "PTConstNoopMetatype", "index_12": "UnknownMetatype", "view_67": "PTReshapeMetatype", "permute_55": "PTTransposeMetatype", @@ -1397,11 +1660,9 @@ "view_68": "PTReshapeMetatype", "permute_56": "PTTransposeMetatype", "reshape_54": "PTReshapeMetatype", - "_param_constant205": "PTConstNoopMetatype", "clone_12": "PTNoopMetatype", "slice_189": "PTGatherMetatype", "zero__12": "UnknownMetatype", - "_param_constant206": "PTConstNoopMetatype", "linear_76": "PTLinearMetatype", "reshape_55": "PTReshapeMetatype", "permute_57": "PTTransposeMetatype", @@ -1418,7 +1679,6 @@ "div_25": "PTDivMetatype", "transpose_24": "PTTransposeMetatype", "matmul_24": "PTMatMulMetatype", - "_param_constant207": "PTConstNoopMetatype", "clamp_12": "UnknownMetatype", "exp_12": "PTExpMetatype", "mul_25": "PTMulMetatype", @@ -1428,8 +1688,6 @@ "matmul_25": "PTMatMulMetatype", "transpose_25": "PTTransposeMetatype", "reshape_56": "PTReshapeMetatype", - "_param_constant208": "PTConstNoopMetatype", - "_param_constant209": "PTConstNoopMetatype", "linear_77": "PTLinearMetatype", "dropout_49": "PTDropoutMetatype", "view_69": "PTReshapeMetatype", @@ -1440,32 +1698,19 @@ "slice_192": "PTGatherMetatype", "slice_193": "PTGatherMetatype", "contiguous_23": "PTNoopMetatype", - "_param_constant210": "PTConstNoopMetatype", - "_param_constant211": "PTConstNoopMetatype", "layer_norm_27": "PTLayerNormMetatype", "add_43": "PTAddMetatype", - "_param_constant212": "PTConstNoopMetatype", - "_param_constant213": "PTConstNoopMetatype", "linear_78": "PTLinearMetatype", "gelu_12": "PTGELUMetatype", "dropout_50": "PTDropoutMetatype", - "_param_constant214": "PTConstNoopMetatype", - "_param_constant215": "PTConstNoopMetatype", "linear_79": "PTLinearMetatype", "dropout_51": "PTDropoutMetatype", - "_param_constant216": "PTConstNoopMetatype", - "_param_constant217": "PTConstNoopMetatype", "layer_norm_28": "PTLayerNormMetatype", "add_44": "PTAddMetatype", - "_tensor_constant80": "PTConstNoopMetatype", - "_param_constant218": "PTConstNoopMetatype", - "_param_constant219": "PTConstNoopMetatype", "linear_80": "PTLinearMetatype", "relu__13": "PTRELUMetatype", - "_param_constant220": "PTConstNoopMetatype", "linear_81": "PTLinearMetatype", "view_70": "PTReshapeMetatype", - "_tensor_constant81": "PTConstNoopMetatype", "index_13": "UnknownMetatype", "view_71": "PTReshapeMetatype", "permute_59": "PTTransposeMetatype", @@ -1478,11 +1723,9 @@ "view_72": "PTReshapeMetatype", "permute_60": "PTTransposeMetatype", "reshape_58": "PTReshapeMetatype", - "_param_constant221": "PTConstNoopMetatype", "clone_13": "PTNoopMetatype", "slice_194": "PTGatherMetatype", "zero__13": "UnknownMetatype", - "_param_constant222": "PTConstNoopMetatype", "linear_82": "PTLinearMetatype", "reshape_59": "PTReshapeMetatype", "permute_61": "PTTransposeMetatype", @@ -1499,53 +1742,43 @@ "div_27": "PTDivMetatype", "transpose_26": "PTTransposeMetatype", "matmul_26": "PTMatMulMetatype", - "_param_constant223": "PTConstNoopMetatype", "clamp_13": "UnknownMetatype", "exp_13": "PTExpMetatype", "mul_27": "PTMulMetatype", "add_45": "PTAddMetatype", "new_zeros_6": "UnknownMetatype", - "_tensor_constant82": "PTConstNoopMetatype", "lift_fresh_copy_54": "UnknownMetatype", "slice_195": "PTGatherMetatype", "slice_196": "PTGatherMetatype", "fill__54": "UnknownMetatype", - "_tensor_constant83": "PTConstNoopMetatype", "lift_fresh_copy_55": "UnknownMetatype", "slice_197": "PTGatherMetatype", "slice_198": "PTGatherMetatype", "fill__55": "UnknownMetatype", - "_tensor_constant84": "PTConstNoopMetatype", "lift_fresh_copy_56": "UnknownMetatype", "slice_199": "PTGatherMetatype", "slice_200": "PTGatherMetatype", "fill__56": "UnknownMetatype", - "_tensor_constant85": "PTConstNoopMetatype", "lift_fresh_copy_57": "UnknownMetatype", "slice_201": "PTGatherMetatype", "slice_202": "PTGatherMetatype", "fill__57": "UnknownMetatype", - "_tensor_constant86": "PTConstNoopMetatype", "lift_fresh_copy_58": "UnknownMetatype", "slice_203": "PTGatherMetatype", "slice_204": "PTGatherMetatype", "fill__58": "UnknownMetatype", - "_tensor_constant87": "PTConstNoopMetatype", "lift_fresh_copy_59": "UnknownMetatype", "slice_205": "PTGatherMetatype", "slice_206": "PTGatherMetatype", "fill__59": "UnknownMetatype", - "_tensor_constant88": "PTConstNoopMetatype", "lift_fresh_copy_60": "UnknownMetatype", "slice_207": "PTGatherMetatype", "slice_208": "PTGatherMetatype", "fill__60": "UnknownMetatype", - "_tensor_constant89": "PTConstNoopMetatype", "lift_fresh_copy_61": "UnknownMetatype", "slice_209": "PTGatherMetatype", "slice_210": "PTGatherMetatype", "fill__61": "UnknownMetatype", - "_tensor_constant90": "PTConstNoopMetatype", "lift_fresh_copy_62": "UnknownMetatype", "slice_211": "PTGatherMetatype", "slice_212": "PTGatherMetatype", @@ -1570,8 +1803,6 @@ "matmul_27": "PTMatMulMetatype", "transpose_27": "PTTransposeMetatype", "reshape_61": "PTReshapeMetatype", - "_param_constant224": "PTConstNoopMetatype", - "_param_constant225": "PTConstNoopMetatype", "linear_83": "PTLinearMetatype", "dropout_53": "PTDropoutMetatype", "view_76": "PTReshapeMetatype", @@ -1583,32 +1814,19 @@ "slice_215": "PTGatherMetatype", "slice_216": "PTGatherMetatype", "contiguous_25": "PTNoopMetatype", - "_param_constant226": "PTConstNoopMetatype", - "_param_constant227": "PTConstNoopMetatype", "layer_norm_29": "PTLayerNormMetatype", "add_47": "PTAddMetatype", - "_param_constant228": "PTConstNoopMetatype", - "_param_constant229": "PTConstNoopMetatype", "linear_84": "PTLinearMetatype", "gelu_13": "PTGELUMetatype", "dropout_54": "PTDropoutMetatype", - "_param_constant230": "PTConstNoopMetatype", - "_param_constant231": "PTConstNoopMetatype", "linear_85": "PTLinearMetatype", "dropout_55": "PTDropoutMetatype", - "_param_constant232": "PTConstNoopMetatype", - "_param_constant233": "PTConstNoopMetatype", "layer_norm_30": "PTLayerNormMetatype", "add_48": "PTAddMetatype", - "_tensor_constant91": "PTConstNoopMetatype", - "_param_constant234": "PTConstNoopMetatype", - "_param_constant235": "PTConstNoopMetatype", "linear_86": "PTLinearMetatype", "relu__14": "PTRELUMetatype", - "_param_constant236": "PTConstNoopMetatype", "linear_87": "PTLinearMetatype", "view_77": "PTReshapeMetatype", - "_tensor_constant92": "PTConstNoopMetatype", "index_14": "UnknownMetatype", "view_78": "PTReshapeMetatype", "permute_64": "PTTransposeMetatype", @@ -1620,11 +1838,9 @@ "view_79": "PTReshapeMetatype", "permute_65": "PTTransposeMetatype", "reshape_63": "PTReshapeMetatype", - "_param_constant237": "PTConstNoopMetatype", "clone_14": "PTNoopMetatype", "slice_217": "PTGatherMetatype", "zero__14": "UnknownMetatype", - "_param_constant238": "PTConstNoopMetatype", "linear_88": "PTLinearMetatype", "reshape_64": "PTReshapeMetatype", "permute_66": "PTTransposeMetatype", @@ -1641,7 +1857,6 @@ "div_29": "PTDivMetatype", "transpose_28": "PTTransposeMetatype", "matmul_28": "PTMatMulMetatype", - "_param_constant239": "PTConstNoopMetatype", "clamp_14": "UnknownMetatype", "exp_14": "PTExpMetatype", "mul_29": "PTMulMetatype", @@ -1651,8 +1866,6 @@ "matmul_29": "PTMatMulMetatype", "transpose_29": "PTTransposeMetatype", "reshape_65": "PTReshapeMetatype", - "_param_constant240": "PTConstNoopMetatype", - "_param_constant241": "PTConstNoopMetatype", "linear_89": "PTLinearMetatype", "dropout_57": "PTDropoutMetatype", "view_80": "PTReshapeMetatype", @@ -1663,32 +1876,19 @@ "slice_220": "PTGatherMetatype", "slice_221": "PTGatherMetatype", "contiguous_27": "PTNoopMetatype", - "_param_constant242": "PTConstNoopMetatype", - "_param_constant243": "PTConstNoopMetatype", "layer_norm_31": "PTLayerNormMetatype", "add_50": "PTAddMetatype", - "_param_constant244": "PTConstNoopMetatype", - "_param_constant245": "PTConstNoopMetatype", "linear_90": "PTLinearMetatype", "gelu_14": "PTGELUMetatype", "dropout_58": "PTDropoutMetatype", - "_param_constant246": "PTConstNoopMetatype", - "_param_constant247": "PTConstNoopMetatype", "linear_91": "PTLinearMetatype", "dropout_59": "PTDropoutMetatype", - "_param_constant248": "PTConstNoopMetatype", - "_param_constant249": "PTConstNoopMetatype", "layer_norm_32": "PTLayerNormMetatype", "add_51": "PTAddMetatype", - "_tensor_constant93": "PTConstNoopMetatype", - "_param_constant250": "PTConstNoopMetatype", - "_param_constant251": "PTConstNoopMetatype", "linear_92": "PTLinearMetatype", "relu__15": "PTRELUMetatype", - "_param_constant252": "PTConstNoopMetatype", "linear_93": "PTLinearMetatype", "view_81": "PTReshapeMetatype", - "_tensor_constant94": "PTConstNoopMetatype", "index_15": "UnknownMetatype", "view_82": "PTReshapeMetatype", "permute_68": "PTTransposeMetatype", @@ -1701,11 +1901,9 @@ "view_83": "PTReshapeMetatype", "permute_69": "PTTransposeMetatype", "reshape_67": "PTReshapeMetatype", - "_param_constant253": "PTConstNoopMetatype", "clone_15": "PTNoopMetatype", "slice_222": "PTGatherMetatype", "zero__15": "UnknownMetatype", - "_param_constant254": "PTConstNoopMetatype", "linear_94": "PTLinearMetatype", "reshape_68": "PTReshapeMetatype", "permute_70": "PTTransposeMetatype", @@ -1722,53 +1920,43 @@ "div_31": "PTDivMetatype", "transpose_30": "PTTransposeMetatype", "matmul_30": "PTMatMulMetatype", - "_param_constant255": "PTConstNoopMetatype", "clamp_15": "UnknownMetatype", "exp_15": "PTExpMetatype", "mul_31": "PTMulMetatype", "add_52": "PTAddMetatype", "new_zeros_7": "UnknownMetatype", - "_tensor_constant95": "PTConstNoopMetatype", "lift_fresh_copy_63": "UnknownMetatype", "slice_223": "PTGatherMetatype", "slice_224": "PTGatherMetatype", "fill__63": "UnknownMetatype", - "_tensor_constant96": "PTConstNoopMetatype", "lift_fresh_copy_64": "UnknownMetatype", "slice_225": "PTGatherMetatype", "slice_226": "PTGatherMetatype", "fill__64": "UnknownMetatype", - "_tensor_constant97": "PTConstNoopMetatype", "lift_fresh_copy_65": "UnknownMetatype", "slice_227": "PTGatherMetatype", "slice_228": "PTGatherMetatype", "fill__65": "UnknownMetatype", - "_tensor_constant98": "PTConstNoopMetatype", "lift_fresh_copy_66": "UnknownMetatype", "slice_229": "PTGatherMetatype", "slice_230": "PTGatherMetatype", "fill__66": "UnknownMetatype", - "_tensor_constant99": "PTConstNoopMetatype", "lift_fresh_copy_67": "UnknownMetatype", "slice_231": "PTGatherMetatype", "slice_232": "PTGatherMetatype", "fill__67": "UnknownMetatype", - "_tensor_constant100": "PTConstNoopMetatype", "lift_fresh_copy_68": "UnknownMetatype", "slice_233": "PTGatherMetatype", "slice_234": "PTGatherMetatype", "fill__68": "UnknownMetatype", - "_tensor_constant101": "PTConstNoopMetatype", "lift_fresh_copy_69": "UnknownMetatype", "slice_235": "PTGatherMetatype", "slice_236": "PTGatherMetatype", "fill__69": "UnknownMetatype", - "_tensor_constant102": "PTConstNoopMetatype", "lift_fresh_copy_70": "UnknownMetatype", "slice_237": "PTGatherMetatype", "slice_238": "PTGatherMetatype", "fill__70": "UnknownMetatype", - "_tensor_constant103": "PTConstNoopMetatype", "lift_fresh_copy_71": "UnknownMetatype", "slice_239": "PTGatherMetatype", "slice_240": "PTGatherMetatype", @@ -1793,8 +1981,6 @@ "matmul_31": "PTMatMulMetatype", "transpose_31": "PTTransposeMetatype", "reshape_70": "PTReshapeMetatype", - "_param_constant256": "PTConstNoopMetatype", - "_param_constant257": "PTConstNoopMetatype", "linear_95": "PTLinearMetatype", "dropout_61": "PTDropoutMetatype", "view_87": "PTReshapeMetatype", @@ -1806,32 +1992,19 @@ "slice_243": "PTGatherMetatype", "slice_244": "PTGatherMetatype", "contiguous_29": "PTNoopMetatype", - "_param_constant258": "PTConstNoopMetatype", - "_param_constant259": "PTConstNoopMetatype", "layer_norm_33": "PTLayerNormMetatype", "add_54": "PTAddMetatype", - "_param_constant260": "PTConstNoopMetatype", - "_param_constant261": "PTConstNoopMetatype", "linear_96": "PTLinearMetatype", "gelu_15": "PTGELUMetatype", "dropout_62": "PTDropoutMetatype", - "_param_constant262": "PTConstNoopMetatype", - "_param_constant263": "PTConstNoopMetatype", "linear_97": "PTLinearMetatype", "dropout_63": "PTDropoutMetatype", - "_param_constant264": "PTConstNoopMetatype", - "_param_constant265": "PTConstNoopMetatype", "layer_norm_34": "PTLayerNormMetatype", "add_55": "PTAddMetatype", - "_tensor_constant104": "PTConstNoopMetatype", - "_param_constant266": "PTConstNoopMetatype", - "_param_constant267": "PTConstNoopMetatype", "linear_98": "PTLinearMetatype", "relu__16": "PTRELUMetatype", - "_param_constant268": "PTConstNoopMetatype", "linear_99": "PTLinearMetatype", "view_88": "PTReshapeMetatype", - "_tensor_constant105": "PTConstNoopMetatype", "index_16": "UnknownMetatype", "view_89": "PTReshapeMetatype", "permute_73": "PTTransposeMetatype", @@ -1843,11 +2016,9 @@ "view_90": "PTReshapeMetatype", "permute_74": "PTTransposeMetatype", "reshape_72": "PTReshapeMetatype", - "_param_constant269": "PTConstNoopMetatype", "clone_16": "PTNoopMetatype", "slice_245": "PTGatherMetatype", "zero__16": "UnknownMetatype", - "_param_constant270": "PTConstNoopMetatype", "linear_100": "PTLinearMetatype", "reshape_73": "PTReshapeMetatype", "permute_75": "PTTransposeMetatype", @@ -1864,7 +2035,6 @@ "div_33": "PTDivMetatype", "transpose_32": "PTTransposeMetatype", "matmul_32": "PTMatMulMetatype", - "_param_constant271": "PTConstNoopMetatype", "clamp_16": "UnknownMetatype", "exp_16": "PTExpMetatype", "mul_33": "PTMulMetatype", @@ -1874,8 +2044,6 @@ "matmul_33": "PTMatMulMetatype", "transpose_33": "PTTransposeMetatype", "reshape_74": "PTReshapeMetatype", - "_param_constant272": "PTConstNoopMetatype", - "_param_constant273": "PTConstNoopMetatype", "linear_101": "PTLinearMetatype", "dropout_65": "PTDropoutMetatype", "view_91": "PTReshapeMetatype", @@ -1886,32 +2054,19 @@ "slice_248": "PTGatherMetatype", "slice_249": "PTGatherMetatype", "contiguous_31": "PTNoopMetatype", - "_param_constant274": "PTConstNoopMetatype", - "_param_constant275": "PTConstNoopMetatype", "layer_norm_35": "PTLayerNormMetatype", "add_57": "PTAddMetatype", - "_param_constant276": "PTConstNoopMetatype", - "_param_constant277": "PTConstNoopMetatype", "linear_102": "PTLinearMetatype", "gelu_16": "PTGELUMetatype", "dropout_66": "PTDropoutMetatype", - "_param_constant278": "PTConstNoopMetatype", - "_param_constant279": "PTConstNoopMetatype", "linear_103": "PTLinearMetatype", "dropout_67": "PTDropoutMetatype", - "_param_constant280": "PTConstNoopMetatype", - "_param_constant281": "PTConstNoopMetatype", "layer_norm_36": "PTLayerNormMetatype", "add_58": "PTAddMetatype", - "_tensor_constant106": "PTConstNoopMetatype", - "_param_constant282": "PTConstNoopMetatype", - "_param_constant283": "PTConstNoopMetatype", "linear_104": "PTLinearMetatype", "relu__17": "PTRELUMetatype", - "_param_constant284": "PTConstNoopMetatype", "linear_105": "PTLinearMetatype", "view_92": "PTReshapeMetatype", - "_tensor_constant107": "PTConstNoopMetatype", "index_17": "UnknownMetatype", "view_93": "PTReshapeMetatype", "permute_77": "PTTransposeMetatype", @@ -1924,11 +2079,9 @@ "view_94": "PTReshapeMetatype", "permute_78": "PTTransposeMetatype", "reshape_76": "PTReshapeMetatype", - "_param_constant285": "PTConstNoopMetatype", "clone_17": "PTNoopMetatype", "slice_250": "PTGatherMetatype", "zero__17": "UnknownMetatype", - "_param_constant286": "PTConstNoopMetatype", "linear_106": "PTLinearMetatype", "reshape_77": "PTReshapeMetatype", "permute_79": "PTTransposeMetatype", @@ -1945,53 +2098,43 @@ "div_35": "PTDivMetatype", "transpose_34": "PTTransposeMetatype", "matmul_34": "PTMatMulMetatype", - "_param_constant287": "PTConstNoopMetatype", "clamp_17": "UnknownMetatype", "exp_17": "PTExpMetatype", "mul_35": "PTMulMetatype", "add_59": "PTAddMetatype", "new_zeros_8": "UnknownMetatype", - "_tensor_constant108": "PTConstNoopMetatype", "lift_fresh_copy_72": "UnknownMetatype", "slice_251": "PTGatherMetatype", "slice_252": "PTGatherMetatype", "fill__72": "UnknownMetatype", - "_tensor_constant109": "PTConstNoopMetatype", "lift_fresh_copy_73": "UnknownMetatype", "slice_253": "PTGatherMetatype", "slice_254": "PTGatherMetatype", "fill__73": "UnknownMetatype", - "_tensor_constant110": "PTConstNoopMetatype", "lift_fresh_copy_74": "UnknownMetatype", "slice_255": "PTGatherMetatype", "slice_256": "PTGatherMetatype", "fill__74": "UnknownMetatype", - "_tensor_constant111": "PTConstNoopMetatype", "lift_fresh_copy_75": "UnknownMetatype", "slice_257": "PTGatherMetatype", "slice_258": "PTGatherMetatype", "fill__75": "UnknownMetatype", - "_tensor_constant112": "PTConstNoopMetatype", "lift_fresh_copy_76": "UnknownMetatype", "slice_259": "PTGatherMetatype", "slice_260": "PTGatherMetatype", "fill__76": "UnknownMetatype", - "_tensor_constant113": "PTConstNoopMetatype", "lift_fresh_copy_77": "UnknownMetatype", "slice_261": "PTGatherMetatype", "slice_262": "PTGatherMetatype", "fill__77": "UnknownMetatype", - "_tensor_constant114": "PTConstNoopMetatype", "lift_fresh_copy_78": "UnknownMetatype", "slice_263": "PTGatherMetatype", "slice_264": "PTGatherMetatype", "fill__78": "UnknownMetatype", - "_tensor_constant115": "PTConstNoopMetatype", "lift_fresh_copy_79": "UnknownMetatype", "slice_265": "PTGatherMetatype", "slice_266": "PTGatherMetatype", "fill__79": "UnknownMetatype", - "_tensor_constant116": "PTConstNoopMetatype", "lift_fresh_copy_80": "UnknownMetatype", "slice_267": "PTGatherMetatype", "slice_268": "PTGatherMetatype", @@ -2016,8 +2159,6 @@ "matmul_35": "PTMatMulMetatype", "transpose_35": "PTTransposeMetatype", "reshape_79": "PTReshapeMetatype", - "_param_constant288": "PTConstNoopMetatype", - "_param_constant289": "PTConstNoopMetatype", "linear_107": "PTLinearMetatype", "dropout_69": "PTDropoutMetatype", "view_98": "PTReshapeMetatype", @@ -2029,32 +2170,19 @@ "slice_271": "PTGatherMetatype", "slice_272": "PTGatherMetatype", "contiguous_33": "PTNoopMetatype", - "_param_constant290": "PTConstNoopMetatype", - "_param_constant291": "PTConstNoopMetatype", "layer_norm_37": "PTLayerNormMetatype", "add_61": "PTAddMetatype", - "_param_constant292": "PTConstNoopMetatype", - "_param_constant293": "PTConstNoopMetatype", "linear_108": "PTLinearMetatype", "gelu_17": "PTGELUMetatype", "dropout_70": "PTDropoutMetatype", - "_param_constant294": "PTConstNoopMetatype", - "_param_constant295": "PTConstNoopMetatype", "linear_109": "PTLinearMetatype", "dropout_71": "PTDropoutMetatype", - "_param_constant296": "PTConstNoopMetatype", - "_param_constant297": "PTConstNoopMetatype", "layer_norm_38": "PTLayerNormMetatype", "add_62": "PTAddMetatype", - "_tensor_constant117": "PTConstNoopMetatype", - "_param_constant298": "PTConstNoopMetatype", - "_param_constant299": "PTConstNoopMetatype", "linear_110": "PTLinearMetatype", "relu__18": "PTRELUMetatype", - "_param_constant300": "PTConstNoopMetatype", "linear_111": "PTLinearMetatype", "view_99": "PTReshapeMetatype", - "_tensor_constant118": "PTConstNoopMetatype", "index_18": "UnknownMetatype", "view_100": "PTReshapeMetatype", "permute_82": "PTTransposeMetatype", @@ -2066,11 +2194,9 @@ "view_101": "PTReshapeMetatype", "permute_83": "PTTransposeMetatype", "reshape_81": "PTReshapeMetatype", - "_param_constant301": "PTConstNoopMetatype", "clone_18": "PTNoopMetatype", "slice_273": "PTGatherMetatype", "zero__18": "UnknownMetatype", - "_param_constant302": "PTConstNoopMetatype", "linear_112": "PTLinearMetatype", "reshape_82": "PTReshapeMetatype", "permute_84": "PTTransposeMetatype", @@ -2087,7 +2213,6 @@ "div_37": "PTDivMetatype", "transpose_36": "PTTransposeMetatype", "matmul_36": "PTMatMulMetatype", - "_param_constant303": "PTConstNoopMetatype", "clamp_18": "UnknownMetatype", "exp_18": "PTExpMetatype", "mul_37": "PTMulMetatype", @@ -2097,8 +2222,6 @@ "matmul_37": "PTMatMulMetatype", "transpose_37": "PTTransposeMetatype", "reshape_83": "PTReshapeMetatype", - "_param_constant304": "PTConstNoopMetatype", - "_param_constant305": "PTConstNoopMetatype", "linear_113": "PTLinearMetatype", "dropout_73": "PTDropoutMetatype", "view_102": "PTReshapeMetatype", @@ -2109,32 +2232,19 @@ "slice_276": "PTGatherMetatype", "slice_277": "PTGatherMetatype", "contiguous_35": "PTNoopMetatype", - "_param_constant306": "PTConstNoopMetatype", - "_param_constant307": "PTConstNoopMetatype", "layer_norm_39": "PTLayerNormMetatype", "add_64": "PTAddMetatype", - "_param_constant308": "PTConstNoopMetatype", - "_param_constant309": "PTConstNoopMetatype", "linear_114": "PTLinearMetatype", "gelu_18": "PTGELUMetatype", "dropout_74": "PTDropoutMetatype", - "_param_constant310": "PTConstNoopMetatype", - "_param_constant311": "PTConstNoopMetatype", "linear_115": "PTLinearMetatype", "dropout_75": "PTDropoutMetatype", - "_param_constant312": "PTConstNoopMetatype", - "_param_constant313": "PTConstNoopMetatype", "layer_norm_40": "PTLayerNormMetatype", "add_65": "PTAddMetatype", - "_tensor_constant119": "PTConstNoopMetatype", - "_param_constant314": "PTConstNoopMetatype", - "_param_constant315": "PTConstNoopMetatype", "linear_116": "PTLinearMetatype", "relu__19": "PTRELUMetatype", - "_param_constant316": "PTConstNoopMetatype", "linear_117": "PTLinearMetatype", "view_103": "PTReshapeMetatype", - "_tensor_constant120": "PTConstNoopMetatype", "index_19": "UnknownMetatype", "view_104": "PTReshapeMetatype", "permute_86": "PTTransposeMetatype", @@ -2147,11 +2257,9 @@ "view_105": "PTReshapeMetatype", "permute_87": "PTTransposeMetatype", "reshape_85": "PTReshapeMetatype", - "_param_constant317": "PTConstNoopMetatype", "clone_19": "PTNoopMetatype", "slice_278": "PTGatherMetatype", "zero__19": "UnknownMetatype", - "_param_constant318": "PTConstNoopMetatype", "linear_118": "PTLinearMetatype", "reshape_86": "PTReshapeMetatype", "permute_88": "PTTransposeMetatype", @@ -2168,53 +2276,43 @@ "div_39": "PTDivMetatype", "transpose_38": "PTTransposeMetatype", "matmul_38": "PTMatMulMetatype", - "_param_constant319": "PTConstNoopMetatype", "clamp_19": "UnknownMetatype", "exp_19": "PTExpMetatype", "mul_39": "PTMulMetatype", "add_66": "PTAddMetatype", "new_zeros_9": "UnknownMetatype", - "_tensor_constant121": "PTConstNoopMetatype", "lift_fresh_copy_81": "UnknownMetatype", "slice_279": "PTGatherMetatype", "slice_280": "PTGatherMetatype", "fill__81": "UnknownMetatype", - "_tensor_constant122": "PTConstNoopMetatype", "lift_fresh_copy_82": "UnknownMetatype", "slice_281": "PTGatherMetatype", "slice_282": "PTGatherMetatype", "fill__82": "UnknownMetatype", - "_tensor_constant123": "PTConstNoopMetatype", "lift_fresh_copy_83": "UnknownMetatype", "slice_283": "PTGatherMetatype", "slice_284": "PTGatherMetatype", "fill__83": "UnknownMetatype", - "_tensor_constant124": "PTConstNoopMetatype", "lift_fresh_copy_84": "UnknownMetatype", "slice_285": "PTGatherMetatype", "slice_286": "PTGatherMetatype", "fill__84": "UnknownMetatype", - "_tensor_constant125": "PTConstNoopMetatype", "lift_fresh_copy_85": "UnknownMetatype", "slice_287": "PTGatherMetatype", "slice_288": "PTGatherMetatype", "fill__85": "UnknownMetatype", - "_tensor_constant126": "PTConstNoopMetatype", "lift_fresh_copy_86": "UnknownMetatype", "slice_289": "PTGatherMetatype", "slice_290": "PTGatherMetatype", "fill__86": "UnknownMetatype", - "_tensor_constant127": "PTConstNoopMetatype", "lift_fresh_copy_87": "UnknownMetatype", "slice_291": "PTGatherMetatype", "slice_292": "PTGatherMetatype", "fill__87": "UnknownMetatype", - "_tensor_constant128": "PTConstNoopMetatype", "lift_fresh_copy_88": "UnknownMetatype", "slice_293": "PTGatherMetatype", "slice_294": "PTGatherMetatype", "fill__88": "UnknownMetatype", - "_tensor_constant129": "PTConstNoopMetatype", "lift_fresh_copy_89": "UnknownMetatype", "slice_295": "PTGatherMetatype", "slice_296": "PTGatherMetatype", @@ -2239,8 +2337,6 @@ "matmul_39": "PTMatMulMetatype", "transpose_39": "PTTransposeMetatype", "reshape_88": "PTReshapeMetatype", - "_param_constant320": "PTConstNoopMetatype", - "_param_constant321": "PTConstNoopMetatype", "linear_119": "PTLinearMetatype", "dropout_77": "PTDropoutMetatype", "view_109": "PTReshapeMetatype", @@ -2252,32 +2348,19 @@ "slice_299": "PTGatherMetatype", "slice_300": "PTGatherMetatype", "contiguous_37": "PTNoopMetatype", - "_param_constant322": "PTConstNoopMetatype", - "_param_constant323": "PTConstNoopMetatype", "layer_norm_41": "PTLayerNormMetatype", "add_68": "PTAddMetatype", - "_param_constant324": "PTConstNoopMetatype", - "_param_constant325": "PTConstNoopMetatype", "linear_120": "PTLinearMetatype", "gelu_19": "PTGELUMetatype", "dropout_78": "PTDropoutMetatype", - "_param_constant326": "PTConstNoopMetatype", - "_param_constant327": "PTConstNoopMetatype", "linear_121": "PTLinearMetatype", "dropout_79": "PTDropoutMetatype", - "_param_constant328": "PTConstNoopMetatype", - "_param_constant329": "PTConstNoopMetatype", "layer_norm_42": "PTLayerNormMetatype", "add_69": "PTAddMetatype", - "_tensor_constant130": "PTConstNoopMetatype", - "_param_constant330": "PTConstNoopMetatype", - "_param_constant331": "PTConstNoopMetatype", "linear_122": "PTLinearMetatype", "relu__20": "PTRELUMetatype", - "_param_constant332": "PTConstNoopMetatype", "linear_123": "PTLinearMetatype", "view_110": "PTReshapeMetatype", - "_tensor_constant131": "PTConstNoopMetatype", "index_20": "UnknownMetatype", "view_111": "PTReshapeMetatype", "permute_91": "PTTransposeMetatype", @@ -2289,11 +2372,9 @@ "view_112": "PTReshapeMetatype", "permute_92": "PTTransposeMetatype", "reshape_90": "PTReshapeMetatype", - "_param_constant333": "PTConstNoopMetatype", "clone_20": "PTNoopMetatype", "slice_301": "PTGatherMetatype", "zero__20": "UnknownMetatype", - "_param_constant334": "PTConstNoopMetatype", "linear_124": "PTLinearMetatype", "reshape_91": "PTReshapeMetatype", "permute_93": "PTTransposeMetatype", @@ -2310,7 +2391,6 @@ "div_41": "PTDivMetatype", "transpose_40": "PTTransposeMetatype", "matmul_40": "PTMatMulMetatype", - "_param_constant335": "PTConstNoopMetatype", "clamp_20": "UnknownMetatype", "exp_20": "PTExpMetatype", "mul_41": "PTMulMetatype", @@ -2320,8 +2400,6 @@ "matmul_41": "PTMatMulMetatype", "transpose_41": "PTTransposeMetatype", "reshape_92": "PTReshapeMetatype", - "_param_constant336": "PTConstNoopMetatype", - "_param_constant337": "PTConstNoopMetatype", "linear_125": "PTLinearMetatype", "dropout_81": "PTDropoutMetatype", "view_113": "PTReshapeMetatype", @@ -2332,32 +2410,19 @@ "slice_304": "PTGatherMetatype", "slice_305": "PTGatherMetatype", "contiguous_39": "PTNoopMetatype", - "_param_constant338": "PTConstNoopMetatype", - "_param_constant339": "PTConstNoopMetatype", "layer_norm_43": "PTLayerNormMetatype", "add_71": "PTAddMetatype", - "_param_constant340": "PTConstNoopMetatype", - "_param_constant341": "PTConstNoopMetatype", "linear_126": "PTLinearMetatype", "gelu_20": "PTGELUMetatype", "dropout_82": "PTDropoutMetatype", - "_param_constant342": "PTConstNoopMetatype", - "_param_constant343": "PTConstNoopMetatype", "linear_127": "PTLinearMetatype", "dropout_83": "PTDropoutMetatype", - "_param_constant344": "PTConstNoopMetatype", - "_param_constant345": "PTConstNoopMetatype", "layer_norm_44": "PTLayerNormMetatype", "add_72": "PTAddMetatype", - "_tensor_constant132": "PTConstNoopMetatype", - "_param_constant346": "PTConstNoopMetatype", - "_param_constant347": "PTConstNoopMetatype", "linear_128": "PTLinearMetatype", "relu__21": "PTRELUMetatype", - "_param_constant348": "PTConstNoopMetatype", "linear_129": "PTLinearMetatype", "view_114": "PTReshapeMetatype", - "_tensor_constant133": "PTConstNoopMetatype", "index_21": "UnknownMetatype", "view_115": "PTReshapeMetatype", "permute_95": "PTTransposeMetatype", @@ -2370,11 +2435,9 @@ "view_116": "PTReshapeMetatype", "permute_96": "PTTransposeMetatype", "reshape_94": "PTReshapeMetatype", - "_param_constant349": "PTConstNoopMetatype", "clone_21": "PTNoopMetatype", "slice_306": "PTGatherMetatype", "zero__21": "UnknownMetatype", - "_param_constant350": "PTConstNoopMetatype", "linear_130": "PTLinearMetatype", "reshape_95": "PTReshapeMetatype", "permute_97": "PTTransposeMetatype", @@ -2391,53 +2454,43 @@ "div_43": "PTDivMetatype", "transpose_42": "PTTransposeMetatype", "matmul_42": "PTMatMulMetatype", - "_param_constant351": "PTConstNoopMetatype", "clamp_21": "UnknownMetatype", "exp_21": "PTExpMetatype", "mul_43": "PTMulMetatype", "add_73": "PTAddMetatype", "new_zeros_10": "UnknownMetatype", - "_tensor_constant134": "PTConstNoopMetatype", "lift_fresh_copy_90": "UnknownMetatype", "slice_307": "PTGatherMetatype", "slice_308": "PTGatherMetatype", "fill__90": "UnknownMetatype", - "_tensor_constant135": "PTConstNoopMetatype", "lift_fresh_copy_91": "UnknownMetatype", "slice_309": "PTGatherMetatype", "slice_310": "PTGatherMetatype", "fill__91": "UnknownMetatype", - "_tensor_constant136": "PTConstNoopMetatype", "lift_fresh_copy_92": "UnknownMetatype", "slice_311": "PTGatherMetatype", "slice_312": "PTGatherMetatype", "fill__92": "UnknownMetatype", - "_tensor_constant137": "PTConstNoopMetatype", "lift_fresh_copy_93": "UnknownMetatype", "slice_313": "PTGatherMetatype", "slice_314": "PTGatherMetatype", "fill__93": "UnknownMetatype", - "_tensor_constant138": "PTConstNoopMetatype", "lift_fresh_copy_94": "UnknownMetatype", "slice_315": "PTGatherMetatype", "slice_316": "PTGatherMetatype", "fill__94": "UnknownMetatype", - "_tensor_constant139": "PTConstNoopMetatype", "lift_fresh_copy_95": "UnknownMetatype", "slice_317": "PTGatherMetatype", "slice_318": "PTGatherMetatype", "fill__95": "UnknownMetatype", - "_tensor_constant140": "PTConstNoopMetatype", "lift_fresh_copy_96": "UnknownMetatype", "slice_319": "PTGatherMetatype", "slice_320": "PTGatherMetatype", "fill__96": "UnknownMetatype", - "_tensor_constant141": "PTConstNoopMetatype", "lift_fresh_copy_97": "UnknownMetatype", "slice_321": "PTGatherMetatype", "slice_322": "PTGatherMetatype", "fill__97": "UnknownMetatype", - "_tensor_constant142": "PTConstNoopMetatype", "lift_fresh_copy_98": "UnknownMetatype", "slice_323": "PTGatherMetatype", "slice_324": "PTGatherMetatype", @@ -2462,8 +2515,6 @@ "matmul_43": "PTMatMulMetatype", "transpose_43": "PTTransposeMetatype", "reshape_97": "PTReshapeMetatype", - "_param_constant352": "PTConstNoopMetatype", - "_param_constant353": "PTConstNoopMetatype", "linear_131": "PTLinearMetatype", "dropout_85": "PTDropoutMetatype", "view_120": "PTReshapeMetatype", @@ -2475,21 +2526,13 @@ "slice_327": "PTGatherMetatype", "slice_328": "PTGatherMetatype", "contiguous_41": "PTNoopMetatype", - "_param_constant354": "PTConstNoopMetatype", - "_param_constant355": "PTConstNoopMetatype", "layer_norm_45": "PTLayerNormMetatype", "add_75": "PTAddMetatype", - "_param_constant356": "PTConstNoopMetatype", - "_param_constant357": "PTConstNoopMetatype", "linear_132": "PTLinearMetatype", "gelu_21": "PTGELUMetatype", "dropout_86": "PTDropoutMetatype", - "_param_constant358": "PTConstNoopMetatype", - "_param_constant359": "PTConstNoopMetatype", "linear_133": "PTLinearMetatype", "dropout_87": "PTDropoutMetatype", - "_param_constant360": "PTConstNoopMetatype", - "_param_constant361": "PTConstNoopMetatype", "layer_norm_46": "PTLayerNormMetatype", "add_76": "PTAddMetatype", "pad_24": "PTPadMetatype", @@ -2506,20 +2549,12 @@ "slice_339": "PTGatherMetatype", "slice_340": "PTGatherMetatype", "cat_2": "PTCatMetatype", - "_param_constant362": "PTConstNoopMetatype", "linear_134": "PTLinearMetatype", - "_param_constant363": "PTConstNoopMetatype", - "_param_constant364": "PTConstNoopMetatype", "layer_norm_47": "PTLayerNormMetatype", - "_tensor_constant143": "PTConstNoopMetatype", - "_param_constant365": "PTConstNoopMetatype", - "_param_constant366": "PTConstNoopMetatype", "linear_135": "PTLinearMetatype", "relu__22": "PTRELUMetatype", - "_param_constant367": "PTConstNoopMetatype", "linear_136": "PTLinearMetatype", "view_121": "PTReshapeMetatype", - "_tensor_constant144": "PTConstNoopMetatype", "index_22": "UnknownMetatype", "view_122": "PTReshapeMetatype", "permute_100": "PTTransposeMetatype", @@ -2531,11 +2566,9 @@ "view_123": "PTReshapeMetatype", "permute_101": "PTTransposeMetatype", "reshape_99": "PTReshapeMetatype", - "_param_constant368": "PTConstNoopMetatype", "clone_22": "PTNoopMetatype", "slice_341": "PTGatherMetatype", "zero__22": "UnknownMetatype", - "_param_constant369": "PTConstNoopMetatype", "linear_137": "PTLinearMetatype", "reshape_100": "PTReshapeMetatype", "permute_102": "PTTransposeMetatype", @@ -2552,7 +2585,6 @@ "div_45": "PTDivMetatype", "transpose_44": "PTTransposeMetatype", "matmul_44": "PTMatMulMetatype", - "_param_constant370": "PTConstNoopMetatype", "clamp_22": "UnknownMetatype", "exp_22": "PTExpMetatype", "mul_45": "PTMulMetatype", @@ -2562,8 +2594,6 @@ "matmul_45": "PTMatMulMetatype", "transpose_45": "PTTransposeMetatype", "reshape_101": "PTReshapeMetatype", - "_param_constant371": "PTConstNoopMetatype", - "_param_constant372": "PTConstNoopMetatype", "linear_138": "PTLinearMetatype", "dropout_89": "PTDropoutMetatype", "view_124": "PTReshapeMetatype", @@ -2574,32 +2604,19 @@ "slice_344": "PTGatherMetatype", "slice_345": "PTGatherMetatype", "contiguous_43": "PTNoopMetatype", - "_param_constant373": "PTConstNoopMetatype", - "_param_constant374": "PTConstNoopMetatype", "layer_norm_48": "PTLayerNormMetatype", "add_78": "PTAddMetatype", - "_param_constant375": "PTConstNoopMetatype", - "_param_constant376": "PTConstNoopMetatype", "linear_139": "PTLinearMetatype", "gelu_22": "PTGELUMetatype", "dropout_90": "PTDropoutMetatype", - "_param_constant377": "PTConstNoopMetatype", - "_param_constant378": "PTConstNoopMetatype", "linear_140": "PTLinearMetatype", "dropout_91": "PTDropoutMetatype", - "_param_constant379": "PTConstNoopMetatype", - "_param_constant380": "PTConstNoopMetatype", "layer_norm_49": "PTLayerNormMetatype", "add_79": "PTAddMetatype", - "_tensor_constant145": "PTConstNoopMetatype", - "_param_constant381": "PTConstNoopMetatype", - "_param_constant382": "PTConstNoopMetatype", "linear_141": "PTLinearMetatype", "relu__23": "PTRELUMetatype", - "_param_constant383": "PTConstNoopMetatype", "linear_142": "PTLinearMetatype", "view_125": "PTReshapeMetatype", - "_tensor_constant146": "PTConstNoopMetatype", "index_23": "UnknownMetatype", "view_126": "PTReshapeMetatype", "permute_104": "PTTransposeMetatype", @@ -2611,11 +2628,9 @@ "view_127": "PTReshapeMetatype", "permute_105": "PTTransposeMetatype", "reshape_103": "PTReshapeMetatype", - "_param_constant384": "PTConstNoopMetatype", "clone_23": "PTNoopMetatype", "slice_346": "PTGatherMetatype", "zero__23": "UnknownMetatype", - "_param_constant385": "PTConstNoopMetatype", "linear_143": "PTLinearMetatype", "reshape_104": "PTReshapeMetatype", "permute_106": "PTTransposeMetatype", @@ -2632,7 +2647,6 @@ "div_47": "PTDivMetatype", "transpose_46": "PTTransposeMetatype", "matmul_46": "PTMatMulMetatype", - "_param_constant386": "PTConstNoopMetatype", "clamp_23": "UnknownMetatype", "exp_23": "PTExpMetatype", "mul_47": "PTMulMetatype", @@ -2642,8 +2656,6 @@ "matmul_47": "PTMatMulMetatype", "transpose_47": "PTTransposeMetatype", "reshape_105": "PTReshapeMetatype", - "_param_constant387": "PTConstNoopMetatype", - "_param_constant388": "PTConstNoopMetatype", "linear_144": "PTLinearMetatype", "dropout_93": "PTDropoutMetatype", "view_128": "PTReshapeMetatype", @@ -2654,31 +2666,19 @@ "slice_349": "PTGatherMetatype", "slice_350": "PTGatherMetatype", "contiguous_45": "PTNoopMetatype", - "_param_constant389": "PTConstNoopMetatype", - "_param_constant390": "PTConstNoopMetatype", "layer_norm_50": "PTLayerNormMetatype", "add_81": "PTAddMetatype", - "_param_constant391": "PTConstNoopMetatype", - "_param_constant392": "PTConstNoopMetatype", "linear_145": "PTLinearMetatype", "gelu_23": "PTGELUMetatype", "dropout_94": "PTDropoutMetatype", - "_param_constant393": "PTConstNoopMetatype", - "_param_constant394": "PTConstNoopMetatype", "linear_146": "PTLinearMetatype", "dropout_95": "PTDropoutMetatype", - "_param_constant395": "PTConstNoopMetatype", - "_param_constant396": "PTConstNoopMetatype", "layer_norm_51": "PTLayerNormMetatype", "add_82": "PTAddMetatype", - "_param_constant397": "PTConstNoopMetatype", - "_param_constant398": "PTConstNoopMetatype", "layer_norm_52": "PTLayerNormMetatype", "permute_108": "PTTransposeMetatype", "adaptive_avg_pool2d": "PTAvgPool2dMetatype", "flatten": "PTReshapeMetatype", - "_param_constant399": "PTConstNoopMetatype", - "_param_constant400": "PTConstNoopMetatype", "linear_147": "PTLinearMetatype", - "output": "PTOutputNoopMetatype" + "output_1": "PTOutputNoopMetatype" } \ No newline at end of file diff --git a/tests/torch/data/reference_graphs/fx/reference_metatypes/synthetic_transformer.json b/tests/torch/data/reference_graphs/fx/reference_metatypes/synthetic_transformer.json new file mode 100644 index 00000000000..ba475c16e61 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/reference_metatypes/synthetic_transformer.json @@ -0,0 +1,12 @@ +{ + "wte_weight": "PTConstNoopMetatype", + "linear_weight": "PTConstNoopMetatype", + "linear_bias": "PTConstNoopMetatype", + "lm_head_weight": "PTConstNoopMetatype", + "lm_head_bias": "PTConstNoopMetatype", + "input_ids": "PTInputNoopMetatype", + "embedding": "PTAtenEmbeddingMetatype", + "linear": "PTLinearMetatype", + "linear_1": "PTLinearMetatype", + "output_1": "PTOutputNoopMetatype" +} \ No newline at end of file diff --git a/tests/torch/data/reference_graphs/fx/reference_metatypes/unet.json b/tests/torch/data/reference_graphs/fx/reference_metatypes/unet.json index fb26ec71994..cf5ba08fc69 100644 --- a/tests/torch/data/reference_graphs/fx/reference_metatypes/unet.json +++ b/tests/torch/data/reference_graphs/fx/reference_metatypes/unet.json @@ -1,277 +1,223 @@ { - "arg0_1": "PTInputNoopMetatype", - "_param_constant0": "PTConstNoopMetatype", - "_param_constant1": "PTConstNoopMetatype", + "down_path_0_block_0_weight": "PTConstNoopMetatype", + "down_path_0_block_0_bias": "PTConstNoopMetatype", + "down_path_0_block_1_weight": "PTConstNoopMetatype", + "down_path_0_block_1_bias": "PTConstNoopMetatype", + "down_path_0_block_3_weight": "PTConstNoopMetatype", + "down_path_0_block_3_bias": "PTConstNoopMetatype", + "down_path_0_block_4_weight": "PTConstNoopMetatype", + "down_path_0_block_4_bias": "PTConstNoopMetatype", + "down_path_1_block_0_weight": "PTConstNoopMetatype", + "down_path_1_block_0_bias": "PTConstNoopMetatype", + "down_path_1_block_1_weight": "PTConstNoopMetatype", + "down_path_1_block_1_bias": "PTConstNoopMetatype", + "down_path_1_block_3_weight": "PTConstNoopMetatype", + "down_path_1_block_3_bias": "PTConstNoopMetatype", + "down_path_1_block_4_weight": "PTConstNoopMetatype", + "down_path_1_block_4_bias": "PTConstNoopMetatype", + "down_path_2_block_0_weight": "PTConstNoopMetatype", + "down_path_2_block_0_bias": "PTConstNoopMetatype", + "down_path_2_block_1_weight": "PTConstNoopMetatype", + "down_path_2_block_1_bias": "PTConstNoopMetatype", + "down_path_2_block_3_weight": "PTConstNoopMetatype", + "down_path_2_block_3_bias": "PTConstNoopMetatype", + "down_path_2_block_4_weight": "PTConstNoopMetatype", + "down_path_2_block_4_bias": "PTConstNoopMetatype", + "down_path_3_block_0_weight": "PTConstNoopMetatype", + "down_path_3_block_0_bias": "PTConstNoopMetatype", + "down_path_3_block_1_weight": "PTConstNoopMetatype", + "down_path_3_block_1_bias": "PTConstNoopMetatype", + "down_path_3_block_3_weight": "PTConstNoopMetatype", + "down_path_3_block_3_bias": "PTConstNoopMetatype", + "down_path_3_block_4_weight": "PTConstNoopMetatype", + "down_path_3_block_4_bias": "PTConstNoopMetatype", + "down_path_4_block_0_weight": "PTConstNoopMetatype", + "down_path_4_block_0_bias": "PTConstNoopMetatype", + "down_path_4_block_1_weight": "PTConstNoopMetatype", + "down_path_4_block_1_bias": "PTConstNoopMetatype", + "down_path_4_block_3_weight": "PTConstNoopMetatype", + "down_path_4_block_3_bias": "PTConstNoopMetatype", + "down_path_4_block_4_weight": "PTConstNoopMetatype", + "down_path_4_block_4_bias": "PTConstNoopMetatype", + "up_path_0_up_weight": "PTConstNoopMetatype", + "up_path_0_up_bias": "PTConstNoopMetatype", + "up_path_0_conv_block_block_0_weight": "PTConstNoopMetatype", + "up_path_0_conv_block_block_0_bias": "PTConstNoopMetatype", + "up_path_0_conv_block_block_1_weight": "PTConstNoopMetatype", + "up_path_0_conv_block_block_1_bias": "PTConstNoopMetatype", + "up_path_0_conv_block_block_3_weight": "PTConstNoopMetatype", + "up_path_0_conv_block_block_3_bias": "PTConstNoopMetatype", + "up_path_0_conv_block_block_4_weight": "PTConstNoopMetatype", + "up_path_0_conv_block_block_4_bias": "PTConstNoopMetatype", + "up_path_1_up_weight": "PTConstNoopMetatype", + "up_path_1_up_bias": "PTConstNoopMetatype", + "up_path_1_conv_block_block_0_weight": "PTConstNoopMetatype", + "up_path_1_conv_block_block_0_bias": "PTConstNoopMetatype", + "up_path_1_conv_block_block_1_weight": "PTConstNoopMetatype", + "up_path_1_conv_block_block_1_bias": "PTConstNoopMetatype", + "up_path_1_conv_block_block_3_weight": "PTConstNoopMetatype", + "up_path_1_conv_block_block_3_bias": "PTConstNoopMetatype", + "up_path_1_conv_block_block_4_weight": "PTConstNoopMetatype", + "up_path_1_conv_block_block_4_bias": "PTConstNoopMetatype", + "up_path_2_up_weight": "PTConstNoopMetatype", + "up_path_2_up_bias": "PTConstNoopMetatype", + "up_path_2_conv_block_block_0_weight": "PTConstNoopMetatype", + "up_path_2_conv_block_block_0_bias": "PTConstNoopMetatype", + "up_path_2_conv_block_block_1_weight": "PTConstNoopMetatype", + "up_path_2_conv_block_block_1_bias": "PTConstNoopMetatype", + "up_path_2_conv_block_block_3_weight": "PTConstNoopMetatype", + "up_path_2_conv_block_block_3_bias": "PTConstNoopMetatype", + "up_path_2_conv_block_block_4_weight": "PTConstNoopMetatype", + "up_path_2_conv_block_block_4_bias": "PTConstNoopMetatype", + "up_path_3_up_weight": "PTConstNoopMetatype", + "up_path_3_up_bias": "PTConstNoopMetatype", + "up_path_3_conv_block_block_0_weight": "PTConstNoopMetatype", + "up_path_3_conv_block_block_0_bias": "PTConstNoopMetatype", + "up_path_3_conv_block_block_1_weight": "PTConstNoopMetatype", + "up_path_3_conv_block_block_1_bias": "PTConstNoopMetatype", + "up_path_3_conv_block_block_3_weight": "PTConstNoopMetatype", + "up_path_3_conv_block_block_3_bias": "PTConstNoopMetatype", + "up_path_3_conv_block_block_4_weight": "PTConstNoopMetatype", + "up_path_3_conv_block_block_4_bias": "PTConstNoopMetatype", + "last_weight": "PTConstNoopMetatype", + "last_bias": "PTConstNoopMetatype", + "down_path_0_block_1_running_mean": "PTConstNoopMetatype", + "down_path_0_block_1_running_var": "PTConstNoopMetatype", + "down_path_0_block_1_num_batches_tracked": "PTConstNoopMetatype", + "down_path_0_block_4_running_mean": "PTConstNoopMetatype", + "down_path_0_block_4_running_var": "PTConstNoopMetatype", + "down_path_0_block_4_num_batches_tracked": "PTConstNoopMetatype", + "down_path_1_block_1_running_mean": "PTConstNoopMetatype", + "down_path_1_block_1_running_var": "PTConstNoopMetatype", + "down_path_1_block_1_num_batches_tracked": "PTConstNoopMetatype", + "down_path_1_block_4_running_mean": "PTConstNoopMetatype", + "down_path_1_block_4_running_var": "PTConstNoopMetatype", + "down_path_1_block_4_num_batches_tracked": "PTConstNoopMetatype", + "down_path_2_block_1_running_mean": "PTConstNoopMetatype", + "down_path_2_block_1_running_var": "PTConstNoopMetatype", + "down_path_2_block_1_num_batches_tracked": "PTConstNoopMetatype", + "down_path_2_block_4_running_mean": "PTConstNoopMetatype", + "down_path_2_block_4_running_var": "PTConstNoopMetatype", + "down_path_2_block_4_num_batches_tracked": "PTConstNoopMetatype", + "down_path_3_block_1_running_mean": "PTConstNoopMetatype", + "down_path_3_block_1_running_var": "PTConstNoopMetatype", + "down_path_3_block_1_num_batches_tracked": "PTConstNoopMetatype", + "down_path_3_block_4_running_mean": "PTConstNoopMetatype", + "down_path_3_block_4_running_var": "PTConstNoopMetatype", + "down_path_3_block_4_num_batches_tracked": "PTConstNoopMetatype", + "down_path_4_block_1_running_mean": "PTConstNoopMetatype", + "down_path_4_block_1_running_var": "PTConstNoopMetatype", + "down_path_4_block_1_num_batches_tracked": "PTConstNoopMetatype", + "down_path_4_block_4_running_mean": "PTConstNoopMetatype", + "down_path_4_block_4_running_var": "PTConstNoopMetatype", + "down_path_4_block_4_num_batches_tracked": "PTConstNoopMetatype", + "up_path_0_conv_block_block_1_running_mean": "PTConstNoopMetatype", + "up_path_0_conv_block_block_1_running_var": "PTConstNoopMetatype", + "up_path_0_conv_block_block_1_num_batches_tracked": "PTConstNoopMetatype", + "up_path_0_conv_block_block_4_running_mean": "PTConstNoopMetatype", + "up_path_0_conv_block_block_4_running_var": "PTConstNoopMetatype", + "up_path_0_conv_block_block_4_num_batches_tracked": "PTConstNoopMetatype", + "up_path_1_conv_block_block_1_running_mean": "PTConstNoopMetatype", + "up_path_1_conv_block_block_1_running_var": "PTConstNoopMetatype", + "up_path_1_conv_block_block_1_num_batches_tracked": "PTConstNoopMetatype", + "up_path_1_conv_block_block_4_running_mean": "PTConstNoopMetatype", + "up_path_1_conv_block_block_4_running_var": "PTConstNoopMetatype", + "up_path_1_conv_block_block_4_num_batches_tracked": "PTConstNoopMetatype", + "up_path_2_conv_block_block_1_running_mean": "PTConstNoopMetatype", + "up_path_2_conv_block_block_1_running_var": "PTConstNoopMetatype", + "up_path_2_conv_block_block_1_num_batches_tracked": "PTConstNoopMetatype", + "up_path_2_conv_block_block_4_running_mean": "PTConstNoopMetatype", + "up_path_2_conv_block_block_4_running_var": "PTConstNoopMetatype", + "up_path_2_conv_block_block_4_num_batches_tracked": "PTConstNoopMetatype", + "up_path_3_conv_block_block_1_running_mean": "PTConstNoopMetatype", + "up_path_3_conv_block_block_1_running_var": "PTConstNoopMetatype", + "up_path_3_conv_block_block_1_num_batches_tracked": "PTConstNoopMetatype", + "up_path_3_conv_block_block_4_running_mean": "PTConstNoopMetatype", + "up_path_3_conv_block_block_4_running_var": "PTConstNoopMetatype", + "up_path_3_conv_block_block_4_num_batches_tracked": "PTConstNoopMetatype", + "x": "PTInputNoopMetatype", "conv2d": "PTConv2dMetatype", - "empty": "UnknownMetatype", - "_param_constant2": "PTConstNoopMetatype", - "_param_constant3": "PTConstNoopMetatype", - "_tensor_constant0": "PTConstNoopMetatype", - "_tensor_constant1": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training": "PTBatchNormMetatype", - "getitem": "PTGatherMetatype", - "getitem_1": "PTGatherMetatype", - "getitem_2": "PTGatherMetatype", + "batch_norm": "PTBatchNormMetatype", "relu": "PTRELUMetatype", - "_param_constant4": "PTConstNoopMetatype", - "_param_constant5": "PTConstNoopMetatype", "conv2d_1": "PTConv2dMetatype", - "empty_1": "UnknownMetatype", - "_param_constant6": "PTConstNoopMetatype", - "_param_constant7": "PTConstNoopMetatype", - "_tensor_constant2": "PTConstNoopMetatype", - "_tensor_constant3": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_1": "PTBatchNormMetatype", - "getitem_3": "PTGatherMetatype", - "getitem_4": "PTGatherMetatype", - "getitem_5": "PTGatherMetatype", + "batch_norm_1": "PTBatchNormMetatype", "relu_1": "PTRELUMetatype", "max_pool2d": "PTMaxPool2dMetatype", - "_param_constant8": "PTConstNoopMetatype", - "_param_constant9": "PTConstNoopMetatype", "conv2d_2": "PTConv2dMetatype", - "empty_2": "UnknownMetatype", - "_param_constant10": "PTConstNoopMetatype", - "_param_constant11": "PTConstNoopMetatype", - "_tensor_constant4": "PTConstNoopMetatype", - "_tensor_constant5": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_2": "PTBatchNormMetatype", - "getitem_6": "PTGatherMetatype", - "getitem_7": "PTGatherMetatype", - "getitem_8": "PTGatherMetatype", + "batch_norm_2": "PTBatchNormMetatype", "relu_2": "PTRELUMetatype", - "_param_constant12": "PTConstNoopMetatype", - "_param_constant13": "PTConstNoopMetatype", "conv2d_3": "PTConv2dMetatype", - "empty_3": "UnknownMetatype", - "_param_constant14": "PTConstNoopMetatype", - "_param_constant15": "PTConstNoopMetatype", - "_tensor_constant6": "PTConstNoopMetatype", - "_tensor_constant7": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_3": "PTBatchNormMetatype", - "getitem_9": "PTGatherMetatype", - "getitem_10": "PTGatherMetatype", - "getitem_11": "PTGatherMetatype", + "batch_norm_3": "PTBatchNormMetatype", "relu_3": "PTRELUMetatype", "max_pool2d_1": "PTMaxPool2dMetatype", - "_param_constant16": "PTConstNoopMetatype", - "_param_constant17": "PTConstNoopMetatype", "conv2d_4": "PTConv2dMetatype", - "empty_4": "UnknownMetatype", - "_param_constant18": "PTConstNoopMetatype", - "_param_constant19": "PTConstNoopMetatype", - "_tensor_constant8": "PTConstNoopMetatype", - "_tensor_constant9": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_4": "PTBatchNormMetatype", - "getitem_12": "PTGatherMetatype", - "getitem_13": "PTGatherMetatype", - "getitem_14": "PTGatherMetatype", + "batch_norm_4": "PTBatchNormMetatype", "relu_4": "PTRELUMetatype", - "_param_constant20": "PTConstNoopMetatype", - "_param_constant21": "PTConstNoopMetatype", "conv2d_5": "PTConv2dMetatype", - "empty_5": "UnknownMetatype", - "_param_constant22": "PTConstNoopMetatype", - "_param_constant23": "PTConstNoopMetatype", - "_tensor_constant10": "PTConstNoopMetatype", - "_tensor_constant11": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_5": "PTBatchNormMetatype", - "getitem_15": "PTGatherMetatype", - "getitem_16": "PTGatherMetatype", - "getitem_17": "PTGatherMetatype", + "batch_norm_5": "PTBatchNormMetatype", "relu_5": "PTRELUMetatype", "max_pool2d_2": "PTMaxPool2dMetatype", - "_param_constant24": "PTConstNoopMetatype", - "_param_constant25": "PTConstNoopMetatype", "conv2d_6": "PTConv2dMetatype", - "empty_6": "UnknownMetatype", - "_param_constant26": "PTConstNoopMetatype", - "_param_constant27": "PTConstNoopMetatype", - "_tensor_constant12": "PTConstNoopMetatype", - "_tensor_constant13": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_6": "PTBatchNormMetatype", - "getitem_18": "PTGatherMetatype", - "getitem_19": "PTGatherMetatype", - "getitem_20": "PTGatherMetatype", + "batch_norm_6": "PTBatchNormMetatype", "relu_6": "PTRELUMetatype", - "_param_constant28": "PTConstNoopMetatype", - "_param_constant29": "PTConstNoopMetatype", "conv2d_7": "PTConv2dMetatype", - "empty_7": "UnknownMetatype", - "_param_constant30": "PTConstNoopMetatype", - "_param_constant31": "PTConstNoopMetatype", - "_tensor_constant14": "PTConstNoopMetatype", - "_tensor_constant15": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_7": "PTBatchNormMetatype", - "getitem_21": "PTGatherMetatype", - "getitem_22": "PTGatherMetatype", - "getitem_23": "PTGatherMetatype", + "batch_norm_7": "PTBatchNormMetatype", "relu_7": "PTRELUMetatype", "max_pool2d_3": "PTMaxPool2dMetatype", - "_param_constant32": "PTConstNoopMetatype", - "_param_constant33": "PTConstNoopMetatype", "conv2d_8": "PTConv2dMetatype", - "empty_8": "UnknownMetatype", - "_param_constant34": "PTConstNoopMetatype", - "_param_constant35": "PTConstNoopMetatype", - "_tensor_constant16": "PTConstNoopMetatype", - "_tensor_constant17": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_8": "PTBatchNormMetatype", - "getitem_24": "PTGatherMetatype", - "getitem_25": "PTGatherMetatype", - "getitem_26": "PTGatherMetatype", + "batch_norm_8": "PTBatchNormMetatype", "relu_8": "PTRELUMetatype", - "_param_constant36": "PTConstNoopMetatype", - "_param_constant37": "PTConstNoopMetatype", "conv2d_9": "PTConv2dMetatype", - "empty_9": "UnknownMetatype", - "_param_constant38": "PTConstNoopMetatype", - "_param_constant39": "PTConstNoopMetatype", - "_tensor_constant18": "PTConstNoopMetatype", - "_tensor_constant19": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_9": "PTBatchNormMetatype", - "getitem_27": "PTGatherMetatype", - "getitem_28": "PTGatherMetatype", - "getitem_29": "PTGatherMetatype", + "batch_norm_9": "PTBatchNormMetatype", "relu_9": "PTRELUMetatype", - "_param_constant40": "PTConstNoopMetatype", - "_param_constant41": "PTConstNoopMetatype", "conv_transpose2d": "PTConvTranspose2dMetatype", "slice_1": "PTGatherMetatype", "slice_2": "PTGatherMetatype", "slice_3": "PTGatherMetatype", "slice_4": "PTGatherMetatype", "cat": "PTCatMetatype", - "_param_constant42": "PTConstNoopMetatype", - "_param_constant43": "PTConstNoopMetatype", "conv2d_10": "PTConv2dMetatype", - "empty_10": "UnknownMetatype", - "_param_constant44": "PTConstNoopMetatype", - "_param_constant45": "PTConstNoopMetatype", - "_tensor_constant20": "PTConstNoopMetatype", - "_tensor_constant21": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_10": "PTBatchNormMetatype", - "getitem_30": "PTGatherMetatype", - "getitem_31": "PTGatherMetatype", - "getitem_32": "PTGatherMetatype", + "batch_norm_10": "PTBatchNormMetatype", "relu_10": "PTRELUMetatype", - "_param_constant46": "PTConstNoopMetatype", - "_param_constant47": "PTConstNoopMetatype", "conv2d_11": "PTConv2dMetatype", - "empty_11": "UnknownMetatype", - "_param_constant48": "PTConstNoopMetatype", - "_param_constant49": "PTConstNoopMetatype", - "_tensor_constant22": "PTConstNoopMetatype", - "_tensor_constant23": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_11": "PTBatchNormMetatype", - "getitem_33": "PTGatherMetatype", - "getitem_34": "PTGatherMetatype", - "getitem_35": "PTGatherMetatype", + "batch_norm_11": "PTBatchNormMetatype", "relu_11": "PTRELUMetatype", - "_param_constant50": "PTConstNoopMetatype", - "_param_constant51": "PTConstNoopMetatype", "conv_transpose2d_1": "PTConvTranspose2dMetatype", "slice_5": "PTGatherMetatype", "slice_6": "PTGatherMetatype", "slice_7": "PTGatherMetatype", "slice_8": "PTGatherMetatype", "cat_1": "PTCatMetatype", - "_param_constant52": "PTConstNoopMetatype", - "_param_constant53": "PTConstNoopMetatype", "conv2d_12": "PTConv2dMetatype", - "empty_12": "UnknownMetatype", - "_param_constant54": "PTConstNoopMetatype", - "_param_constant55": "PTConstNoopMetatype", - "_tensor_constant24": "PTConstNoopMetatype", - "_tensor_constant25": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_12": "PTBatchNormMetatype", - "getitem_36": "PTGatherMetatype", - "getitem_37": "PTGatherMetatype", - "getitem_38": "PTGatherMetatype", + "batch_norm_12": "PTBatchNormMetatype", "relu_12": "PTRELUMetatype", - "_param_constant56": "PTConstNoopMetatype", - "_param_constant57": "PTConstNoopMetatype", "conv2d_13": "PTConv2dMetatype", - "empty_13": "UnknownMetatype", - "_param_constant58": "PTConstNoopMetatype", - "_param_constant59": "PTConstNoopMetatype", - "_tensor_constant26": "PTConstNoopMetatype", - "_tensor_constant27": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_13": "PTBatchNormMetatype", - "getitem_39": "PTGatherMetatype", - "getitem_40": "PTGatherMetatype", - "getitem_41": "PTGatherMetatype", + "batch_norm_13": "PTBatchNormMetatype", "relu_13": "PTRELUMetatype", - "_param_constant60": "PTConstNoopMetatype", - "_param_constant61": "PTConstNoopMetatype", "conv_transpose2d_2": "PTConvTranspose2dMetatype", "slice_9": "PTGatherMetatype", "slice_10": "PTGatherMetatype", "slice_11": "PTGatherMetatype", "slice_12": "PTGatherMetatype", "cat_2": "PTCatMetatype", - "_param_constant62": "PTConstNoopMetatype", - "_param_constant63": "PTConstNoopMetatype", "conv2d_14": "PTConv2dMetatype", - "empty_14": "UnknownMetatype", - "_param_constant64": "PTConstNoopMetatype", - "_param_constant65": "PTConstNoopMetatype", - "_tensor_constant28": "PTConstNoopMetatype", - "_tensor_constant29": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_14": "PTBatchNormMetatype", - "getitem_42": "PTGatherMetatype", - "getitem_43": "PTGatherMetatype", - "getitem_44": "PTGatherMetatype", + "batch_norm_14": "PTBatchNormMetatype", "relu_14": "PTRELUMetatype", - "_param_constant66": "PTConstNoopMetatype", - "_param_constant67": "PTConstNoopMetatype", "conv2d_15": "PTConv2dMetatype", - "empty_15": "UnknownMetatype", - "_param_constant68": "PTConstNoopMetatype", - "_param_constant69": "PTConstNoopMetatype", - "_tensor_constant30": "PTConstNoopMetatype", - "_tensor_constant31": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_15": "PTBatchNormMetatype", - "getitem_45": "PTGatherMetatype", - "getitem_46": "PTGatherMetatype", - "getitem_47": "PTGatherMetatype", + "batch_norm_15": "PTBatchNormMetatype", "relu_15": "PTRELUMetatype", - "_param_constant70": "PTConstNoopMetatype", - "_param_constant71": "PTConstNoopMetatype", "conv_transpose2d_3": "PTConvTranspose2dMetatype", "slice_13": "PTGatherMetatype", "slice_14": "PTGatherMetatype", "slice_15": "PTGatherMetatype", "slice_16": "PTGatherMetatype", "cat_3": "PTCatMetatype", - "_param_constant72": "PTConstNoopMetatype", - "_param_constant73": "PTConstNoopMetatype", "conv2d_16": "PTConv2dMetatype", - "empty_16": "UnknownMetatype", - "_param_constant74": "PTConstNoopMetatype", - "_param_constant75": "PTConstNoopMetatype", - "_tensor_constant32": "PTConstNoopMetatype", - "_tensor_constant33": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_16": "PTBatchNormMetatype", - "getitem_48": "PTGatherMetatype", - "getitem_49": "PTGatherMetatype", - "getitem_50": "PTGatherMetatype", + "batch_norm_16": "PTBatchNormMetatype", "relu_16": "PTRELUMetatype", - "_param_constant76": "PTConstNoopMetatype", - "_param_constant77": "PTConstNoopMetatype", "conv2d_17": "PTConv2dMetatype", - "empty_17": "UnknownMetatype", - "_param_constant78": "PTConstNoopMetatype", - "_param_constant79": "PTConstNoopMetatype", - "_tensor_constant34": "PTConstNoopMetatype", - "_tensor_constant35": "PTConstNoopMetatype", - "_native_batch_norm_legit_no_training_17": "PTBatchNormMetatype", - "getitem_51": "PTGatherMetatype", - "getitem_52": "PTGatherMetatype", - "getitem_53": "PTGatherMetatype", + "batch_norm_17": "PTBatchNormMetatype", "relu_17": "PTRELUMetatype", - "_param_constant80": "PTConstNoopMetatype", - "_param_constant81": "PTConstNoopMetatype", "conv2d_18": "PTConv2dMetatype", - "output": "PTOutputNoopMetatype" + "output_1": "PTOutputNoopMetatype" } \ No newline at end of file diff --git a/tests/torch/data/reference_graphs/fx/reference_metatypes/vit_b_16.json b/tests/torch/data/reference_graphs/fx/reference_metatypes/vit_b_16.json index 2b9eb7fa783..42484583d7a 100644 --- a/tests/torch/data/reference_graphs/fx/reference_metatypes/vit_b_16.json +++ b/tests/torch/data/reference_graphs/fx/reference_metatypes/vit_b_16.json @@ -1,22 +1,166 @@ { - "arg0_1": "PTInputNoopMetatype", - "_param_constant0": "PTConstNoopMetatype", - "_param_constant1": "PTConstNoopMetatype", + "class_token": "PTConstNoopMetatype", + "encoder_pos_embedding": "PTConstNoopMetatype", + "conv_proj_weight": "PTConstNoopMetatype", + "conv_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_0_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_1_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_2_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_3_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_4_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_5_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_6_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_7_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_8_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_9_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_10_mlp_3_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_ln_1_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_ln_1_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_self_attention_in_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_self_attention_in_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_self_attention_out_proj_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_self_attention_out_proj_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_ln_2_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_ln_2_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_mlp_0_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_mlp_0_bias": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_mlp_3_weight": "PTConstNoopMetatype", + "encoder_layers_encoder_layer_11_mlp_3_bias": "PTConstNoopMetatype", + "encoder_ln_weight": "PTConstNoopMetatype", + "encoder_ln_bias": "PTConstNoopMetatype", + "heads_head_weight": "PTConstNoopMetatype", + "heads_head_bias": "PTConstNoopMetatype", + "x": "PTInputNoopMetatype", "conv2d": "PTConv2dMetatype", "reshape": "PTReshapeMetatype", "permute": "PTTransposeMetatype", - "_param_constant2": "PTConstNoopMetatype", "expand": "PTExpandMetatype", "cat": "PTCatMetatype", - "_param_constant3": "PTConstNoopMetatype", "add": "PTAddMetatype", "dropout": "PTDropoutMetatype", - "_param_constant4": "PTConstNoopMetatype", - "_param_constant5": "PTConstNoopMetatype", "layer_norm": "PTLayerNormMetatype", "transpose": "PTTransposeMetatype", - "_param_constant6": "PTConstNoopMetatype", - "_param_constant7": "PTConstNoopMetatype", "linear": "PTLinearMetatype", "unflatten": "PTReshapeMetatype", "unsqueeze": "PTReshapeMetatype", @@ -38,32 +182,20 @@ "scaled_dot_product_attention": "PTScaledDotProductAttentionMetatype", "permute_1": "PTTransposeMetatype", "view_6": "PTReshapeMetatype", - "_param_constant8": "PTConstNoopMetatype", - "_param_constant9": "PTConstNoopMetatype", "linear_1": "PTLinearMetatype", "view_7": "PTReshapeMetatype", "transpose_5": "PTTransposeMetatype", "dropout_1": "PTDropoutMetatype", "add_1": "PTAddMetatype", - "_param_constant10": "PTConstNoopMetatype", - "_param_constant11": "PTConstNoopMetatype", "layer_norm_1": "PTLayerNormMetatype", - "_param_constant12": "PTConstNoopMetatype", - "_param_constant13": "PTConstNoopMetatype", "linear_2": "PTLinearMetatype", "gelu": "PTGELUMetatype", "dropout_2": "PTDropoutMetatype", - "_param_constant14": "PTConstNoopMetatype", - "_param_constant15": "PTConstNoopMetatype", "linear_3": "PTLinearMetatype", "dropout_3": "PTDropoutMetatype", "add_2": "PTAddMetatype", - "_param_constant16": "PTConstNoopMetatype", - "_param_constant17": "PTConstNoopMetatype", "layer_norm_2": "PTLayerNormMetatype", "transpose_6": "PTTransposeMetatype", - "_param_constant18": "PTConstNoopMetatype", - "_param_constant19": "PTConstNoopMetatype", "linear_4": "PTLinearMetatype", "unflatten_1": "PTReshapeMetatype", "unsqueeze_1": "PTReshapeMetatype", @@ -85,32 +217,20 @@ "scaled_dot_product_attention_1": "PTScaledDotProductAttentionMetatype", "permute_2": "PTTransposeMetatype", "view_14": "PTReshapeMetatype", - "_param_constant20": "PTConstNoopMetatype", - "_param_constant21": "PTConstNoopMetatype", "linear_5": "PTLinearMetatype", "view_15": "PTReshapeMetatype", "transpose_11": "PTTransposeMetatype", "dropout_4": "PTDropoutMetatype", "add_3": "PTAddMetatype", - "_param_constant22": "PTConstNoopMetatype", - "_param_constant23": "PTConstNoopMetatype", "layer_norm_3": "PTLayerNormMetatype", - "_param_constant24": "PTConstNoopMetatype", - "_param_constant25": "PTConstNoopMetatype", "linear_6": "PTLinearMetatype", "gelu_1": "PTGELUMetatype", "dropout_5": "PTDropoutMetatype", - "_param_constant26": "PTConstNoopMetatype", - "_param_constant27": "PTConstNoopMetatype", "linear_7": "PTLinearMetatype", "dropout_6": "PTDropoutMetatype", "add_4": "PTAddMetatype", - "_param_constant28": "PTConstNoopMetatype", - "_param_constant29": "PTConstNoopMetatype", "layer_norm_4": "PTLayerNormMetatype", "transpose_12": "PTTransposeMetatype", - "_param_constant30": "PTConstNoopMetatype", - "_param_constant31": "PTConstNoopMetatype", "linear_8": "PTLinearMetatype", "unflatten_2": "PTReshapeMetatype", "unsqueeze_2": "PTReshapeMetatype", @@ -132,32 +252,20 @@ "scaled_dot_product_attention_2": "PTScaledDotProductAttentionMetatype", "permute_3": "PTTransposeMetatype", "view_22": "PTReshapeMetatype", - "_param_constant32": "PTConstNoopMetatype", - "_param_constant33": "PTConstNoopMetatype", "linear_9": "PTLinearMetatype", "view_23": "PTReshapeMetatype", "transpose_17": "PTTransposeMetatype", "dropout_7": "PTDropoutMetatype", "add_5": "PTAddMetatype", - "_param_constant34": "PTConstNoopMetatype", - "_param_constant35": "PTConstNoopMetatype", "layer_norm_5": "PTLayerNormMetatype", - "_param_constant36": "PTConstNoopMetatype", - "_param_constant37": "PTConstNoopMetatype", "linear_10": "PTLinearMetatype", "gelu_2": "PTGELUMetatype", "dropout_8": "PTDropoutMetatype", - "_param_constant38": "PTConstNoopMetatype", - "_param_constant39": "PTConstNoopMetatype", "linear_11": "PTLinearMetatype", "dropout_9": "PTDropoutMetatype", "add_6": "PTAddMetatype", - "_param_constant40": "PTConstNoopMetatype", - "_param_constant41": "PTConstNoopMetatype", "layer_norm_6": "PTLayerNormMetatype", "transpose_18": "PTTransposeMetatype", - "_param_constant42": "PTConstNoopMetatype", - "_param_constant43": "PTConstNoopMetatype", "linear_12": "PTLinearMetatype", "unflatten_3": "PTReshapeMetatype", "unsqueeze_3": "PTReshapeMetatype", @@ -179,32 +287,20 @@ "scaled_dot_product_attention_3": "PTScaledDotProductAttentionMetatype", "permute_4": "PTTransposeMetatype", "view_30": "PTReshapeMetatype", - "_param_constant44": "PTConstNoopMetatype", - "_param_constant45": "PTConstNoopMetatype", "linear_13": "PTLinearMetatype", "view_31": "PTReshapeMetatype", "transpose_23": "PTTransposeMetatype", "dropout_10": "PTDropoutMetatype", "add_7": "PTAddMetatype", - "_param_constant46": "PTConstNoopMetatype", - "_param_constant47": "PTConstNoopMetatype", "layer_norm_7": "PTLayerNormMetatype", - "_param_constant48": "PTConstNoopMetatype", - "_param_constant49": "PTConstNoopMetatype", "linear_14": "PTLinearMetatype", "gelu_3": "PTGELUMetatype", "dropout_11": "PTDropoutMetatype", - "_param_constant50": "PTConstNoopMetatype", - "_param_constant51": "PTConstNoopMetatype", "linear_15": "PTLinearMetatype", "dropout_12": "PTDropoutMetatype", "add_8": "PTAddMetatype", - "_param_constant52": "PTConstNoopMetatype", - "_param_constant53": "PTConstNoopMetatype", "layer_norm_8": "PTLayerNormMetatype", "transpose_24": "PTTransposeMetatype", - "_param_constant54": "PTConstNoopMetatype", - "_param_constant55": "PTConstNoopMetatype", "linear_16": "PTLinearMetatype", "unflatten_4": "PTReshapeMetatype", "unsqueeze_4": "PTReshapeMetatype", @@ -226,32 +322,20 @@ "scaled_dot_product_attention_4": "PTScaledDotProductAttentionMetatype", "permute_5": "PTTransposeMetatype", "view_38": "PTReshapeMetatype", - "_param_constant56": "PTConstNoopMetatype", - "_param_constant57": "PTConstNoopMetatype", "linear_17": "PTLinearMetatype", "view_39": "PTReshapeMetatype", "transpose_29": "PTTransposeMetatype", "dropout_13": "PTDropoutMetatype", "add_9": "PTAddMetatype", - "_param_constant58": "PTConstNoopMetatype", - "_param_constant59": "PTConstNoopMetatype", "layer_norm_9": "PTLayerNormMetatype", - "_param_constant60": "PTConstNoopMetatype", - "_param_constant61": "PTConstNoopMetatype", "linear_18": "PTLinearMetatype", "gelu_4": "PTGELUMetatype", "dropout_14": "PTDropoutMetatype", - "_param_constant62": "PTConstNoopMetatype", - "_param_constant63": "PTConstNoopMetatype", "linear_19": "PTLinearMetatype", "dropout_15": "PTDropoutMetatype", "add_10": "PTAddMetatype", - "_param_constant64": "PTConstNoopMetatype", - "_param_constant65": "PTConstNoopMetatype", "layer_norm_10": "PTLayerNormMetatype", "transpose_30": "PTTransposeMetatype", - "_param_constant66": "PTConstNoopMetatype", - "_param_constant67": "PTConstNoopMetatype", "linear_20": "PTLinearMetatype", "unflatten_5": "PTReshapeMetatype", "unsqueeze_5": "PTReshapeMetatype", @@ -273,32 +357,20 @@ "scaled_dot_product_attention_5": "PTScaledDotProductAttentionMetatype", "permute_6": "PTTransposeMetatype", "view_46": "PTReshapeMetatype", - "_param_constant68": "PTConstNoopMetatype", - "_param_constant69": "PTConstNoopMetatype", "linear_21": "PTLinearMetatype", "view_47": "PTReshapeMetatype", "transpose_35": "PTTransposeMetatype", "dropout_16": "PTDropoutMetatype", "add_11": "PTAddMetatype", - "_param_constant70": "PTConstNoopMetatype", - "_param_constant71": "PTConstNoopMetatype", "layer_norm_11": "PTLayerNormMetatype", - "_param_constant72": "PTConstNoopMetatype", - "_param_constant73": "PTConstNoopMetatype", "linear_22": "PTLinearMetatype", "gelu_5": "PTGELUMetatype", "dropout_17": "PTDropoutMetatype", - "_param_constant74": "PTConstNoopMetatype", - "_param_constant75": "PTConstNoopMetatype", "linear_23": "PTLinearMetatype", "dropout_18": "PTDropoutMetatype", "add_12": "PTAddMetatype", - "_param_constant76": "PTConstNoopMetatype", - "_param_constant77": "PTConstNoopMetatype", "layer_norm_12": "PTLayerNormMetatype", "transpose_36": "PTTransposeMetatype", - "_param_constant78": "PTConstNoopMetatype", - "_param_constant79": "PTConstNoopMetatype", "linear_24": "PTLinearMetatype", "unflatten_6": "PTReshapeMetatype", "unsqueeze_6": "PTReshapeMetatype", @@ -320,32 +392,20 @@ "scaled_dot_product_attention_6": "PTScaledDotProductAttentionMetatype", "permute_7": "PTTransposeMetatype", "view_54": "PTReshapeMetatype", - "_param_constant80": "PTConstNoopMetatype", - "_param_constant81": "PTConstNoopMetatype", "linear_25": "PTLinearMetatype", "view_55": "PTReshapeMetatype", "transpose_41": "PTTransposeMetatype", "dropout_19": "PTDropoutMetatype", "add_13": "PTAddMetatype", - "_param_constant82": "PTConstNoopMetatype", - "_param_constant83": "PTConstNoopMetatype", "layer_norm_13": "PTLayerNormMetatype", - "_param_constant84": "PTConstNoopMetatype", - "_param_constant85": "PTConstNoopMetatype", "linear_26": "PTLinearMetatype", "gelu_6": "PTGELUMetatype", "dropout_20": "PTDropoutMetatype", - "_param_constant86": "PTConstNoopMetatype", - "_param_constant87": "PTConstNoopMetatype", "linear_27": "PTLinearMetatype", "dropout_21": "PTDropoutMetatype", "add_14": "PTAddMetatype", - "_param_constant88": "PTConstNoopMetatype", - "_param_constant89": "PTConstNoopMetatype", "layer_norm_14": "PTLayerNormMetatype", "transpose_42": "PTTransposeMetatype", - "_param_constant90": "PTConstNoopMetatype", - "_param_constant91": "PTConstNoopMetatype", "linear_28": "PTLinearMetatype", "unflatten_7": "PTReshapeMetatype", "unsqueeze_7": "PTReshapeMetatype", @@ -367,32 +427,20 @@ "scaled_dot_product_attention_7": "PTScaledDotProductAttentionMetatype", "permute_8": "PTTransposeMetatype", "view_62": "PTReshapeMetatype", - "_param_constant92": "PTConstNoopMetatype", - "_param_constant93": "PTConstNoopMetatype", "linear_29": "PTLinearMetatype", "view_63": "PTReshapeMetatype", "transpose_47": "PTTransposeMetatype", "dropout_22": "PTDropoutMetatype", "add_15": "PTAddMetatype", - "_param_constant94": "PTConstNoopMetatype", - "_param_constant95": "PTConstNoopMetatype", "layer_norm_15": "PTLayerNormMetatype", - "_param_constant96": "PTConstNoopMetatype", - "_param_constant97": "PTConstNoopMetatype", "linear_30": "PTLinearMetatype", "gelu_7": "PTGELUMetatype", "dropout_23": "PTDropoutMetatype", - "_param_constant98": "PTConstNoopMetatype", - "_param_constant99": "PTConstNoopMetatype", "linear_31": "PTLinearMetatype", "dropout_24": "PTDropoutMetatype", "add_16": "PTAddMetatype", - "_param_constant100": "PTConstNoopMetatype", - "_param_constant101": "PTConstNoopMetatype", "layer_norm_16": "PTLayerNormMetatype", "transpose_48": "PTTransposeMetatype", - "_param_constant102": "PTConstNoopMetatype", - "_param_constant103": "PTConstNoopMetatype", "linear_32": "PTLinearMetatype", "unflatten_8": "PTReshapeMetatype", "unsqueeze_8": "PTReshapeMetatype", @@ -414,32 +462,20 @@ "scaled_dot_product_attention_8": "PTScaledDotProductAttentionMetatype", "permute_9": "PTTransposeMetatype", "view_70": "PTReshapeMetatype", - "_param_constant104": "PTConstNoopMetatype", - "_param_constant105": "PTConstNoopMetatype", "linear_33": "PTLinearMetatype", "view_71": "PTReshapeMetatype", "transpose_53": "PTTransposeMetatype", "dropout_25": "PTDropoutMetatype", "add_17": "PTAddMetatype", - "_param_constant106": "PTConstNoopMetatype", - "_param_constant107": "PTConstNoopMetatype", "layer_norm_17": "PTLayerNormMetatype", - "_param_constant108": "PTConstNoopMetatype", - "_param_constant109": "PTConstNoopMetatype", "linear_34": "PTLinearMetatype", "gelu_8": "PTGELUMetatype", "dropout_26": "PTDropoutMetatype", - "_param_constant110": "PTConstNoopMetatype", - "_param_constant111": "PTConstNoopMetatype", "linear_35": "PTLinearMetatype", "dropout_27": "PTDropoutMetatype", "add_18": "PTAddMetatype", - "_param_constant112": "PTConstNoopMetatype", - "_param_constant113": "PTConstNoopMetatype", "layer_norm_18": "PTLayerNormMetatype", "transpose_54": "PTTransposeMetatype", - "_param_constant114": "PTConstNoopMetatype", - "_param_constant115": "PTConstNoopMetatype", "linear_36": "PTLinearMetatype", "unflatten_9": "PTReshapeMetatype", "unsqueeze_9": "PTReshapeMetatype", @@ -461,32 +497,20 @@ "scaled_dot_product_attention_9": "PTScaledDotProductAttentionMetatype", "permute_10": "PTTransposeMetatype", "view_78": "PTReshapeMetatype", - "_param_constant116": "PTConstNoopMetatype", - "_param_constant117": "PTConstNoopMetatype", "linear_37": "PTLinearMetatype", "view_79": "PTReshapeMetatype", "transpose_59": "PTTransposeMetatype", "dropout_28": "PTDropoutMetatype", "add_19": "PTAddMetatype", - "_param_constant118": "PTConstNoopMetatype", - "_param_constant119": "PTConstNoopMetatype", "layer_norm_19": "PTLayerNormMetatype", - "_param_constant120": "PTConstNoopMetatype", - "_param_constant121": "PTConstNoopMetatype", "linear_38": "PTLinearMetatype", "gelu_9": "PTGELUMetatype", "dropout_29": "PTDropoutMetatype", - "_param_constant122": "PTConstNoopMetatype", - "_param_constant123": "PTConstNoopMetatype", "linear_39": "PTLinearMetatype", "dropout_30": "PTDropoutMetatype", "add_20": "PTAddMetatype", - "_param_constant124": "PTConstNoopMetatype", - "_param_constant125": "PTConstNoopMetatype", "layer_norm_20": "PTLayerNormMetatype", "transpose_60": "PTTransposeMetatype", - "_param_constant126": "PTConstNoopMetatype", - "_param_constant127": "PTConstNoopMetatype", "linear_40": "PTLinearMetatype", "unflatten_10": "PTReshapeMetatype", "unsqueeze_10": "PTReshapeMetatype", @@ -508,32 +532,20 @@ "scaled_dot_product_attention_10": "PTScaledDotProductAttentionMetatype", "permute_11": "PTTransposeMetatype", "view_86": "PTReshapeMetatype", - "_param_constant128": "PTConstNoopMetatype", - "_param_constant129": "PTConstNoopMetatype", "linear_41": "PTLinearMetatype", "view_87": "PTReshapeMetatype", "transpose_65": "PTTransposeMetatype", "dropout_31": "PTDropoutMetatype", "add_21": "PTAddMetatype", - "_param_constant130": "PTConstNoopMetatype", - "_param_constant131": "PTConstNoopMetatype", "layer_norm_21": "PTLayerNormMetatype", - "_param_constant132": "PTConstNoopMetatype", - "_param_constant133": "PTConstNoopMetatype", "linear_42": "PTLinearMetatype", "gelu_10": "PTGELUMetatype", "dropout_32": "PTDropoutMetatype", - "_param_constant134": "PTConstNoopMetatype", - "_param_constant135": "PTConstNoopMetatype", "linear_43": "PTLinearMetatype", "dropout_33": "PTDropoutMetatype", "add_22": "PTAddMetatype", - "_param_constant136": "PTConstNoopMetatype", - "_param_constant137": "PTConstNoopMetatype", "layer_norm_22": "PTLayerNormMetatype", "transpose_66": "PTTransposeMetatype", - "_param_constant138": "PTConstNoopMetatype", - "_param_constant139": "PTConstNoopMetatype", "linear_44": "PTLinearMetatype", "unflatten_11": "PTReshapeMetatype", "unsqueeze_11": "PTReshapeMetatype", @@ -555,33 +567,21 @@ "scaled_dot_product_attention_11": "PTScaledDotProductAttentionMetatype", "permute_12": "PTTransposeMetatype", "view_94": "PTReshapeMetatype", - "_param_constant140": "PTConstNoopMetatype", - "_param_constant141": "PTConstNoopMetatype", "linear_45": "PTLinearMetatype", "view_95": "PTReshapeMetatype", "transpose_71": "PTTransposeMetatype", "dropout_34": "PTDropoutMetatype", "add_23": "PTAddMetatype", - "_param_constant142": "PTConstNoopMetatype", - "_param_constant143": "PTConstNoopMetatype", "layer_norm_23": "PTLayerNormMetatype", - "_param_constant144": "PTConstNoopMetatype", - "_param_constant145": "PTConstNoopMetatype", "linear_46": "PTLinearMetatype", "gelu_11": "PTGELUMetatype", "dropout_35": "PTDropoutMetatype", - "_param_constant146": "PTConstNoopMetatype", - "_param_constant147": "PTConstNoopMetatype", "linear_47": "PTLinearMetatype", "dropout_36": "PTDropoutMetatype", "add_24": "PTAddMetatype", - "_param_constant148": "PTConstNoopMetatype", - "_param_constant149": "PTConstNoopMetatype", "layer_norm_24": "PTLayerNormMetatype", "slice_1": "PTGatherMetatype", "select_36": "PTGatherMetatype", - "_param_constant150": "PTConstNoopMetatype", - "_param_constant151": "PTConstNoopMetatype", "linear_48": "PTLinearMetatype", - "output": "PTOutputNoopMetatype" + "output_1": "PTOutputNoopMetatype" } \ No newline at end of file diff --git a/tests/torch/data/reference_graphs/fx/resnet18.dot b/tests/torch/data/reference_graphs/fx/resnet18.dot index 562374583ff..a566ccc6d7a 100644 --- a/tests/torch/data/reference_graphs/fx/resnet18.dot +++ b/tests/torch/data/reference_graphs/fx/resnet18.dot @@ -1,495 +1,375 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; -"1 _param_constant0" [id=1, type=get_attr]; -"2 conv2d" [id=2, type=conv2d]; -"3 empty" [id=3, type=empty]; -"4 _param_constant1" [id=4, type=get_attr]; -"5 _param_constant2" [id=5, type=get_attr]; -"6 _tensor_constant0" [id=6, type=get_attr]; -"7 _tensor_constant1" [id=7, type=get_attr]; -"8 _native_batch_norm_legit_no_training" [id=8, type=_native_batch_norm_legit_no_training]; -"9 getitem" [id=9, type=__getitem__]; -"10 getitem_1" [id=10, type=__getitem__]; -"11 getitem_2" [id=11, type=__getitem__]; -"12 relu_" [id=12, type=relu_]; -"13 max_pool2d" [id=13, type=max_pool2d]; -"14 _param_constant3" [id=14, type=get_attr]; -"15 conv2d_1" [id=15, type=conv2d]; -"16 empty_1" [id=16, type=empty]; -"17 _param_constant4" [id=17, type=get_attr]; -"18 _param_constant5" [id=18, type=get_attr]; -"19 _tensor_constant2" [id=19, type=get_attr]; -"20 _tensor_constant3" [id=20, type=get_attr]; -"21 _native_batch_norm_legit_no_training_1" [id=21, type=_native_batch_norm_legit_no_training]; -"22 getitem_3" [id=22, type=__getitem__]; -"23 getitem_4" [id=23, type=__getitem__]; -"24 getitem_5" [id=24, type=__getitem__]; -"25 relu__1" [id=25, type=relu_]; -"26 _param_constant6" [id=26, type=get_attr]; -"27 conv2d_2" [id=27, type=conv2d]; -"28 empty_2" [id=28, type=empty]; -"29 _param_constant7" [id=29, type=get_attr]; -"30 _param_constant8" [id=30, type=get_attr]; -"31 _tensor_constant4" [id=31, type=get_attr]; -"32 _tensor_constant5" [id=32, type=get_attr]; -"33 _native_batch_norm_legit_no_training_2" [id=33, type=_native_batch_norm_legit_no_training]; -"34 getitem_6" [id=34, type=__getitem__]; -"35 getitem_7" [id=35, type=__getitem__]; -"36 getitem_8" [id=36, type=__getitem__]; -"37 add_" [id=37, type=add_]; -"38 relu__2" [id=38, type=relu_]; -"39 _param_constant9" [id=39, type=get_attr]; -"40 conv2d_3" [id=40, type=conv2d]; -"41 empty_3" [id=41, type=empty]; -"42 _param_constant10" [id=42, type=get_attr]; -"43 _param_constant11" [id=43, type=get_attr]; -"44 _tensor_constant6" [id=44, type=get_attr]; -"45 _tensor_constant7" [id=45, type=get_attr]; -"46 _native_batch_norm_legit_no_training_3" [id=46, type=_native_batch_norm_legit_no_training]; -"47 getitem_9" [id=47, type=__getitem__]; -"48 getitem_10" [id=48, type=__getitem__]; -"49 getitem_11" [id=49, type=__getitem__]; -"50 relu__3" [id=50, type=relu_]; -"51 _param_constant12" [id=51, type=get_attr]; -"52 conv2d_4" [id=52, type=conv2d]; -"53 empty_4" [id=53, type=empty]; -"54 _param_constant13" [id=54, type=get_attr]; -"55 _param_constant14" [id=55, type=get_attr]; -"56 _tensor_constant8" [id=56, type=get_attr]; -"57 _tensor_constant9" [id=57, type=get_attr]; -"58 _native_batch_norm_legit_no_training_4" [id=58, type=_native_batch_norm_legit_no_training]; -"59 getitem_12" [id=59, type=__getitem__]; -"60 getitem_13" [id=60, type=__getitem__]; -"61 getitem_14" [id=61, type=__getitem__]; -"62 add__1" [id=62, type=add_]; -"63 relu__4" [id=63, type=relu_]; -"64 _param_constant15" [id=64, type=get_attr]; -"65 conv2d_5" [id=65, type=conv2d]; -"66 empty_5" [id=66, type=empty]; -"67 _param_constant16" [id=67, type=get_attr]; -"68 _param_constant17" [id=68, type=get_attr]; -"69 _tensor_constant10" [id=69, type=get_attr]; -"70 _tensor_constant11" [id=70, type=get_attr]; -"71 _native_batch_norm_legit_no_training_5" [id=71, type=_native_batch_norm_legit_no_training]; -"72 getitem_15" [id=72, type=__getitem__]; -"73 getitem_16" [id=73, type=__getitem__]; -"74 getitem_17" [id=74, type=__getitem__]; -"75 relu__5" [id=75, type=relu_]; -"76 _param_constant18" [id=76, type=get_attr]; -"77 conv2d_6" [id=77, type=conv2d]; -"78 empty_6" [id=78, type=empty]; -"79 _param_constant19" [id=79, type=get_attr]; -"80 _param_constant20" [id=80, type=get_attr]; -"81 _tensor_constant12" [id=81, type=get_attr]; -"82 _tensor_constant13" [id=82, type=get_attr]; -"83 _native_batch_norm_legit_no_training_6" [id=83, type=_native_batch_norm_legit_no_training]; -"84 getitem_18" [id=84, type=__getitem__]; -"85 getitem_19" [id=85, type=__getitem__]; -"86 getitem_20" [id=86, type=__getitem__]; -"87 _param_constant21" [id=87, type=get_attr]; -"88 conv2d_7" [id=88, type=conv2d]; -"89 empty_7" [id=89, type=empty]; -"90 _param_constant22" [id=90, type=get_attr]; -"91 _param_constant23" [id=91, type=get_attr]; -"92 _tensor_constant14" [id=92, type=get_attr]; -"93 _tensor_constant15" [id=93, type=get_attr]; -"94 _native_batch_norm_legit_no_training_7" [id=94, type=_native_batch_norm_legit_no_training]; -"95 getitem_21" [id=95, type=__getitem__]; -"96 getitem_22" [id=96, type=__getitem__]; -"97 getitem_23" [id=97, type=__getitem__]; -"98 add__2" [id=98, type=add_]; -"99 relu__6" [id=99, type=relu_]; -"100 _param_constant24" [id=100, type=get_attr]; -"101 conv2d_8" [id=101, type=conv2d]; -"102 empty_8" [id=102, type=empty]; -"103 _param_constant25" [id=103, type=get_attr]; -"104 _param_constant26" [id=104, type=get_attr]; -"105 _tensor_constant16" [id=105, type=get_attr]; -"106 _tensor_constant17" [id=106, type=get_attr]; -"107 _native_batch_norm_legit_no_training_8" [id=107, type=_native_batch_norm_legit_no_training]; -"108 getitem_24" [id=108, type=__getitem__]; -"109 getitem_25" [id=109, type=__getitem__]; -"110 getitem_26" [id=110, type=__getitem__]; -"111 relu__7" [id=111, type=relu_]; -"112 _param_constant27" [id=112, type=get_attr]; -"113 conv2d_9" [id=113, type=conv2d]; -"114 empty_9" [id=114, type=empty]; -"115 _param_constant28" [id=115, type=get_attr]; -"116 _param_constant29" [id=116, type=get_attr]; -"117 _tensor_constant18" [id=117, type=get_attr]; -"118 _tensor_constant19" [id=118, type=get_attr]; -"119 _native_batch_norm_legit_no_training_9" [id=119, type=_native_batch_norm_legit_no_training]; -"120 getitem_27" [id=120, type=__getitem__]; -"121 getitem_28" [id=121, type=__getitem__]; -"122 getitem_29" [id=122, type=__getitem__]; -"123 add__3" [id=123, type=add_]; -"124 relu__8" [id=124, type=relu_]; -"125 _param_constant30" [id=125, type=get_attr]; -"126 conv2d_10" [id=126, type=conv2d]; -"127 empty_10" [id=127, type=empty]; -"128 _param_constant31" [id=128, type=get_attr]; -"129 _param_constant32" [id=129, type=get_attr]; -"130 _tensor_constant20" [id=130, type=get_attr]; -"131 _tensor_constant21" [id=131, type=get_attr]; -"132 _native_batch_norm_legit_no_training_10" [id=132, type=_native_batch_norm_legit_no_training]; -"133 getitem_30" [id=133, type=__getitem__]; -"134 getitem_31" [id=134, type=__getitem__]; -"135 getitem_32" [id=135, type=__getitem__]; -"136 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type=_native_batch_norm_legit_no_training]; -"156 getitem_36" [id=156, type=__getitem__]; -"157 getitem_37" [id=157, type=__getitem__]; -"158 getitem_38" [id=158, type=__getitem__]; -"159 add__4" [id=159, type=add_]; -"160 relu__10" [id=160, type=relu_]; -"161 _param_constant39" [id=161, type=get_attr]; -"162 conv2d_13" [id=162, type=conv2d]; -"163 empty_13" [id=163, type=empty]; -"164 _param_constant40" [id=164, type=get_attr]; -"165 _param_constant41" [id=165, type=get_attr]; -"166 _tensor_constant26" [id=166, type=get_attr]; -"167 _tensor_constant27" [id=167, type=get_attr]; -"168 _native_batch_norm_legit_no_training_13" [id=168, type=_native_batch_norm_legit_no_training]; -"169 getitem_39" [id=169, type=__getitem__]; -"170 getitem_40" [id=170, type=__getitem__]; -"171 getitem_41" [id=171, type=__getitem__]; -"172 relu__11" [id=172, type=relu_]; -"173 _param_constant42" [id=173, type=get_attr]; -"174 conv2d_14" [id=174, type=conv2d]; -"175 empty_14" [id=175, type=empty]; -"176 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[id=195, type=__getitem__]; -"196 getitem_47" [id=196, type=__getitem__]; -"197 relu__13" [id=197, type=relu_]; -"198 _param_constant48" [id=198, type=get_attr]; -"199 conv2d_16" [id=199, type=conv2d]; -"200 empty_16" [id=200, type=empty]; -"201 _param_constant49" [id=201, type=get_attr]; -"202 _param_constant50" [id=202, type=get_attr]; -"203 _tensor_constant32" [id=203, type=get_attr]; -"204 _tensor_constant33" [id=204, type=get_attr]; -"205 _native_batch_norm_legit_no_training_16" [id=205, type=_native_batch_norm_legit_no_training]; -"206 getitem_48" [id=206, type=__getitem__]; -"207 getitem_49" [id=207, type=__getitem__]; -"208 getitem_50" [id=208, type=__getitem__]; -"209 _param_constant51" [id=209, type=get_attr]; -"210 conv2d_17" [id=210, type=conv2d]; -"211 empty_17" [id=211, type=empty]; -"212 _param_constant52" [id=212, type=get_attr]; -"213 _param_constant53" [id=213, type=get_attr]; -"214 _tensor_constant34" [id=214, type=get_attr]; -"215 _tensor_constant35" [id=215, type=get_attr]; -"216 _native_batch_norm_legit_no_training_17" [id=216, type=_native_batch_norm_legit_no_training]; -"217 getitem_51" [id=217, type=__getitem__]; -"218 getitem_52" [id=218, type=__getitem__]; -"219 getitem_53" [id=219, type=__getitem__]; -"220 add__6" [id=220, type=add_]; -"221 relu__14" [id=221, type=relu_]; -"222 _param_constant54" [id=222, type=get_attr]; -"223 conv2d_18" [id=223, type=conv2d]; -"224 empty_18" [id=224, type=empty]; -"225 _param_constant55" [id=225, type=get_attr]; -"226 _param_constant56" [id=226, type=get_attr]; -"227 _tensor_constant36" [id=227, type=get_attr]; -"228 _tensor_constant37" [id=228, type=get_attr]; -"229 _native_batch_norm_legit_no_training_18" [id=229, type=_native_batch_norm_legit_no_training]; -"230 getitem_54" [id=230, type=__getitem__]; -"231 getitem_55" [id=231, type=__getitem__]; -"232 getitem_56" [id=232, type=__getitem__]; -"233 relu__15" [id=233, type=relu_]; -"234 _param_constant57" [id=234, type=get_attr]; -"235 conv2d_19" [id=235, type=conv2d]; -"236 empty_19" [id=236, type=empty]; -"237 _param_constant58" [id=237, type=get_attr]; -"238 _param_constant59" [id=238, type=get_attr]; -"239 _tensor_constant38" [id=239, type=get_attr]; -"240 _tensor_constant39" [id=240, type=get_attr]; -"241 _native_batch_norm_legit_no_training_19" [id=241, type=_native_batch_norm_legit_no_training]; -"242 getitem_57" [id=242, type=__getitem__]; -"243 getitem_58" [id=243, type=__getitem__]; -"244 getitem_59" [id=244, type=__getitem__]; -"245 add__7" [id=245, type=add_]; -"246 relu__16" [id=246, type=relu_]; -"247 adaptive_avg_pool2d" [id=247, type=adaptive_avg_pool2d]; -"248 flatten" [id=248, type=flatten]; -"249 _param_constant60" [id=249, type=get_attr]; -"250 _param_constant61" [id=250, type=get_attr]; -"251 linear" [id=251, type=linear]; -"252 output" [id=252, type=output]; -"0 arg0_1" -> "2 conv2d"; -"1 _param_constant0" -> "2 conv2d"; -"2 conv2d" -> "8 _native_batch_norm_legit_no_training"; -"4 _param_constant1" -> "8 _native_batch_norm_legit_no_training"; -"5 _param_constant2" -> "8 _native_batch_norm_legit_no_training"; -"6 _tensor_constant0" -> "8 _native_batch_norm_legit_no_training"; -"7 _tensor_constant1" -> "8 _native_batch_norm_legit_no_training"; -"8 _native_batch_norm_legit_no_training" -> "9 getitem"; -"8 _native_batch_norm_legit_no_training" -> "10 getitem_1"; -"8 _native_batch_norm_legit_no_training" -> "11 getitem_2"; -"9 getitem" -> "12 relu_"; -"12 relu_" -> "13 max_pool2d"; -"13 max_pool2d" -> "15 conv2d_1"; -"13 max_pool2d" -> "37 add_"; -"14 _param_constant3" -> "15 conv2d_1"; -"15 conv2d_1" -> "21 _native_batch_norm_legit_no_training_1"; -"17 _param_constant4" -> "21 _native_batch_norm_legit_no_training_1"; -"18 _param_constant5" -> "21 _native_batch_norm_legit_no_training_1"; -"19 _tensor_constant2" -> "21 _native_batch_norm_legit_no_training_1"; -"20 _tensor_constant3" -> "21 _native_batch_norm_legit_no_training_1"; -"21 _native_batch_norm_legit_no_training_1" -> "22 getitem_3"; -"21 _native_batch_norm_legit_no_training_1" -> "23 getitem_4"; -"21 _native_batch_norm_legit_no_training_1" -> "24 getitem_5"; -"22 getitem_3" -> "25 relu__1"; -"25 relu__1" -> "27 conv2d_2"; -"26 _param_constant6" -> "27 conv2d_2"; -"27 conv2d_2" -> "33 _native_batch_norm_legit_no_training_2"; -"29 _param_constant7" -> "33 _native_batch_norm_legit_no_training_2"; -"30 _param_constant8" -> "33 _native_batch_norm_legit_no_training_2"; -"31 _tensor_constant4" -> "33 _native_batch_norm_legit_no_training_2"; -"32 _tensor_constant5" -> "33 _native_batch_norm_legit_no_training_2"; -"33 _native_batch_norm_legit_no_training_2" -> "34 getitem_6"; -"33 _native_batch_norm_legit_no_training_2" -> "35 getitem_7"; -"33 _native_batch_norm_legit_no_training_2" -> "36 getitem_8"; -"34 getitem_6" -> "37 add_"; -"37 add_" -> "38 relu__2"; -"38 relu__2" -> "40 conv2d_3"; -"38 relu__2" -> "62 add__1"; -"39 _param_constant9" -> "40 conv2d_3"; -"40 conv2d_3" -> "46 _native_batch_norm_legit_no_training_3"; -"42 _param_constant10" -> "46 _native_batch_norm_legit_no_training_3"; -"43 _param_constant11" -> "46 _native_batch_norm_legit_no_training_3"; -"44 _tensor_constant6" -> "46 _native_batch_norm_legit_no_training_3"; -"45 _tensor_constant7" -> "46 _native_batch_norm_legit_no_training_3"; -"46 _native_batch_norm_legit_no_training_3" -> "47 getitem_9"; -"46 _native_batch_norm_legit_no_training_3" -> "48 getitem_10"; -"46 _native_batch_norm_legit_no_training_3" -> "49 getitem_11"; -"47 getitem_9" -> "50 relu__3"; -"50 relu__3" -> "52 conv2d_4"; -"51 _param_constant12" -> "52 conv2d_4"; -"52 conv2d_4" -> "58 _native_batch_norm_legit_no_training_4"; -"54 _param_constant13" -> "58 _native_batch_norm_legit_no_training_4"; -"55 _param_constant14" -> "58 _native_batch_norm_legit_no_training_4"; -"56 _tensor_constant8" -> "58 _native_batch_norm_legit_no_training_4"; -"57 _tensor_constant9" -> "58 _native_batch_norm_legit_no_training_4"; -"58 _native_batch_norm_legit_no_training_4" -> "59 getitem_12"; -"58 _native_batch_norm_legit_no_training_4" -> "60 getitem_13"; -"58 _native_batch_norm_legit_no_training_4" -> "61 getitem_14"; -"59 getitem_12" -> "62 add__1"; -"62 add__1" -> "63 relu__4"; -"63 relu__4" -> "65 conv2d_5"; -"63 relu__4" -> "88 conv2d_7"; -"64 _param_constant15" -> "65 conv2d_5"; -"65 conv2d_5" -> "71 _native_batch_norm_legit_no_training_5"; -"67 _param_constant16" -> "71 _native_batch_norm_legit_no_training_5"; -"68 _param_constant17" -> "71 _native_batch_norm_legit_no_training_5"; -"69 _tensor_constant10" -> "71 _native_batch_norm_legit_no_training_5"; -"70 _tensor_constant11" -> "71 _native_batch_norm_legit_no_training_5"; -"71 _native_batch_norm_legit_no_training_5" -> "72 getitem_15"; -"71 _native_batch_norm_legit_no_training_5" -> "73 getitem_16"; -"71 _native_batch_norm_legit_no_training_5" -> "74 getitem_17"; -"72 getitem_15" -> "75 relu__5"; -"75 relu__5" -> "77 conv2d_6"; -"76 _param_constant18" -> "77 conv2d_6"; -"77 conv2d_6" -> "83 _native_batch_norm_legit_no_training_6"; -"79 _param_constant19" -> "83 _native_batch_norm_legit_no_training_6"; -"80 _param_constant20" -> "83 _native_batch_norm_legit_no_training_6"; -"81 _tensor_constant12" -> "83 _native_batch_norm_legit_no_training_6"; -"82 _tensor_constant13" -> "83 _native_batch_norm_legit_no_training_6"; -"83 _native_batch_norm_legit_no_training_6" -> "84 getitem_18"; -"83 _native_batch_norm_legit_no_training_6" -> "85 getitem_19"; -"83 _native_batch_norm_legit_no_training_6" -> "86 getitem_20"; -"84 getitem_18" -> "98 add__2"; -"87 _param_constant21" -> "88 conv2d_7"; -"88 conv2d_7" -> "94 _native_batch_norm_legit_no_training_7"; -"90 _param_constant22" -> "94 _native_batch_norm_legit_no_training_7"; -"91 _param_constant23" -> "94 _native_batch_norm_legit_no_training_7"; -"92 _tensor_constant14" -> "94 _native_batch_norm_legit_no_training_7"; -"93 _tensor_constant15" -> "94 _native_batch_norm_legit_no_training_7"; -"94 _native_batch_norm_legit_no_training_7" -> "95 getitem_21"; -"94 _native_batch_norm_legit_no_training_7" -> "96 getitem_22"; -"94 _native_batch_norm_legit_no_training_7" -> "97 getitem_23"; -"95 getitem_21" -> "98 add__2"; -"98 add__2" -> "99 relu__6"; -"99 relu__6" -> "101 conv2d_8"; -"99 relu__6" -> "123 add__3"; -"100 _param_constant24" -> "101 conv2d_8"; -"101 conv2d_8" -> "107 _native_batch_norm_legit_no_training_8"; -"103 _param_constant25" -> "107 _native_batch_norm_legit_no_training_8"; -"104 _param_constant26" -> "107 _native_batch_norm_legit_no_training_8"; -"105 _tensor_constant16" -> "107 _native_batch_norm_legit_no_training_8"; -"106 _tensor_constant17" -> "107 _native_batch_norm_legit_no_training_8"; -"107 _native_batch_norm_legit_no_training_8" -> "108 getitem_24"; -"107 _native_batch_norm_legit_no_training_8" -> "109 getitem_25"; -"107 _native_batch_norm_legit_no_training_8" -> "110 getitem_26"; -"108 getitem_24" -> "111 relu__7"; -"111 relu__7" -> "113 conv2d_9"; -"112 _param_constant27" -> "113 conv2d_9"; -"113 conv2d_9" -> "119 _native_batch_norm_legit_no_training_9"; -"115 _param_constant28" -> "119 _native_batch_norm_legit_no_training_9"; -"116 _param_constant29" -> "119 _native_batch_norm_legit_no_training_9"; -"117 _tensor_constant18" -> "119 _native_batch_norm_legit_no_training_9"; -"118 _tensor_constant19" -> "119 _native_batch_norm_legit_no_training_9"; -"119 _native_batch_norm_legit_no_training_9" -> "120 getitem_27"; -"119 _native_batch_norm_legit_no_training_9" -> "121 getitem_28"; -"119 _native_batch_norm_legit_no_training_9" -> "122 getitem_29"; -"120 getitem_27" -> "123 add__3"; -"123 add__3" -> "124 relu__8"; -"124 relu__8" -> "126 conv2d_10"; -"124 relu__8" -> "149 conv2d_12"; -"125 _param_constant30" -> "126 conv2d_10"; -"126 conv2d_10" -> "132 _native_batch_norm_legit_no_training_10"; -"128 _param_constant31" -> "132 _native_batch_norm_legit_no_training_10"; -"129 _param_constant32" -> "132 _native_batch_norm_legit_no_training_10"; -"130 _tensor_constant20" -> "132 _native_batch_norm_legit_no_training_10"; -"131 _tensor_constant21" -> "132 _native_batch_norm_legit_no_training_10"; -"132 _native_batch_norm_legit_no_training_10" -> "133 getitem_30"; -"132 _native_batch_norm_legit_no_training_10" -> "134 getitem_31"; -"132 _native_batch_norm_legit_no_training_10" -> "135 getitem_32"; -"133 getitem_30" -> "136 relu__9"; -"136 relu__9" -> "138 conv2d_11"; -"137 _param_constant33" -> "138 conv2d_11"; -"138 conv2d_11" -> "144 _native_batch_norm_legit_no_training_11"; -"140 _param_constant34" -> "144 _native_batch_norm_legit_no_training_11"; -"141 _param_constant35" -> "144 _native_batch_norm_legit_no_training_11"; -"142 _tensor_constant22" -> "144 _native_batch_norm_legit_no_training_11"; -"143 _tensor_constant23" -> "144 _native_batch_norm_legit_no_training_11"; -"144 _native_batch_norm_legit_no_training_11" -> "145 getitem_33"; -"144 _native_batch_norm_legit_no_training_11" -> "146 getitem_34"; -"144 _native_batch_norm_legit_no_training_11" -> "147 getitem_35"; -"145 getitem_33" -> "159 add__4"; -"148 _param_constant36" -> "149 conv2d_12"; -"149 conv2d_12" -> "155 _native_batch_norm_legit_no_training_12"; -"151 _param_constant37" -> "155 _native_batch_norm_legit_no_training_12"; -"152 _param_constant38" -> "155 _native_batch_norm_legit_no_training_12"; -"153 _tensor_constant24" -> "155 _native_batch_norm_legit_no_training_12"; -"154 _tensor_constant25" -> "155 _native_batch_norm_legit_no_training_12"; -"155 _native_batch_norm_legit_no_training_12" -> "156 getitem_36"; -"155 _native_batch_norm_legit_no_training_12" -> "157 getitem_37"; -"155 _native_batch_norm_legit_no_training_12" -> "158 getitem_38"; -"156 getitem_36" -> "159 add__4"; -"159 add__4" -> "160 relu__10"; -"160 relu__10" -> "162 conv2d_13"; -"160 relu__10" -> "184 add__5"; -"161 _param_constant39" -> "162 conv2d_13"; -"162 conv2d_13" -> "168 _native_batch_norm_legit_no_training_13"; -"164 _param_constant40" -> "168 _native_batch_norm_legit_no_training_13"; -"165 _param_constant41" -> "168 _native_batch_norm_legit_no_training_13"; -"166 _tensor_constant26" -> "168 _native_batch_norm_legit_no_training_13"; -"167 _tensor_constant27" -> "168 _native_batch_norm_legit_no_training_13"; -"168 _native_batch_norm_legit_no_training_13" -> "169 getitem_39"; -"168 _native_batch_norm_legit_no_training_13" -> "170 getitem_40"; -"168 _native_batch_norm_legit_no_training_13" -> "171 getitem_41"; -"169 getitem_39" -> "172 relu__11"; -"172 relu__11" -> "174 conv2d_14"; -"173 _param_constant42" -> "174 conv2d_14"; -"174 conv2d_14" -> "180 _native_batch_norm_legit_no_training_14"; -"176 _param_constant43" -> "180 _native_batch_norm_legit_no_training_14"; -"177 _param_constant44" -> "180 _native_batch_norm_legit_no_training_14"; -"178 _tensor_constant28" -> "180 _native_batch_norm_legit_no_training_14"; -"179 _tensor_constant29" -> "180 _native_batch_norm_legit_no_training_14"; -"180 _native_batch_norm_legit_no_training_14" -> "181 getitem_42"; -"180 _native_batch_norm_legit_no_training_14" -> "182 getitem_43"; -"180 _native_batch_norm_legit_no_training_14" -> "183 getitem_44"; -"181 getitem_42" -> "184 add__5"; -"184 add__5" -> "185 relu__12"; -"185 relu__12" -> "187 conv2d_15"; -"185 relu__12" -> "210 conv2d_17"; -"186 _param_constant45" -> "187 conv2d_15"; -"187 conv2d_15" -> "193 _native_batch_norm_legit_no_training_15"; -"189 _param_constant46" -> "193 _native_batch_norm_legit_no_training_15"; -"190 _param_constant47" -> "193 _native_batch_norm_legit_no_training_15"; -"191 _tensor_constant30" -> "193 _native_batch_norm_legit_no_training_15"; -"192 _tensor_constant31" -> "193 _native_batch_norm_legit_no_training_15"; -"193 _native_batch_norm_legit_no_training_15" -> "194 getitem_45"; -"193 _native_batch_norm_legit_no_training_15" -> "195 getitem_46"; -"193 _native_batch_norm_legit_no_training_15" -> "196 getitem_47"; -"194 getitem_45" -> "197 relu__13"; -"197 relu__13" -> "199 conv2d_16"; -"198 _param_constant48" -> "199 conv2d_16"; -"199 conv2d_16" -> "205 _native_batch_norm_legit_no_training_16"; -"201 _param_constant49" -> "205 _native_batch_norm_legit_no_training_16"; -"202 _param_constant50" -> "205 _native_batch_norm_legit_no_training_16"; -"203 _tensor_constant32" -> "205 _native_batch_norm_legit_no_training_16"; -"204 _tensor_constant33" -> "205 _native_batch_norm_legit_no_training_16"; -"205 _native_batch_norm_legit_no_training_16" -> "206 getitem_48"; -"205 _native_batch_norm_legit_no_training_16" -> "207 getitem_49"; -"205 _native_batch_norm_legit_no_training_16" -> "208 getitem_50"; -"206 getitem_48" -> "220 add__6"; -"209 _param_constant51" -> "210 conv2d_17"; -"210 conv2d_17" -> "216 _native_batch_norm_legit_no_training_17"; -"212 _param_constant52" -> "216 _native_batch_norm_legit_no_training_17"; -"213 _param_constant53" -> "216 _native_batch_norm_legit_no_training_17"; -"214 _tensor_constant34" -> "216 _native_batch_norm_legit_no_training_17"; -"215 _tensor_constant35" -> "216 _native_batch_norm_legit_no_training_17"; -"216 _native_batch_norm_legit_no_training_17" -> "217 getitem_51"; -"216 _native_batch_norm_legit_no_training_17" -> "218 getitem_52"; -"216 _native_batch_norm_legit_no_training_17" -> "219 getitem_53"; -"217 getitem_51" -> "220 add__6"; -"220 add__6" -> "221 relu__14"; -"221 relu__14" -> "223 conv2d_18"; -"221 relu__14" -> "245 add__7"; -"222 _param_constant54" -> "223 conv2d_18"; -"223 conv2d_18" -> "229 _native_batch_norm_legit_no_training_18"; -"225 _param_constant55" -> "229 _native_batch_norm_legit_no_training_18"; -"226 _param_constant56" -> "229 _native_batch_norm_legit_no_training_18"; -"227 _tensor_constant36" -> "229 _native_batch_norm_legit_no_training_18"; -"228 _tensor_constant37" -> "229 _native_batch_norm_legit_no_training_18"; -"229 _native_batch_norm_legit_no_training_18" -> "230 getitem_54"; -"229 _native_batch_norm_legit_no_training_18" -> "231 getitem_55"; -"229 _native_batch_norm_legit_no_training_18" -> "232 getitem_56"; -"230 getitem_54" -> "233 relu__15"; -"233 relu__15" -> "235 conv2d_19"; -"234 _param_constant57" -> "235 conv2d_19"; -"235 conv2d_19" -> "241 _native_batch_norm_legit_no_training_19"; -"237 _param_constant58" -> "241 _native_batch_norm_legit_no_training_19"; -"238 _param_constant59" -> "241 _native_batch_norm_legit_no_training_19"; -"239 _tensor_constant38" -> "241 _native_batch_norm_legit_no_training_19"; -"240 _tensor_constant39" -> "241 _native_batch_norm_legit_no_training_19"; -"241 _native_batch_norm_legit_no_training_19" -> "242 getitem_57"; -"241 _native_batch_norm_legit_no_training_19" -> "243 getitem_58"; -"241 _native_batch_norm_legit_no_training_19" -> "244 getitem_59"; -"242 getitem_57" -> "245 add__7"; -"245 add__7" -> "246 relu__16"; -"246 relu__16" -> "247 adaptive_avg_pool2d"; -"247 adaptive_avg_pool2d" -> "248 flatten"; -"248 flatten" -> "251 linear"; -"249 _param_constant60" -> "251 linear"; -"250 _param_constant61" -> "251 linear"; -"251 linear" -> "252 output"; +"0 conv1_weight" [id=0, type=get_attr]; +"1 bn1_weight" [id=1, type=get_attr]; +"2 bn1_bias" [id=2, type=get_attr]; +"3 layer1_0_conv1_weight" [id=3, type=get_attr]; +"4 layer1_0_bn1_weight" [id=4, type=get_attr]; +"5 layer1_0_bn1_bias" [id=5, type=get_attr]; +"6 layer1_0_conv2_weight" [id=6, type=get_attr]; +"7 layer1_0_bn2_weight" [id=7, type=get_attr]; +"8 layer1_0_bn2_bias" [id=8, type=get_attr]; +"9 layer1_1_conv1_weight" [id=9, type=get_attr]; +"10 layer1_1_bn1_weight" [id=10, type=get_attr]; +"11 layer1_1_bn1_bias" [id=11, type=get_attr]; +"12 layer1_1_conv2_weight" [id=12, type=get_attr]; +"13 layer1_1_bn2_weight" [id=13, type=get_attr]; +"14 layer1_1_bn2_bias" [id=14, type=get_attr]; +"15 layer2_0_conv1_weight" [id=15, type=get_attr]; +"16 layer2_0_bn1_weight" [id=16, type=get_attr]; +"17 layer2_0_bn1_bias" [id=17, type=get_attr]; +"18 layer2_0_conv2_weight" [id=18, type=get_attr]; +"19 layer2_0_bn2_weight" [id=19, type=get_attr]; +"20 layer2_0_bn2_bias" [id=20, type=get_attr]; +"21 layer2_0_downsample_0_weight" [id=21, type=get_attr]; +"22 layer2_0_downsample_1_weight" [id=22, type=get_attr]; +"23 layer2_0_downsample_1_bias" [id=23, type=get_attr]; +"24 layer2_1_conv1_weight" [id=24, type=get_attr]; +"25 layer2_1_bn1_weight" [id=25, type=get_attr]; +"26 layer2_1_bn1_bias" [id=26, type=get_attr]; +"27 layer2_1_conv2_weight" [id=27, type=get_attr]; +"28 layer2_1_bn2_weight" [id=28, type=get_attr]; +"29 layer2_1_bn2_bias" [id=29, type=get_attr]; +"30 layer3_0_conv1_weight" [id=30, type=get_attr]; +"31 layer3_0_bn1_weight" [id=31, type=get_attr]; +"32 layer3_0_bn1_bias" [id=32, type=get_attr]; +"33 layer3_0_conv2_weight" [id=33, type=get_attr]; +"34 layer3_0_bn2_weight" [id=34, type=get_attr]; +"35 layer3_0_bn2_bias" [id=35, type=get_attr]; +"36 layer3_0_downsample_0_weight" [id=36, type=get_attr]; +"37 layer3_0_downsample_1_weight" [id=37, type=get_attr]; +"38 layer3_0_downsample_1_bias" [id=38, type=get_attr]; +"39 layer3_1_conv1_weight" [id=39, type=get_attr]; +"40 layer3_1_bn1_weight" [id=40, type=get_attr]; +"41 layer3_1_bn1_bias" [id=41, type=get_attr]; +"42 layer3_1_conv2_weight" [id=42, type=get_attr]; +"43 layer3_1_bn2_weight" [id=43, type=get_attr]; +"44 layer3_1_bn2_bias" [id=44, type=get_attr]; +"45 layer4_0_conv1_weight" [id=45, type=get_attr]; +"46 layer4_0_bn1_weight" [id=46, type=get_attr]; +"47 layer4_0_bn1_bias" [id=47, type=get_attr]; +"48 layer4_0_conv2_weight" [id=48, type=get_attr]; +"49 layer4_0_bn2_weight" [id=49, type=get_attr]; +"50 layer4_0_bn2_bias" [id=50, type=get_attr]; +"51 layer4_0_downsample_0_weight" [id=51, type=get_attr]; +"52 layer4_0_downsample_1_weight" [id=52, type=get_attr]; +"53 layer4_0_downsample_1_bias" [id=53, type=get_attr]; +"54 layer4_1_conv1_weight" [id=54, type=get_attr]; +"55 layer4_1_bn1_weight" [id=55, type=get_attr]; +"56 layer4_1_bn1_bias" [id=56, type=get_attr]; +"57 layer4_1_conv2_weight" [id=57, type=get_attr]; +"58 layer4_1_bn2_weight" [id=58, type=get_attr]; +"59 layer4_1_bn2_bias" [id=59, type=get_attr]; +"60 fc_weight" [id=60, type=get_attr]; +"61 fc_bias" [id=61, type=get_attr]; +"62 bn1_running_mean" [id=62, type=get_attr]; +"63 bn1_running_var" [id=63, type=get_attr]; +"64 bn1_num_batches_tracked" [id=64, type=get_attr]; +"65 layer1_0_bn1_running_mean" [id=65, type=get_attr]; +"66 layer1_0_bn1_running_var" [id=66, type=get_attr]; +"67 layer1_0_bn1_num_batches_tracked" [id=67, type=get_attr]; +"68 layer1_0_bn2_running_mean" [id=68, type=get_attr]; +"69 layer1_0_bn2_running_var" [id=69, type=get_attr]; +"70 layer1_0_bn2_num_batches_tracked" [id=70, type=get_attr]; +"71 layer1_1_bn1_running_mean" [id=71, type=get_attr]; +"72 layer1_1_bn1_running_var" [id=72, type=get_attr]; +"73 layer1_1_bn1_num_batches_tracked" [id=73, type=get_attr]; +"74 layer1_1_bn2_running_mean" [id=74, type=get_attr]; +"75 layer1_1_bn2_running_var" [id=75, type=get_attr]; +"76 layer1_1_bn2_num_batches_tracked" [id=76, type=get_attr]; +"77 layer2_0_bn1_running_mean" [id=77, type=get_attr]; +"78 layer2_0_bn1_running_var" [id=78, type=get_attr]; +"79 layer2_0_bn1_num_batches_tracked" [id=79, type=get_attr]; +"80 layer2_0_bn2_running_mean" [id=80, type=get_attr]; +"81 layer2_0_bn2_running_var" [id=81, type=get_attr]; +"82 layer2_0_bn2_num_batches_tracked" [id=82, type=get_attr]; +"83 layer2_0_downsample_1_running_mean" [id=83, type=get_attr]; +"84 layer2_0_downsample_1_running_var" [id=84, type=get_attr]; +"85 layer2_0_downsample_1_num_batches_tracked" [id=85, type=get_attr]; +"86 layer2_1_bn1_running_mean" [id=86, type=get_attr]; +"87 layer2_1_bn1_running_var" [id=87, type=get_attr]; +"88 layer2_1_bn1_num_batches_tracked" [id=88, type=get_attr]; +"89 layer2_1_bn2_running_mean" [id=89, type=get_attr]; +"90 layer2_1_bn2_running_var" [id=90, type=get_attr]; +"91 layer2_1_bn2_num_batches_tracked" [id=91, type=get_attr]; +"92 layer3_0_bn1_running_mean" [id=92, type=get_attr]; +"93 layer3_0_bn1_running_var" [id=93, type=get_attr]; +"94 layer3_0_bn1_num_batches_tracked" [id=94, type=get_attr]; +"95 layer3_0_bn2_running_mean" [id=95, type=get_attr]; +"96 layer3_0_bn2_running_var" [id=96, type=get_attr]; +"97 layer3_0_bn2_num_batches_tracked" [id=97, type=get_attr]; +"98 layer3_0_downsample_1_running_mean" [id=98, type=get_attr]; +"99 layer3_0_downsample_1_running_var" [id=99, type=get_attr]; +"100 layer3_0_downsample_1_num_batches_tracked" [id=100, type=get_attr]; +"101 layer3_1_bn1_running_mean" [id=101, type=get_attr]; +"102 layer3_1_bn1_running_var" [id=102, type=get_attr]; +"103 layer3_1_bn1_num_batches_tracked" [id=103, type=get_attr]; +"104 layer3_1_bn2_running_mean" [id=104, type=get_attr]; +"105 layer3_1_bn2_running_var" [id=105, type=get_attr]; +"106 layer3_1_bn2_num_batches_tracked" [id=106, type=get_attr]; +"107 layer4_0_bn1_running_mean" [id=107, type=get_attr]; +"108 layer4_0_bn1_running_var" [id=108, type=get_attr]; +"109 layer4_0_bn1_num_batches_tracked" [id=109, type=get_attr]; +"110 layer4_0_bn2_running_mean" [id=110, type=get_attr]; +"111 layer4_0_bn2_running_var" [id=111, type=get_attr]; +"112 layer4_0_bn2_num_batches_tracked" [id=112, type=get_attr]; +"113 layer4_0_downsample_1_running_mean" [id=113, type=get_attr]; +"114 layer4_0_downsample_1_running_var" [id=114, type=get_attr]; +"115 layer4_0_downsample_1_num_batches_tracked" [id=115, type=get_attr]; +"116 layer4_1_bn1_running_mean" [id=116, type=get_attr]; +"117 layer4_1_bn1_running_var" [id=117, type=get_attr]; +"118 layer4_1_bn1_num_batches_tracked" [id=118, type=get_attr]; +"119 layer4_1_bn2_running_mean" [id=119, type=get_attr]; +"120 layer4_1_bn2_running_var" [id=120, type=get_attr]; +"121 layer4_1_bn2_num_batches_tracked" [id=121, type=get_attr]; +"122 x" [id=122, type=input]; +"123 conv2d" [id=123, type=conv2d]; +"124 batch_norm" [id=124, type=batch_norm]; +"125 relu_" [id=125, type=relu_]; +"126 max_pool2d" [id=126, type=max_pool2d]; +"127 conv2d_1" [id=127, type=conv2d]; +"128 batch_norm_1" [id=128, type=batch_norm]; +"129 relu__1" [id=129, type=relu_]; +"130 conv2d_2" [id=130, type=conv2d]; +"131 batch_norm_2" [id=131, type=batch_norm]; +"132 add_" [id=132, type=add_]; +"133 relu__2" [id=133, type=relu_]; +"134 conv2d_3" [id=134, type=conv2d]; +"135 batch_norm_3" [id=135, type=batch_norm]; +"136 relu__3" [id=136, type=relu_]; +"137 conv2d_4" [id=137, type=conv2d]; +"138 batch_norm_4" [id=138, type=batch_norm]; +"139 add__1" [id=139, type=add_]; +"140 relu__4" [id=140, type=relu_]; +"141 conv2d_5" [id=141, type=conv2d]; +"142 batch_norm_5" [id=142, type=batch_norm]; +"143 relu__5" [id=143, type=relu_]; +"144 conv2d_6" [id=144, type=conv2d]; +"145 batch_norm_6" [id=145, type=batch_norm]; +"146 conv2d_7" [id=146, type=conv2d]; +"147 batch_norm_7" [id=147, type=batch_norm]; +"148 add__2" [id=148, type=add_]; +"149 relu__6" [id=149, type=relu_]; +"150 conv2d_8" [id=150, type=conv2d]; +"151 batch_norm_8" [id=151, type=batch_norm]; +"152 relu__7" [id=152, type=relu_]; +"153 conv2d_9" [id=153, type=conv2d]; +"154 batch_norm_9" [id=154, type=batch_norm]; +"155 add__3" [id=155, type=add_]; +"156 relu__8" [id=156, type=relu_]; +"157 conv2d_10" [id=157, type=conv2d]; +"158 batch_norm_10" [id=158, type=batch_norm]; +"159 relu__9" [id=159, type=relu_]; +"160 conv2d_11" [id=160, type=conv2d]; +"161 batch_norm_11" [id=161, type=batch_norm]; +"162 conv2d_12" [id=162, type=conv2d]; +"163 batch_norm_12" [id=163, type=batch_norm]; +"164 add__4" [id=164, type=add_]; +"165 relu__10" [id=165, type=relu_]; +"166 conv2d_13" [id=166, type=conv2d]; +"167 batch_norm_13" [id=167, type=batch_norm]; +"168 relu__11" [id=168, type=relu_]; +"169 conv2d_14" [id=169, type=conv2d]; +"170 batch_norm_14" [id=170, type=batch_norm]; +"171 add__5" [id=171, type=add_]; +"172 relu__12" [id=172, type=relu_]; +"173 conv2d_15" [id=173, type=conv2d]; +"174 batch_norm_15" [id=174, type=batch_norm]; +"175 relu__13" [id=175, type=relu_]; +"176 conv2d_16" [id=176, type=conv2d]; +"177 batch_norm_16" [id=177, type=batch_norm]; +"178 conv2d_17" [id=178, type=conv2d]; +"179 batch_norm_17" [id=179, type=batch_norm]; +"180 add__6" [id=180, type=add_]; +"181 relu__14" [id=181, type=relu_]; +"182 conv2d_18" [id=182, type=conv2d]; +"183 batch_norm_18" [id=183, type=batch_norm]; +"184 relu__15" [id=184, type=relu_]; +"185 conv2d_19" [id=185, type=conv2d]; +"186 batch_norm_19" [id=186, type=batch_norm]; +"187 add__7" [id=187, type=add_]; +"188 relu__16" [id=188, type=relu_]; +"189 adaptive_avg_pool2d" [id=189, type=adaptive_avg_pool2d]; +"190 flatten" [id=190, type=flatten]; +"191 linear" [id=191, type=linear]; +"192 output_1" [id=192, type=output]; +"0 conv1_weight" -> "123 conv2d" [label="(64, 3, 7, 7)", style=solid]; +"1 bn1_weight" -> "124 batch_norm" [label="(64,)", style=solid]; +"2 bn1_bias" -> "124 batch_norm" [label="(64,)", style=solid]; +"3 layer1_0_conv1_weight" -> "127 conv2d_1" [label="(64, 64, 3, 3)", style=solid]; +"4 layer1_0_bn1_weight" -> "128 batch_norm_1" [label="(64,)", style=solid]; +"5 layer1_0_bn1_bias" -> "128 batch_norm_1" [label="(64,)", style=solid]; +"6 layer1_0_conv2_weight" -> "130 conv2d_2" [label="(64, 64, 3, 3)", style=solid]; +"7 layer1_0_bn2_weight" -> "131 batch_norm_2" [label="(64,)", style=solid]; +"8 layer1_0_bn2_bias" -> "131 batch_norm_2" [label="(64,)", style=solid]; +"9 layer1_1_conv1_weight" -> "134 conv2d_3" [label="(64, 64, 3, 3)", style=solid]; +"10 layer1_1_bn1_weight" -> "135 batch_norm_3" [label="(64,)", style=solid]; +"11 layer1_1_bn1_bias" -> "135 batch_norm_3" [label="(64,)", style=solid]; +"12 layer1_1_conv2_weight" -> "137 conv2d_4" [label="(64, 64, 3, 3)", style=solid]; +"13 layer1_1_bn2_weight" -> "138 batch_norm_4" [label="(64,)", style=solid]; +"14 layer1_1_bn2_bias" -> "138 batch_norm_4" [label="(64,)", style=solid]; +"15 layer2_0_conv1_weight" -> "141 conv2d_5" [label="(128, 64, 3, 3)", style=solid]; +"16 layer2_0_bn1_weight" -> "142 batch_norm_5" [label="(128,)", style=solid]; +"17 layer2_0_bn1_bias" -> "142 batch_norm_5" [label="(128,)", style=solid]; +"18 layer2_0_conv2_weight" -> "144 conv2d_6" [label="(128, 128, 3, 3)", style=solid]; +"19 layer2_0_bn2_weight" -> "145 batch_norm_6" [label="(128,)", style=solid]; +"20 layer2_0_bn2_bias" -> "145 batch_norm_6" [label="(128,)", style=solid]; +"21 layer2_0_downsample_0_weight" -> "146 conv2d_7" [label="(128, 64, 1, 1)", style=solid]; +"22 layer2_0_downsample_1_weight" -> "147 batch_norm_7" [label="(128,)", style=solid]; +"23 layer2_0_downsample_1_bias" -> "147 batch_norm_7" [label="(128,)", style=solid]; +"24 layer2_1_conv1_weight" -> "150 conv2d_8" [label="(128, 128, 3, 3)", style=solid]; +"25 layer2_1_bn1_weight" -> "151 batch_norm_8" [label="(128,)", style=solid]; +"26 layer2_1_bn1_bias" -> "151 batch_norm_8" [label="(128,)", style=solid]; +"27 layer2_1_conv2_weight" -> "153 conv2d_9" [label="(128, 128, 3, 3)", style=solid]; +"28 layer2_1_bn2_weight" -> "154 batch_norm_9" [label="(128,)", style=solid]; +"29 layer2_1_bn2_bias" -> "154 batch_norm_9" [label="(128,)", style=solid]; +"30 layer3_0_conv1_weight" -> "157 conv2d_10" [label="(256, 128, 3, 3)", style=solid]; +"31 layer3_0_bn1_weight" -> "158 batch_norm_10" [label="(256,)", style=solid]; +"32 layer3_0_bn1_bias" -> "158 batch_norm_10" [label="(256,)", style=solid]; +"33 layer3_0_conv2_weight" -> "160 conv2d_11" [label="(256, 256, 3, 3)", style=solid]; +"34 layer3_0_bn2_weight" -> "161 batch_norm_11" [label="(256,)", style=solid]; +"35 layer3_0_bn2_bias" -> "161 batch_norm_11" [label="(256,)", style=solid]; +"36 layer3_0_downsample_0_weight" -> "162 conv2d_12" [label="(256, 128, 1, 1)", style=solid]; +"37 layer3_0_downsample_1_weight" -> "163 batch_norm_12" [label="(256,)", style=solid]; +"38 layer3_0_downsample_1_bias" -> "163 batch_norm_12" [label="(256,)", style=solid]; +"39 layer3_1_conv1_weight" -> "166 conv2d_13" [label="(256, 256, 3, 3)", style=solid]; +"40 layer3_1_bn1_weight" -> "167 batch_norm_13" [label="(256,)", style=solid]; +"41 layer3_1_bn1_bias" -> "167 batch_norm_13" [label="(256,)", style=solid]; +"42 layer3_1_conv2_weight" -> "169 conv2d_14" [label="(256, 256, 3, 3)", style=solid]; +"43 layer3_1_bn2_weight" -> "170 batch_norm_14" [label="(256,)", style=solid]; +"44 layer3_1_bn2_bias" -> "170 batch_norm_14" [label="(256,)", style=solid]; +"45 layer4_0_conv1_weight" -> "173 conv2d_15" [label="(512, 256, 3, 3)", style=solid]; +"46 layer4_0_bn1_weight" -> "174 batch_norm_15" [label="(512,)", style=solid]; +"47 layer4_0_bn1_bias" -> "174 batch_norm_15" [label="(512,)", style=solid]; +"48 layer4_0_conv2_weight" -> "176 conv2d_16" [label="(512, 512, 3, 3)", style=solid]; +"49 layer4_0_bn2_weight" -> "177 batch_norm_16" [label="(512,)", style=solid]; +"50 layer4_0_bn2_bias" -> "177 batch_norm_16" [label="(512,)", style=solid]; +"51 layer4_0_downsample_0_weight" -> "178 conv2d_17" [label="(512, 256, 1, 1)", style=solid]; +"52 layer4_0_downsample_1_weight" -> "179 batch_norm_17" [label="(512,)", style=solid]; +"53 layer4_0_downsample_1_bias" -> "179 batch_norm_17" [label="(512,)", style=solid]; +"54 layer4_1_conv1_weight" -> "182 conv2d_18" [label="(512, 512, 3, 3)", style=solid]; +"55 layer4_1_bn1_weight" -> "183 batch_norm_18" [label="(512,)", style=solid]; +"56 layer4_1_bn1_bias" -> "183 batch_norm_18" [label="(512,)", style=solid]; +"57 layer4_1_conv2_weight" -> "185 conv2d_19" [label="(512, 512, 3, 3)", style=solid]; +"58 layer4_1_bn2_weight" -> "186 batch_norm_19" [label="(512,)", style=solid]; +"59 layer4_1_bn2_bias" -> "186 batch_norm_19" [label="(512,)", style=solid]; +"60 fc_weight" -> "191 linear" [label="(1000, 512)", style=solid]; +"61 fc_bias" -> "191 linear" [label="(1000,)", style=solid]; +"62 bn1_running_mean" -> "124 batch_norm" [label="(64,)", style=solid]; +"63 bn1_running_var" -> "124 batch_norm" [label="(64,)", style=solid]; +"65 layer1_0_bn1_running_mean" -> "128 batch_norm_1" [label="(64,)", style=solid]; +"66 layer1_0_bn1_running_var" -> "128 batch_norm_1" [label="(64,)", style=solid]; +"68 layer1_0_bn2_running_mean" -> "131 batch_norm_2" [label="(64,)", style=solid]; +"69 layer1_0_bn2_running_var" -> "131 batch_norm_2" [label="(64,)", style=solid]; +"71 layer1_1_bn1_running_mean" -> "135 batch_norm_3" [label="(64,)", style=solid]; +"72 layer1_1_bn1_running_var" -> "135 batch_norm_3" [label="(64,)", style=solid]; +"74 layer1_1_bn2_running_mean" -> "138 batch_norm_4" [label="(64,)", style=solid]; +"75 layer1_1_bn2_running_var" -> "138 batch_norm_4" [label="(64,)", style=solid]; +"77 layer2_0_bn1_running_mean" -> "142 batch_norm_5" [label="(128,)", style=solid]; +"78 layer2_0_bn1_running_var" -> "142 batch_norm_5" [label="(128,)", style=solid]; +"80 layer2_0_bn2_running_mean" -> "145 batch_norm_6" [label="(128,)", style=solid]; +"81 layer2_0_bn2_running_var" -> "145 batch_norm_6" [label="(128,)", style=solid]; +"83 layer2_0_downsample_1_running_mean" -> "147 batch_norm_7" [label="(128,)", style=solid]; +"84 layer2_0_downsample_1_running_var" -> "147 batch_norm_7" [label="(128,)", style=solid]; +"86 layer2_1_bn1_running_mean" -> "151 batch_norm_8" [label="(128,)", style=solid]; +"87 layer2_1_bn1_running_var" -> "151 batch_norm_8" [label="(128,)", style=solid]; +"89 layer2_1_bn2_running_mean" -> "154 batch_norm_9" [label="(128,)", style=solid]; +"90 layer2_1_bn2_running_var" -> "154 batch_norm_9" [label="(128,)", style=solid]; +"92 layer3_0_bn1_running_mean" -> "158 batch_norm_10" [label="(256,)", style=solid]; +"93 layer3_0_bn1_running_var" -> "158 batch_norm_10" [label="(256,)", style=solid]; +"95 layer3_0_bn2_running_mean" -> "161 batch_norm_11" [label="(256,)", style=solid]; +"96 layer3_0_bn2_running_var" -> "161 batch_norm_11" [label="(256,)", style=solid]; +"98 layer3_0_downsample_1_running_mean" -> "163 batch_norm_12" [label="(256,)", style=solid]; +"99 layer3_0_downsample_1_running_var" -> "163 batch_norm_12" [label="(256,)", style=solid]; +"101 layer3_1_bn1_running_mean" -> "167 batch_norm_13" [label="(256,)", style=solid]; +"102 layer3_1_bn1_running_var" -> "167 batch_norm_13" [label="(256,)", style=solid]; +"104 layer3_1_bn2_running_mean" -> "170 batch_norm_14" [label="(256,)", style=solid]; +"105 layer3_1_bn2_running_var" -> "170 batch_norm_14" [label="(256,)", style=solid]; +"107 layer4_0_bn1_running_mean" -> "174 batch_norm_15" [label="(512,)", style=solid]; +"108 layer4_0_bn1_running_var" -> "174 batch_norm_15" [label="(512,)", style=solid]; +"110 layer4_0_bn2_running_mean" -> "177 batch_norm_16" [label="(512,)", style=solid]; +"111 layer4_0_bn2_running_var" -> "177 batch_norm_16" [label="(512,)", style=solid]; +"113 layer4_0_downsample_1_running_mean" -> "179 batch_norm_17" [label="(512,)", style=solid]; +"114 layer4_0_downsample_1_running_var" -> "179 batch_norm_17" [label="(512,)", style=solid]; +"116 layer4_1_bn1_running_mean" -> "183 batch_norm_18" [label="(512,)", style=solid]; +"117 layer4_1_bn1_running_var" -> "183 batch_norm_18" [label="(512,)", style=solid]; +"119 layer4_1_bn2_running_mean" -> "186 batch_norm_19" [label="(512,)", style=solid]; +"120 layer4_1_bn2_running_var" -> "186 batch_norm_19" [label="(512,)", style=solid]; +"122 x" -> "123 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"123 conv2d" -> "124 batch_norm" [label="(1, 64, 112, 112)", style=solid]; +"124 batch_norm" -> "125 relu_" [label="(1, 64, 112, 112)", style=solid]; +"125 relu_" -> "126 max_pool2d" [label="(1, 64, 112, 112)", style=solid]; +"126 max_pool2d" -> "127 conv2d_1" [label="(1, 64, 56, 56)", style=solid]; +"126 max_pool2d" -> "132 add_" [label="(1, 64, 56, 56)", style=solid]; +"127 conv2d_1" -> "128 batch_norm_1" [label="(1, 64, 56, 56)", style=solid]; +"128 batch_norm_1" -> "129 relu__1" [label="(1, 64, 56, 56)", style=solid]; +"129 relu__1" -> "130 conv2d_2" [label="(1, 64, 56, 56)", style=solid]; +"130 conv2d_2" -> "131 batch_norm_2" [label="(1, 64, 56, 56)", style=solid]; +"131 batch_norm_2" -> "132 add_" [label="(1, 64, 56, 56)", style=solid]; +"132 add_" -> "133 relu__2" [label="(1, 64, 56, 56)", style=solid]; +"133 relu__2" -> "134 conv2d_3" [label="(1, 64, 56, 56)", style=solid]; +"133 relu__2" -> "139 add__1" [label="(1, 64, 56, 56)", style=solid]; +"134 conv2d_3" -> "135 batch_norm_3" [label="(1, 64, 56, 56)", style=solid]; +"135 batch_norm_3" -> "136 relu__3" [label="(1, 64, 56, 56)", style=solid]; +"136 relu__3" -> "137 conv2d_4" [label="(1, 64, 56, 56)", style=solid]; +"137 conv2d_4" -> "138 batch_norm_4" [label="(1, 64, 56, 56)", style=solid]; +"138 batch_norm_4" -> "139 add__1" [label="(1, 64, 56, 56)", style=solid]; +"139 add__1" -> "140 relu__4" [label="(1, 64, 56, 56)", style=solid]; +"140 relu__4" -> "141 conv2d_5" [label="(1, 64, 56, 56)", style=solid]; +"140 relu__4" -> "146 conv2d_7" [label="(1, 64, 56, 56)", style=solid]; +"141 conv2d_5" -> "142 batch_norm_5" [label="(1, 128, 28, 28)", style=solid]; +"142 batch_norm_5" -> "143 relu__5" [label="(1, 128, 28, 28)", style=solid]; +"143 relu__5" -> "144 conv2d_6" [label="(1, 128, 28, 28)", style=solid]; +"144 conv2d_6" -> "145 batch_norm_6" [label="(1, 128, 28, 28)", style=solid]; +"145 batch_norm_6" -> "148 add__2" [label="(1, 128, 28, 28)", style=solid]; +"146 conv2d_7" -> "147 batch_norm_7" [label="(1, 128, 28, 28)", style=solid]; +"147 batch_norm_7" -> "148 add__2" [label="(1, 128, 28, 28)", style=solid]; +"148 add__2" -> "149 relu__6" [label="(1, 128, 28, 28)", style=solid]; +"149 relu__6" -> "150 conv2d_8" [label="(1, 128, 28, 28)", style=solid]; +"149 relu__6" -> "155 add__3" [label="(1, 128, 28, 28)", style=solid]; +"150 conv2d_8" -> "151 batch_norm_8" [label="(1, 128, 28, 28)", style=solid]; +"151 batch_norm_8" -> "152 relu__7" [label="(1, 128, 28, 28)", style=solid]; +"152 relu__7" -> "153 conv2d_9" [label="(1, 128, 28, 28)", style=solid]; +"153 conv2d_9" -> "154 batch_norm_9" [label="(1, 128, 28, 28)", style=solid]; +"154 batch_norm_9" -> "155 add__3" [label="(1, 128, 28, 28)", style=solid]; +"155 add__3" -> "156 relu__8" [label="(1, 128, 28, 28)", style=solid]; +"156 relu__8" -> "157 conv2d_10" [label="(1, 128, 28, 28)", style=solid]; +"156 relu__8" -> "162 conv2d_12" [label="(1, 128, 28, 28)", style=solid]; +"157 conv2d_10" -> "158 batch_norm_10" [label="(1, 256, 14, 14)", style=solid]; +"158 batch_norm_10" -> "159 relu__9" [label="(1, 256, 14, 14)", style=solid]; +"159 relu__9" -> "160 conv2d_11" [label="(1, 256, 14, 14)", style=solid]; +"160 conv2d_11" -> "161 batch_norm_11" [label="(1, 256, 14, 14)", style=solid]; +"161 batch_norm_11" -> "164 add__4" [label="(1, 256, 14, 14)", style=solid]; +"162 conv2d_12" -> "163 batch_norm_12" [label="(1, 256, 14, 14)", style=solid]; +"163 batch_norm_12" -> "164 add__4" [label="(1, 256, 14, 14)", style=solid]; +"164 add__4" -> "165 relu__10" [label="(1, 256, 14, 14)", style=solid]; +"165 relu__10" -> "166 conv2d_13" [label="(1, 256, 14, 14)", style=solid]; +"165 relu__10" -> "171 add__5" [label="(1, 256, 14, 14)", style=solid]; +"166 conv2d_13" -> "167 batch_norm_13" [label="(1, 256, 14, 14)", style=solid]; +"167 batch_norm_13" -> "168 relu__11" [label="(1, 256, 14, 14)", style=solid]; +"168 relu__11" -> "169 conv2d_14" [label="(1, 256, 14, 14)", style=solid]; +"169 conv2d_14" -> "170 batch_norm_14" [label="(1, 256, 14, 14)", style=solid]; +"170 batch_norm_14" -> "171 add__5" [label="(1, 256, 14, 14)", style=solid]; +"171 add__5" -> "172 relu__12" [label="(1, 256, 14, 14)", style=solid]; +"172 relu__12" -> "173 conv2d_15" [label="(1, 256, 14, 14)", style=solid]; +"172 relu__12" -> "178 conv2d_17" [label="(1, 256, 14, 14)", style=solid]; +"173 conv2d_15" -> "174 batch_norm_15" [label="(1, 512, 7, 7)", style=solid]; +"174 batch_norm_15" -> "175 relu__13" [label="(1, 512, 7, 7)", style=solid]; +"175 relu__13" -> "176 conv2d_16" [label="(1, 512, 7, 7)", style=solid]; +"176 conv2d_16" -> "177 batch_norm_16" [label="(1, 512, 7, 7)", style=solid]; +"177 batch_norm_16" -> "180 add__6" [label="(1, 512, 7, 7)", style=solid]; +"178 conv2d_17" -> "179 batch_norm_17" [label="(1, 512, 7, 7)", style=solid]; +"179 batch_norm_17" -> "180 add__6" [label="(1, 512, 7, 7)", style=solid]; +"180 add__6" -> "181 relu__14" [label="(1, 512, 7, 7)", style=solid]; +"181 relu__14" -> "182 conv2d_18" [label="(1, 512, 7, 7)", style=solid]; +"181 relu__14" -> "187 add__7" [label="(1, 512, 7, 7)", style=solid]; +"182 conv2d_18" -> "183 batch_norm_18" [label="(1, 512, 7, 7)", style=solid]; +"183 batch_norm_18" -> "184 relu__15" [label="(1, 512, 7, 7)", style=solid]; +"184 relu__15" -> "185 conv2d_19" [label="(1, 512, 7, 7)", style=solid]; +"185 conv2d_19" -> "186 batch_norm_19" [label="(1, 512, 7, 7)", style=solid]; +"186 batch_norm_19" -> "187 add__7" [label="(1, 512, 7, 7)", style=solid]; +"187 add__7" -> "188 relu__16" [label="(1, 512, 7, 7)", style=solid]; +"188 relu__16" -> "189 adaptive_avg_pool2d" [label="(1, 512, 7, 7)", style=solid]; +"189 adaptive_avg_pool2d" -> "190 flatten" [label="(1, 512, 1, 1)", style=solid]; +"190 flatten" -> "191 linear" [label="(1, 512)", style=solid]; +"191 linear" -> "192 output_1" [label="(1, 1000)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/swin_v2_s.dot b/tests/torch/data/reference_graphs/fx/swin_v2_s.dot index 95a0b21237d..1371ee266f8 100644 --- a/tests/torch/data/reference_graphs/fx/swin_v2_s.dot +++ b/tests/torch/data/reference_graphs/fx/swin_v2_s.dot @@ -1,5610 +1,5610 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; -"1 _param_constant0" [id=1, type=get_attr]; -"2 _param_constant1" [id=2, type=get_attr]; -"3 conv2d" [id=3, type=conv2d]; -"4 permute" [id=4, type=permute]; -"5 _param_constant2" [id=5, type=get_attr]; -"6 _param_constant3" [id=6, type=get_attr]; -"7 layer_norm" [id=7, type=layer_norm]; -"8 _tensor_constant0" [id=8, type=get_attr]; -"9 _param_constant4" [id=9, type=get_attr]; -"10 _param_constant5" [id=10, type=get_attr]; -"11 linear" [id=11, type=linear]; -"12 relu_" [id=12, type=relu_]; -"13 _param_constant6" [id=13, type=get_attr]; -"14 linear_1" [id=14, type=linear]; -"15 view" [id=15, type=view]; -"16 _tensor_constant1" [id=16, type=get_attr]; -"17 index" [id=17, type=index]; -"18 view_1" [id=18, type=view]; -"19 permute_1" [id=19, type=permute]; -"20 contiguous" [id=20, type=contiguous]; -"21 unsqueeze" [id=21, type=unsqueeze]; -"22 sigmoid" [id=22, type=sigmoid]; -"23 mul" [id=23, type=mul]; -"24 pad" [id=24, type=pad]; -"25 view_2" [id=25, type=view]; -"26 permute_2" [id=26, type=permute]; -"27 reshape" [id=27, type=reshape]; -"28 _param_constant7" [id=28, type=get_attr]; -"29 clone" [id=29, type=clone]; -"30 slice_1" [id=30, type=slice]; -"31 zero_" [id=31, type=zero_]; -"32 _param_constant8" [id=32, type=get_attr]; -"33 linear_2" [id=33, type=linear]; -"34 reshape_1" [id=34, type=reshape]; -"35 permute_3" [id=35, type=permute]; -"36 select" [id=36, type=select]; -"37 select_1" [id=37, type=select]; -"38 select_2" [id=38, type=select]; -"39 linalg_vector_norm" [id=39, type=linalg_vector_norm]; -"40 clamp_min" [id=40, type=clamp_min]; -"41 expand_as" [id=41, type=expand_as]; -"42 div" [id=42, type=div]; -"43 linalg_vector_norm_1" [id=43, type=linalg_vector_norm]; -"44 clamp_min_1" [id=44, type=clamp_min]; -"45 expand_as_1" [id=45, type=expand_as]; -"46 div_1" [id=46, type=div]; -"47 transpose" [id=47, type=transpose]; -"48 matmul" [id=48, type=matmul]; -"49 _param_constant9" [id=49, type=get_attr]; -"50 clamp" [id=50, type=clamp]; -"51 exp" [id=51, type=exp]; -"52 mul_1" [id=52, type=mul]; -"53 add" [id=53, type=add]; -"54 softmax" [id=54, type=softmax]; -"55 dropout" [id=55, type=dropout]; -"56 matmul_1" [id=56, type=matmul]; -"57 transpose_1" [id=57, type=transpose]; -"58 reshape_2" [id=58, type=reshape]; -"59 _param_constant10" [id=59, type=get_attr]; -"60 _param_constant11" [id=60, type=get_attr]; -"61 linear_3" [id=61, type=linear]; -"62 dropout_1" [id=62, type=dropout]; -"63 view_3" [id=63, type=view]; -"64 permute_4" [id=64, type=permute]; -"65 reshape_3" [id=65, type=reshape]; -"66 slice_2" [id=66, type=slice]; -"67 slice_3" [id=67, type=slice]; -"68 _param_constant12" [id=68, type=get_attr]; -"69 _param_constant13" [id=69, type=get_attr]; -"70 layer_norm_1" [id=70, type=layer_norm]; -"71 add_1" [id=71, type=add]; -"72 _param_constant14" [id=72, type=get_attr]; -"73 _param_constant15" [id=73, type=get_attr]; -"74 linear_4" [id=74, type=linear]; -"75 gelu" [id=75, type=gelu]; -"76 dropout_2" [id=76, type=dropout]; -"77 _param_constant16" [id=77, type=get_attr]; -"78 _param_constant17" [id=78, type=get_attr]; -"79 linear_5" [id=79, type=linear]; -"80 dropout_3" [id=80, type=dropout]; -"81 _param_constant18" [id=81, type=get_attr]; -"82 _param_constant19" [id=82, type=get_attr]; -"83 layer_norm_2" [id=83, type=layer_norm]; -"84 add_2" [id=84, type=add]; -"85 _tensor_constant2" [id=85, type=get_attr]; -"86 _param_constant20" [id=86, type=get_attr]; -"87 _param_constant21" [id=87, type=get_attr]; -"88 linear_6" [id=88, type=linear]; -"89 relu__1" [id=89, type=relu_]; -"90 _param_constant22" [id=90, type=get_attr]; -"91 linear_7" [id=91, type=linear]; -"92 view_4" [id=92, type=view]; -"93 _tensor_constant3" [id=93, type=get_attr]; -"94 index_1" [id=94, type=index]; -"95 view_5" [id=95, type=view]; -"96 permute_5" [id=96, type=permute]; -"97 contiguous_1" [id=97, type=contiguous]; -"98 unsqueeze_1" [id=98, type=unsqueeze]; -"99 sigmoid_1" [id=99, type=sigmoid]; -"100 mul_2" [id=100, type=mul]; -"101 pad_1" [id=101, type=pad]; -"102 roll" [id=102, type=roll]; -"103 view_6" [id=103, type=view]; -"104 permute_6" [id=104, type=permute]; -"105 reshape_4" [id=105, type=reshape]; -"106 _param_constant23" [id=106, type=get_attr]; -"107 clone_1" [id=107, type=clone]; -"108 slice_4" [id=108, type=slice]; -"109 zero__1" [id=109, type=zero_]; -"110 _param_constant24" [id=110, type=get_attr]; -"111 linear_8" [id=111, type=linear]; -"112 reshape_5" [id=112, type=reshape]; -"113 permute_7" [id=113, type=permute]; -"114 select_3" [id=114, type=select]; -"115 select_4" [id=115, type=select]; -"116 select_5" [id=116, type=select]; -"117 linalg_vector_norm_2" [id=117, type=linalg_vector_norm]; -"118 clamp_min_2" [id=118, type=clamp_min]; -"119 expand_as_2" [id=119, type=expand_as]; -"120 div_2" [id=120, type=div]; -"121 linalg_vector_norm_3" [id=121, type=linalg_vector_norm]; -"122 clamp_min_3" [id=122, type=clamp_min]; -"123 expand_as_3" [id=123, type=expand_as]; -"124 div_3" [id=124, type=div]; -"125 transpose_2" [id=125, type=transpose]; -"126 matmul_2" [id=126, type=matmul]; -"127 _param_constant25" [id=127, type=get_attr]; -"128 clamp_1" [id=128, type=clamp]; -"129 exp_1" [id=129, type=exp]; -"130 mul_3" [id=130, type=mul]; -"131 add_3" [id=131, type=add]; -"132 new_zeros" [id=132, type=new_zeros]; -"133 _tensor_constant4" [id=133, type=get_attr]; -"134 lift_fresh_copy" [id=134, type=lift_fresh_copy]; -"135 slice_5" [id=135, type=slice]; -"136 slice_6" [id=136, type=slice]; -"137 fill_" [id=137, type=fill_]; -"138 _tensor_constant5" [id=138, type=get_attr]; -"139 lift_fresh_copy_1" [id=139, type=lift_fresh_copy]; -"140 slice_7" [id=140, type=slice]; -"141 slice_8" [id=141, type=slice]; -"142 fill__1" [id=142, type=fill_]; -"143 _tensor_constant6" [id=143, type=get_attr]; -"144 lift_fresh_copy_2" [id=144, type=lift_fresh_copy]; -"145 slice_9" [id=145, type=slice]; -"146 slice_10" [id=146, type=slice]; -"147 fill__2" [id=147, type=fill_]; -"148 _tensor_constant7" [id=148, type=get_attr]; -"149 lift_fresh_copy_3" [id=149, type=lift_fresh_copy]; -"150 slice_11" [id=150, type=slice]; -"151 slice_12" [id=151, type=slice]; -"152 fill__3" [id=152, type=fill_]; -"153 _tensor_constant8" [id=153, type=get_attr]; -"154 lift_fresh_copy_4" [id=154, type=lift_fresh_copy]; -"155 slice_13" [id=155, type=slice]; -"156 slice_14" [id=156, type=slice]; -"157 fill__4" [id=157, type=fill_]; -"158 _tensor_constant9" [id=158, type=get_attr]; -"159 lift_fresh_copy_5" [id=159, type=lift_fresh_copy]; -"160 slice_15" [id=160, type=slice]; -"161 slice_16" [id=161, type=slice]; -"162 fill__5" [id=162, type=fill_]; -"163 _tensor_constant10" [id=163, type=get_attr]; -"164 lift_fresh_copy_6" [id=164, type=lift_fresh_copy]; -"165 slice_17" [id=165, type=slice]; -"166 slice_18" [id=166, type=slice]; -"167 fill__6" [id=167, type=fill_]; -"168 _tensor_constant11" [id=168, type=get_attr]; -"169 lift_fresh_copy_7" [id=169, type=lift_fresh_copy]; -"170 slice_19" [id=170, type=slice]; -"171 slice_20" [id=171, type=slice]; -"172 fill__7" [id=172, type=fill_]; -"173 _tensor_constant12" [id=173, type=get_attr]; -"174 lift_fresh_copy_8" [id=174, type=lift_fresh_copy]; -"175 slice_21" [id=175, type=slice]; -"176 slice_22" [id=176, type=slice]; -"177 fill__8" [id=177, type=fill_]; -"178 view_7" [id=178, type=view]; -"179 permute_8" [id=179, type=permute]; -"180 reshape_6" [id=180, type=reshape]; -"181 unsqueeze_2" [id=181, type=unsqueeze]; -"182 unsqueeze_3" [id=182, type=unsqueeze]; -"183 sub" [id=183, type=sub]; -"184 ne" [id=184, type=ne]; -"185 masked_fill" [id=185, type=masked_fill]; -"186 eq" [id=186, type=eq]; -"187 masked_fill_1" [id=187, type=masked_fill]; -"188 view_8" [id=188, type=view]; -"189 unsqueeze_4" [id=189, type=unsqueeze]; -"190 unsqueeze_5" [id=190, type=unsqueeze]; -"191 add_4" [id=191, type=add]; -"192 view_9" [id=192, type=view]; -"193 softmax_1" [id=193, type=softmax]; -"194 dropout_4" [id=194, type=dropout]; -"195 matmul_3" [id=195, type=matmul]; -"196 transpose_3" [id=196, type=transpose]; -"197 reshape_7" [id=197, type=reshape]; -"198 _param_constant26" [id=198, type=get_attr]; -"199 _param_constant27" [id=199, type=get_attr]; -"200 linear_9" [id=200, type=linear]; -"201 dropout_5" [id=201, type=dropout]; -"202 view_10" [id=202, type=view]; -"203 permute_9" [id=203, type=permute]; -"204 reshape_8" [id=204, type=reshape]; -"205 roll_1" [id=205, type=roll]; -"206 slice_23" [id=206, type=slice]; -"207 slice_24" [id=207, type=slice]; -"208 _param_constant28" [id=208, type=get_attr]; -"209 _param_constant29" [id=209, type=get_attr]; -"210 layer_norm_3" [id=210, type=layer_norm]; -"211 add_5" [id=211, type=add]; -"212 _param_constant30" [id=212, type=get_attr]; -"213 _param_constant31" [id=213, type=get_attr]; -"214 linear_10" [id=214, type=linear]; -"215 gelu_1" [id=215, type=gelu]; -"216 dropout_6" [id=216, type=dropout]; -"217 _param_constant32" [id=217, type=get_attr]; -"218 _param_constant33" [id=218, type=get_attr]; -"219 linear_11" [id=219, type=linear]; -"220 dropout_7" [id=220, type=dropout]; -"221 _param_constant34" [id=221, type=get_attr]; -"222 _param_constant35" [id=222, type=get_attr]; -"223 layer_norm_4" [id=223, type=layer_norm]; -"224 add_6" [id=224, type=add]; -"225 pad_2" [id=225, type=pad]; -"226 slice_25" [id=226, type=slice]; -"227 slice_26" [id=227, type=slice]; -"228 slice_27" [id=228, type=slice]; -"229 slice_28" [id=229, type=slice]; -"230 slice_29" [id=230, type=slice]; -"231 slice_30" [id=231, type=slice]; -"232 slice_31" [id=232, type=slice]; -"233 slice_32" [id=233, type=slice]; -"234 slice_33" [id=234, type=slice]; -"235 slice_34" [id=235, type=slice]; -"236 slice_35" [id=236, type=slice]; -"237 slice_36" [id=237, type=slice]; -"238 cat" [id=238, type=cat]; -"239 _param_constant36" [id=239, type=get_attr]; -"240 linear_12" [id=240, type=linear]; -"241 _param_constant37" [id=241, type=get_attr]; -"242 _param_constant38" [id=242, type=get_attr]; -"243 layer_norm_5" [id=243, type=layer_norm]; -"244 _tensor_constant13" [id=244, type=get_attr]; -"245 _param_constant39" [id=245, type=get_attr]; -"246 _param_constant40" [id=246, type=get_attr]; -"247 linear_13" [id=247, type=linear]; -"248 relu__2" [id=248, type=relu_]; -"249 _param_constant41" [id=249, type=get_attr]; -"250 linear_14" [id=250, type=linear]; -"251 view_11" [id=251, type=view]; -"252 _tensor_constant14" [id=252, type=get_attr]; -"253 index_2" [id=253, type=index]; -"254 view_12" [id=254, type=view]; -"255 permute_10" [id=255, type=permute]; -"256 contiguous_2" [id=256, type=contiguous]; -"257 unsqueeze_6" [id=257, type=unsqueeze]; -"258 sigmoid_2" [id=258, type=sigmoid]; -"259 mul_4" [id=259, type=mul]; -"260 pad_3" [id=260, type=pad]; -"261 view_13" [id=261, type=view]; -"262 permute_11" [id=262, type=permute]; -"263 reshape_9" [id=263, type=reshape]; -"264 _param_constant42" [id=264, type=get_attr]; -"265 clone_2" [id=265, type=clone]; -"266 slice_37" [id=266, type=slice]; -"267 zero__2" [id=267, type=zero_]; -"268 _param_constant43" [id=268, type=get_attr]; -"269 linear_15" [id=269, type=linear]; -"270 reshape_10" [id=270, type=reshape]; -"271 permute_12" [id=271, type=permute]; -"272 select_6" [id=272, type=select]; -"273 select_7" [id=273, type=select]; -"274 select_8" [id=274, type=select]; -"275 linalg_vector_norm_4" [id=275, type=linalg_vector_norm]; -"276 clamp_min_4" [id=276, type=clamp_min]; -"277 expand_as_4" [id=277, type=expand_as]; -"278 div_4" [id=278, type=div]; -"279 linalg_vector_norm_5" [id=279, type=linalg_vector_norm]; -"280 clamp_min_5" [id=280, type=clamp_min]; -"281 expand_as_5" [id=281, type=expand_as]; -"282 div_5" [id=282, type=div]; -"283 transpose_4" [id=283, type=transpose]; -"284 matmul_4" [id=284, type=matmul]; -"285 _param_constant44" [id=285, type=get_attr]; -"286 clamp_2" [id=286, type=clamp]; -"287 exp_2" [id=287, type=exp]; -"288 mul_5" [id=288, type=mul]; -"289 add_7" [id=289, type=add]; -"290 softmax_2" [id=290, type=softmax]; -"291 dropout_8" [id=291, type=dropout]; -"292 matmul_5" [id=292, type=matmul]; -"293 transpose_5" [id=293, type=transpose]; -"294 reshape_11" [id=294, type=reshape]; -"295 _param_constant45" [id=295, type=get_attr]; -"296 _param_constant46" [id=296, type=get_attr]; -"297 linear_16" [id=297, type=linear]; -"298 dropout_9" [id=298, type=dropout]; -"299 view_14" [id=299, type=view]; -"300 permute_13" [id=300, type=permute]; -"301 reshape_12" [id=301, type=reshape]; -"302 slice_38" [id=302, type=slice]; -"303 slice_39" [id=303, type=slice]; -"304 slice_40" [id=304, type=slice]; -"305 slice_41" [id=305, type=slice]; -"306 contiguous_3" [id=306, type=contiguous]; -"307 _param_constant47" [id=307, type=get_attr]; -"308 _param_constant48" [id=308, type=get_attr]; -"309 layer_norm_6" [id=309, type=layer_norm]; -"310 add_8" [id=310, type=add]; -"311 _param_constant49" [id=311, type=get_attr]; -"312 _param_constant50" [id=312, type=get_attr]; -"313 linear_17" [id=313, type=linear]; -"314 gelu_2" [id=314, type=gelu]; -"315 dropout_10" [id=315, type=dropout]; -"316 _param_constant51" [id=316, type=get_attr]; -"317 _param_constant52" [id=317, type=get_attr]; -"318 linear_18" [id=318, type=linear]; -"319 dropout_11" [id=319, type=dropout]; -"320 _param_constant53" [id=320, type=get_attr]; -"321 _param_constant54" [id=321, type=get_attr]; -"322 layer_norm_7" [id=322, type=layer_norm]; -"323 add_9" [id=323, type=add]; -"324 _tensor_constant15" [id=324, type=get_attr]; -"325 _param_constant55" [id=325, type=get_attr]; -"326 _param_constant56" [id=326, type=get_attr]; -"327 linear_19" [id=327, type=linear]; -"328 relu__3" [id=328, type=relu_]; -"329 _param_constant57" [id=329, type=get_attr]; -"330 linear_20" [id=330, type=linear]; -"331 view_15" [id=331, type=view]; -"332 _tensor_constant16" [id=332, type=get_attr]; -"333 index_3" [id=333, type=index]; -"334 view_16" [id=334, type=view]; -"335 permute_14" [id=335, type=permute]; -"336 contiguous_4" [id=336, type=contiguous]; -"337 unsqueeze_7" [id=337, type=unsqueeze]; -"338 sigmoid_3" [id=338, type=sigmoid]; -"339 mul_6" [id=339, type=mul]; -"340 pad_4" [id=340, type=pad]; -"341 roll_2" [id=341, type=roll]; -"342 view_17" [id=342, type=view]; -"343 permute_15" [id=343, type=permute]; -"344 reshape_13" [id=344, type=reshape]; -"345 _param_constant58" [id=345, type=get_attr]; -"346 clone_3" [id=346, type=clone]; -"347 slice_42" [id=347, type=slice]; -"348 zero__3" [id=348, type=zero_]; -"349 _param_constant59" [id=349, type=get_attr]; -"350 linear_21" [id=350, type=linear]; -"351 reshape_14" [id=351, type=reshape]; -"352 permute_16" [id=352, type=permute]; -"353 select_9" [id=353, type=select]; -"354 select_10" [id=354, type=select]; -"355 select_11" [id=355, type=select]; -"356 linalg_vector_norm_6" [id=356, type=linalg_vector_norm]; -"357 clamp_min_6" [id=357, type=clamp_min]; -"358 expand_as_6" [id=358, type=expand_as]; -"359 div_6" [id=359, type=div]; -"360 linalg_vector_norm_7" [id=360, type=linalg_vector_norm]; -"361 clamp_min_7" [id=361, type=clamp_min]; -"362 expand_as_7" [id=362, type=expand_as]; -"363 div_7" [id=363, type=div]; -"364 transpose_6" [id=364, type=transpose]; -"365 matmul_6" [id=365, type=matmul]; -"366 _param_constant60" [id=366, type=get_attr]; -"367 clamp_3" [id=367, type=clamp]; -"368 exp_3" [id=368, type=exp]; -"369 mul_7" [id=369, type=mul]; -"370 add_10" [id=370, type=add]; -"371 new_zeros_1" [id=371, type=new_zeros]; -"372 _tensor_constant17" [id=372, type=get_attr]; -"373 lift_fresh_copy_9" [id=373, type=lift_fresh_copy]; -"374 slice_43" [id=374, type=slice]; -"375 slice_44" [id=375, type=slice]; -"376 fill__9" [id=376, type=fill_]; -"377 _tensor_constant18" [id=377, type=get_attr]; -"378 lift_fresh_copy_10" [id=378, type=lift_fresh_copy]; -"379 slice_45" [id=379, type=slice]; -"380 slice_46" [id=380, type=slice]; -"381 fill__10" [id=381, type=fill_]; -"382 _tensor_constant19" [id=382, type=get_attr]; -"383 lift_fresh_copy_11" [id=383, type=lift_fresh_copy]; -"384 slice_47" [id=384, type=slice]; -"385 slice_48" [id=385, type=slice]; -"386 fill__11" [id=386, type=fill_]; -"387 _tensor_constant20" [id=387, type=get_attr]; -"388 lift_fresh_copy_12" [id=388, type=lift_fresh_copy]; -"389 slice_49" [id=389, type=slice]; -"390 slice_50" [id=390, type=slice]; -"391 fill__12" [id=391, type=fill_]; -"392 _tensor_constant21" [id=392, type=get_attr]; -"393 lift_fresh_copy_13" [id=393, type=lift_fresh_copy]; -"394 slice_51" [id=394, type=slice]; -"395 slice_52" [id=395, type=slice]; -"396 fill__13" [id=396, type=fill_]; -"397 _tensor_constant22" [id=397, type=get_attr]; -"398 lift_fresh_copy_14" [id=398, type=lift_fresh_copy]; -"399 slice_53" [id=399, type=slice]; -"400 slice_54" [id=400, type=slice]; -"401 fill__14" [id=401, type=fill_]; -"402 _tensor_constant23" [id=402, type=get_attr]; -"403 lift_fresh_copy_15" [id=403, type=lift_fresh_copy]; -"404 slice_55" [id=404, type=slice]; -"405 slice_56" [id=405, type=slice]; -"406 fill__15" [id=406, type=fill_]; -"407 _tensor_constant24" [id=407, type=get_attr]; -"408 lift_fresh_copy_16" [id=408, type=lift_fresh_copy]; -"409 slice_57" [id=409, type=slice]; -"410 slice_58" [id=410, type=slice]; -"411 fill__16" [id=411, type=fill_]; -"412 _tensor_constant25" [id=412, type=get_attr]; -"413 lift_fresh_copy_17" [id=413, type=lift_fresh_copy]; -"414 slice_59" [id=414, type=slice]; -"415 slice_60" [id=415, type=slice]; -"416 fill__17" [id=416, type=fill_]; -"417 view_18" [id=417, type=view]; -"418 permute_17" [id=418, type=permute]; -"419 reshape_15" [id=419, type=reshape]; -"420 unsqueeze_8" [id=420, type=unsqueeze]; -"421 unsqueeze_9" [id=421, type=unsqueeze]; -"422 sub_1" [id=422, type=sub]; -"423 ne_1" [id=423, type=ne]; -"424 masked_fill_2" [id=424, type=masked_fill]; -"425 eq_1" [id=425, type=eq]; -"426 masked_fill_3" [id=426, type=masked_fill]; -"427 view_19" [id=427, type=view]; -"428 unsqueeze_10" [id=428, type=unsqueeze]; -"429 unsqueeze_11" [id=429, type=unsqueeze]; -"430 add_11" [id=430, type=add]; -"431 view_20" [id=431, type=view]; -"432 softmax_3" [id=432, type=softmax]; -"433 dropout_12" [id=433, type=dropout]; -"434 matmul_7" [id=434, type=matmul]; -"435 transpose_7" [id=435, type=transpose]; -"436 reshape_16" [id=436, type=reshape]; -"437 _param_constant61" [id=437, type=get_attr]; -"438 _param_constant62" [id=438, type=get_attr]; -"439 linear_22" [id=439, type=linear]; -"440 dropout_13" 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[id=863, type=slice]; +"864 slice_54" [id=864, type=slice]; +"865 fill__14" [id=865, type=fill_]; +"866 lift_fresh_copy_15" [id=866, type=lift_fresh_copy]; +"867 slice_55" [id=867, type=slice]; +"868 slice_56" [id=868, type=slice]; +"869 fill__15" [id=869, type=fill_]; +"870 lift_fresh_copy_16" [id=870, type=lift_fresh_copy]; +"871 slice_57" [id=871, type=slice]; +"872 slice_58" [id=872, type=slice]; +"873 fill__16" [id=873, type=fill_]; +"874 lift_fresh_copy_17" [id=874, type=lift_fresh_copy]; +"875 slice_59" [id=875, type=slice]; +"876 slice_60" [id=876, type=slice]; +"877 fill__17" [id=877, type=fill_]; +"878 view_18" [id=878, type=view]; +"879 permute_17" [id=879, type=permute]; +"880 reshape_15" [id=880, type=reshape]; +"881 unsqueeze_8" [id=881, type=unsqueeze]; +"882 unsqueeze_9" [id=882, type=unsqueeze]; +"883 sub_1" [id=883, type=sub]; +"884 ne_1" [id=884, type=ne]; +"885 masked_fill_2" [id=885, type=masked_fill]; +"886 eq_1" [id=886, type=eq]; +"887 masked_fill_3" [id=887, 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layer_norm_11" [id=987, type=layer_norm]; +"988 add_15" [id=988, type=add]; +"989 linear_30" [id=989, type=linear]; +"990 gelu_4" [id=990, type=gelu]; +"991 dropout_18" [id=991, type=dropout]; +"992 linear_31" [id=992, type=linear]; +"993 dropout_19" [id=993, type=dropout]; +"994 layer_norm_12" [id=994, type=layer_norm]; +"995 add_16" [id=995, type=add]; +"996 linear_32" [id=996, type=linear]; +"997 relu__5" [id=997, type=relu_]; +"998 linear_33" [id=998, type=linear]; +"999 view_26" [id=999, type=view]; +"1000 index_5" [id=1000, type=index]; +"1001 view_27" [id=1001, type=view]; +"1002 permute_23" [id=1002, type=permute]; +"1003 contiguous_8" [id=1003, type=contiguous]; +"1004 unsqueeze_13" [id=1004, type=unsqueeze]; +"1005 sigmoid_5" [id=1005, type=sigmoid]; +"1006 mul_10" [id=1006, type=mul]; +"1007 pad_7" [id=1007, type=pad]; +"1008 roll_4" [id=1008, type=roll]; +"1009 view_28" [id=1009, type=view]; +"1010 permute_24" [id=1010, type=permute]; +"1011 reshape_22" [id=1011, type=reshape]; +"1012 clone_5" [id=1012, type=clone]; +"1013 slice_82" [id=1013, type=slice]; +"1014 zero__5" [id=1014, type=zero_]; +"1015 linear_34" [id=1015, type=linear]; +"1016 reshape_23" [id=1016, type=reshape]; +"1017 permute_25" [id=1017, type=permute]; +"1018 select_15" [id=1018, type=select]; +"1019 select_16" [id=1019, type=select]; +"1020 select_17" [id=1020, type=select]; +"1021 linalg_vector_norm_10" [id=1021, type=linalg_vector_norm]; +"1022 clamp_min_10" [id=1022, type=clamp_min]; +"1023 expand_as_10" [id=1023, type=expand_as]; +"1024 div_10" [id=1024, type=div]; +"1025 linalg_vector_norm_11" [id=1025, type=linalg_vector_norm]; +"1026 clamp_min_11" [id=1026, type=clamp_min]; +"1027 expand_as_11" [id=1027, type=expand_as]; +"1028 div_11" [id=1028, type=div]; +"1029 transpose_10" [id=1029, type=transpose]; +"1030 matmul_10" [id=1030, type=matmul]; +"1031 clamp_5" [id=1031, type=clamp]; +"1032 exp_5" [id=1032, type=exp]; +"1033 mul_11" [id=1033, type=mul]; +"1034 add_17" [id=1034, type=add]; +"1035 new_zeros_2" [id=1035, type=new_zeros]; +"1036 lift_fresh_copy_18" [id=1036, type=lift_fresh_copy]; +"1037 slice_83" [id=1037, type=slice]; +"1038 slice_84" [id=1038, type=slice]; +"1039 fill__18" [id=1039, type=fill_]; +"1040 lift_fresh_copy_19" [id=1040, type=lift_fresh_copy]; +"1041 slice_85" [id=1041, type=slice]; +"1042 slice_86" [id=1042, type=slice]; +"1043 fill__19" [id=1043, type=fill_]; +"1044 lift_fresh_copy_20" [id=1044, type=lift_fresh_copy]; +"1045 slice_87" [id=1045, type=slice]; +"1046 slice_88" [id=1046, type=slice]; +"1047 fill__20" [id=1047, type=fill_]; +"1048 lift_fresh_copy_21" [id=1048, type=lift_fresh_copy]; +"1049 slice_89" [id=1049, type=slice]; +"1050 slice_90" [id=1050, type=slice]; +"1051 fill__21" [id=1051, type=fill_]; +"1052 lift_fresh_copy_22" [id=1052, type=lift_fresh_copy]; +"1053 slice_91" [id=1053, type=slice]; +"1054 slice_92" [id=1054, type=slice]; +"1055 fill__22" [id=1055, type=fill_]; +"1056 lift_fresh_copy_23" [id=1056, type=lift_fresh_copy]; +"1057 slice_93" [id=1057, type=slice]; +"1058 slice_94" [id=1058, type=slice]; +"1059 fill__23" [id=1059, type=fill_]; +"1060 lift_fresh_copy_24" [id=1060, type=lift_fresh_copy]; +"1061 slice_95" [id=1061, type=slice]; +"1062 slice_96" [id=1062, type=slice]; +"1063 fill__24" [id=1063, type=fill_]; +"1064 lift_fresh_copy_25" [id=1064, type=lift_fresh_copy]; +"1065 slice_97" [id=1065, type=slice]; +"1066 slice_98" [id=1066, type=slice]; +"1067 fill__25" [id=1067, type=fill_]; +"1068 lift_fresh_copy_26" [id=1068, type=lift_fresh_copy]; +"1069 slice_99" [id=1069, type=slice]; +"1070 slice_100" [id=1070, type=slice]; +"1071 fill__26" [id=1071, type=fill_]; +"1072 view_29" [id=1072, type=view]; +"1073 permute_26" [id=1073, type=permute]; +"1074 reshape_24" [id=1074, type=reshape]; +"1075 unsqueeze_14" [id=1075, type=unsqueeze]; +"1076 unsqueeze_15" [id=1076, type=unsqueeze]; +"1077 sub_2" [id=1077, type=sub]; +"1078 ne_2" [id=1078, type=ne]; +"1079 masked_fill_4" [id=1079, type=masked_fill]; +"1080 eq_2" [id=1080, type=eq]; +"1081 masked_fill_5" [id=1081, type=masked_fill]; +"1082 view_30" [id=1082, type=view]; +"1083 unsqueeze_16" [id=1083, type=unsqueeze]; +"1084 unsqueeze_17" [id=1084, type=unsqueeze]; +"1085 add_18" [id=1085, type=add]; +"1086 view_31" [id=1086, type=view]; +"1087 softmax_5" [id=1087, type=softmax]; +"1088 dropout_20" [id=1088, type=dropout]; +"1089 matmul_11" [id=1089, type=matmul]; +"1090 transpose_11" [id=1090, type=transpose]; +"1091 reshape_25" [id=1091, type=reshape]; +"1092 linear_35" [id=1092, type=linear]; +"1093 dropout_21" [id=1093, type=dropout]; +"1094 view_32" [id=1094, type=view]; +"1095 permute_27" [id=1095, type=permute]; +"1096 reshape_26" [id=1096, type=reshape]; +"1097 roll_5" [id=1097, type=roll]; +"1098 slice_101" [id=1098, type=slice]; +"1099 slice_102" [id=1099, type=slice]; +"1100 slice_103" [id=1100, type=slice]; +"1101 slice_104" [id=1101, type=slice]; +"1102 contiguous_9" [id=1102, type=contiguous]; +"1103 layer_norm_13" [id=1103, type=layer_norm]; +"1104 add_19" [id=1104, type=add]; +"1105 linear_36" [id=1105, type=linear]; +"1106 gelu_5" [id=1106, type=gelu]; +"1107 dropout_22" [id=1107, type=dropout]; +"1108 linear_37" [id=1108, type=linear]; +"1109 dropout_23" [id=1109, type=dropout]; +"1110 layer_norm_14" [id=1110, type=layer_norm]; +"1111 add_20" [id=1111, type=add]; +"1112 linear_38" [id=1112, type=linear]; +"1113 relu__6" [id=1113, type=relu_]; +"1114 linear_39" [id=1114, type=linear]; +"1115 view_33" [id=1115, type=view]; +"1116 index_6" [id=1116, type=index]; +"1117 view_34" [id=1117, type=view]; +"1118 permute_28" [id=1118, type=permute]; +"1119 contiguous_10" [id=1119, type=contiguous]; +"1120 unsqueeze_18" [id=1120, type=unsqueeze]; +"1121 sigmoid_6" [id=1121, type=sigmoid]; +"1122 mul_12" [id=1122, type=mul]; +"1123 pad_8" [id=1123, type=pad]; +"1124 view_35" [id=1124, type=view]; +"1125 permute_29" [id=1125, type=permute]; +"1126 reshape_27" 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add_49" [id=1861, type=add]; +"1862 softmax_14" [id=1862, type=softmax]; +"1863 dropout_56" [id=1863, type=dropout]; +"1864 matmul_29" [id=1864, type=matmul]; +"1865 transpose_29" [id=1865, type=transpose]; +"1866 reshape_65" [id=1866, type=reshape]; +"1867 linear_89" [id=1867, type=linear]; +"1868 dropout_57" [id=1868, type=dropout]; +"1869 view_80" [id=1869, type=view]; +"1870 permute_67" [id=1870, type=permute]; +"1871 reshape_66" [id=1871, type=reshape]; +"1872 slice_218" [id=1872, type=slice]; +"1873 slice_219" [id=1873, type=slice]; +"1874 slice_220" [id=1874, type=slice]; +"1875 slice_221" [id=1875, type=slice]; +"1876 contiguous_27" [id=1876, type=contiguous]; +"1877 layer_norm_31" [id=1877, type=layer_norm]; +"1878 add_50" [id=1878, type=add]; +"1879 linear_90" [id=1879, type=linear]; +"1880 gelu_14" [id=1880, type=gelu]; +"1881 dropout_58" [id=1881, type=dropout]; +"1882 linear_91" [id=1882, type=linear]; +"1883 dropout_59" [id=1883, type=dropout]; +"1884 layer_norm_32" [id=1884, type=layer_norm]; +"1885 add_51" [id=1885, type=add]; +"1886 linear_92" [id=1886, type=linear]; +"1887 relu__15" [id=1887, type=relu_]; +"1888 linear_93" [id=1888, type=linear]; +"1889 view_81" [id=1889, type=view]; +"1890 index_15" [id=1890, type=index]; +"1891 view_82" [id=1891, type=view]; +"1892 permute_68" [id=1892, type=permute]; +"1893 contiguous_28" [id=1893, type=contiguous]; +"1894 unsqueeze_43" [id=1894, type=unsqueeze]; +"1895 sigmoid_15" [id=1895, type=sigmoid]; +"1896 mul_30" [id=1896, type=mul]; +"1897 pad_17" [id=1897, type=pad]; +"1898 roll_14" [id=1898, type=roll]; +"1899 view_83" [id=1899, type=view]; +"1900 permute_69" [id=1900, type=permute]; +"1901 reshape_67" [id=1901, type=reshape]; +"1902 clone_15" [id=1902, type=clone]; +"1903 slice_222" [id=1903, type=slice]; +"1904 zero__15" [id=1904, type=zero_]; +"1905 linear_94" [id=1905, type=linear]; +"1906 reshape_68" [id=1906, type=reshape]; +"1907 permute_70" [id=1907, type=permute]; +"1908 select_45" [id=1908, type=select]; +"1909 select_46" [id=1909, type=select]; +"1910 select_47" [id=1910, type=select]; +"1911 linalg_vector_norm_30" [id=1911, type=linalg_vector_norm]; +"1912 clamp_min_30" [id=1912, type=clamp_min]; +"1913 expand_as_30" [id=1913, type=expand_as]; +"1914 div_30" [id=1914, type=div]; +"1915 linalg_vector_norm_31" [id=1915, type=linalg_vector_norm]; +"1916 clamp_min_31" [id=1916, type=clamp_min]; +"1917 expand_as_31" [id=1917, type=expand_as]; +"1918 div_31" [id=1918, type=div]; +"1919 transpose_30" [id=1919, type=transpose]; +"1920 matmul_30" [id=1920, type=matmul]; +"1921 clamp_15" [id=1921, type=clamp]; +"1922 exp_15" [id=1922, type=exp]; +"1923 mul_31" [id=1923, type=mul]; +"1924 add_52" [id=1924, type=add]; +"1925 new_zeros_7" [id=1925, type=new_zeros]; +"1926 lift_fresh_copy_63" [id=1926, type=lift_fresh_copy]; +"1927 slice_223" [id=1927, type=slice]; +"1928 slice_224" [id=1928, type=slice]; +"1929 fill__63" [id=1929, type=fill_]; +"1930 lift_fresh_copy_64" [id=1930, type=lift_fresh_copy]; +"1931 slice_225" [id=1931, type=slice]; +"1932 slice_226" [id=1932, type=slice]; +"1933 fill__64" [id=1933, type=fill_]; +"1934 lift_fresh_copy_65" [id=1934, type=lift_fresh_copy]; +"1935 slice_227" [id=1935, type=slice]; +"1936 slice_228" [id=1936, type=slice]; +"1937 fill__65" [id=1937, type=fill_]; +"1938 lift_fresh_copy_66" [id=1938, type=lift_fresh_copy]; +"1939 slice_229" [id=1939, type=slice]; +"1940 slice_230" [id=1940, type=slice]; +"1941 fill__66" [id=1941, type=fill_]; +"1942 lift_fresh_copy_67" [id=1942, type=lift_fresh_copy]; +"1943 slice_231" [id=1943, type=slice]; +"1944 slice_232" [id=1944, type=slice]; +"1945 fill__67" [id=1945, type=fill_]; +"1946 lift_fresh_copy_68" [id=1946, type=lift_fresh_copy]; +"1947 slice_233" [id=1947, type=slice]; +"1948 slice_234" [id=1948, type=slice]; +"1949 fill__68" [id=1949, type=fill_]; +"1950 lift_fresh_copy_69" [id=1950, type=lift_fresh_copy]; +"1951 slice_235" [id=1951, type=slice]; +"1952 slice_236" [id=1952, type=slice]; +"1953 fill__69" [id=1953, type=fill_]; +"1954 lift_fresh_copy_70" [id=1954, type=lift_fresh_copy]; +"1955 slice_237" [id=1955, type=slice]; +"1956 slice_238" [id=1956, type=slice]; +"1957 fill__70" [id=1957, type=fill_]; +"1958 lift_fresh_copy_71" [id=1958, type=lift_fresh_copy]; +"1959 slice_239" [id=1959, type=slice]; +"1960 slice_240" [id=1960, type=slice]; +"1961 fill__71" [id=1961, type=fill_]; +"1962 view_84" [id=1962, type=view]; +"1963 permute_71" [id=1963, type=permute]; +"1964 reshape_69" [id=1964, type=reshape]; +"1965 unsqueeze_44" [id=1965, type=unsqueeze]; +"1966 unsqueeze_45" [id=1966, type=unsqueeze]; +"1967 sub_7" [id=1967, type=sub]; +"1968 ne_7" [id=1968, type=ne]; +"1969 masked_fill_14" [id=1969, type=masked_fill]; +"1970 eq_7" [id=1970, type=eq]; +"1971 masked_fill_15" [id=1971, type=masked_fill]; +"1972 view_85" [id=1972, type=view]; +"1973 unsqueeze_46" [id=1973, type=unsqueeze]; +"1974 unsqueeze_47" [id=1974, type=unsqueeze]; 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type=linear]; +"1999 dropout_63" [id=1999, type=dropout]; +"2000 layer_norm_34" [id=2000, type=layer_norm]; +"2001 add_55" [id=2001, type=add]; +"2002 linear_98" [id=2002, type=linear]; +"2003 relu__16" [id=2003, type=relu_]; +"2004 linear_99" [id=2004, type=linear]; +"2005 view_88" [id=2005, type=view]; +"2006 index_16" [id=2006, type=index]; +"2007 view_89" [id=2007, type=view]; +"2008 permute_73" [id=2008, type=permute]; +"2009 contiguous_30" [id=2009, type=contiguous]; +"2010 unsqueeze_48" [id=2010, type=unsqueeze]; +"2011 sigmoid_16" [id=2011, type=sigmoid]; +"2012 mul_32" [id=2012, type=mul]; +"2013 pad_18" [id=2013, type=pad]; +"2014 view_90" [id=2014, type=view]; +"2015 permute_74" [id=2015, type=permute]; +"2016 reshape_72" [id=2016, type=reshape]; +"2017 clone_16" [id=2017, type=clone]; +"2018 slice_245" [id=2018, type=slice]; +"2019 zero__16" [id=2019, type=zero_]; +"2020 linear_100" [id=2020, type=linear]; +"2021 reshape_73" [id=2021, type=reshape]; +"2022 permute_75" [id=2022, type=permute]; +"2023 select_48" [id=2023, type=select]; +"2024 select_49" [id=2024, type=select]; +"2025 select_50" [id=2025, type=select]; +"2026 linalg_vector_norm_32" [id=2026, type=linalg_vector_norm]; +"2027 clamp_min_32" [id=2027, type=clamp_min]; +"2028 expand_as_32" [id=2028, type=expand_as]; +"2029 div_32" [id=2029, type=div]; +"2030 linalg_vector_norm_33" [id=2030, type=linalg_vector_norm]; +"2031 clamp_min_33" [id=2031, type=clamp_min]; +"2032 expand_as_33" [id=2032, type=expand_as]; +"2033 div_33" [id=2033, type=div]; +"2034 transpose_32" [id=2034, type=transpose]; +"2035 matmul_32" [id=2035, type=matmul]; +"2036 clamp_16" [id=2036, type=clamp]; +"2037 exp_16" [id=2037, type=exp]; +"2038 mul_33" [id=2038, type=mul]; +"2039 add_56" [id=2039, type=add]; +"2040 softmax_16" [id=2040, type=softmax]; +"2041 dropout_64" [id=2041, type=dropout]; +"2042 matmul_33" [id=2042, type=matmul]; +"2043 transpose_33" [id=2043, type=transpose]; +"2044 reshape_74" [id=2044, type=reshape]; +"2045 linear_101" [id=2045, type=linear]; +"2046 dropout_65" [id=2046, type=dropout]; +"2047 view_91" [id=2047, type=view]; +"2048 permute_76" [id=2048, type=permute]; +"2049 reshape_75" [id=2049, type=reshape]; +"2050 slice_246" [id=2050, type=slice]; +"2051 slice_247" [id=2051, type=slice]; +"2052 slice_248" [id=2052, type=slice]; +"2053 slice_249" [id=2053, type=slice]; +"2054 contiguous_31" [id=2054, type=contiguous]; +"2055 layer_norm_35" [id=2055, type=layer_norm]; +"2056 add_57" [id=2056, type=add]; +"2057 linear_102" [id=2057, type=linear]; +"2058 gelu_16" [id=2058, type=gelu]; +"2059 dropout_66" [id=2059, type=dropout]; +"2060 linear_103" [id=2060, type=linear]; +"2061 dropout_67" [id=2061, type=dropout]; +"2062 layer_norm_36" [id=2062, type=layer_norm]; +"2063 add_58" [id=2063, type=add]; +"2064 linear_104" [id=2064, type=linear]; +"2065 relu__17" [id=2065, type=relu_]; +"2066 linear_105" [id=2066, type=linear]; +"2067 view_92" [id=2067, type=view]; +"2068 index_17" [id=2068, type=index]; +"2069 view_93" [id=2069, type=view]; +"2070 permute_77" [id=2070, type=permute]; +"2071 contiguous_32" [id=2071, type=contiguous]; +"2072 unsqueeze_49" [id=2072, type=unsqueeze]; +"2073 sigmoid_17" [id=2073, type=sigmoid]; +"2074 mul_34" [id=2074, type=mul]; +"2075 pad_19" [id=2075, type=pad]; +"2076 roll_16" [id=2076, type=roll]; +"2077 view_94" [id=2077, type=view]; +"2078 permute_78" [id=2078, type=permute]; +"2079 reshape_76" [id=2079, type=reshape]; +"2080 clone_17" [id=2080, type=clone]; +"2081 slice_250" [id=2081, type=slice]; +"2082 zero__17" [id=2082, type=zero_]; +"2083 linear_106" [id=2083, type=linear]; +"2084 reshape_77" [id=2084, type=reshape]; +"2085 permute_79" [id=2085, type=permute]; +"2086 select_51" [id=2086, type=select]; +"2087 select_52" [id=2087, type=select]; +"2088 select_53" [id=2088, type=select]; +"2089 linalg_vector_norm_34" [id=2089, type=linalg_vector_norm]; +"2090 clamp_min_34" [id=2090, type=clamp_min]; +"2091 expand_as_34" [id=2091, type=expand_as]; +"2092 div_34" [id=2092, type=div]; +"2093 linalg_vector_norm_35" [id=2093, type=linalg_vector_norm]; +"2094 clamp_min_35" [id=2094, type=clamp_min]; +"2095 expand_as_35" [id=2095, type=expand_as]; +"2096 div_35" [id=2096, type=div]; +"2097 transpose_34" [id=2097, type=transpose]; +"2098 matmul_34" [id=2098, type=matmul]; +"2099 clamp_17" [id=2099, type=clamp]; +"2100 exp_17" [id=2100, type=exp]; +"2101 mul_35" [id=2101, type=mul]; +"2102 add_59" [id=2102, type=add]; +"2103 new_zeros_8" [id=2103, type=new_zeros]; +"2104 lift_fresh_copy_72" [id=2104, type=lift_fresh_copy]; +"2105 slice_251" [id=2105, type=slice]; +"2106 slice_252" [id=2106, type=slice]; +"2107 fill__72" [id=2107, type=fill_]; +"2108 lift_fresh_copy_73" [id=2108, type=lift_fresh_copy]; +"2109 slice_253" [id=2109, type=slice]; +"2110 slice_254" [id=2110, type=slice]; +"2111 fill__73" [id=2111, type=fill_]; +"2112 lift_fresh_copy_74" [id=2112, type=lift_fresh_copy]; +"2113 slice_255" [id=2113, type=slice]; +"2114 slice_256" [id=2114, type=slice]; +"2115 fill__74" [id=2115, type=fill_]; +"2116 lift_fresh_copy_75" [id=2116, type=lift_fresh_copy]; +"2117 slice_257" [id=2117, type=slice]; +"2118 slice_258" [id=2118, type=slice]; +"2119 fill__75" [id=2119, type=fill_]; +"2120 lift_fresh_copy_76" [id=2120, type=lift_fresh_copy]; +"2121 slice_259" [id=2121, type=slice]; +"2122 slice_260" [id=2122, type=slice]; +"2123 fill__76" [id=2123, type=fill_]; +"2124 lift_fresh_copy_77" [id=2124, type=lift_fresh_copy]; +"2125 slice_261" [id=2125, type=slice]; +"2126 slice_262" [id=2126, type=slice]; +"2127 fill__77" [id=2127, type=fill_]; +"2128 lift_fresh_copy_78" [id=2128, type=lift_fresh_copy]; +"2129 slice_263" [id=2129, type=slice]; +"2130 slice_264" [id=2130, type=slice]; +"2131 fill__78" [id=2131, type=fill_]; +"2132 lift_fresh_copy_79" [id=2132, type=lift_fresh_copy]; +"2133 slice_265" [id=2133, type=slice]; +"2134 slice_266" [id=2134, type=slice]; +"2135 fill__79" [id=2135, type=fill_]; +"2136 lift_fresh_copy_80" [id=2136, type=lift_fresh_copy]; +"2137 slice_267" [id=2137, type=slice]; +"2138 slice_268" [id=2138, type=slice]; +"2139 fill__80" [id=2139, type=fill_]; +"2140 view_95" [id=2140, type=view]; +"2141 permute_80" [id=2141, type=permute]; +"2142 reshape_78" [id=2142, type=reshape]; +"2143 unsqueeze_50" [id=2143, type=unsqueeze]; +"2144 unsqueeze_51" [id=2144, type=unsqueeze]; +"2145 sub_8" [id=2145, type=sub]; +"2146 ne_8" [id=2146, type=ne]; +"2147 masked_fill_16" [id=2147, type=masked_fill]; +"2148 eq_8" [id=2148, type=eq]; +"2149 masked_fill_17" [id=2149, type=masked_fill]; +"2150 view_96" [id=2150, type=view]; +"2151 unsqueeze_52" [id=2151, type=unsqueeze]; +"2152 unsqueeze_53" [id=2152, type=unsqueeze]; +"2153 add_60" [id=2153, type=add]; +"2154 view_97" [id=2154, type=view]; +"2155 softmax_17" [id=2155, type=softmax]; +"2156 dropout_68" [id=2156, type=dropout]; +"2157 matmul_35" [id=2157, type=matmul]; +"2158 transpose_35" 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type=reshape]; +"2572 permute_102" [id=2572, type=permute]; +"2573 select_66" [id=2573, type=select]; +"2574 select_67" [id=2574, type=select]; +"2575 select_68" [id=2575, type=select]; +"2576 linalg_vector_norm_44" [id=2576, type=linalg_vector_norm]; +"2577 clamp_min_44" [id=2577, type=clamp_min]; +"2578 expand_as_44" [id=2578, type=expand_as]; +"2579 div_44" [id=2579, type=div]; +"2580 linalg_vector_norm_45" [id=2580, type=linalg_vector_norm]; +"2581 clamp_min_45" [id=2581, type=clamp_min]; +"2582 expand_as_45" [id=2582, type=expand_as]; +"2583 div_45" [id=2583, type=div]; +"2584 transpose_44" [id=2584, type=transpose]; +"2585 matmul_44" [id=2585, type=matmul]; +"2586 clamp_22" [id=2586, type=clamp]; +"2587 exp_22" [id=2587, type=exp]; +"2588 mul_45" [id=2588, type=mul]; +"2589 add_77" [id=2589, type=add]; +"2590 softmax_22" [id=2590, type=softmax]; +"2591 dropout_88" [id=2591, type=dropout]; +"2592 matmul_45" [id=2592, type=matmul]; +"2593 transpose_45" [id=2593, type=transpose]; +"2594 reshape_101" [id=2594, type=reshape]; +"2595 linear_138" [id=2595, type=linear]; +"2596 dropout_89" [id=2596, type=dropout]; +"2597 view_124" [id=2597, type=view]; +"2598 permute_103" [id=2598, type=permute]; +"2599 reshape_102" [id=2599, type=reshape]; +"2600 slice_342" [id=2600, type=slice]; +"2601 slice_343" [id=2601, type=slice]; +"2602 slice_344" [id=2602, type=slice]; +"2603 slice_345" [id=2603, type=slice]; +"2604 contiguous_43" [id=2604, type=contiguous]; +"2605 layer_norm_48" [id=2605, type=layer_norm]; +"2606 add_78" [id=2606, type=add]; +"2607 linear_139" [id=2607, type=linear]; +"2608 gelu_22" [id=2608, type=gelu]; +"2609 dropout_90" [id=2609, type=dropout]; +"2610 linear_140" [id=2610, type=linear]; +"2611 dropout_91" [id=2611, type=dropout]; +"2612 layer_norm_49" [id=2612, type=layer_norm]; +"2613 add_79" [id=2613, type=add]; +"2614 linear_141" [id=2614, type=linear]; +"2615 relu__23" [id=2615, type=relu_]; +"2616 linear_142" [id=2616, type=linear]; +"2617 view_125" [id=2617, type=view]; +"2618 index_23" [id=2618, type=index]; +"2619 view_126" [id=2619, type=view]; +"2620 permute_104" [id=2620, type=permute]; +"2621 contiguous_44" [id=2621, type=contiguous]; +"2622 unsqueeze_67" [id=2622, type=unsqueeze]; +"2623 sigmoid_23" [id=2623, type=sigmoid]; +"2624 mul_46" [id=2624, type=mul]; +"2625 pad_26" [id=2625, type=pad]; +"2626 view_127" [id=2626, type=view]; +"2627 permute_105" [id=2627, type=permute]; +"2628 reshape_103" [id=2628, type=reshape]; +"2629 clone_23" [id=2629, type=clone]; +"2630 slice_346" [id=2630, type=slice]; +"2631 zero__23" [id=2631, type=zero_]; +"2632 linear_143" [id=2632, type=linear]; +"2633 reshape_104" [id=2633, type=reshape]; +"2634 permute_106" [id=2634, type=permute]; +"2635 select_69" [id=2635, type=select]; +"2636 select_70" [id=2636, type=select]; +"2637 select_71" [id=2637, type=select]; +"2638 linalg_vector_norm_46" [id=2638, type=linalg_vector_norm]; +"2639 clamp_min_46" [id=2639, type=clamp_min]; +"2640 expand_as_46" [id=2640, type=expand_as]; +"2641 div_46" [id=2641, type=div]; +"2642 linalg_vector_norm_47" [id=2642, type=linalg_vector_norm]; +"2643 clamp_min_47" [id=2643, type=clamp_min]; +"2644 expand_as_47" [id=2644, type=expand_as]; +"2645 div_47" [id=2645, type=div]; +"2646 transpose_46" [id=2646, type=transpose]; +"2647 matmul_46" [id=2647, type=matmul]; +"2648 clamp_23" [id=2648, type=clamp]; +"2649 exp_23" [id=2649, type=exp]; +"2650 mul_47" [id=2650, type=mul]; +"2651 add_80" [id=2651, type=add]; +"2652 softmax_23" [id=2652, type=softmax]; +"2653 dropout_92" [id=2653, type=dropout]; +"2654 matmul_47" [id=2654, type=matmul]; +"2655 transpose_47" [id=2655, type=transpose]; +"2656 reshape_105" [id=2656, type=reshape]; +"2657 linear_144" [id=2657, type=linear]; +"2658 dropout_93" [id=2658, type=dropout]; +"2659 view_128" [id=2659, type=view]; +"2660 permute_107" [id=2660, type=permute]; +"2661 reshape_106" [id=2661, type=reshape]; +"2662 slice_347" [id=2662, type=slice]; +"2663 slice_348" [id=2663, type=slice]; +"2664 slice_349" [id=2664, type=slice]; +"2665 slice_350" [id=2665, type=slice]; +"2666 contiguous_45" [id=2666, type=contiguous]; +"2667 layer_norm_50" [id=2667, type=layer_norm]; +"2668 add_81" [id=2668, type=add]; +"2669 linear_145" [id=2669, type=linear]; +"2670 gelu_23" [id=2670, type=gelu]; +"2671 dropout_94" [id=2671, type=dropout]; +"2672 linear_146" [id=2672, type=linear]; +"2673 dropout_95" [id=2673, type=dropout]; +"2674 layer_norm_51" [id=2674, type=layer_norm]; +"2675 add_82" [id=2675, type=add]; +"2676 layer_norm_52" [id=2676, type=layer_norm]; +"2677 permute_108" [id=2677, type=permute]; +"2678 adaptive_avg_pool2d" [id=2678, type=adaptive_avg_pool2d]; +"2679 flatten" [id=2679, type=flatten]; "2680 linear_147" [id=2680, type=linear]; -"2681 output" [id=2681, type=output]; -"0 arg0_1" -> "3 conv2d"; -"1 _param_constant0" -> "3 conv2d"; -"2 _param_constant1" -> "3 conv2d"; -"3 conv2d" -> "4 permute"; -"4 permute" -> "7 layer_norm"; -"5 _param_constant2" -> "7 layer_norm"; -"6 _param_constant3" -> "7 layer_norm"; -"7 layer_norm" -> "24 pad"; -"7 layer_norm" -> "71 add_1"; -"8 _tensor_constant0" -> "11 linear"; -"9 _param_constant4" -> "11 linear"; -"10 _param_constant5" -> "11 linear"; -"11 linear" -> "12 relu_"; -"12 relu_" -> "14 linear_1"; -"13 _param_constant6" -> "14 linear_1"; -"14 linear_1" -> "15 view"; -"15 view" -> "17 index"; -"16 _tensor_constant1" -> "17 index"; -"17 index" -> "18 view_1"; -"18 view_1" -> "19 permute_1"; -"19 permute_1" -> "20 contiguous"; -"20 contiguous" -> "21 unsqueeze"; -"21 unsqueeze" -> "22 sigmoid"; -"22 sigmoid" -> "23 mul"; -"23 mul" -> "53 add"; -"24 pad" -> "25 view_2"; -"25 view_2" -> "26 permute_2"; -"26 permute_2" -> "27 reshape"; -"27 reshape" -> "33 linear_2"; -"28 _param_constant7" -> "29 clone"; -"29 clone" -> "30 slice_1"; -"29 clone" -> "33 linear_2"; -"30 slice_1" -> "31 zero_"; -"32 _param_constant8" -> "33 linear_2"; -"33 linear_2" -> "34 reshape_1"; -"34 reshape_1" -> "35 permute_3"; -"35 permute_3" -> "36 select"; -"35 permute_3" -> "37 select_1"; -"35 permute_3" -> "38 select_2"; -"36 select" -> "39 linalg_vector_norm"; -"36 select" -> "41 expand_as"; -"36 select" -> "42 div"; -"37 select_1" -> "43 linalg_vector_norm_1"; -"37 select_1" -> "45 expand_as_1"; -"37 select_1" -> "46 div_1"; -"38 select_2" -> "56 matmul_1"; -"39 linalg_vector_norm" -> "40 clamp_min"; -"40 clamp_min" -> "41 expand_as"; -"41 expand_as" -> "42 div"; -"42 div" -> "48 matmul"; -"43 linalg_vector_norm_1" -> "44 clamp_min_1"; -"44 clamp_min_1" -> "45 expand_as_1"; -"45 expand_as_1" -> "46 div_1"; -"46 div_1" -> "47 transpose"; -"47 transpose" -> "48 matmul"; -"48 matmul" -> "52 mul_1"; -"49 _param_constant9" -> "50 clamp"; -"50 clamp" -> "51 exp"; -"51 exp" -> "52 mul_1"; -"52 mul_1" -> "53 add"; -"53 add" -> "54 softmax"; -"54 softmax" -> "55 dropout"; -"55 dropout" -> "56 matmul_1"; -"56 matmul_1" -> "57 transpose_1"; -"57 transpose_1" -> "58 reshape_2"; -"58 reshape_2" -> "61 linear_3"; -"59 _param_constant10" -> "61 linear_3"; -"60 _param_constant11" -> "61 linear_3"; -"61 linear_3" -> "62 dropout_1"; -"62 dropout_1" -> "63 view_3"; -"63 view_3" -> "64 permute_4"; -"64 permute_4" -> "65 reshape_3"; -"65 reshape_3" -> "66 slice_2"; -"66 slice_2" -> "67 slice_3"; -"67 slice_3" -> "70 layer_norm_1"; -"68 _param_constant12" -> "70 layer_norm_1"; -"69 _param_constant13" -> "70 layer_norm_1"; -"70 layer_norm_1" -> "71 add_1"; -"71 add_1" -> "74 linear_4"; -"71 add_1" -> "84 add_2"; -"72 _param_constant14" -> "74 linear_4"; -"73 _param_constant15" -> "74 linear_4"; -"74 linear_4" -> "75 gelu"; -"75 gelu" -> "76 dropout_2"; -"76 dropout_2" -> "79 linear_5"; -"77 _param_constant16" -> "79 linear_5"; -"78 _param_constant17" -> "79 linear_5"; -"79 linear_5" -> "80 dropout_3"; -"80 dropout_3" -> "83 layer_norm_2"; -"81 _param_constant18" -> "83 layer_norm_2"; -"82 _param_constant19" -> "83 layer_norm_2"; -"83 layer_norm_2" -> "84 add_2"; -"84 add_2" -> "101 pad_1"; -"84 add_2" -> "211 add_5"; -"85 _tensor_constant2" -> "88 linear_6"; -"86 _param_constant20" -> "88 linear_6"; -"87 _param_constant21" -> "88 linear_6"; -"88 linear_6" -> "89 relu__1"; -"89 relu__1" -> "91 linear_7"; -"90 _param_constant22" -> "91 linear_7"; -"91 linear_7" -> "92 view_4"; -"92 view_4" -> "94 index_1"; -"93 _tensor_constant3" -> "94 index_1"; -"94 index_1" -> "95 view_5"; -"95 view_5" -> "96 permute_5"; -"96 permute_5" -> "97 contiguous_1"; -"97 contiguous_1" -> "98 unsqueeze_1"; -"98 unsqueeze_1" -> "99 sigmoid_1"; -"99 sigmoid_1" -> "100 mul_2"; -"100 mul_2" -> "131 add_3"; -"101 pad_1" -> "102 roll"; -"102 roll" -> "103 view_6"; -"103 view_6" -> "104 permute_6"; -"104 permute_6" -> "105 reshape_4"; -"105 reshape_4" -> "111 linear_8"; -"105 reshape_4" -> "132 new_zeros"; -"106 _param_constant23" -> "107 clone_1"; -"107 clone_1" -> "108 slice_4"; -"107 clone_1" -> "111 linear_8"; -"108 slice_4" -> "109 zero__1"; -"110 _param_constant24" -> "111 linear_8"; -"111 linear_8" -> "112 reshape_5"; -"112 reshape_5" -> "113 permute_7"; -"113 permute_7" -> "114 select_3"; -"113 permute_7" -> "115 select_4"; -"113 permute_7" -> "116 select_5"; -"114 select_3" -> "117 linalg_vector_norm_2"; -"114 select_3" -> "119 expand_as_2"; -"114 select_3" -> "120 div_2"; -"115 select_4" -> "121 linalg_vector_norm_3"; -"115 select_4" -> "123 expand_as_3"; -"115 select_4" -> "124 div_3"; -"116 select_5" -> "195 matmul_3"; -"117 linalg_vector_norm_2" -> "118 clamp_min_2"; -"118 clamp_min_2" -> "119 expand_as_2"; -"119 expand_as_2" -> "120 div_2"; -"120 div_2" -> "126 matmul_2"; -"121 linalg_vector_norm_3" -> "122 clamp_min_3"; -"122 clamp_min_3" -> "123 expand_as_3"; -"123 expand_as_3" -> "124 div_3"; -"124 div_3" -> "125 transpose_2"; -"125 transpose_2" -> "126 matmul_2"; -"126 matmul_2" -> "130 mul_3"; -"127 _param_constant25" -> "128 clamp_1"; -"128 clamp_1" -> "129 exp_1"; -"129 exp_1" -> "130 mul_3"; -"130 mul_3" -> "131 add_3"; -"131 add_3" -> "188 view_8"; -"132 new_zeros" -> "135 slice_5"; -"132 new_zeros" -> "140 slice_7"; -"132 new_zeros" -> "145 slice_9"; -"132 new_zeros" -> "150 slice_11"; -"132 new_zeros" -> "155 slice_13"; -"132 new_zeros" -> "160 slice_15"; -"132 new_zeros" -> "165 slice_17"; -"132 new_zeros" -> "170 slice_19"; -"132 new_zeros" -> "175 slice_21"; -"132 new_zeros" -> "178 view_7"; -"133 _tensor_constant4" -> "134 lift_fresh_copy"; -"134 lift_fresh_copy" -> "137 fill_"; -"135 slice_5" -> "136 slice_6"; -"136 slice_6" -> "137 fill_"; -"138 _tensor_constant5" -> "139 lift_fresh_copy_1"; -"139 lift_fresh_copy_1" -> "142 fill__1"; -"140 slice_7" -> "141 slice_8"; -"141 slice_8" -> "142 fill__1"; -"143 _tensor_constant6" -> "144 lift_fresh_copy_2"; -"144 lift_fresh_copy_2" -> "147 fill__2"; -"145 slice_9" -> "146 slice_10"; -"146 slice_10" -> "147 fill__2"; -"148 _tensor_constant7" -> "149 lift_fresh_copy_3"; -"149 lift_fresh_copy_3" -> "152 fill__3"; -"150 slice_11" -> "151 slice_12"; -"151 slice_12" -> "152 fill__3"; -"153 _tensor_constant8" -> "154 lift_fresh_copy_4"; -"154 lift_fresh_copy_4" -> "157 fill__4"; -"155 slice_13" -> "156 slice_14"; -"156 slice_14" -> "157 fill__4"; -"158 _tensor_constant9" -> "159 lift_fresh_copy_5"; -"159 lift_fresh_copy_5" -> "162 fill__5"; -"160 slice_15" -> "161 slice_16"; -"161 slice_16" -> "162 fill__5"; -"163 _tensor_constant10" -> "164 lift_fresh_copy_6"; -"164 lift_fresh_copy_6" -> "167 fill__6"; -"165 slice_17" -> "166 slice_18"; -"166 slice_18" -> "167 fill__6"; -"168 _tensor_constant11" -> "169 lift_fresh_copy_7"; -"169 lift_fresh_copy_7" -> "172 fill__7"; -"170 slice_19" -> "171 slice_20"; -"171 slice_20" -> "172 fill__7"; -"173 _tensor_constant12" -> "174 lift_fresh_copy_8"; -"174 lift_fresh_copy_8" -> "177 fill__8"; -"175 slice_21" -> "176 slice_22"; -"176 slice_22" -> "177 fill__8"; -"178 view_7" -> "179 permute_8"; -"179 permute_8" -> "180 reshape_6"; -"180 reshape_6" -> "181 unsqueeze_2"; -"180 reshape_6" -> "182 unsqueeze_3"; -"181 unsqueeze_2" -> "183 sub"; -"182 unsqueeze_3" -> "183 sub"; -"183 sub" -> "184 ne"; -"183 sub" -> "185 masked_fill"; -"183 sub" -> "186 eq"; -"184 ne" -> "185 masked_fill"; -"185 masked_fill" -> "187 masked_fill_1"; -"186 eq" -> "187 masked_fill_1"; -"187 masked_fill_1" -> "189 unsqueeze_4"; -"188 view_8" -> "191 add_4"; -"189 unsqueeze_4" -> "190 unsqueeze_5"; -"190 unsqueeze_5" -> "191 add_4"; -"191 add_4" -> "192 view_9"; -"192 view_9" -> "193 softmax_1"; -"193 softmax_1" -> "194 dropout_4"; -"194 dropout_4" -> "195 matmul_3"; -"195 matmul_3" -> "196 transpose_3"; -"196 transpose_3" -> "197 reshape_7"; -"197 reshape_7" -> "200 linear_9"; -"198 _param_constant26" -> "200 linear_9"; -"199 _param_constant27" -> "200 linear_9"; -"200 linear_9" -> "201 dropout_5"; -"201 dropout_5" -> "202 view_10"; -"202 view_10" -> "203 permute_9"; -"203 permute_9" -> "204 reshape_8"; -"204 reshape_8" -> "205 roll_1"; -"205 roll_1" -> "206 slice_23"; -"206 slice_23" -> "207 slice_24"; -"207 slice_24" -> "210 layer_norm_3"; -"208 _param_constant28" -> "210 layer_norm_3"; -"209 _param_constant29" -> "210 layer_norm_3"; -"210 layer_norm_3" -> "211 add_5"; -"211 add_5" -> "214 linear_10"; -"211 add_5" -> "224 add_6"; -"212 _param_constant30" -> "214 linear_10"; -"213 _param_constant31" -> "214 linear_10"; -"214 linear_10" -> "215 gelu_1"; -"215 gelu_1" -> "216 dropout_6"; -"216 dropout_6" -> "219 linear_11"; -"217 _param_constant32" -> "219 linear_11"; -"218 _param_constant33" -> "219 linear_11"; -"219 linear_11" -> "220 dropout_7"; -"220 dropout_7" -> "223 layer_norm_4"; -"221 _param_constant34" -> "223 layer_norm_4"; -"222 _param_constant35" -> "223 layer_norm_4"; -"223 layer_norm_4" -> "224 add_6"; -"224 add_6" -> "225 pad_2"; -"225 pad_2" -> "226 slice_25"; -"225 pad_2" -> "229 slice_28"; -"225 pad_2" -> "232 slice_31"; -"225 pad_2" -> "235 slice_34"; -"226 slice_25" -> "227 slice_26"; -"227 slice_26" -> "228 slice_27"; -"228 slice_27" -> "238 cat"; -"229 slice_28" -> "230 slice_29"; -"230 slice_29" -> "231 slice_30"; -"231 slice_30" -> "238 cat"; -"232 slice_31" -> "233 slice_32"; -"233 slice_32" -> "234 slice_33"; -"234 slice_33" -> "238 cat"; -"235 slice_34" -> "236 slice_35"; -"236 slice_35" -> "237 slice_36"; -"237 slice_36" -> "238 cat"; -"238 cat" -> "240 linear_12"; -"239 _param_constant36" -> "240 linear_12"; -"240 linear_12" -> "243 layer_norm_5"; -"241 _param_constant37" -> "243 layer_norm_5"; -"242 _param_constant38" -> "243 layer_norm_5"; -"243 layer_norm_5" -> "260 pad_3"; -"243 layer_norm_5" -> "310 add_8"; -"244 _tensor_constant13" -> "247 linear_13"; -"245 _param_constant39" -> "247 linear_13"; -"246 _param_constant40" -> "247 linear_13"; -"247 linear_13" -> "248 relu__2"; -"248 relu__2" -> "250 linear_14"; -"249 _param_constant41" -> "250 linear_14"; -"250 linear_14" -> "251 view_11"; -"251 view_11" -> "253 index_2"; -"252 _tensor_constant14" -> "253 index_2"; -"253 index_2" -> "254 view_12"; -"254 view_12" -> "255 permute_10"; -"255 permute_10" -> "256 contiguous_2"; -"256 contiguous_2" -> "257 unsqueeze_6"; -"257 unsqueeze_6" -> "258 sigmoid_2"; -"258 sigmoid_2" -> "259 mul_4"; -"259 mul_4" -> "289 add_7"; -"260 pad_3" -> "261 view_13"; -"261 view_13" -> "262 permute_11"; -"262 permute_11" -> "263 reshape_9"; -"263 reshape_9" -> "269 linear_15"; -"264 _param_constant42" -> "265 clone_2"; -"265 clone_2" -> "266 slice_37"; -"265 clone_2" -> "269 linear_15"; -"266 slice_37" -> "267 zero__2"; -"268 _param_constant43" -> "269 linear_15"; -"269 linear_15" -> "270 reshape_10"; -"270 reshape_10" -> "271 permute_12"; -"271 permute_12" -> "272 select_6"; -"271 permute_12" -> "273 select_7"; -"271 permute_12" -> "274 select_8"; -"272 select_6" -> "275 linalg_vector_norm_4"; -"272 select_6" -> "277 expand_as_4"; -"272 select_6" -> "278 div_4"; -"273 select_7" -> "279 linalg_vector_norm_5"; -"273 select_7" -> "281 expand_as_5"; -"273 select_7" -> "282 div_5"; -"274 select_8" -> "292 matmul_5"; -"275 linalg_vector_norm_4" -> "276 clamp_min_4"; -"276 clamp_min_4" -> "277 expand_as_4"; -"277 expand_as_4" -> "278 div_4"; -"278 div_4" -> "284 matmul_4"; -"279 linalg_vector_norm_5" -> "280 clamp_min_5"; -"280 clamp_min_5" -> "281 expand_as_5"; -"281 expand_as_5" -> "282 div_5"; -"282 div_5" -> "283 transpose_4"; -"283 transpose_4" -> "284 matmul_4"; -"284 matmul_4" -> "288 mul_5"; -"285 _param_constant44" -> "286 clamp_2"; -"286 clamp_2" -> "287 exp_2"; -"287 exp_2" -> "288 mul_5"; -"288 mul_5" -> "289 add_7"; -"289 add_7" -> "290 softmax_2"; -"290 softmax_2" -> "291 dropout_8"; -"291 dropout_8" -> "292 matmul_5"; -"292 matmul_5" -> "293 transpose_5"; -"293 transpose_5" -> "294 reshape_11"; -"294 reshape_11" -> "297 linear_16"; -"295 _param_constant45" -> "297 linear_16"; -"296 _param_constant46" -> "297 linear_16"; -"297 linear_16" -> "298 dropout_9"; -"298 dropout_9" -> "299 view_14"; -"299 view_14" -> "300 permute_13"; -"300 permute_13" -> "301 reshape_12"; -"301 reshape_12" -> "302 slice_38"; -"302 slice_38" -> "303 slice_39"; -"303 slice_39" -> "304 slice_40"; -"304 slice_40" -> "305 slice_41"; -"305 slice_41" -> "306 contiguous_3"; -"306 contiguous_3" -> "309 layer_norm_6"; -"307 _param_constant47" -> "309 layer_norm_6"; -"308 _param_constant48" -> "309 layer_norm_6"; -"309 layer_norm_6" -> "310 add_8"; -"310 add_8" -> "313 linear_17"; -"310 add_8" -> "323 add_9"; -"311 _param_constant49" -> "313 linear_17"; -"312 _param_constant50" -> "313 linear_17"; -"313 linear_17" -> "314 gelu_2"; -"314 gelu_2" -> "315 dropout_10"; -"315 dropout_10" -> "318 linear_18"; -"316 _param_constant51" -> "318 linear_18"; -"317 _param_constant52" -> "318 linear_18"; -"318 linear_18" -> "319 dropout_11"; -"319 dropout_11" -> "322 layer_norm_7"; -"320 _param_constant53" -> "322 layer_norm_7"; -"321 _param_constant54" -> "322 layer_norm_7"; -"322 layer_norm_7" -> "323 add_9"; -"323 add_9" -> "340 pad_4"; -"323 add_9" -> "453 add_12"; -"324 _tensor_constant15" -> "327 linear_19"; -"325 _param_constant55" -> "327 linear_19"; -"326 _param_constant56" -> "327 linear_19"; -"327 linear_19" -> "328 relu__3"; -"328 relu__3" -> "330 linear_20"; -"329 _param_constant57" -> "330 linear_20"; -"330 linear_20" -> "331 view_15"; -"331 view_15" -> "333 index_3"; -"332 _tensor_constant16" -> "333 index_3"; -"333 index_3" -> "334 view_16"; -"334 view_16" -> "335 permute_14"; -"335 permute_14" -> "336 contiguous_4"; -"336 contiguous_4" -> "337 unsqueeze_7"; -"337 unsqueeze_7" -> "338 sigmoid_3"; -"338 sigmoid_3" -> "339 mul_6"; -"339 mul_6" -> "370 add_10"; -"340 pad_4" -> "341 roll_2"; -"341 roll_2" -> "342 view_17"; -"342 view_17" -> "343 permute_15"; -"343 permute_15" -> "344 reshape_13"; -"344 reshape_13" -> "350 linear_21"; -"344 reshape_13" -> "371 new_zeros_1"; -"345 _param_constant58" -> "346 clone_3"; -"346 clone_3" -> "347 slice_42"; -"346 clone_3" -> "350 linear_21"; -"347 slice_42" -> "348 zero__3"; -"349 _param_constant59" -> "350 linear_21"; -"350 linear_21" -> "351 reshape_14"; -"351 reshape_14" -> "352 permute_16"; -"352 permute_16" -> "353 select_9"; -"352 permute_16" -> "354 select_10"; -"352 permute_16" -> "355 select_11"; -"353 select_9" -> "356 linalg_vector_norm_6"; -"353 select_9" -> "358 expand_as_6"; -"353 select_9" -> "359 div_6"; -"354 select_10" -> "360 linalg_vector_norm_7"; -"354 select_10" -> "362 expand_as_7"; -"354 select_10" -> "363 div_7"; -"355 select_11" -> "434 matmul_7"; -"356 linalg_vector_norm_6" -> "357 clamp_min_6"; -"357 clamp_min_6" -> "358 expand_as_6"; -"358 expand_as_6" -> "359 div_6"; -"359 div_6" -> "365 matmul_6"; -"360 linalg_vector_norm_7" -> "361 clamp_min_7"; -"361 clamp_min_7" -> "362 expand_as_7"; -"362 expand_as_7" -> "363 div_7"; -"363 div_7" -> "364 transpose_6"; -"364 transpose_6" -> "365 matmul_6"; -"365 matmul_6" -> "369 mul_7"; -"366 _param_constant60" -> "367 clamp_3"; -"367 clamp_3" -> "368 exp_3"; -"368 exp_3" -> "369 mul_7"; -"369 mul_7" -> "370 add_10"; -"370 add_10" -> "427 view_19"; -"371 new_zeros_1" -> "374 slice_43"; -"371 new_zeros_1" -> "379 slice_45"; -"371 new_zeros_1" -> "384 slice_47"; -"371 new_zeros_1" -> "389 slice_49"; -"371 new_zeros_1" -> "394 slice_51"; -"371 new_zeros_1" -> "399 slice_53"; -"371 new_zeros_1" -> "404 slice_55"; -"371 new_zeros_1" -> "409 slice_57"; -"371 new_zeros_1" -> "414 slice_59"; -"371 new_zeros_1" -> "417 view_18"; -"372 _tensor_constant17" -> "373 lift_fresh_copy_9"; -"373 lift_fresh_copy_9" -> "376 fill__9"; -"374 slice_43" -> "375 slice_44"; -"375 slice_44" -> "376 fill__9"; -"377 _tensor_constant18" -> "378 lift_fresh_copy_10"; -"378 lift_fresh_copy_10" -> "381 fill__10"; -"379 slice_45" -> "380 slice_46"; -"380 slice_46" -> "381 fill__10"; -"382 _tensor_constant19" -> "383 lift_fresh_copy_11"; -"383 lift_fresh_copy_11" -> "386 fill__11"; -"384 slice_47" -> "385 slice_48"; -"385 slice_48" -> "386 fill__11"; -"387 _tensor_constant20" -> "388 lift_fresh_copy_12"; -"388 lift_fresh_copy_12" -> "391 fill__12"; -"389 slice_49" -> "390 slice_50"; -"390 slice_50" -> "391 fill__12"; -"392 _tensor_constant21" -> "393 lift_fresh_copy_13"; -"393 lift_fresh_copy_13" -> "396 fill__13"; -"394 slice_51" -> "395 slice_52"; -"395 slice_52" -> "396 fill__13"; -"397 _tensor_constant22" -> "398 lift_fresh_copy_14"; -"398 lift_fresh_copy_14" -> "401 fill__14"; -"399 slice_53" -> "400 slice_54"; -"400 slice_54" -> "401 fill__14"; -"402 _tensor_constant23" -> "403 lift_fresh_copy_15"; -"403 lift_fresh_copy_15" -> "406 fill__15"; -"404 slice_55" -> "405 slice_56"; -"405 slice_56" -> "406 fill__15"; -"407 _tensor_constant24" -> "408 lift_fresh_copy_16"; -"408 lift_fresh_copy_16" -> "411 fill__16"; -"409 slice_57" -> "410 slice_58"; -"410 slice_58" -> "411 fill__16"; -"412 _tensor_constant25" -> "413 lift_fresh_copy_17"; -"413 lift_fresh_copy_17" -> "416 fill__17"; -"414 slice_59" -> "415 slice_60"; -"415 slice_60" -> "416 fill__17"; -"417 view_18" -> "418 permute_17"; -"418 permute_17" -> "419 reshape_15"; -"419 reshape_15" -> "420 unsqueeze_8"; -"419 reshape_15" -> "421 unsqueeze_9"; -"420 unsqueeze_8" -> "422 sub_1"; -"421 unsqueeze_9" -> "422 sub_1"; -"422 sub_1" -> "423 ne_1"; -"422 sub_1" -> "424 masked_fill_2"; -"422 sub_1" -> "425 eq_1"; -"423 ne_1" -> "424 masked_fill_2"; -"424 masked_fill_2" -> "426 masked_fill_3"; -"425 eq_1" -> "426 masked_fill_3"; -"426 masked_fill_3" -> "428 unsqueeze_10"; -"427 view_19" -> "430 add_11"; -"428 unsqueeze_10" -> "429 unsqueeze_11"; -"429 unsqueeze_11" -> "430 add_11"; -"430 add_11" -> "431 view_20"; -"431 view_20" -> "432 softmax_3"; -"432 softmax_3" -> "433 dropout_12"; -"433 dropout_12" -> "434 matmul_7"; -"434 matmul_7" -> "435 transpose_7"; -"435 transpose_7" -> "436 reshape_16"; -"436 reshape_16" -> "439 linear_22"; -"437 _param_constant61" -> "439 linear_22"; -"438 _param_constant62" -> "439 linear_22"; -"439 linear_22" -> "440 dropout_13"; -"440 dropout_13" -> "441 view_21"; -"441 view_21" -> "442 permute_18"; -"442 permute_18" -> "443 reshape_17"; -"443 reshape_17" -> "444 roll_3"; -"444 roll_3" -> "445 slice_61"; -"445 slice_61" -> "446 slice_62"; -"446 slice_62" -> "447 slice_63"; -"447 slice_63" -> "448 slice_64"; -"448 slice_64" -> "449 contiguous_5"; -"449 contiguous_5" -> "452 layer_norm_8"; -"450 _param_constant63" -> "452 layer_norm_8"; -"451 _param_constant64" -> "452 layer_norm_8"; -"452 layer_norm_8" -> "453 add_12"; -"453 add_12" -> "456 linear_23"; -"453 add_12" -> "466 add_13"; -"454 _param_constant65" -> "456 linear_23"; -"455 _param_constant66" -> "456 linear_23"; -"456 linear_23" -> "457 gelu_3"; -"457 gelu_3" -> "458 dropout_14"; -"458 dropout_14" -> "461 linear_24"; -"459 _param_constant67" -> "461 linear_24"; -"460 _param_constant68" -> "461 linear_24"; -"461 linear_24" -> "462 dropout_15"; -"462 dropout_15" -> "465 layer_norm_9"; -"463 _param_constant69" -> "465 layer_norm_9"; -"464 _param_constant70" -> "465 layer_norm_9"; -"465 layer_norm_9" -> "466 add_13"; -"466 add_13" -> "467 pad_5"; -"467 pad_5" -> "468 slice_65"; -"467 pad_5" -> "471 slice_68"; -"467 pad_5" -> "474 slice_71"; -"467 pad_5" -> "477 slice_74"; -"468 slice_65" -> "469 slice_66"; -"469 slice_66" -> "470 slice_67"; -"470 slice_67" -> "480 cat_1"; -"471 slice_68" -> "472 slice_69"; -"472 slice_69" -> "473 slice_70"; -"473 slice_70" -> "480 cat_1"; -"474 slice_71" -> "475 slice_72"; -"475 slice_72" -> "476 slice_73"; -"476 slice_73" -> "480 cat_1"; -"477 slice_74" -> "478 slice_75"; -"478 slice_75" -> "479 slice_76"; -"479 slice_76" -> "480 cat_1"; -"480 cat_1" -> "482 linear_25"; -"481 _param_constant71" -> "482 linear_25"; -"482 linear_25" -> "485 layer_norm_10"; -"483 _param_constant72" -> "485 layer_norm_10"; -"484 _param_constant73" -> "485 layer_norm_10"; -"485 layer_norm_10" -> "502 pad_6"; -"485 layer_norm_10" -> "552 add_15"; -"486 _tensor_constant26" -> "489 linear_26"; -"487 _param_constant74" -> "489 linear_26"; -"488 _param_constant75" -> "489 linear_26"; -"489 linear_26" -> "490 relu__4"; -"490 relu__4" -> "492 linear_27"; -"491 _param_constant76" -> "492 linear_27"; -"492 linear_27" -> "493 view_22"; -"493 view_22" -> "495 index_4"; -"494 _tensor_constant27" -> "495 index_4"; -"495 index_4" -> "496 view_23"; -"496 view_23" -> "497 permute_19"; -"497 permute_19" -> "498 contiguous_6"; -"498 contiguous_6" -> "499 unsqueeze_12"; -"499 unsqueeze_12" -> "500 sigmoid_4"; -"500 sigmoid_4" -> "501 mul_8"; -"501 mul_8" -> "531 add_14"; -"502 pad_6" -> "503 view_24"; -"503 view_24" -> "504 permute_20"; -"504 permute_20" -> "505 reshape_18"; -"505 reshape_18" -> "511 linear_28"; -"506 _param_constant77" -> "507 clone_4"; -"507 clone_4" -> "508 slice_77"; -"507 clone_4" -> "511 linear_28"; -"508 slice_77" -> "509 zero__4"; -"510 _param_constant78" -> "511 linear_28"; -"511 linear_28" -> "512 reshape_19"; -"512 reshape_19" -> "513 permute_21"; -"513 permute_21" -> "514 select_12"; -"513 permute_21" -> "515 select_13"; -"513 permute_21" -> "516 select_14"; -"514 select_12" -> "517 linalg_vector_norm_8"; -"514 select_12" -> "519 expand_as_8"; -"514 select_12" -> "520 div_8"; -"515 select_13" -> "521 linalg_vector_norm_9"; -"515 select_13" -> "523 expand_as_9"; -"515 select_13" -> "524 div_9"; -"516 select_14" -> "534 matmul_9"; -"517 linalg_vector_norm_8" -> "518 clamp_min_8"; -"518 clamp_min_8" -> "519 expand_as_8"; -"519 expand_as_8" -> "520 div_8"; -"520 div_8" -> "526 matmul_8"; -"521 linalg_vector_norm_9" -> "522 clamp_min_9"; -"522 clamp_min_9" -> "523 expand_as_9"; -"523 expand_as_9" -> "524 div_9"; -"524 div_9" -> "525 transpose_8"; -"525 transpose_8" -> "526 matmul_8"; -"526 matmul_8" -> "530 mul_9"; -"527 _param_constant79" -> "528 clamp_4"; -"528 clamp_4" -> "529 exp_4"; -"529 exp_4" -> "530 mul_9"; -"530 mul_9" -> "531 add_14"; -"531 add_14" -> "532 softmax_4"; -"532 softmax_4" -> "533 dropout_16"; -"533 dropout_16" -> "534 matmul_9"; -"534 matmul_9" -> "535 transpose_9"; -"535 transpose_9" -> "536 reshape_20"; -"536 reshape_20" -> "539 linear_29"; -"537 _param_constant80" -> "539 linear_29"; -"538 _param_constant81" -> "539 linear_29"; -"539 linear_29" -> "540 dropout_17"; -"540 dropout_17" -> "541 view_25"; -"541 view_25" -> "542 permute_22"; -"542 permute_22" -> "543 reshape_21"; -"543 reshape_21" -> "544 slice_78"; -"544 slice_78" -> "545 slice_79"; -"545 slice_79" -> "546 slice_80"; -"546 slice_80" -> "547 slice_81"; -"547 slice_81" -> "548 contiguous_7"; -"548 contiguous_7" -> "551 layer_norm_11"; -"549 _param_constant82" -> "551 layer_norm_11"; -"550 _param_constant83" -> "551 layer_norm_11"; -"551 layer_norm_11" -> "552 add_15"; -"552 add_15" -> "555 linear_30"; -"552 add_15" -> "565 add_16"; -"553 _param_constant84" -> "555 linear_30"; -"554 _param_constant85" -> "555 linear_30"; -"555 linear_30" -> "556 gelu_4"; -"556 gelu_4" -> "557 dropout_18"; -"557 dropout_18" -> "560 linear_31"; -"558 _param_constant86" -> "560 linear_31"; -"559 _param_constant87" -> "560 linear_31"; -"560 linear_31" -> "561 dropout_19"; -"561 dropout_19" -> "564 layer_norm_12"; -"562 _param_constant88" -> "564 layer_norm_12"; -"563 _param_constant89" -> "564 layer_norm_12"; -"564 layer_norm_12" -> "565 add_16"; -"565 add_16" -> "582 pad_7"; -"565 add_16" -> "695 add_19"; -"566 _tensor_constant28" -> "569 linear_32"; -"567 _param_constant90" -> "569 linear_32"; -"568 _param_constant91" -> "569 linear_32"; -"569 linear_32" -> "570 relu__5"; -"570 relu__5" -> "572 linear_33"; -"571 _param_constant92" -> "572 linear_33"; -"572 linear_33" -> "573 view_26"; -"573 view_26" -> "575 index_5"; -"574 _tensor_constant29" -> "575 index_5"; -"575 index_5" -> "576 view_27"; -"576 view_27" -> "577 permute_23"; -"577 permute_23" -> "578 contiguous_8"; -"578 contiguous_8" -> "579 unsqueeze_13"; -"579 unsqueeze_13" -> "580 sigmoid_5"; -"580 sigmoid_5" -> "581 mul_10"; -"581 mul_10" -> "612 add_17"; -"582 pad_7" -> "583 roll_4"; -"583 roll_4" -> "584 view_28"; -"584 view_28" -> "585 permute_24"; -"585 permute_24" -> "586 reshape_22"; -"586 reshape_22" -> "592 linear_34"; -"586 reshape_22" -> "613 new_zeros_2"; -"587 _param_constant93" -> "588 clone_5"; -"588 clone_5" -> "589 slice_82"; -"588 clone_5" -> "592 linear_34"; -"589 slice_82" -> "590 zero__5"; -"591 _param_constant94" -> "592 linear_34"; -"592 linear_34" -> "593 reshape_23"; -"593 reshape_23" -> "594 permute_25"; -"594 permute_25" -> "595 select_15"; -"594 permute_25" -> "596 select_16"; -"594 permute_25" -> "597 select_17"; -"595 select_15" -> "598 linalg_vector_norm_10"; -"595 select_15" -> "600 expand_as_10"; -"595 select_15" -> "601 div_10"; -"596 select_16" -> "602 linalg_vector_norm_11"; -"596 select_16" -> "604 expand_as_11"; -"596 select_16" -> "605 div_11"; -"597 select_17" -> "676 matmul_11"; -"598 linalg_vector_norm_10" -> "599 clamp_min_10"; -"599 clamp_min_10" -> "600 expand_as_10"; -"600 expand_as_10" -> "601 div_10"; -"601 div_10" -> "607 matmul_10"; -"602 linalg_vector_norm_11" -> "603 clamp_min_11"; -"603 clamp_min_11" -> "604 expand_as_11"; -"604 expand_as_11" -> "605 div_11"; -"605 div_11" -> "606 transpose_10"; -"606 transpose_10" -> "607 matmul_10"; -"607 matmul_10" -> "611 mul_11"; -"608 _param_constant95" -> "609 clamp_5"; -"609 clamp_5" -> "610 exp_5"; -"610 exp_5" -> "611 mul_11"; -"611 mul_11" -> "612 add_17"; -"612 add_17" -> "669 view_30"; -"613 new_zeros_2" -> "616 slice_83"; -"613 new_zeros_2" -> "621 slice_85"; -"613 new_zeros_2" -> "626 slice_87"; -"613 new_zeros_2" -> "631 slice_89"; -"613 new_zeros_2" -> "636 slice_91"; -"613 new_zeros_2" -> "641 slice_93"; -"613 new_zeros_2" -> "646 slice_95"; -"613 new_zeros_2" -> "651 slice_97"; -"613 new_zeros_2" -> "656 slice_99"; -"613 new_zeros_2" -> "659 view_29"; -"614 _tensor_constant30" -> "615 lift_fresh_copy_18"; -"615 lift_fresh_copy_18" -> "618 fill__18"; -"616 slice_83" -> "617 slice_84"; -"617 slice_84" -> "618 fill__18"; -"619 _tensor_constant31" -> "620 lift_fresh_copy_19"; -"620 lift_fresh_copy_19" -> "623 fill__19"; -"621 slice_85" -> "622 slice_86"; -"622 slice_86" -> "623 fill__19"; -"624 _tensor_constant32" -> "625 lift_fresh_copy_20"; -"625 lift_fresh_copy_20" -> "628 fill__20"; -"626 slice_87" -> "627 slice_88"; -"627 slice_88" -> "628 fill__20"; -"629 _tensor_constant33" -> "630 lift_fresh_copy_21"; -"630 lift_fresh_copy_21" -> "633 fill__21"; -"631 slice_89" -> "632 slice_90"; -"632 slice_90" -> "633 fill__21"; -"634 _tensor_constant34" -> "635 lift_fresh_copy_22"; -"635 lift_fresh_copy_22" -> "638 fill__22"; -"636 slice_91" -> "637 slice_92"; -"637 slice_92" -> "638 fill__22"; -"639 _tensor_constant35" -> "640 lift_fresh_copy_23"; -"640 lift_fresh_copy_23" -> "643 fill__23"; -"641 slice_93" -> "642 slice_94"; -"642 slice_94" -> "643 fill__23"; -"644 _tensor_constant36" -> "645 lift_fresh_copy_24"; -"645 lift_fresh_copy_24" -> "648 fill__24"; -"646 slice_95" -> "647 slice_96"; -"647 slice_96" -> "648 fill__24"; -"649 _tensor_constant37" -> "650 lift_fresh_copy_25"; -"650 lift_fresh_copy_25" -> "653 fill__25"; -"651 slice_97" -> "652 slice_98"; -"652 slice_98" -> "653 fill__25"; -"654 _tensor_constant38" -> "655 lift_fresh_copy_26"; -"655 lift_fresh_copy_26" -> "658 fill__26"; -"656 slice_99" -> "657 slice_100"; -"657 slice_100" -> "658 fill__26"; -"659 view_29" -> "660 permute_26"; -"660 permute_26" -> "661 reshape_24"; -"661 reshape_24" -> "662 unsqueeze_14"; -"661 reshape_24" -> "663 unsqueeze_15"; -"662 unsqueeze_14" -> "664 sub_2"; -"663 unsqueeze_15" -> "664 sub_2"; -"664 sub_2" -> "665 ne_2"; -"664 sub_2" -> "666 masked_fill_4"; -"664 sub_2" -> "667 eq_2"; -"665 ne_2" -> "666 masked_fill_4"; -"666 masked_fill_4" -> "668 masked_fill_5"; -"667 eq_2" -> "668 masked_fill_5"; -"668 masked_fill_5" -> "670 unsqueeze_16"; -"669 view_30" -> "672 add_18"; -"670 unsqueeze_16" -> "671 unsqueeze_17"; -"671 unsqueeze_17" -> "672 add_18"; -"672 add_18" -> "673 view_31"; -"673 view_31" -> "674 softmax_5"; -"674 softmax_5" -> "675 dropout_20"; -"675 dropout_20" -> "676 matmul_11"; -"676 matmul_11" -> "677 transpose_11"; -"677 transpose_11" -> "678 reshape_25"; -"678 reshape_25" -> "681 linear_35"; -"679 _param_constant96" -> "681 linear_35"; -"680 _param_constant97" -> "681 linear_35"; -"681 linear_35" -> "682 dropout_21"; -"682 dropout_21" -> "683 view_32"; -"683 view_32" -> "684 permute_27"; -"684 permute_27" -> "685 reshape_26"; -"685 reshape_26" -> "686 roll_5"; -"686 roll_5" -> "687 slice_101"; -"687 slice_101" -> "688 slice_102"; -"688 slice_102" -> "689 slice_103"; -"689 slice_103" -> "690 slice_104"; -"690 slice_104" -> "691 contiguous_9"; -"691 contiguous_9" -> "694 layer_norm_13"; -"692 _param_constant98" -> "694 layer_norm_13"; -"693 _param_constant99" -> "694 layer_norm_13"; -"694 layer_norm_13" -> "695 add_19"; -"695 add_19" -> "698 linear_36"; -"695 add_19" -> "708 add_20"; -"696 _param_constant100" -> "698 linear_36"; -"697 _param_constant101" -> "698 linear_36"; -"698 linear_36" -> "699 gelu_5"; -"699 gelu_5" -> "700 dropout_22"; -"700 dropout_22" -> "703 linear_37"; -"701 _param_constant102" -> "703 linear_37"; -"702 _param_constant103" -> "703 linear_37"; -"703 linear_37" -> "704 dropout_23"; -"704 dropout_23" -> "707 layer_norm_14"; -"705 _param_constant104" -> "707 layer_norm_14"; -"706 _param_constant105" -> "707 layer_norm_14"; -"707 layer_norm_14" -> "708 add_20"; -"708 add_20" -> "725 pad_8"; -"708 add_20" -> "775 add_22"; -"709 _tensor_constant39" -> "712 linear_38"; -"710 _param_constant106" -> "712 linear_38"; -"711 _param_constant107" -> "712 linear_38"; -"712 linear_38" -> "713 relu__6"; -"713 relu__6" -> "715 linear_39"; -"714 _param_constant108" -> "715 linear_39"; -"715 linear_39" -> "716 view_33"; -"716 view_33" -> "718 index_6"; -"717 _tensor_constant40" -> "718 index_6"; -"718 index_6" -> "719 view_34"; -"719 view_34" -> "720 permute_28"; -"720 permute_28" -> "721 contiguous_10"; -"721 contiguous_10" -> "722 unsqueeze_18"; -"722 unsqueeze_18" -> "723 sigmoid_6"; -"723 sigmoid_6" -> "724 mul_12"; -"724 mul_12" -> "754 add_21"; -"725 pad_8" -> "726 view_35"; -"726 view_35" -> "727 permute_29"; -"727 permute_29" -> "728 reshape_27"; -"728 reshape_27" -> "734 linear_40"; -"729 _param_constant109" -> "730 clone_6"; -"730 clone_6" -> "731 slice_105"; -"730 clone_6" -> "734 linear_40"; -"731 slice_105" -> "732 zero__6"; -"733 _param_constant110" -> "734 linear_40"; -"734 linear_40" -> "735 reshape_28"; -"735 reshape_28" -> "736 permute_30"; -"736 permute_30" -> "737 select_18"; -"736 permute_30" -> "738 select_19"; -"736 permute_30" -> "739 select_20"; -"737 select_18" -> "740 linalg_vector_norm_12"; -"737 select_18" -> "742 expand_as_12"; -"737 select_18" -> "743 div_12"; -"738 select_19" -> "744 linalg_vector_norm_13"; -"738 select_19" -> "746 expand_as_13"; -"738 select_19" -> "747 div_13"; -"739 select_20" -> "757 matmul_13"; -"740 linalg_vector_norm_12" -> "741 clamp_min_12"; -"741 clamp_min_12" -> "742 expand_as_12"; -"742 expand_as_12" -> "743 div_12"; -"743 div_12" -> "749 matmul_12"; -"744 linalg_vector_norm_13" -> "745 clamp_min_13"; -"745 clamp_min_13" -> "746 expand_as_13"; -"746 expand_as_13" -> "747 div_13"; -"747 div_13" -> "748 transpose_12"; -"748 transpose_12" -> "749 matmul_12"; -"749 matmul_12" -> "753 mul_13"; -"750 _param_constant111" -> "751 clamp_6"; -"751 clamp_6" -> "752 exp_6"; -"752 exp_6" -> "753 mul_13"; -"753 mul_13" -> "754 add_21"; -"754 add_21" -> "755 softmax_6"; -"755 softmax_6" -> "756 dropout_24"; -"756 dropout_24" -> "757 matmul_13"; -"757 matmul_13" -> "758 transpose_13"; -"758 transpose_13" -> "759 reshape_29"; -"759 reshape_29" -> "762 linear_41"; -"760 _param_constant112" -> "762 linear_41"; -"761 _param_constant113" -> "762 linear_41"; -"762 linear_41" -> "763 dropout_25"; -"763 dropout_25" -> "764 view_36"; -"764 view_36" -> "765 permute_31"; -"765 permute_31" -> "766 reshape_30"; -"766 reshape_30" -> "767 slice_106"; -"767 slice_106" -> "768 slice_107"; -"768 slice_107" -> "769 slice_108"; -"769 slice_108" -> "770 slice_109"; -"770 slice_109" -> "771 contiguous_11"; -"771 contiguous_11" -> "774 layer_norm_15"; -"772 _param_constant114" -> "774 layer_norm_15"; -"773 _param_constant115" -> "774 layer_norm_15"; -"774 layer_norm_15" -> "775 add_22"; -"775 add_22" -> "778 linear_42"; -"775 add_22" -> "788 add_23"; -"776 _param_constant116" -> "778 linear_42"; -"777 _param_constant117" -> "778 linear_42"; -"778 linear_42" -> "779 gelu_6"; -"779 gelu_6" -> "780 dropout_26"; -"780 dropout_26" -> "783 linear_43"; -"781 _param_constant118" -> "783 linear_43"; -"782 _param_constant119" -> "783 linear_43"; -"783 linear_43" -> "784 dropout_27"; -"784 dropout_27" -> "787 layer_norm_16"; -"785 _param_constant120" -> "787 layer_norm_16"; -"786 _param_constant121" -> "787 layer_norm_16"; -"787 layer_norm_16" -> "788 add_23"; -"788 add_23" -> "805 pad_9"; -"788 add_23" -> "918 add_26"; -"789 _tensor_constant41" -> "792 linear_44"; -"790 _param_constant122" -> "792 linear_44"; -"791 _param_constant123" -> "792 linear_44"; -"792 linear_44" -> "793 relu__7"; -"793 relu__7" -> "795 linear_45"; -"794 _param_constant124" -> "795 linear_45"; -"795 linear_45" -> "796 view_37"; -"796 view_37" -> "798 index_7"; -"797 _tensor_constant42" -> "798 index_7"; -"798 index_7" -> "799 view_38"; -"799 view_38" -> "800 permute_32"; -"800 permute_32" -> "801 contiguous_12"; -"801 contiguous_12" -> "802 unsqueeze_19"; -"802 unsqueeze_19" -> "803 sigmoid_7"; -"803 sigmoid_7" -> "804 mul_14"; -"804 mul_14" -> "835 add_24"; -"805 pad_9" -> "806 roll_6"; -"806 roll_6" -> "807 view_39"; -"807 view_39" -> "808 permute_33"; -"808 permute_33" -> "809 reshape_31"; -"809 reshape_31" -> "815 linear_46"; -"809 reshape_31" -> "836 new_zeros_3"; -"810 _param_constant125" -> "811 clone_7"; -"811 clone_7" -> "812 slice_110"; -"811 clone_7" -> "815 linear_46"; -"812 slice_110" -> "813 zero__7"; -"814 _param_constant126" -> "815 linear_46"; -"815 linear_46" -> "816 reshape_32"; -"816 reshape_32" -> "817 permute_34"; -"817 permute_34" -> "818 select_21"; -"817 permute_34" -> "819 select_22"; -"817 permute_34" -> "820 select_23"; -"818 select_21" -> "821 linalg_vector_norm_14"; -"818 select_21" -> "823 expand_as_14"; -"818 select_21" -> "824 div_14"; -"819 select_22" -> "825 linalg_vector_norm_15"; -"819 select_22" -> "827 expand_as_15"; -"819 select_22" -> "828 div_15"; -"820 select_23" -> "899 matmul_15"; -"821 linalg_vector_norm_14" -> "822 clamp_min_14"; -"822 clamp_min_14" -> "823 expand_as_14"; -"823 expand_as_14" -> "824 div_14"; -"824 div_14" -> "830 matmul_14"; -"825 linalg_vector_norm_15" -> "826 clamp_min_15"; -"826 clamp_min_15" -> "827 expand_as_15"; -"827 expand_as_15" -> "828 div_15"; -"828 div_15" -> "829 transpose_14"; -"829 transpose_14" -> "830 matmul_14"; -"830 matmul_14" -> "834 mul_15"; -"831 _param_constant127" -> "832 clamp_7"; -"832 clamp_7" -> "833 exp_7"; -"833 exp_7" -> "834 mul_15"; -"834 mul_15" -> "835 add_24"; -"835 add_24" -> "892 view_41"; -"836 new_zeros_3" -> "839 slice_111"; -"836 new_zeros_3" -> "844 slice_113"; -"836 new_zeros_3" -> "849 slice_115"; -"836 new_zeros_3" -> "854 slice_117"; -"836 new_zeros_3" -> "859 slice_119"; -"836 new_zeros_3" -> "864 slice_121"; -"836 new_zeros_3" -> "869 slice_123"; -"836 new_zeros_3" -> "874 slice_125"; -"836 new_zeros_3" -> "879 slice_127"; -"836 new_zeros_3" -> "882 view_40"; -"837 _tensor_constant43" -> "838 lift_fresh_copy_27"; -"838 lift_fresh_copy_27" -> "841 fill__27"; -"839 slice_111" -> "840 slice_112"; -"840 slice_112" -> "841 fill__27"; -"842 _tensor_constant44" -> "843 lift_fresh_copy_28"; -"843 lift_fresh_copy_28" -> "846 fill__28"; -"844 slice_113" -> "845 slice_114"; -"845 slice_114" -> "846 fill__28"; -"847 _tensor_constant45" -> "848 lift_fresh_copy_29"; -"848 lift_fresh_copy_29" -> "851 fill__29"; -"849 slice_115" -> "850 slice_116"; -"850 slice_116" -> "851 fill__29"; -"852 _tensor_constant46" -> "853 lift_fresh_copy_30"; -"853 lift_fresh_copy_30" -> "856 fill__30"; -"854 slice_117" -> "855 slice_118"; -"855 slice_118" -> "856 fill__30"; -"857 _tensor_constant47" -> "858 lift_fresh_copy_31"; -"858 lift_fresh_copy_31" -> "861 fill__31"; -"859 slice_119" -> "860 slice_120"; -"860 slice_120" -> "861 fill__31"; -"862 _tensor_constant48" -> "863 lift_fresh_copy_32"; -"863 lift_fresh_copy_32" -> "866 fill__32"; -"864 slice_121" -> "865 slice_122"; -"865 slice_122" -> "866 fill__32"; -"867 _tensor_constant49" -> "868 lift_fresh_copy_33"; -"868 lift_fresh_copy_33" -> "871 fill__33"; -"869 slice_123" -> "870 slice_124"; -"870 slice_124" -> "871 fill__33"; -"872 _tensor_constant50" -> "873 lift_fresh_copy_34"; -"873 lift_fresh_copy_34" -> "876 fill__34"; -"874 slice_125" -> "875 slice_126"; -"875 slice_126" -> "876 fill__34"; -"877 _tensor_constant51" -> "878 lift_fresh_copy_35"; -"878 lift_fresh_copy_35" -> "881 fill__35"; -"879 slice_127" -> "880 slice_128"; -"880 slice_128" -> "881 fill__35"; -"882 view_40" -> "883 permute_35"; -"883 permute_35" -> "884 reshape_33"; -"884 reshape_33" -> "885 unsqueeze_20"; -"884 reshape_33" -> "886 unsqueeze_21"; -"885 unsqueeze_20" -> "887 sub_3"; -"886 unsqueeze_21" -> "887 sub_3"; -"887 sub_3" -> "888 ne_3"; -"887 sub_3" -> "889 masked_fill_6"; -"887 sub_3" -> "890 eq_3"; -"888 ne_3" -> "889 masked_fill_6"; -"889 masked_fill_6" -> "891 masked_fill_7"; -"890 eq_3" -> "891 masked_fill_7"; -"891 masked_fill_7" -> "893 unsqueeze_22"; -"892 view_41" -> "895 add_25"; -"893 unsqueeze_22" -> "894 unsqueeze_23"; -"894 unsqueeze_23" -> "895 add_25"; -"895 add_25" -> "896 view_42"; -"896 view_42" -> "897 softmax_7"; -"897 softmax_7" -> "898 dropout_28"; -"898 dropout_28" -> "899 matmul_15"; -"899 matmul_15" -> "900 transpose_15"; -"900 transpose_15" -> "901 reshape_34"; -"901 reshape_34" -> "904 linear_47"; -"902 _param_constant128" -> "904 linear_47"; -"903 _param_constant129" -> "904 linear_47"; -"904 linear_47" -> "905 dropout_29"; -"905 dropout_29" -> "906 view_43"; -"906 view_43" -> "907 permute_36"; -"907 permute_36" -> "908 reshape_35"; -"908 reshape_35" -> "909 roll_7"; -"909 roll_7" -> "910 slice_129"; -"910 slice_129" -> "911 slice_130"; -"911 slice_130" -> "912 slice_131"; -"912 slice_131" -> "913 slice_132"; -"913 slice_132" -> "914 contiguous_13"; -"914 contiguous_13" -> "917 layer_norm_17"; -"915 _param_constant130" -> "917 layer_norm_17"; -"916 _param_constant131" -> "917 layer_norm_17"; -"917 layer_norm_17" -> "918 add_26"; -"918 add_26" -> "921 linear_48"; -"918 add_26" -> "931 add_27"; -"919 _param_constant132" -> "921 linear_48"; -"920 _param_constant133" -> "921 linear_48"; -"921 linear_48" -> "922 gelu_7"; -"922 gelu_7" -> "923 dropout_30"; -"923 dropout_30" -> "926 linear_49"; -"924 _param_constant134" -> "926 linear_49"; -"925 _param_constant135" -> "926 linear_49"; -"926 linear_49" -> "927 dropout_31"; -"927 dropout_31" -> "930 layer_norm_18"; -"928 _param_constant136" -> "930 layer_norm_18"; -"929 _param_constant137" -> "930 layer_norm_18"; -"930 layer_norm_18" -> "931 add_27"; -"931 add_27" -> "948 pad_10"; -"931 add_27" -> "998 add_29"; -"932 _tensor_constant52" -> "935 linear_50"; -"933 _param_constant138" -> "935 linear_50"; -"934 _param_constant139" -> "935 linear_50"; -"935 linear_50" -> "936 relu__8"; -"936 relu__8" -> "938 linear_51"; -"937 _param_constant140" -> "938 linear_51"; -"938 linear_51" -> "939 view_44"; -"939 view_44" -> "941 index_8"; -"940 _tensor_constant53" -> "941 index_8"; -"941 index_8" -> "942 view_45"; -"942 view_45" -> "943 permute_37"; -"943 permute_37" -> "944 contiguous_14"; -"944 contiguous_14" -> "945 unsqueeze_24"; -"945 unsqueeze_24" -> "946 sigmoid_8"; -"946 sigmoid_8" -> "947 mul_16"; -"947 mul_16" -> "977 add_28"; -"948 pad_10" -> "949 view_46"; -"949 view_46" -> "950 permute_38"; -"950 permute_38" -> "951 reshape_36"; -"951 reshape_36" -> "957 linear_52"; -"952 _param_constant141" -> "953 clone_8"; -"953 clone_8" -> "954 slice_133"; -"953 clone_8" -> "957 linear_52"; -"954 slice_133" -> "955 zero__8"; -"956 _param_constant142" -> "957 linear_52"; -"957 linear_52" -> "958 reshape_37"; -"958 reshape_37" -> "959 permute_39"; -"959 permute_39" -> "960 select_24"; -"959 permute_39" -> "961 select_25"; -"959 permute_39" -> "962 select_26"; -"960 select_24" -> "963 linalg_vector_norm_16"; -"960 select_24" -> "965 expand_as_16"; -"960 select_24" -> "966 div_16"; -"961 select_25" -> "967 linalg_vector_norm_17"; -"961 select_25" -> "969 expand_as_17"; -"961 select_25" -> "970 div_17"; -"962 select_26" -> "980 matmul_17"; -"963 linalg_vector_norm_16" -> "964 clamp_min_16"; -"964 clamp_min_16" -> "965 expand_as_16"; -"965 expand_as_16" -> "966 div_16"; -"966 div_16" -> "972 matmul_16"; -"967 linalg_vector_norm_17" -> "968 clamp_min_17"; -"968 clamp_min_17" -> "969 expand_as_17"; -"969 expand_as_17" -> "970 div_17"; -"970 div_17" -> "971 transpose_16"; -"971 transpose_16" -> "972 matmul_16"; -"972 matmul_16" -> "976 mul_17"; -"973 _param_constant143" -> "974 clamp_8"; -"974 clamp_8" -> "975 exp_8"; -"975 exp_8" -> "976 mul_17"; -"976 mul_17" -> "977 add_28"; -"977 add_28" -> "978 softmax_8"; -"978 softmax_8" -> "979 dropout_32"; -"979 dropout_32" -> "980 matmul_17"; -"980 matmul_17" -> "981 transpose_17"; -"981 transpose_17" -> "982 reshape_38"; -"982 reshape_38" -> "985 linear_53"; -"983 _param_constant144" -> "985 linear_53"; -"984 _param_constant145" -> "985 linear_53"; -"985 linear_53" -> "986 dropout_33"; -"986 dropout_33" -> "987 view_47"; -"987 view_47" -> "988 permute_40"; -"988 permute_40" -> "989 reshape_39"; -"989 reshape_39" -> "990 slice_134"; -"990 slice_134" -> "991 slice_135"; -"991 slice_135" -> "992 slice_136"; -"992 slice_136" -> "993 slice_137"; -"993 slice_137" -> "994 contiguous_15"; -"994 contiguous_15" -> "997 layer_norm_19"; -"995 _param_constant146" -> "997 layer_norm_19"; -"996 _param_constant147" -> "997 layer_norm_19"; -"997 layer_norm_19" -> "998 add_29"; -"998 add_29" -> "1001 linear_54"; -"998 add_29" -> "1011 add_30"; -"999 _param_constant148" -> "1001 linear_54"; -"1000 _param_constant149" -> "1001 linear_54"; -"1001 linear_54" -> "1002 gelu_8"; -"1002 gelu_8" -> "1003 dropout_34"; -"1003 dropout_34" -> "1006 linear_55"; -"1004 _param_constant150" -> "1006 linear_55"; -"1005 _param_constant151" -> "1006 linear_55"; -"1006 linear_55" -> "1007 dropout_35"; -"1007 dropout_35" -> "1010 layer_norm_20"; -"1008 _param_constant152" -> "1010 layer_norm_20"; -"1009 _param_constant153" -> "1010 layer_norm_20"; -"1010 layer_norm_20" -> "1011 add_30"; -"1011 add_30" -> "1028 pad_11"; -"1011 add_30" -> "1141 add_33"; -"1012 _tensor_constant54" -> "1015 linear_56"; -"1013 _param_constant154" -> "1015 linear_56"; -"1014 _param_constant155" -> "1015 linear_56"; -"1015 linear_56" -> "1016 relu__9"; -"1016 relu__9" -> "1018 linear_57"; -"1017 _param_constant156" -> "1018 linear_57"; -"1018 linear_57" -> "1019 view_48"; -"1019 view_48" -> "1021 index_9"; -"1020 _tensor_constant55" -> "1021 index_9"; -"1021 index_9" -> "1022 view_49"; -"1022 view_49" -> "1023 permute_41"; -"1023 permute_41" -> "1024 contiguous_16"; -"1024 contiguous_16" -> "1025 unsqueeze_25"; -"1025 unsqueeze_25" -> "1026 sigmoid_9"; -"1026 sigmoid_9" -> "1027 mul_18"; -"1027 mul_18" -> "1058 add_31"; -"1028 pad_11" -> "1029 roll_8"; -"1029 roll_8" -> "1030 view_50"; -"1030 view_50" -> "1031 permute_42"; -"1031 permute_42" -> "1032 reshape_40"; -"1032 reshape_40" -> "1038 linear_58"; -"1032 reshape_40" -> "1059 new_zeros_4"; -"1033 _param_constant157" -> "1034 clone_9"; -"1034 clone_9" -> "1035 slice_138"; -"1034 clone_9" -> "1038 linear_58"; -"1035 slice_138" -> "1036 zero__9"; -"1037 _param_constant158" -> "1038 linear_58"; -"1038 linear_58" -> "1039 reshape_41"; -"1039 reshape_41" -> "1040 permute_43"; -"1040 permute_43" -> "1041 select_27"; -"1040 permute_43" -> "1042 select_28"; -"1040 permute_43" -> "1043 select_29"; -"1041 select_27" -> "1044 linalg_vector_norm_18"; -"1041 select_27" -> "1046 expand_as_18"; -"1041 select_27" -> "1047 div_18"; -"1042 select_28" -> "1048 linalg_vector_norm_19"; -"1042 select_28" -> "1050 expand_as_19"; -"1042 select_28" -> "1051 div_19"; -"1043 select_29" -> "1122 matmul_19"; -"1044 linalg_vector_norm_18" -> "1045 clamp_min_18"; -"1045 clamp_min_18" -> "1046 expand_as_18"; -"1046 expand_as_18" -> "1047 div_18"; -"1047 div_18" -> "1053 matmul_18"; -"1048 linalg_vector_norm_19" -> "1049 clamp_min_19"; -"1049 clamp_min_19" -> "1050 expand_as_19"; -"1050 expand_as_19" -> "1051 div_19"; -"1051 div_19" -> "1052 transpose_18"; -"1052 transpose_18" -> "1053 matmul_18"; -"1053 matmul_18" -> "1057 mul_19"; -"1054 _param_constant159" -> "1055 clamp_9"; -"1055 clamp_9" -> "1056 exp_9"; -"1056 exp_9" -> "1057 mul_19"; -"1057 mul_19" -> "1058 add_31"; -"1058 add_31" -> "1115 view_52"; -"1059 new_zeros_4" -> "1062 slice_139"; -"1059 new_zeros_4" -> "1067 slice_141"; -"1059 new_zeros_4" -> "1072 slice_143"; -"1059 new_zeros_4" -> "1077 slice_145"; -"1059 new_zeros_4" -> "1082 slice_147"; -"1059 new_zeros_4" -> "1087 slice_149"; -"1059 new_zeros_4" -> "1092 slice_151"; -"1059 new_zeros_4" -> "1097 slice_153"; -"1059 new_zeros_4" -> "1102 slice_155"; -"1059 new_zeros_4" -> "1105 view_51"; -"1060 _tensor_constant56" -> "1061 lift_fresh_copy_36"; -"1061 lift_fresh_copy_36" -> "1064 fill__36"; -"1062 slice_139" -> "1063 slice_140"; -"1063 slice_140" -> "1064 fill__36"; -"1065 _tensor_constant57" -> "1066 lift_fresh_copy_37"; -"1066 lift_fresh_copy_37" -> "1069 fill__37"; -"1067 slice_141" -> "1068 slice_142"; -"1068 slice_142" -> "1069 fill__37"; -"1070 _tensor_constant58" -> "1071 lift_fresh_copy_38"; -"1071 lift_fresh_copy_38" -> "1074 fill__38"; -"1072 slice_143" -> "1073 slice_144"; -"1073 slice_144" -> "1074 fill__38"; -"1075 _tensor_constant59" -> "1076 lift_fresh_copy_39"; -"1076 lift_fresh_copy_39" -> "1079 fill__39"; -"1077 slice_145" -> "1078 slice_146"; -"1078 slice_146" -> "1079 fill__39"; -"1080 _tensor_constant60" -> "1081 lift_fresh_copy_40"; -"1081 lift_fresh_copy_40" -> "1084 fill__40"; -"1082 slice_147" -> "1083 slice_148"; -"1083 slice_148" -> "1084 fill__40"; -"1085 _tensor_constant61" -> "1086 lift_fresh_copy_41"; -"1086 lift_fresh_copy_41" -> "1089 fill__41"; -"1087 slice_149" -> "1088 slice_150"; -"1088 slice_150" -> "1089 fill__41"; -"1090 _tensor_constant62" -> "1091 lift_fresh_copy_42"; -"1091 lift_fresh_copy_42" -> "1094 fill__42"; -"1092 slice_151" -> "1093 slice_152"; -"1093 slice_152" -> "1094 fill__42"; -"1095 _tensor_constant63" -> "1096 lift_fresh_copy_43"; -"1096 lift_fresh_copy_43" -> "1099 fill__43"; -"1097 slice_153" -> "1098 slice_154"; -"1098 slice_154" -> "1099 fill__43"; -"1100 _tensor_constant64" -> "1101 lift_fresh_copy_44"; -"1101 lift_fresh_copy_44" -> "1104 fill__44"; -"1102 slice_155" -> "1103 slice_156"; -"1103 slice_156" -> "1104 fill__44"; -"1105 view_51" -> "1106 permute_44"; -"1106 permute_44" -> "1107 reshape_42"; -"1107 reshape_42" -> "1108 unsqueeze_26"; -"1107 reshape_42" -> "1109 unsqueeze_27"; -"1108 unsqueeze_26" -> "1110 sub_4"; -"1109 unsqueeze_27" -> "1110 sub_4"; -"1110 sub_4" -> "1111 ne_4"; -"1110 sub_4" -> "1112 masked_fill_8"; -"1110 sub_4" -> "1113 eq_4"; -"1111 ne_4" -> "1112 masked_fill_8"; -"1112 masked_fill_8" -> "1114 masked_fill_9"; -"1113 eq_4" -> "1114 masked_fill_9"; -"1114 masked_fill_9" -> "1116 unsqueeze_28"; -"1115 view_52" -> "1118 add_32"; -"1116 unsqueeze_28" -> "1117 unsqueeze_29"; -"1117 unsqueeze_29" -> "1118 add_32"; -"1118 add_32" -> "1119 view_53"; -"1119 view_53" -> "1120 softmax_9"; -"1120 softmax_9" -> "1121 dropout_36"; -"1121 dropout_36" -> "1122 matmul_19"; -"1122 matmul_19" -> "1123 transpose_19"; -"1123 transpose_19" -> "1124 reshape_43"; -"1124 reshape_43" -> "1127 linear_59"; -"1125 _param_constant160" -> "1127 linear_59"; -"1126 _param_constant161" -> "1127 linear_59"; -"1127 linear_59" -> "1128 dropout_37"; -"1128 dropout_37" -> "1129 view_54"; -"1129 view_54" -> "1130 permute_45"; -"1130 permute_45" -> "1131 reshape_44"; -"1131 reshape_44" -> "1132 roll_9"; -"1132 roll_9" -> "1133 slice_157"; -"1133 slice_157" -> "1134 slice_158"; -"1134 slice_158" -> "1135 slice_159"; -"1135 slice_159" -> "1136 slice_160"; -"1136 slice_160" -> "1137 contiguous_17"; -"1137 contiguous_17" -> "1140 layer_norm_21"; -"1138 _param_constant162" -> "1140 layer_norm_21"; -"1139 _param_constant163" -> "1140 layer_norm_21"; -"1140 layer_norm_21" -> "1141 add_33"; -"1141 add_33" -> "1144 linear_60"; -"1141 add_33" -> "1154 add_34"; -"1142 _param_constant164" -> "1144 linear_60"; -"1143 _param_constant165" -> "1144 linear_60"; -"1144 linear_60" -> "1145 gelu_9"; -"1145 gelu_9" -> "1146 dropout_38"; -"1146 dropout_38" -> "1149 linear_61"; -"1147 _param_constant166" -> "1149 linear_61"; -"1148 _param_constant167" -> "1149 linear_61"; -"1149 linear_61" -> "1150 dropout_39"; -"1150 dropout_39" -> "1153 layer_norm_22"; -"1151 _param_constant168" -> "1153 layer_norm_22"; -"1152 _param_constant169" -> "1153 layer_norm_22"; -"1153 layer_norm_22" -> "1154 add_34"; -"1154 add_34" -> "1171 pad_12"; -"1154 add_34" -> "1221 add_36"; -"1155 _tensor_constant65" -> "1158 linear_62"; -"1156 _param_constant170" -> "1158 linear_62"; -"1157 _param_constant171" -> "1158 linear_62"; -"1158 linear_62" -> "1159 relu__10"; -"1159 relu__10" -> "1161 linear_63"; -"1160 _param_constant172" -> "1161 linear_63"; -"1161 linear_63" -> "1162 view_55"; -"1162 view_55" -> "1164 index_10"; -"1163 _tensor_constant66" -> "1164 index_10"; -"1164 index_10" -> "1165 view_56"; -"1165 view_56" -> "1166 permute_46"; -"1166 permute_46" -> "1167 contiguous_18"; -"1167 contiguous_18" -> "1168 unsqueeze_30"; -"1168 unsqueeze_30" -> "1169 sigmoid_10"; -"1169 sigmoid_10" -> "1170 mul_20"; -"1170 mul_20" -> "1200 add_35"; -"1171 pad_12" -> "1172 view_57"; -"1172 view_57" -> "1173 permute_47"; -"1173 permute_47" -> "1174 reshape_45"; -"1174 reshape_45" -> "1180 linear_64"; -"1175 _param_constant173" -> "1176 clone_10"; -"1176 clone_10" -> "1177 slice_161"; -"1176 clone_10" -> "1180 linear_64"; -"1177 slice_161" -> "1178 zero__10"; -"1179 _param_constant174" -> "1180 linear_64"; -"1180 linear_64" -> "1181 reshape_46"; -"1181 reshape_46" -> "1182 permute_48"; -"1182 permute_48" -> "1183 select_30"; -"1182 permute_48" -> "1184 select_31"; -"1182 permute_48" -> "1185 select_32"; -"1183 select_30" -> "1186 linalg_vector_norm_20"; -"1183 select_30" -> "1188 expand_as_20"; -"1183 select_30" -> "1189 div_20"; -"1184 select_31" -> "1190 linalg_vector_norm_21"; -"1184 select_31" -> "1192 expand_as_21"; -"1184 select_31" -> "1193 div_21"; -"1185 select_32" -> "1203 matmul_21"; -"1186 linalg_vector_norm_20" -> "1187 clamp_min_20"; -"1187 clamp_min_20" -> "1188 expand_as_20"; -"1188 expand_as_20" -> "1189 div_20"; -"1189 div_20" -> "1195 matmul_20"; -"1190 linalg_vector_norm_21" -> "1191 clamp_min_21"; -"1191 clamp_min_21" -> "1192 expand_as_21"; -"1192 expand_as_21" -> "1193 div_21"; -"1193 div_21" -> "1194 transpose_20"; -"1194 transpose_20" -> "1195 matmul_20"; -"1195 matmul_20" -> "1199 mul_21"; -"1196 _param_constant175" -> "1197 clamp_10"; -"1197 clamp_10" -> "1198 exp_10"; -"1198 exp_10" -> "1199 mul_21"; -"1199 mul_21" -> "1200 add_35"; -"1200 add_35" -> "1201 softmax_10"; -"1201 softmax_10" -> "1202 dropout_40"; -"1202 dropout_40" -> "1203 matmul_21"; -"1203 matmul_21" -> "1204 transpose_21"; -"1204 transpose_21" -> "1205 reshape_47"; -"1205 reshape_47" -> "1208 linear_65"; -"1206 _param_constant176" -> "1208 linear_65"; -"1207 _param_constant177" -> "1208 linear_65"; -"1208 linear_65" -> "1209 dropout_41"; -"1209 dropout_41" -> "1210 view_58"; -"1210 view_58" -> "1211 permute_49"; -"1211 permute_49" -> "1212 reshape_48"; -"1212 reshape_48" -> "1213 slice_162"; -"1213 slice_162" -> "1214 slice_163"; -"1214 slice_163" -> "1215 slice_164"; -"1215 slice_164" -> "1216 slice_165"; -"1216 slice_165" -> "1217 contiguous_19"; -"1217 contiguous_19" -> "1220 layer_norm_23"; -"1218 _param_constant178" -> "1220 layer_norm_23"; -"1219 _param_constant179" -> "1220 layer_norm_23"; -"1220 layer_norm_23" -> "1221 add_36"; -"1221 add_36" -> "1224 linear_66"; -"1221 add_36" -> "1234 add_37"; -"1222 _param_constant180" -> "1224 linear_66"; -"1223 _param_constant181" -> "1224 linear_66"; -"1224 linear_66" -> "1225 gelu_10"; -"1225 gelu_10" -> "1226 dropout_42"; -"1226 dropout_42" -> "1229 linear_67"; -"1227 _param_constant182" -> "1229 linear_67"; -"1228 _param_constant183" -> "1229 linear_67"; -"1229 linear_67" -> "1230 dropout_43"; -"1230 dropout_43" -> "1233 layer_norm_24"; -"1231 _param_constant184" -> "1233 layer_norm_24"; -"1232 _param_constant185" -> "1233 layer_norm_24"; -"1233 layer_norm_24" -> "1234 add_37"; -"1234 add_37" -> "1251 pad_13"; -"1234 add_37" -> "1364 add_40"; -"1235 _tensor_constant67" -> "1238 linear_68"; -"1236 _param_constant186" -> "1238 linear_68"; -"1237 _param_constant187" -> "1238 linear_68"; -"1238 linear_68" -> "1239 relu__11"; -"1239 relu__11" -> "1241 linear_69"; -"1240 _param_constant188" -> "1241 linear_69"; -"1241 linear_69" -> "1242 view_59"; -"1242 view_59" -> "1244 index_11"; -"1243 _tensor_constant68" -> "1244 index_11"; -"1244 index_11" -> "1245 view_60"; -"1245 view_60" -> "1246 permute_50"; -"1246 permute_50" -> "1247 contiguous_20"; -"1247 contiguous_20" -> "1248 unsqueeze_31"; -"1248 unsqueeze_31" -> "1249 sigmoid_11"; -"1249 sigmoid_11" -> "1250 mul_22"; -"1250 mul_22" -> "1281 add_38"; -"1251 pad_13" -> "1252 roll_10"; -"1252 roll_10" -> "1253 view_61"; -"1253 view_61" -> "1254 permute_51"; -"1254 permute_51" -> "1255 reshape_49"; -"1255 reshape_49" -> "1261 linear_70"; -"1255 reshape_49" -> "1282 new_zeros_5"; -"1256 _param_constant189" -> "1257 clone_11"; -"1257 clone_11" -> "1258 slice_166"; -"1257 clone_11" -> "1261 linear_70"; -"1258 slice_166" -> "1259 zero__11"; -"1260 _param_constant190" -> "1261 linear_70"; -"1261 linear_70" -> "1262 reshape_50"; -"1262 reshape_50" -> "1263 permute_52"; -"1263 permute_52" -> "1264 select_33"; -"1263 permute_52" -> "1265 select_34"; -"1263 permute_52" -> "1266 select_35"; -"1264 select_33" -> "1267 linalg_vector_norm_22"; -"1264 select_33" -> "1269 expand_as_22"; -"1264 select_33" -> "1270 div_22"; -"1265 select_34" -> "1271 linalg_vector_norm_23"; -"1265 select_34" -> "1273 expand_as_23"; -"1265 select_34" -> "1274 div_23"; -"1266 select_35" -> "1345 matmul_23"; -"1267 linalg_vector_norm_22" -> "1268 clamp_min_22"; -"1268 clamp_min_22" -> "1269 expand_as_22"; -"1269 expand_as_22" -> "1270 div_22"; -"1270 div_22" -> "1276 matmul_22"; -"1271 linalg_vector_norm_23" -> "1272 clamp_min_23"; -"1272 clamp_min_23" -> "1273 expand_as_23"; -"1273 expand_as_23" -> "1274 div_23"; -"1274 div_23" -> "1275 transpose_22"; -"1275 transpose_22" -> "1276 matmul_22"; -"1276 matmul_22" -> "1280 mul_23"; -"1277 _param_constant191" -> "1278 clamp_11"; -"1278 clamp_11" -> "1279 exp_11"; -"1279 exp_11" -> "1280 mul_23"; -"1280 mul_23" -> "1281 add_38"; -"1281 add_38" -> "1338 view_63"; -"1282 new_zeros_5" -> "1285 slice_167"; -"1282 new_zeros_5" -> "1290 slice_169"; -"1282 new_zeros_5" -> "1295 slice_171"; -"1282 new_zeros_5" -> "1300 slice_173"; -"1282 new_zeros_5" -> "1305 slice_175"; -"1282 new_zeros_5" -> "1310 slice_177"; -"1282 new_zeros_5" -> "1315 slice_179"; -"1282 new_zeros_5" -> "1320 slice_181"; -"1282 new_zeros_5" -> "1325 slice_183"; -"1282 new_zeros_5" -> "1328 view_62"; -"1283 _tensor_constant69" -> "1284 lift_fresh_copy_45"; -"1284 lift_fresh_copy_45" -> "1287 fill__45"; -"1285 slice_167" -> "1286 slice_168"; -"1286 slice_168" -> "1287 fill__45"; -"1288 _tensor_constant70" -> "1289 lift_fresh_copy_46"; -"1289 lift_fresh_copy_46" -> "1292 fill__46"; -"1290 slice_169" -> "1291 slice_170"; -"1291 slice_170" -> "1292 fill__46"; -"1293 _tensor_constant71" -> "1294 lift_fresh_copy_47"; -"1294 lift_fresh_copy_47" -> "1297 fill__47"; -"1295 slice_171" -> "1296 slice_172"; -"1296 slice_172" -> "1297 fill__47"; -"1298 _tensor_constant72" -> "1299 lift_fresh_copy_48"; -"1299 lift_fresh_copy_48" -> "1302 fill__48"; -"1300 slice_173" -> "1301 slice_174"; -"1301 slice_174" -> "1302 fill__48"; -"1303 _tensor_constant73" -> "1304 lift_fresh_copy_49"; -"1304 lift_fresh_copy_49" -> "1307 fill__49"; -"1305 slice_175" -> "1306 slice_176"; -"1306 slice_176" -> "1307 fill__49"; -"1308 _tensor_constant74" -> "1309 lift_fresh_copy_50"; -"1309 lift_fresh_copy_50" -> "1312 fill__50"; -"1310 slice_177" -> "1311 slice_178"; -"1311 slice_178" -> "1312 fill__50"; -"1313 _tensor_constant75" -> "1314 lift_fresh_copy_51"; -"1314 lift_fresh_copy_51" -> "1317 fill__51"; -"1315 slice_179" -> "1316 slice_180"; -"1316 slice_180" -> "1317 fill__51"; -"1318 _tensor_constant76" -> "1319 lift_fresh_copy_52"; -"1319 lift_fresh_copy_52" -> "1322 fill__52"; -"1320 slice_181" -> "1321 slice_182"; -"1321 slice_182" -> "1322 fill__52"; -"1323 _tensor_constant77" -> "1324 lift_fresh_copy_53"; -"1324 lift_fresh_copy_53" -> "1327 fill__53"; -"1325 slice_183" -> "1326 slice_184"; -"1326 slice_184" -> "1327 fill__53"; -"1328 view_62" -> "1329 permute_53"; -"1329 permute_53" -> "1330 reshape_51"; -"1330 reshape_51" -> "1331 unsqueeze_32"; -"1330 reshape_51" -> "1332 unsqueeze_33"; -"1331 unsqueeze_32" -> "1333 sub_5"; -"1332 unsqueeze_33" -> "1333 sub_5"; -"1333 sub_5" -> "1334 ne_5"; -"1333 sub_5" -> "1335 masked_fill_10"; -"1333 sub_5" -> "1336 eq_5"; -"1334 ne_5" -> "1335 masked_fill_10"; -"1335 masked_fill_10" -> "1337 masked_fill_11"; -"1336 eq_5" -> "1337 masked_fill_11"; -"1337 masked_fill_11" -> "1339 unsqueeze_34"; -"1338 view_63" -> "1341 add_39"; -"1339 unsqueeze_34" -> "1340 unsqueeze_35"; -"1340 unsqueeze_35" -> "1341 add_39"; -"1341 add_39" -> "1342 view_64"; -"1342 view_64" -> "1343 softmax_11"; -"1343 softmax_11" -> "1344 dropout_44"; -"1344 dropout_44" -> "1345 matmul_23"; -"1345 matmul_23" -> "1346 transpose_23"; -"1346 transpose_23" -> "1347 reshape_52"; -"1347 reshape_52" -> "1350 linear_71"; -"1348 _param_constant192" -> "1350 linear_71"; -"1349 _param_constant193" -> "1350 linear_71"; -"1350 linear_71" -> "1351 dropout_45"; -"1351 dropout_45" -> "1352 view_65"; -"1352 view_65" -> "1353 permute_54"; -"1353 permute_54" -> "1354 reshape_53"; -"1354 reshape_53" -> "1355 roll_11"; -"1355 roll_11" -> "1356 slice_185"; -"1356 slice_185" -> "1357 slice_186"; -"1357 slice_186" -> "1358 slice_187"; -"1358 slice_187" -> "1359 slice_188"; -"1359 slice_188" -> "1360 contiguous_21"; -"1360 contiguous_21" -> "1363 layer_norm_25"; -"1361 _param_constant194" -> "1363 layer_norm_25"; -"1362 _param_constant195" -> "1363 layer_norm_25"; -"1363 layer_norm_25" -> "1364 add_40"; -"1364 add_40" -> "1367 linear_72"; -"1364 add_40" -> "1377 add_41"; -"1365 _param_constant196" -> "1367 linear_72"; -"1366 _param_constant197" -> "1367 linear_72"; -"1367 linear_72" -> "1368 gelu_11"; -"1368 gelu_11" -> "1369 dropout_46"; -"1369 dropout_46" -> "1372 linear_73"; -"1370 _param_constant198" -> "1372 linear_73"; -"1371 _param_constant199" -> "1372 linear_73"; -"1372 linear_73" -> "1373 dropout_47"; -"1373 dropout_47" -> "1376 layer_norm_26"; -"1374 _param_constant200" -> "1376 layer_norm_26"; -"1375 _param_constant201" -> "1376 layer_norm_26"; -"1376 layer_norm_26" -> "1377 add_41"; -"1377 add_41" -> "1394 pad_14"; -"1377 add_41" -> "1444 add_43"; -"1378 _tensor_constant78" -> "1381 linear_74"; -"1379 _param_constant202" -> "1381 linear_74"; -"1380 _param_constant203" -> "1381 linear_74"; -"1381 linear_74" -> "1382 relu__12"; -"1382 relu__12" -> "1384 linear_75"; -"1383 _param_constant204" -> "1384 linear_75"; -"1384 linear_75" -> "1385 view_66"; -"1385 view_66" -> "1387 index_12"; -"1386 _tensor_constant79" -> "1387 index_12"; -"1387 index_12" -> "1388 view_67"; -"1388 view_67" -> "1389 permute_55"; -"1389 permute_55" -> "1390 contiguous_22"; -"1390 contiguous_22" -> "1391 unsqueeze_36"; -"1391 unsqueeze_36" -> "1392 sigmoid_12"; -"1392 sigmoid_12" -> "1393 mul_24"; -"1393 mul_24" -> "1423 add_42"; -"1394 pad_14" -> "1395 view_68"; -"1395 view_68" -> "1396 permute_56"; -"1396 permute_56" -> "1397 reshape_54"; -"1397 reshape_54" -> "1403 linear_76"; -"1398 _param_constant205" -> "1399 clone_12"; -"1399 clone_12" -> "1400 slice_189"; -"1399 clone_12" -> "1403 linear_76"; -"1400 slice_189" -> "1401 zero__12"; -"1402 _param_constant206" -> "1403 linear_76"; -"1403 linear_76" -> "1404 reshape_55"; -"1404 reshape_55" -> "1405 permute_57"; -"1405 permute_57" -> "1406 select_36"; -"1405 permute_57" -> "1407 select_37"; -"1405 permute_57" -> "1408 select_38"; -"1406 select_36" -> "1409 linalg_vector_norm_24"; -"1406 select_36" -> "1411 expand_as_24"; -"1406 select_36" -> "1412 div_24"; -"1407 select_37" -> "1413 linalg_vector_norm_25"; -"1407 select_37" -> "1415 expand_as_25"; -"1407 select_37" -> "1416 div_25"; -"1408 select_38" -> "1426 matmul_25"; -"1409 linalg_vector_norm_24" -> "1410 clamp_min_24"; -"1410 clamp_min_24" -> "1411 expand_as_24"; -"1411 expand_as_24" -> "1412 div_24"; -"1412 div_24" -> "1418 matmul_24"; -"1413 linalg_vector_norm_25" -> "1414 clamp_min_25"; -"1414 clamp_min_25" -> "1415 expand_as_25"; -"1415 expand_as_25" -> "1416 div_25"; -"1416 div_25" -> "1417 transpose_24"; -"1417 transpose_24" -> "1418 matmul_24"; -"1418 matmul_24" -> "1422 mul_25"; -"1419 _param_constant207" -> "1420 clamp_12"; -"1420 clamp_12" -> "1421 exp_12"; -"1421 exp_12" -> "1422 mul_25"; -"1422 mul_25" -> "1423 add_42"; -"1423 add_42" -> "1424 softmax_12"; -"1424 softmax_12" -> "1425 dropout_48"; -"1425 dropout_48" -> "1426 matmul_25"; -"1426 matmul_25" -> "1427 transpose_25"; -"1427 transpose_25" -> "1428 reshape_56"; -"1428 reshape_56" -> "1431 linear_77"; -"1429 _param_constant208" -> "1431 linear_77"; -"1430 _param_constant209" -> "1431 linear_77"; -"1431 linear_77" -> "1432 dropout_49"; -"1432 dropout_49" -> "1433 view_69"; -"1433 view_69" -> "1434 permute_58"; -"1434 permute_58" -> "1435 reshape_57"; -"1435 reshape_57" -> "1436 slice_190"; -"1436 slice_190" -> "1437 slice_191"; -"1437 slice_191" -> "1438 slice_192"; -"1438 slice_192" -> "1439 slice_193"; -"1439 slice_193" -> "1440 contiguous_23"; -"1440 contiguous_23" -> "1443 layer_norm_27"; -"1441 _param_constant210" -> "1443 layer_norm_27"; -"1442 _param_constant211" -> "1443 layer_norm_27"; -"1443 layer_norm_27" -> "1444 add_43"; -"1444 add_43" -> "1447 linear_78"; -"1444 add_43" -> "1457 add_44"; -"1445 _param_constant212" -> "1447 linear_78"; -"1446 _param_constant213" -> "1447 linear_78"; -"1447 linear_78" -> "1448 gelu_12"; -"1448 gelu_12" -> "1449 dropout_50"; -"1449 dropout_50" -> "1452 linear_79"; -"1450 _param_constant214" -> "1452 linear_79"; -"1451 _param_constant215" -> "1452 linear_79"; -"1452 linear_79" -> "1453 dropout_51"; -"1453 dropout_51" -> "1456 layer_norm_28"; -"1454 _param_constant216" -> "1456 layer_norm_28"; -"1455 _param_constant217" -> "1456 layer_norm_28"; -"1456 layer_norm_28" -> "1457 add_44"; -"1457 add_44" -> "1474 pad_15"; -"1457 add_44" -> "1587 add_47"; -"1458 _tensor_constant80" -> "1461 linear_80"; -"1459 _param_constant218" -> "1461 linear_80"; -"1460 _param_constant219" -> "1461 linear_80"; -"1461 linear_80" -> "1462 relu__13"; -"1462 relu__13" -> "1464 linear_81"; -"1463 _param_constant220" -> "1464 linear_81"; -"1464 linear_81" -> "1465 view_70"; -"1465 view_70" -> "1467 index_13"; -"1466 _tensor_constant81" -> "1467 index_13"; -"1467 index_13" -> "1468 view_71"; -"1468 view_71" -> "1469 permute_59"; -"1469 permute_59" -> "1470 contiguous_24"; -"1470 contiguous_24" -> "1471 unsqueeze_37"; -"1471 unsqueeze_37" -> "1472 sigmoid_13"; -"1472 sigmoid_13" -> "1473 mul_26"; -"1473 mul_26" -> "1504 add_45"; -"1474 pad_15" -> "1475 roll_12"; -"1475 roll_12" -> "1476 view_72"; -"1476 view_72" -> "1477 permute_60"; -"1477 permute_60" -> "1478 reshape_58"; -"1478 reshape_58" -> "1484 linear_82"; -"1478 reshape_58" -> "1505 new_zeros_6"; -"1479 _param_constant221" -> "1480 clone_13"; -"1480 clone_13" -> "1481 slice_194"; -"1480 clone_13" -> "1484 linear_82"; -"1481 slice_194" -> "1482 zero__13"; -"1483 _param_constant222" -> "1484 linear_82"; -"1484 linear_82" -> "1485 reshape_59"; -"1485 reshape_59" -> "1486 permute_61"; -"1486 permute_61" -> "1487 select_39"; -"1486 permute_61" -> "1488 select_40"; -"1486 permute_61" -> "1489 select_41"; -"1487 select_39" -> "1490 linalg_vector_norm_26"; -"1487 select_39" -> "1492 expand_as_26"; -"1487 select_39" -> "1493 div_26"; -"1488 select_40" -> "1494 linalg_vector_norm_27"; -"1488 select_40" -> "1496 expand_as_27"; -"1488 select_40" -> "1497 div_27"; -"1489 select_41" -> "1568 matmul_27"; -"1490 linalg_vector_norm_26" -> "1491 clamp_min_26"; -"1491 clamp_min_26" -> "1492 expand_as_26"; -"1492 expand_as_26" -> "1493 div_26"; -"1493 div_26" -> "1499 matmul_26"; -"1494 linalg_vector_norm_27" -> "1495 clamp_min_27"; -"1495 clamp_min_27" -> "1496 expand_as_27"; -"1496 expand_as_27" -> "1497 div_27"; -"1497 div_27" -> "1498 transpose_26"; -"1498 transpose_26" -> "1499 matmul_26"; -"1499 matmul_26" -> "1503 mul_27"; -"1500 _param_constant223" -> "1501 clamp_13"; -"1501 clamp_13" -> "1502 exp_13"; -"1502 exp_13" -> "1503 mul_27"; -"1503 mul_27" -> "1504 add_45"; -"1504 add_45" -> "1561 view_74"; -"1505 new_zeros_6" -> "1508 slice_195"; -"1505 new_zeros_6" -> "1513 slice_197"; -"1505 new_zeros_6" -> "1518 slice_199"; -"1505 new_zeros_6" -> "1523 slice_201"; -"1505 new_zeros_6" -> "1528 slice_203"; -"1505 new_zeros_6" -> "1533 slice_205"; -"1505 new_zeros_6" -> "1538 slice_207"; -"1505 new_zeros_6" -> "1543 slice_209"; -"1505 new_zeros_6" -> "1548 slice_211"; -"1505 new_zeros_6" -> "1551 view_73"; -"1506 _tensor_constant82" -> "1507 lift_fresh_copy_54"; -"1507 lift_fresh_copy_54" -> "1510 fill__54"; -"1508 slice_195" -> "1509 slice_196"; -"1509 slice_196" -> "1510 fill__54"; -"1511 _tensor_constant83" -> "1512 lift_fresh_copy_55"; -"1512 lift_fresh_copy_55" -> "1515 fill__55"; -"1513 slice_197" -> "1514 slice_198"; -"1514 slice_198" -> "1515 fill__55"; -"1516 _tensor_constant84" -> "1517 lift_fresh_copy_56"; -"1517 lift_fresh_copy_56" -> "1520 fill__56"; -"1518 slice_199" -> "1519 slice_200"; -"1519 slice_200" -> "1520 fill__56"; -"1521 _tensor_constant85" -> "1522 lift_fresh_copy_57"; -"1522 lift_fresh_copy_57" -> "1525 fill__57"; -"1523 slice_201" -> "1524 slice_202"; -"1524 slice_202" -> "1525 fill__57"; -"1526 _tensor_constant86" -> "1527 lift_fresh_copy_58"; -"1527 lift_fresh_copy_58" -> "1530 fill__58"; -"1528 slice_203" -> "1529 slice_204"; -"1529 slice_204" -> "1530 fill__58"; -"1531 _tensor_constant87" -> "1532 lift_fresh_copy_59"; -"1532 lift_fresh_copy_59" -> "1535 fill__59"; -"1533 slice_205" -> "1534 slice_206"; -"1534 slice_206" -> "1535 fill__59"; -"1536 _tensor_constant88" -> "1537 lift_fresh_copy_60"; -"1537 lift_fresh_copy_60" -> "1540 fill__60"; -"1538 slice_207" -> "1539 slice_208"; -"1539 slice_208" -> "1540 fill__60"; -"1541 _tensor_constant89" -> "1542 lift_fresh_copy_61"; -"1542 lift_fresh_copy_61" -> "1545 fill__61"; -"1543 slice_209" -> "1544 slice_210"; -"1544 slice_210" -> "1545 fill__61"; -"1546 _tensor_constant90" -> "1547 lift_fresh_copy_62"; -"1547 lift_fresh_copy_62" -> "1550 fill__62"; -"1548 slice_211" -> "1549 slice_212"; -"1549 slice_212" -> "1550 fill__62"; -"1551 view_73" -> "1552 permute_62"; -"1552 permute_62" -> "1553 reshape_60"; -"1553 reshape_60" -> "1554 unsqueeze_38"; -"1553 reshape_60" -> "1555 unsqueeze_39"; -"1554 unsqueeze_38" -> "1556 sub_6"; -"1555 unsqueeze_39" -> "1556 sub_6"; -"1556 sub_6" -> "1557 ne_6"; -"1556 sub_6" -> "1558 masked_fill_12"; -"1556 sub_6" -> "1559 eq_6"; -"1557 ne_6" -> "1558 masked_fill_12"; -"1558 masked_fill_12" -> "1560 masked_fill_13"; -"1559 eq_6" -> "1560 masked_fill_13"; -"1560 masked_fill_13" -> "1562 unsqueeze_40"; -"1561 view_74" -> "1564 add_46"; -"1562 unsqueeze_40" -> "1563 unsqueeze_41"; -"1563 unsqueeze_41" -> "1564 add_46"; -"1564 add_46" -> "1565 view_75"; -"1565 view_75" -> "1566 softmax_13"; -"1566 softmax_13" -> "1567 dropout_52"; -"1567 dropout_52" -> "1568 matmul_27"; -"1568 matmul_27" -> "1569 transpose_27"; -"1569 transpose_27" -> "1570 reshape_61"; -"1570 reshape_61" -> "1573 linear_83"; -"1571 _param_constant224" -> "1573 linear_83"; -"1572 _param_constant225" -> "1573 linear_83"; -"1573 linear_83" -> "1574 dropout_53"; -"1574 dropout_53" -> "1575 view_76"; -"1575 view_76" -> "1576 permute_63"; -"1576 permute_63" -> "1577 reshape_62"; -"1577 reshape_62" -> "1578 roll_13"; -"1578 roll_13" -> "1579 slice_213"; -"1579 slice_213" -> "1580 slice_214"; -"1580 slice_214" -> "1581 slice_215"; -"1581 slice_215" -> "1582 slice_216"; -"1582 slice_216" -> "1583 contiguous_25"; -"1583 contiguous_25" -> "1586 layer_norm_29"; -"1584 _param_constant226" -> "1586 layer_norm_29"; -"1585 _param_constant227" -> "1586 layer_norm_29"; -"1586 layer_norm_29" -> "1587 add_47"; -"1587 add_47" -> "1590 linear_84"; -"1587 add_47" -> "1600 add_48"; -"1588 _param_constant228" -> "1590 linear_84"; -"1589 _param_constant229" -> "1590 linear_84"; -"1590 linear_84" -> "1591 gelu_13"; -"1591 gelu_13" -> "1592 dropout_54"; -"1592 dropout_54" -> "1595 linear_85"; -"1593 _param_constant230" -> "1595 linear_85"; -"1594 _param_constant231" -> "1595 linear_85"; -"1595 linear_85" -> "1596 dropout_55"; -"1596 dropout_55" -> "1599 layer_norm_30"; -"1597 _param_constant232" -> "1599 layer_norm_30"; -"1598 _param_constant233" -> "1599 layer_norm_30"; -"1599 layer_norm_30" -> "1600 add_48"; -"1600 add_48" -> "1617 pad_16"; -"1600 add_48" -> "1667 add_50"; -"1601 _tensor_constant91" -> "1604 linear_86"; -"1602 _param_constant234" -> "1604 linear_86"; -"1603 _param_constant235" -> "1604 linear_86"; -"1604 linear_86" -> "1605 relu__14"; -"1605 relu__14" -> "1607 linear_87"; -"1606 _param_constant236" -> "1607 linear_87"; -"1607 linear_87" -> "1608 view_77"; -"1608 view_77" -> "1610 index_14"; -"1609 _tensor_constant92" -> "1610 index_14"; -"1610 index_14" -> "1611 view_78"; -"1611 view_78" -> "1612 permute_64"; -"1612 permute_64" -> "1613 contiguous_26"; -"1613 contiguous_26" -> "1614 unsqueeze_42"; -"1614 unsqueeze_42" -> "1615 sigmoid_14"; -"1615 sigmoid_14" -> "1616 mul_28"; -"1616 mul_28" -> "1646 add_49"; -"1617 pad_16" -> "1618 view_79"; -"1618 view_79" -> "1619 permute_65"; -"1619 permute_65" -> "1620 reshape_63"; -"1620 reshape_63" -> "1626 linear_88"; -"1621 _param_constant237" -> "1622 clone_14"; -"1622 clone_14" -> "1623 slice_217"; -"1622 clone_14" -> "1626 linear_88"; -"1623 slice_217" -> "1624 zero__14"; -"1625 _param_constant238" -> "1626 linear_88"; -"1626 linear_88" -> "1627 reshape_64"; -"1627 reshape_64" -> "1628 permute_66"; -"1628 permute_66" -> "1629 select_42"; -"1628 permute_66" -> "1630 select_43"; -"1628 permute_66" -> "1631 select_44"; -"1629 select_42" -> "1632 linalg_vector_norm_28"; -"1629 select_42" -> "1634 expand_as_28"; -"1629 select_42" -> "1635 div_28"; -"1630 select_43" -> "1636 linalg_vector_norm_29"; -"1630 select_43" -> "1638 expand_as_29"; -"1630 select_43" -> "1639 div_29"; -"1631 select_44" -> "1649 matmul_29"; -"1632 linalg_vector_norm_28" -> "1633 clamp_min_28"; -"1633 clamp_min_28" -> "1634 expand_as_28"; -"1634 expand_as_28" -> "1635 div_28"; -"1635 div_28" -> "1641 matmul_28"; -"1636 linalg_vector_norm_29" -> "1637 clamp_min_29"; -"1637 clamp_min_29" -> "1638 expand_as_29"; -"1638 expand_as_29" -> "1639 div_29"; -"1639 div_29" -> "1640 transpose_28"; -"1640 transpose_28" -> "1641 matmul_28"; -"1641 matmul_28" -> "1645 mul_29"; -"1642 _param_constant239" -> "1643 clamp_14"; -"1643 clamp_14" -> "1644 exp_14"; -"1644 exp_14" -> "1645 mul_29"; -"1645 mul_29" -> "1646 add_49"; -"1646 add_49" -> "1647 softmax_14"; -"1647 softmax_14" -> "1648 dropout_56"; -"1648 dropout_56" -> "1649 matmul_29"; -"1649 matmul_29" -> "1650 transpose_29"; -"1650 transpose_29" -> "1651 reshape_65"; -"1651 reshape_65" -> "1654 linear_89"; -"1652 _param_constant240" -> "1654 linear_89"; -"1653 _param_constant241" -> "1654 linear_89"; -"1654 linear_89" -> "1655 dropout_57"; -"1655 dropout_57" -> "1656 view_80"; -"1656 view_80" -> "1657 permute_67"; -"1657 permute_67" -> "1658 reshape_66"; -"1658 reshape_66" -> "1659 slice_218"; -"1659 slice_218" -> "1660 slice_219"; -"1660 slice_219" -> "1661 slice_220"; -"1661 slice_220" -> "1662 slice_221"; -"1662 slice_221" -> "1663 contiguous_27"; -"1663 contiguous_27" -> "1666 layer_norm_31"; -"1664 _param_constant242" -> "1666 layer_norm_31"; -"1665 _param_constant243" -> "1666 layer_norm_31"; -"1666 layer_norm_31" -> "1667 add_50"; -"1667 add_50" -> "1670 linear_90"; -"1667 add_50" -> "1680 add_51"; -"1668 _param_constant244" -> "1670 linear_90"; -"1669 _param_constant245" -> "1670 linear_90"; -"1670 linear_90" -> "1671 gelu_14"; -"1671 gelu_14" -> "1672 dropout_58"; -"1672 dropout_58" -> "1675 linear_91"; -"1673 _param_constant246" -> "1675 linear_91"; -"1674 _param_constant247" -> "1675 linear_91"; -"1675 linear_91" -> "1676 dropout_59"; -"1676 dropout_59" -> "1679 layer_norm_32"; -"1677 _param_constant248" -> "1679 layer_norm_32"; -"1678 _param_constant249" -> "1679 layer_norm_32"; -"1679 layer_norm_32" -> "1680 add_51"; -"1680 add_51" -> "1697 pad_17"; -"1680 add_51" -> "1810 add_54"; -"1681 _tensor_constant93" -> "1684 linear_92"; -"1682 _param_constant250" -> "1684 linear_92"; -"1683 _param_constant251" -> "1684 linear_92"; -"1684 linear_92" -> "1685 relu__15"; -"1685 relu__15" -> "1687 linear_93"; -"1686 _param_constant252" -> "1687 linear_93"; -"1687 linear_93" -> "1688 view_81"; -"1688 view_81" -> "1690 index_15"; -"1689 _tensor_constant94" -> "1690 index_15"; -"1690 index_15" -> "1691 view_82"; -"1691 view_82" -> "1692 permute_68"; -"1692 permute_68" -> "1693 contiguous_28"; -"1693 contiguous_28" -> "1694 unsqueeze_43"; -"1694 unsqueeze_43" -> "1695 sigmoid_15"; -"1695 sigmoid_15" -> "1696 mul_30"; -"1696 mul_30" -> "1727 add_52"; -"1697 pad_17" -> "1698 roll_14"; -"1698 roll_14" -> "1699 view_83"; -"1699 view_83" -> "1700 permute_69"; -"1700 permute_69" -> "1701 reshape_67"; -"1701 reshape_67" -> "1707 linear_94"; -"1701 reshape_67" -> "1728 new_zeros_7"; -"1702 _param_constant253" -> "1703 clone_15"; -"1703 clone_15" -> "1704 slice_222"; -"1703 clone_15" -> "1707 linear_94"; -"1704 slice_222" -> "1705 zero__15"; -"1706 _param_constant254" -> "1707 linear_94"; -"1707 linear_94" -> "1708 reshape_68"; -"1708 reshape_68" -> "1709 permute_70"; -"1709 permute_70" -> "1710 select_45"; -"1709 permute_70" -> "1711 select_46"; -"1709 permute_70" -> "1712 select_47"; -"1710 select_45" -> "1713 linalg_vector_norm_30"; -"1710 select_45" -> "1715 expand_as_30"; -"1710 select_45" -> "1716 div_30"; -"1711 select_46" -> "1717 linalg_vector_norm_31"; -"1711 select_46" -> "1719 expand_as_31"; -"1711 select_46" -> "1720 div_31"; -"1712 select_47" -> "1791 matmul_31"; -"1713 linalg_vector_norm_30" -> "1714 clamp_min_30"; -"1714 clamp_min_30" -> "1715 expand_as_30"; -"1715 expand_as_30" -> "1716 div_30"; -"1716 div_30" -> "1722 matmul_30"; -"1717 linalg_vector_norm_31" -> "1718 clamp_min_31"; -"1718 clamp_min_31" -> "1719 expand_as_31"; -"1719 expand_as_31" -> "1720 div_31"; -"1720 div_31" -> "1721 transpose_30"; -"1721 transpose_30" -> "1722 matmul_30"; -"1722 matmul_30" -> "1726 mul_31"; -"1723 _param_constant255" -> "1724 clamp_15"; -"1724 clamp_15" -> "1725 exp_15"; -"1725 exp_15" -> "1726 mul_31"; -"1726 mul_31" -> "1727 add_52"; -"1727 add_52" -> "1784 view_85"; -"1728 new_zeros_7" -> "1731 slice_223"; -"1728 new_zeros_7" -> "1736 slice_225"; -"1728 new_zeros_7" -> "1741 slice_227"; -"1728 new_zeros_7" -> "1746 slice_229"; -"1728 new_zeros_7" -> "1751 slice_231"; -"1728 new_zeros_7" -> "1756 slice_233"; -"1728 new_zeros_7" -> "1761 slice_235"; -"1728 new_zeros_7" -> "1766 slice_237"; -"1728 new_zeros_7" -> "1771 slice_239"; -"1728 new_zeros_7" -> "1774 view_84"; -"1729 _tensor_constant95" -> "1730 lift_fresh_copy_63"; -"1730 lift_fresh_copy_63" -> "1733 fill__63"; -"1731 slice_223" -> "1732 slice_224"; -"1732 slice_224" -> "1733 fill__63"; -"1734 _tensor_constant96" -> "1735 lift_fresh_copy_64"; -"1735 lift_fresh_copy_64" -> "1738 fill__64"; -"1736 slice_225" -> "1737 slice_226"; -"1737 slice_226" -> "1738 fill__64"; -"1739 _tensor_constant97" -> "1740 lift_fresh_copy_65"; -"1740 lift_fresh_copy_65" -> "1743 fill__65"; -"1741 slice_227" -> "1742 slice_228"; -"1742 slice_228" -> "1743 fill__65"; -"1744 _tensor_constant98" -> "1745 lift_fresh_copy_66"; -"1745 lift_fresh_copy_66" -> "1748 fill__66"; -"1746 slice_229" -> "1747 slice_230"; -"1747 slice_230" -> "1748 fill__66"; -"1749 _tensor_constant99" -> "1750 lift_fresh_copy_67"; -"1750 lift_fresh_copy_67" -> "1753 fill__67"; -"1751 slice_231" -> "1752 slice_232"; -"1752 slice_232" -> "1753 fill__67"; -"1754 _tensor_constant100" -> "1755 lift_fresh_copy_68"; -"1755 lift_fresh_copy_68" -> "1758 fill__68"; -"1756 slice_233" -> "1757 slice_234"; -"1757 slice_234" -> "1758 fill__68"; -"1759 _tensor_constant101" -> "1760 lift_fresh_copy_69"; -"1760 lift_fresh_copy_69" -> "1763 fill__69"; -"1761 slice_235" -> "1762 slice_236"; -"1762 slice_236" -> "1763 fill__69"; -"1764 _tensor_constant102" -> "1765 lift_fresh_copy_70"; -"1765 lift_fresh_copy_70" -> "1768 fill__70"; -"1766 slice_237" -> "1767 slice_238"; -"1767 slice_238" -> "1768 fill__70"; -"1769 _tensor_constant103" -> "1770 lift_fresh_copy_71"; -"1770 lift_fresh_copy_71" -> "1773 fill__71"; -"1771 slice_239" -> "1772 slice_240"; -"1772 slice_240" -> "1773 fill__71"; -"1774 view_84" -> "1775 permute_71"; -"1775 permute_71" -> "1776 reshape_69"; -"1776 reshape_69" -> "1777 unsqueeze_44"; -"1776 reshape_69" -> "1778 unsqueeze_45"; -"1777 unsqueeze_44" -> "1779 sub_7"; -"1778 unsqueeze_45" -> "1779 sub_7"; -"1779 sub_7" -> "1780 ne_7"; -"1779 sub_7" -> "1781 masked_fill_14"; -"1779 sub_7" -> "1782 eq_7"; -"1780 ne_7" -> "1781 masked_fill_14"; -"1781 masked_fill_14" -> "1783 masked_fill_15"; -"1782 eq_7" -> "1783 masked_fill_15"; -"1783 masked_fill_15" -> "1785 unsqueeze_46"; -"1784 view_85" -> "1787 add_53"; -"1785 unsqueeze_46" -> "1786 unsqueeze_47"; -"1786 unsqueeze_47" -> "1787 add_53"; -"1787 add_53" -> "1788 view_86"; -"1788 view_86" -> "1789 softmax_15"; -"1789 softmax_15" -> "1790 dropout_60"; -"1790 dropout_60" -> "1791 matmul_31"; -"1791 matmul_31" -> "1792 transpose_31"; -"1792 transpose_31" -> "1793 reshape_70"; -"1793 reshape_70" -> "1796 linear_95"; -"1794 _param_constant256" -> "1796 linear_95"; -"1795 _param_constant257" -> "1796 linear_95"; -"1796 linear_95" -> "1797 dropout_61"; -"1797 dropout_61" -> "1798 view_87"; -"1798 view_87" -> "1799 permute_72"; -"1799 permute_72" -> "1800 reshape_71"; -"1800 reshape_71" -> "1801 roll_15"; -"1801 roll_15" -> "1802 slice_241"; -"1802 slice_241" -> "1803 slice_242"; -"1803 slice_242" -> "1804 slice_243"; -"1804 slice_243" -> "1805 slice_244"; -"1805 slice_244" -> "1806 contiguous_29"; -"1806 contiguous_29" -> "1809 layer_norm_33"; -"1807 _param_constant258" -> "1809 layer_norm_33"; -"1808 _param_constant259" -> "1809 layer_norm_33"; -"1809 layer_norm_33" -> "1810 add_54"; -"1810 add_54" -> "1813 linear_96"; -"1810 add_54" -> "1823 add_55"; -"1811 _param_constant260" -> "1813 linear_96"; -"1812 _param_constant261" -> "1813 linear_96"; -"1813 linear_96" -> "1814 gelu_15"; -"1814 gelu_15" -> "1815 dropout_62"; -"1815 dropout_62" -> "1818 linear_97"; -"1816 _param_constant262" -> "1818 linear_97"; -"1817 _param_constant263" -> "1818 linear_97"; -"1818 linear_97" -> "1819 dropout_63"; -"1819 dropout_63" -> "1822 layer_norm_34"; -"1820 _param_constant264" -> "1822 layer_norm_34"; -"1821 _param_constant265" -> "1822 layer_norm_34"; -"1822 layer_norm_34" -> "1823 add_55"; -"1823 add_55" -> "1840 pad_18"; -"1823 add_55" -> "1890 add_57"; -"1824 _tensor_constant104" -> "1827 linear_98"; -"1825 _param_constant266" -> "1827 linear_98"; -"1826 _param_constant267" -> "1827 linear_98"; -"1827 linear_98" -> "1828 relu__16"; -"1828 relu__16" -> "1830 linear_99"; -"1829 _param_constant268" -> "1830 linear_99"; -"1830 linear_99" -> "1831 view_88"; -"1831 view_88" -> "1833 index_16"; -"1832 _tensor_constant105" -> "1833 index_16"; -"1833 index_16" -> "1834 view_89"; -"1834 view_89" -> "1835 permute_73"; -"1835 permute_73" -> "1836 contiguous_30"; -"1836 contiguous_30" -> "1837 unsqueeze_48"; -"1837 unsqueeze_48" -> "1838 sigmoid_16"; -"1838 sigmoid_16" -> "1839 mul_32"; -"1839 mul_32" -> "1869 add_56"; -"1840 pad_18" -> "1841 view_90"; -"1841 view_90" -> "1842 permute_74"; -"1842 permute_74" -> "1843 reshape_72"; -"1843 reshape_72" -> "1849 linear_100"; -"1844 _param_constant269" -> "1845 clone_16"; -"1845 clone_16" -> "1846 slice_245"; -"1845 clone_16" -> "1849 linear_100"; -"1846 slice_245" -> "1847 zero__16"; -"1848 _param_constant270" -> "1849 linear_100"; -"1849 linear_100" -> "1850 reshape_73"; -"1850 reshape_73" -> "1851 permute_75"; -"1851 permute_75" -> "1852 select_48"; -"1851 permute_75" -> "1853 select_49"; -"1851 permute_75" -> "1854 select_50"; -"1852 select_48" -> "1855 linalg_vector_norm_32"; -"1852 select_48" -> "1857 expand_as_32"; -"1852 select_48" -> "1858 div_32"; -"1853 select_49" -> "1859 linalg_vector_norm_33"; -"1853 select_49" -> "1861 expand_as_33"; -"1853 select_49" -> "1862 div_33"; -"1854 select_50" -> "1872 matmul_33"; -"1855 linalg_vector_norm_32" -> "1856 clamp_min_32"; -"1856 clamp_min_32" -> "1857 expand_as_32"; -"1857 expand_as_32" -> "1858 div_32"; -"1858 div_32" -> "1864 matmul_32"; -"1859 linalg_vector_norm_33" -> "1860 clamp_min_33"; -"1860 clamp_min_33" -> "1861 expand_as_33"; -"1861 expand_as_33" -> "1862 div_33"; -"1862 div_33" -> "1863 transpose_32"; -"1863 transpose_32" -> "1864 matmul_32"; -"1864 matmul_32" -> "1868 mul_33"; -"1865 _param_constant271" -> "1866 clamp_16"; -"1866 clamp_16" -> "1867 exp_16"; -"1867 exp_16" -> "1868 mul_33"; -"1868 mul_33" -> "1869 add_56"; -"1869 add_56" -> "1870 softmax_16"; -"1870 softmax_16" -> "1871 dropout_64"; -"1871 dropout_64" -> "1872 matmul_33"; -"1872 matmul_33" -> "1873 transpose_33"; -"1873 transpose_33" -> "1874 reshape_74"; -"1874 reshape_74" -> "1877 linear_101"; -"1875 _param_constant272" -> "1877 linear_101"; -"1876 _param_constant273" -> "1877 linear_101"; -"1877 linear_101" -> "1878 dropout_65"; -"1878 dropout_65" -> "1879 view_91"; -"1879 view_91" -> "1880 permute_76"; -"1880 permute_76" -> "1881 reshape_75"; -"1881 reshape_75" -> "1882 slice_246"; -"1882 slice_246" -> "1883 slice_247"; -"1883 slice_247" -> "1884 slice_248"; -"1884 slice_248" -> "1885 slice_249"; -"1885 slice_249" -> "1886 contiguous_31"; -"1886 contiguous_31" -> "1889 layer_norm_35"; -"1887 _param_constant274" -> "1889 layer_norm_35"; -"1888 _param_constant275" -> "1889 layer_norm_35"; -"1889 layer_norm_35" -> "1890 add_57"; -"1890 add_57" -> "1893 linear_102"; -"1890 add_57" -> "1903 add_58"; -"1891 _param_constant276" -> "1893 linear_102"; -"1892 _param_constant277" -> "1893 linear_102"; -"1893 linear_102" -> "1894 gelu_16"; -"1894 gelu_16" -> "1895 dropout_66"; -"1895 dropout_66" -> "1898 linear_103"; -"1896 _param_constant278" -> "1898 linear_103"; -"1897 _param_constant279" -> "1898 linear_103"; -"1898 linear_103" -> "1899 dropout_67"; -"1899 dropout_67" -> "1902 layer_norm_36"; -"1900 _param_constant280" -> "1902 layer_norm_36"; -"1901 _param_constant281" -> "1902 layer_norm_36"; -"1902 layer_norm_36" -> "1903 add_58"; -"1903 add_58" -> "1920 pad_19"; -"1903 add_58" -> "2033 add_61"; -"1904 _tensor_constant106" -> "1907 linear_104"; -"1905 _param_constant282" -> "1907 linear_104"; -"1906 _param_constant283" -> "1907 linear_104"; -"1907 linear_104" -> "1908 relu__17"; -"1908 relu__17" -> "1910 linear_105"; -"1909 _param_constant284" -> "1910 linear_105"; -"1910 linear_105" -> "1911 view_92"; -"1911 view_92" -> "1913 index_17"; -"1912 _tensor_constant107" -> "1913 index_17"; -"1913 index_17" -> "1914 view_93"; -"1914 view_93" -> "1915 permute_77"; -"1915 permute_77" -> "1916 contiguous_32"; -"1916 contiguous_32" -> "1917 unsqueeze_49"; -"1917 unsqueeze_49" -> "1918 sigmoid_17"; -"1918 sigmoid_17" -> "1919 mul_34"; -"1919 mul_34" -> "1950 add_59"; -"1920 pad_19" -> "1921 roll_16"; -"1921 roll_16" -> "1922 view_94"; -"1922 view_94" -> "1923 permute_78"; -"1923 permute_78" -> "1924 reshape_76"; -"1924 reshape_76" -> "1930 linear_106"; -"1924 reshape_76" -> "1951 new_zeros_8"; -"1925 _param_constant285" -> "1926 clone_17"; -"1926 clone_17" -> "1927 slice_250"; -"1926 clone_17" -> "1930 linear_106"; -"1927 slice_250" -> "1928 zero__17"; -"1929 _param_constant286" -> "1930 linear_106"; -"1930 linear_106" -> "1931 reshape_77"; -"1931 reshape_77" -> "1932 permute_79"; -"1932 permute_79" -> "1933 select_51"; -"1932 permute_79" -> "1934 select_52"; -"1932 permute_79" -> "1935 select_53"; -"1933 select_51" -> "1936 linalg_vector_norm_34"; -"1933 select_51" -> "1938 expand_as_34"; -"1933 select_51" -> "1939 div_34"; -"1934 select_52" -> "1940 linalg_vector_norm_35"; -"1934 select_52" -> "1942 expand_as_35"; -"1934 select_52" -> "1943 div_35"; -"1935 select_53" -> "2014 matmul_35"; -"1936 linalg_vector_norm_34" -> "1937 clamp_min_34"; -"1937 clamp_min_34" -> "1938 expand_as_34"; -"1938 expand_as_34" -> "1939 div_34"; -"1939 div_34" -> "1945 matmul_34"; -"1940 linalg_vector_norm_35" -> "1941 clamp_min_35"; -"1941 clamp_min_35" -> "1942 expand_as_35"; -"1942 expand_as_35" -> "1943 div_35"; -"1943 div_35" -> "1944 transpose_34"; -"1944 transpose_34" -> "1945 matmul_34"; -"1945 matmul_34" -> "1949 mul_35"; -"1946 _param_constant287" -> "1947 clamp_17"; -"1947 clamp_17" -> "1948 exp_17"; -"1948 exp_17" -> "1949 mul_35"; -"1949 mul_35" -> "1950 add_59"; -"1950 add_59" -> "2007 view_96"; -"1951 new_zeros_8" -> "1954 slice_251"; -"1951 new_zeros_8" -> "1959 slice_253"; -"1951 new_zeros_8" -> "1964 slice_255"; -"1951 new_zeros_8" -> "1969 slice_257"; -"1951 new_zeros_8" -> "1974 slice_259"; -"1951 new_zeros_8" -> "1979 slice_261"; -"1951 new_zeros_8" -> "1984 slice_263"; -"1951 new_zeros_8" -> "1989 slice_265"; -"1951 new_zeros_8" -> "1994 slice_267"; -"1951 new_zeros_8" -> "1997 view_95"; -"1952 _tensor_constant108" -> "1953 lift_fresh_copy_72"; -"1953 lift_fresh_copy_72" -> "1956 fill__72"; -"1954 slice_251" -> "1955 slice_252"; -"1955 slice_252" -> "1956 fill__72"; -"1957 _tensor_constant109" -> "1958 lift_fresh_copy_73"; -"1958 lift_fresh_copy_73" -> "1961 fill__73"; -"1959 slice_253" -> "1960 slice_254"; -"1960 slice_254" -> "1961 fill__73"; -"1962 _tensor_constant110" -> "1963 lift_fresh_copy_74"; -"1963 lift_fresh_copy_74" -> "1966 fill__74"; -"1964 slice_255" -> "1965 slice_256"; -"1965 slice_256" -> "1966 fill__74"; -"1967 _tensor_constant111" -> "1968 lift_fresh_copy_75"; -"1968 lift_fresh_copy_75" -> "1971 fill__75"; -"1969 slice_257" -> "1970 slice_258"; -"1970 slice_258" -> "1971 fill__75"; -"1972 _tensor_constant112" -> "1973 lift_fresh_copy_76"; -"1973 lift_fresh_copy_76" -> "1976 fill__76"; -"1974 slice_259" -> "1975 slice_260"; -"1975 slice_260" -> "1976 fill__76"; -"1977 _tensor_constant113" -> "1978 lift_fresh_copy_77"; -"1978 lift_fresh_copy_77" -> "1981 fill__77"; -"1979 slice_261" -> "1980 slice_262"; -"1980 slice_262" -> "1981 fill__77"; -"1982 _tensor_constant114" -> "1983 lift_fresh_copy_78"; -"1983 lift_fresh_copy_78" -> "1986 fill__78"; -"1984 slice_263" -> "1985 slice_264"; -"1985 slice_264" -> "1986 fill__78"; -"1987 _tensor_constant115" -> "1988 lift_fresh_copy_79"; -"1988 lift_fresh_copy_79" -> "1991 fill__79"; -"1989 slice_265" -> "1990 slice_266"; -"1990 slice_266" -> "1991 fill__79"; -"1992 _tensor_constant116" -> "1993 lift_fresh_copy_80"; -"1993 lift_fresh_copy_80" -> "1996 fill__80"; -"1994 slice_267" -> "1995 slice_268"; -"1995 slice_268" -> "1996 fill__80"; -"1997 view_95" -> "1998 permute_80"; -"1998 permute_80" -> "1999 reshape_78"; -"1999 reshape_78" -> "2000 unsqueeze_50"; -"1999 reshape_78" -> "2001 unsqueeze_51"; -"2000 unsqueeze_50" -> "2002 sub_8"; -"2001 unsqueeze_51" -> "2002 sub_8"; -"2002 sub_8" -> "2003 ne_8"; -"2002 sub_8" -> "2004 masked_fill_16"; -"2002 sub_8" -> "2005 eq_8"; -"2003 ne_8" -> "2004 masked_fill_16"; -"2004 masked_fill_16" -> "2006 masked_fill_17"; -"2005 eq_8" -> "2006 masked_fill_17"; -"2006 masked_fill_17" -> "2008 unsqueeze_52"; -"2007 view_96" -> "2010 add_60"; -"2008 unsqueeze_52" -> "2009 unsqueeze_53"; -"2009 unsqueeze_53" -> "2010 add_60"; -"2010 add_60" -> "2011 view_97"; -"2011 view_97" -> "2012 softmax_17"; -"2012 softmax_17" -> "2013 dropout_68"; -"2013 dropout_68" -> "2014 matmul_35"; -"2014 matmul_35" -> "2015 transpose_35"; -"2015 transpose_35" -> "2016 reshape_79"; -"2016 reshape_79" -> "2019 linear_107"; -"2017 _param_constant288" -> "2019 linear_107"; -"2018 _param_constant289" -> "2019 linear_107"; -"2019 linear_107" -> "2020 dropout_69"; -"2020 dropout_69" -> "2021 view_98"; -"2021 view_98" -> "2022 permute_81"; -"2022 permute_81" -> "2023 reshape_80"; -"2023 reshape_80" -> "2024 roll_17"; -"2024 roll_17" -> "2025 slice_269"; -"2025 slice_269" -> "2026 slice_270"; -"2026 slice_270" -> "2027 slice_271"; -"2027 slice_271" -> "2028 slice_272"; -"2028 slice_272" -> "2029 contiguous_33"; -"2029 contiguous_33" -> "2032 layer_norm_37"; -"2030 _param_constant290" -> "2032 layer_norm_37"; -"2031 _param_constant291" -> "2032 layer_norm_37"; -"2032 layer_norm_37" -> "2033 add_61"; -"2033 add_61" -> "2036 linear_108"; -"2033 add_61" -> "2046 add_62"; -"2034 _param_constant292" -> "2036 linear_108"; -"2035 _param_constant293" -> "2036 linear_108"; -"2036 linear_108" -> "2037 gelu_17"; -"2037 gelu_17" -> "2038 dropout_70"; -"2038 dropout_70" -> "2041 linear_109"; -"2039 _param_constant294" -> "2041 linear_109"; -"2040 _param_constant295" -> "2041 linear_109"; -"2041 linear_109" -> "2042 dropout_71"; -"2042 dropout_71" -> "2045 layer_norm_38"; -"2043 _param_constant296" -> "2045 layer_norm_38"; -"2044 _param_constant297" -> "2045 layer_norm_38"; -"2045 layer_norm_38" -> "2046 add_62"; -"2046 add_62" -> "2063 pad_20"; -"2046 add_62" -> "2113 add_64"; -"2047 _tensor_constant117" -> "2050 linear_110"; -"2048 _param_constant298" -> "2050 linear_110"; -"2049 _param_constant299" -> "2050 linear_110"; -"2050 linear_110" -> "2051 relu__18"; -"2051 relu__18" -> "2053 linear_111"; -"2052 _param_constant300" -> "2053 linear_111"; -"2053 linear_111" -> "2054 view_99"; -"2054 view_99" -> "2056 index_18"; -"2055 _tensor_constant118" -> "2056 index_18"; -"2056 index_18" -> "2057 view_100"; -"2057 view_100" -> "2058 permute_82"; -"2058 permute_82" -> "2059 contiguous_34"; -"2059 contiguous_34" -> "2060 unsqueeze_54"; -"2060 unsqueeze_54" -> "2061 sigmoid_18"; -"2061 sigmoid_18" -> "2062 mul_36"; -"2062 mul_36" -> "2092 add_63"; -"2063 pad_20" -> "2064 view_101"; -"2064 view_101" -> "2065 permute_83"; -"2065 permute_83" -> "2066 reshape_81"; -"2066 reshape_81" -> "2072 linear_112"; -"2067 _param_constant301" -> "2068 clone_18"; -"2068 clone_18" -> "2069 slice_273"; -"2068 clone_18" -> "2072 linear_112"; -"2069 slice_273" -> "2070 zero__18"; -"2071 _param_constant302" -> "2072 linear_112"; -"2072 linear_112" -> "2073 reshape_82"; -"2073 reshape_82" -> "2074 permute_84"; -"2074 permute_84" -> "2075 select_54"; -"2074 permute_84" -> "2076 select_55"; -"2074 permute_84" -> "2077 select_56"; -"2075 select_54" -> "2078 linalg_vector_norm_36"; -"2075 select_54" -> "2080 expand_as_36"; -"2075 select_54" -> "2081 div_36"; -"2076 select_55" -> "2082 linalg_vector_norm_37"; -"2076 select_55" -> "2084 expand_as_37"; -"2076 select_55" -> "2085 div_37"; -"2077 select_56" -> "2095 matmul_37"; -"2078 linalg_vector_norm_36" -> "2079 clamp_min_36"; -"2079 clamp_min_36" -> "2080 expand_as_36"; -"2080 expand_as_36" -> "2081 div_36"; -"2081 div_36" -> "2087 matmul_36"; -"2082 linalg_vector_norm_37" -> "2083 clamp_min_37"; -"2083 clamp_min_37" -> "2084 expand_as_37"; -"2084 expand_as_37" -> "2085 div_37"; -"2085 div_37" -> "2086 transpose_36"; -"2086 transpose_36" -> "2087 matmul_36"; -"2087 matmul_36" -> "2091 mul_37"; -"2088 _param_constant303" -> "2089 clamp_18"; -"2089 clamp_18" -> "2090 exp_18"; -"2090 exp_18" -> "2091 mul_37"; -"2091 mul_37" -> "2092 add_63"; -"2092 add_63" -> "2093 softmax_18"; -"2093 softmax_18" -> "2094 dropout_72"; -"2094 dropout_72" -> "2095 matmul_37"; -"2095 matmul_37" -> "2096 transpose_37"; -"2096 transpose_37" -> "2097 reshape_83"; -"2097 reshape_83" -> "2100 linear_113"; -"2098 _param_constant304" -> "2100 linear_113"; -"2099 _param_constant305" -> "2100 linear_113"; -"2100 linear_113" -> "2101 dropout_73"; -"2101 dropout_73" -> "2102 view_102"; -"2102 view_102" -> "2103 permute_85"; -"2103 permute_85" -> "2104 reshape_84"; -"2104 reshape_84" -> "2105 slice_274"; -"2105 slice_274" -> "2106 slice_275"; -"2106 slice_275" -> "2107 slice_276"; -"2107 slice_276" -> "2108 slice_277"; -"2108 slice_277" -> "2109 contiguous_35"; -"2109 contiguous_35" -> "2112 layer_norm_39"; -"2110 _param_constant306" -> "2112 layer_norm_39"; -"2111 _param_constant307" -> "2112 layer_norm_39"; -"2112 layer_norm_39" -> "2113 add_64"; -"2113 add_64" -> "2116 linear_114"; -"2113 add_64" -> "2126 add_65"; -"2114 _param_constant308" -> "2116 linear_114"; -"2115 _param_constant309" -> "2116 linear_114"; -"2116 linear_114" -> "2117 gelu_18"; -"2117 gelu_18" -> "2118 dropout_74"; -"2118 dropout_74" -> "2121 linear_115"; -"2119 _param_constant310" -> "2121 linear_115"; -"2120 _param_constant311" -> "2121 linear_115"; -"2121 linear_115" -> "2122 dropout_75"; -"2122 dropout_75" -> "2125 layer_norm_40"; -"2123 _param_constant312" -> "2125 layer_norm_40"; -"2124 _param_constant313" -> "2125 layer_norm_40"; -"2125 layer_norm_40" -> "2126 add_65"; -"2126 add_65" -> "2143 pad_21"; -"2126 add_65" -> "2256 add_68"; -"2127 _tensor_constant119" -> "2130 linear_116"; -"2128 _param_constant314" -> "2130 linear_116"; -"2129 _param_constant315" -> "2130 linear_116"; -"2130 linear_116" -> "2131 relu__19"; -"2131 relu__19" -> "2133 linear_117"; -"2132 _param_constant316" -> "2133 linear_117"; -"2133 linear_117" -> "2134 view_103"; -"2134 view_103" -> "2136 index_19"; -"2135 _tensor_constant120" -> "2136 index_19"; -"2136 index_19" -> "2137 view_104"; -"2137 view_104" -> "2138 permute_86"; -"2138 permute_86" -> "2139 contiguous_36"; -"2139 contiguous_36" -> "2140 unsqueeze_55"; -"2140 unsqueeze_55" -> "2141 sigmoid_19"; -"2141 sigmoid_19" -> "2142 mul_38"; -"2142 mul_38" -> "2173 add_66"; -"2143 pad_21" -> "2144 roll_18"; -"2144 roll_18" -> "2145 view_105"; -"2145 view_105" -> "2146 permute_87"; -"2146 permute_87" -> "2147 reshape_85"; -"2147 reshape_85" -> "2153 linear_118"; -"2147 reshape_85" -> "2174 new_zeros_9"; -"2148 _param_constant317" -> "2149 clone_19"; -"2149 clone_19" -> "2150 slice_278"; -"2149 clone_19" -> "2153 linear_118"; -"2150 slice_278" -> "2151 zero__19"; -"2152 _param_constant318" -> "2153 linear_118"; -"2153 linear_118" -> "2154 reshape_86"; -"2154 reshape_86" -> "2155 permute_88"; -"2155 permute_88" -> "2156 select_57"; -"2155 permute_88" -> "2157 select_58"; -"2155 permute_88" -> "2158 select_59"; -"2156 select_57" -> "2159 linalg_vector_norm_38"; -"2156 select_57" -> "2161 expand_as_38"; -"2156 select_57" -> "2162 div_38"; -"2157 select_58" -> "2163 linalg_vector_norm_39"; -"2157 select_58" -> "2165 expand_as_39"; -"2157 select_58" -> "2166 div_39"; -"2158 select_59" -> "2237 matmul_39"; -"2159 linalg_vector_norm_38" -> "2160 clamp_min_38"; -"2160 clamp_min_38" -> "2161 expand_as_38"; -"2161 expand_as_38" -> "2162 div_38"; -"2162 div_38" -> "2168 matmul_38"; -"2163 linalg_vector_norm_39" -> "2164 clamp_min_39"; -"2164 clamp_min_39" -> "2165 expand_as_39"; -"2165 expand_as_39" -> "2166 div_39"; -"2166 div_39" -> "2167 transpose_38"; -"2167 transpose_38" -> "2168 matmul_38"; -"2168 matmul_38" -> "2172 mul_39"; -"2169 _param_constant319" -> "2170 clamp_19"; -"2170 clamp_19" -> "2171 exp_19"; -"2171 exp_19" -> "2172 mul_39"; -"2172 mul_39" -> "2173 add_66"; -"2173 add_66" -> "2230 view_107"; -"2174 new_zeros_9" -> "2177 slice_279"; -"2174 new_zeros_9" -> "2182 slice_281"; -"2174 new_zeros_9" -> "2187 slice_283"; -"2174 new_zeros_9" -> "2192 slice_285"; -"2174 new_zeros_9" -> "2197 slice_287"; -"2174 new_zeros_9" -> "2202 slice_289"; -"2174 new_zeros_9" -> "2207 slice_291"; -"2174 new_zeros_9" -> "2212 slice_293"; -"2174 new_zeros_9" -> "2217 slice_295"; -"2174 new_zeros_9" -> "2220 view_106"; -"2175 _tensor_constant121" -> "2176 lift_fresh_copy_81"; -"2176 lift_fresh_copy_81" -> "2179 fill__81"; -"2177 slice_279" -> "2178 slice_280"; -"2178 slice_280" -> "2179 fill__81"; -"2180 _tensor_constant122" -> "2181 lift_fresh_copy_82"; -"2181 lift_fresh_copy_82" -> "2184 fill__82"; -"2182 slice_281" -> "2183 slice_282"; -"2183 slice_282" -> "2184 fill__82"; -"2185 _tensor_constant123" -> "2186 lift_fresh_copy_83"; -"2186 lift_fresh_copy_83" -> "2189 fill__83"; -"2187 slice_283" -> "2188 slice_284"; -"2188 slice_284" -> "2189 fill__83"; -"2190 _tensor_constant124" -> "2191 lift_fresh_copy_84"; -"2191 lift_fresh_copy_84" -> "2194 fill__84"; -"2192 slice_285" -> "2193 slice_286"; -"2193 slice_286" -> "2194 fill__84"; -"2195 _tensor_constant125" -> "2196 lift_fresh_copy_85"; -"2196 lift_fresh_copy_85" -> "2199 fill__85"; -"2197 slice_287" -> "2198 slice_288"; -"2198 slice_288" -> "2199 fill__85"; -"2200 _tensor_constant126" -> "2201 lift_fresh_copy_86"; -"2201 lift_fresh_copy_86" -> "2204 fill__86"; -"2202 slice_289" -> "2203 slice_290"; -"2203 slice_290" -> "2204 fill__86"; -"2205 _tensor_constant127" -> "2206 lift_fresh_copy_87"; -"2206 lift_fresh_copy_87" -> "2209 fill__87"; -"2207 slice_291" -> "2208 slice_292"; -"2208 slice_292" -> "2209 fill__87"; -"2210 _tensor_constant128" -> "2211 lift_fresh_copy_88"; -"2211 lift_fresh_copy_88" -> "2214 fill__88"; -"2212 slice_293" -> "2213 slice_294"; -"2213 slice_294" -> "2214 fill__88"; -"2215 _tensor_constant129" -> "2216 lift_fresh_copy_89"; -"2216 lift_fresh_copy_89" -> "2219 fill__89"; -"2217 slice_295" -> "2218 slice_296"; -"2218 slice_296" -> "2219 fill__89"; -"2220 view_106" -> "2221 permute_89"; -"2221 permute_89" -> "2222 reshape_87"; -"2222 reshape_87" -> "2223 unsqueeze_56"; -"2222 reshape_87" -> "2224 unsqueeze_57"; -"2223 unsqueeze_56" -> "2225 sub_9"; -"2224 unsqueeze_57" -> "2225 sub_9"; -"2225 sub_9" -> "2226 ne_9"; -"2225 sub_9" -> "2227 masked_fill_18"; -"2225 sub_9" -> "2228 eq_9"; -"2226 ne_9" -> "2227 masked_fill_18"; -"2227 masked_fill_18" -> "2229 masked_fill_19"; -"2228 eq_9" -> "2229 masked_fill_19"; -"2229 masked_fill_19" -> "2231 unsqueeze_58"; -"2230 view_107" -> "2233 add_67"; -"2231 unsqueeze_58" -> "2232 unsqueeze_59"; -"2232 unsqueeze_59" -> "2233 add_67"; -"2233 add_67" -> "2234 view_108"; -"2234 view_108" -> "2235 softmax_19"; -"2235 softmax_19" -> "2236 dropout_76"; -"2236 dropout_76" -> "2237 matmul_39"; -"2237 matmul_39" -> "2238 transpose_39"; -"2238 transpose_39" -> "2239 reshape_88"; -"2239 reshape_88" -> "2242 linear_119"; -"2240 _param_constant320" -> "2242 linear_119"; -"2241 _param_constant321" -> "2242 linear_119"; -"2242 linear_119" -> "2243 dropout_77"; -"2243 dropout_77" -> "2244 view_109"; -"2244 view_109" -> "2245 permute_90"; -"2245 permute_90" -> "2246 reshape_89"; -"2246 reshape_89" -> "2247 roll_19"; -"2247 roll_19" -> "2248 slice_297"; -"2248 slice_297" -> "2249 slice_298"; -"2249 slice_298" -> "2250 slice_299"; -"2250 slice_299" -> "2251 slice_300"; -"2251 slice_300" -> "2252 contiguous_37"; -"2252 contiguous_37" -> "2255 layer_norm_41"; -"2253 _param_constant322" -> "2255 layer_norm_41"; -"2254 _param_constant323" -> "2255 layer_norm_41"; -"2255 layer_norm_41" -> "2256 add_68"; -"2256 add_68" -> "2259 linear_120"; -"2256 add_68" -> "2269 add_69"; -"2257 _param_constant324" -> "2259 linear_120"; -"2258 _param_constant325" -> "2259 linear_120"; -"2259 linear_120" -> "2260 gelu_19"; -"2260 gelu_19" -> "2261 dropout_78"; -"2261 dropout_78" -> "2264 linear_121"; -"2262 _param_constant326" -> "2264 linear_121"; -"2263 _param_constant327" -> "2264 linear_121"; -"2264 linear_121" -> "2265 dropout_79"; -"2265 dropout_79" -> "2268 layer_norm_42"; -"2266 _param_constant328" -> "2268 layer_norm_42"; -"2267 _param_constant329" -> "2268 layer_norm_42"; -"2268 layer_norm_42" -> "2269 add_69"; -"2269 add_69" -> "2286 pad_22"; -"2269 add_69" -> "2336 add_71"; -"2270 _tensor_constant130" -> "2273 linear_122"; -"2271 _param_constant330" -> "2273 linear_122"; -"2272 _param_constant331" -> "2273 linear_122"; -"2273 linear_122" -> "2274 relu__20"; -"2274 relu__20" -> "2276 linear_123"; -"2275 _param_constant332" -> "2276 linear_123"; -"2276 linear_123" -> "2277 view_110"; -"2277 view_110" -> "2279 index_20"; -"2278 _tensor_constant131" -> "2279 index_20"; -"2279 index_20" -> "2280 view_111"; -"2280 view_111" -> "2281 permute_91"; -"2281 permute_91" -> "2282 contiguous_38"; -"2282 contiguous_38" -> "2283 unsqueeze_60"; -"2283 unsqueeze_60" -> "2284 sigmoid_20"; -"2284 sigmoid_20" -> "2285 mul_40"; -"2285 mul_40" -> "2315 add_70"; -"2286 pad_22" -> "2287 view_112"; -"2287 view_112" -> "2288 permute_92"; -"2288 permute_92" -> "2289 reshape_90"; -"2289 reshape_90" -> "2295 linear_124"; -"2290 _param_constant333" -> "2291 clone_20"; -"2291 clone_20" -> "2292 slice_301"; -"2291 clone_20" -> "2295 linear_124"; -"2292 slice_301" -> "2293 zero__20"; -"2294 _param_constant334" -> "2295 linear_124"; -"2295 linear_124" -> "2296 reshape_91"; -"2296 reshape_91" -> "2297 permute_93"; -"2297 permute_93" -> "2298 select_60"; -"2297 permute_93" -> "2299 select_61"; -"2297 permute_93" -> "2300 select_62"; -"2298 select_60" -> "2301 linalg_vector_norm_40"; -"2298 select_60" -> "2303 expand_as_40"; -"2298 select_60" -> "2304 div_40"; -"2299 select_61" -> "2305 linalg_vector_norm_41"; -"2299 select_61" -> "2307 expand_as_41"; -"2299 select_61" -> "2308 div_41"; -"2300 select_62" -> "2318 matmul_41"; -"2301 linalg_vector_norm_40" -> "2302 clamp_min_40"; -"2302 clamp_min_40" -> "2303 expand_as_40"; -"2303 expand_as_40" -> "2304 div_40"; -"2304 div_40" -> "2310 matmul_40"; -"2305 linalg_vector_norm_41" -> "2306 clamp_min_41"; -"2306 clamp_min_41" -> "2307 expand_as_41"; -"2307 expand_as_41" -> "2308 div_41"; -"2308 div_41" -> "2309 transpose_40"; -"2309 transpose_40" -> "2310 matmul_40"; -"2310 matmul_40" -> "2314 mul_41"; -"2311 _param_constant335" -> "2312 clamp_20"; -"2312 clamp_20" -> "2313 exp_20"; -"2313 exp_20" -> "2314 mul_41"; -"2314 mul_41" -> "2315 add_70"; -"2315 add_70" -> "2316 softmax_20"; -"2316 softmax_20" -> "2317 dropout_80"; -"2317 dropout_80" -> "2318 matmul_41"; -"2318 matmul_41" -> "2319 transpose_41"; -"2319 transpose_41" -> "2320 reshape_92"; -"2320 reshape_92" -> "2323 linear_125"; -"2321 _param_constant336" -> "2323 linear_125"; -"2322 _param_constant337" -> "2323 linear_125"; -"2323 linear_125" -> "2324 dropout_81"; -"2324 dropout_81" -> "2325 view_113"; -"2325 view_113" -> "2326 permute_94"; -"2326 permute_94" -> "2327 reshape_93"; -"2327 reshape_93" -> "2328 slice_302"; -"2328 slice_302" -> "2329 slice_303"; -"2329 slice_303" -> "2330 slice_304"; -"2330 slice_304" -> "2331 slice_305"; -"2331 slice_305" -> "2332 contiguous_39"; -"2332 contiguous_39" -> "2335 layer_norm_43"; -"2333 _param_constant338" -> "2335 layer_norm_43"; -"2334 _param_constant339" -> "2335 layer_norm_43"; -"2335 layer_norm_43" -> "2336 add_71"; -"2336 add_71" -> "2339 linear_126"; -"2336 add_71" -> "2349 add_72"; -"2337 _param_constant340" -> "2339 linear_126"; -"2338 _param_constant341" -> "2339 linear_126"; -"2339 linear_126" -> "2340 gelu_20"; -"2340 gelu_20" -> "2341 dropout_82"; -"2341 dropout_82" -> "2344 linear_127"; -"2342 _param_constant342" -> "2344 linear_127"; -"2343 _param_constant343" -> "2344 linear_127"; -"2344 linear_127" -> "2345 dropout_83"; -"2345 dropout_83" -> "2348 layer_norm_44"; -"2346 _param_constant344" -> "2348 layer_norm_44"; -"2347 _param_constant345" -> "2348 layer_norm_44"; -"2348 layer_norm_44" -> "2349 add_72"; -"2349 add_72" -> "2366 pad_23"; -"2349 add_72" -> "2479 add_75"; -"2350 _tensor_constant132" -> "2353 linear_128"; -"2351 _param_constant346" -> "2353 linear_128"; -"2352 _param_constant347" -> "2353 linear_128"; -"2353 linear_128" -> "2354 relu__21"; -"2354 relu__21" -> "2356 linear_129"; -"2355 _param_constant348" -> "2356 linear_129"; -"2356 linear_129" -> "2357 view_114"; -"2357 view_114" -> "2359 index_21"; -"2358 _tensor_constant133" -> "2359 index_21"; -"2359 index_21" -> "2360 view_115"; -"2360 view_115" -> "2361 permute_95"; -"2361 permute_95" -> "2362 contiguous_40"; -"2362 contiguous_40" -> "2363 unsqueeze_61"; -"2363 unsqueeze_61" -> "2364 sigmoid_21"; -"2364 sigmoid_21" -> "2365 mul_42"; -"2365 mul_42" -> "2396 add_73"; -"2366 pad_23" -> "2367 roll_20"; -"2367 roll_20" -> "2368 view_116"; -"2368 view_116" -> "2369 permute_96"; -"2369 permute_96" -> "2370 reshape_94"; -"2370 reshape_94" -> "2376 linear_130"; -"2370 reshape_94" -> "2397 new_zeros_10"; -"2371 _param_constant349" -> "2372 clone_21"; -"2372 clone_21" -> "2373 slice_306"; -"2372 clone_21" -> "2376 linear_130"; -"2373 slice_306" -> "2374 zero__21"; -"2375 _param_constant350" -> "2376 linear_130"; -"2376 linear_130" -> "2377 reshape_95"; -"2377 reshape_95" -> "2378 permute_97"; -"2378 permute_97" -> "2379 select_63"; -"2378 permute_97" -> "2380 select_64"; -"2378 permute_97" -> "2381 select_65"; -"2379 select_63" -> "2382 linalg_vector_norm_42"; -"2379 select_63" -> "2384 expand_as_42"; -"2379 select_63" -> "2385 div_42"; -"2380 select_64" -> "2386 linalg_vector_norm_43"; -"2380 select_64" -> "2388 expand_as_43"; -"2380 select_64" -> "2389 div_43"; -"2381 select_65" -> "2460 matmul_43"; -"2382 linalg_vector_norm_42" -> "2383 clamp_min_42"; -"2383 clamp_min_42" -> "2384 expand_as_42"; -"2384 expand_as_42" -> "2385 div_42"; -"2385 div_42" -> "2391 matmul_42"; -"2386 linalg_vector_norm_43" -> "2387 clamp_min_43"; -"2387 clamp_min_43" -> "2388 expand_as_43"; -"2388 expand_as_43" -> "2389 div_43"; -"2389 div_43" -> "2390 transpose_42"; -"2390 transpose_42" -> "2391 matmul_42"; -"2391 matmul_42" -> "2395 mul_43"; -"2392 _param_constant351" -> "2393 clamp_21"; -"2393 clamp_21" -> "2394 exp_21"; -"2394 exp_21" -> "2395 mul_43"; -"2395 mul_43" -> "2396 add_73"; -"2396 add_73" -> "2453 view_118"; -"2397 new_zeros_10" -> "2400 slice_307"; -"2397 new_zeros_10" -> "2405 slice_309"; -"2397 new_zeros_10" -> "2410 slice_311"; -"2397 new_zeros_10" -> "2415 slice_313"; -"2397 new_zeros_10" -> "2420 slice_315"; -"2397 new_zeros_10" -> "2425 slice_317"; -"2397 new_zeros_10" -> "2430 slice_319"; -"2397 new_zeros_10" -> "2435 slice_321"; -"2397 new_zeros_10" -> "2440 slice_323"; -"2397 new_zeros_10" -> "2443 view_117"; -"2398 _tensor_constant134" -> "2399 lift_fresh_copy_90"; -"2399 lift_fresh_copy_90" -> "2402 fill__90"; -"2400 slice_307" -> "2401 slice_308"; -"2401 slice_308" -> "2402 fill__90"; -"2403 _tensor_constant135" -> "2404 lift_fresh_copy_91"; -"2404 lift_fresh_copy_91" -> "2407 fill__91"; -"2405 slice_309" -> "2406 slice_310"; -"2406 slice_310" -> "2407 fill__91"; -"2408 _tensor_constant136" -> "2409 lift_fresh_copy_92"; -"2409 lift_fresh_copy_92" -> "2412 fill__92"; -"2410 slice_311" -> "2411 slice_312"; -"2411 slice_312" -> "2412 fill__92"; -"2413 _tensor_constant137" -> "2414 lift_fresh_copy_93"; -"2414 lift_fresh_copy_93" -> "2417 fill__93"; -"2415 slice_313" -> "2416 slice_314"; -"2416 slice_314" -> "2417 fill__93"; -"2418 _tensor_constant138" -> "2419 lift_fresh_copy_94"; -"2419 lift_fresh_copy_94" -> "2422 fill__94"; -"2420 slice_315" -> "2421 slice_316"; -"2421 slice_316" -> "2422 fill__94"; -"2423 _tensor_constant139" -> "2424 lift_fresh_copy_95"; -"2424 lift_fresh_copy_95" -> "2427 fill__95"; -"2425 slice_317" -> "2426 slice_318"; -"2426 slice_318" -> "2427 fill__95"; -"2428 _tensor_constant140" -> "2429 lift_fresh_copy_96"; -"2429 lift_fresh_copy_96" -> "2432 fill__96"; -"2430 slice_319" -> "2431 slice_320"; -"2431 slice_320" -> "2432 fill__96"; -"2433 _tensor_constant141" -> "2434 lift_fresh_copy_97"; -"2434 lift_fresh_copy_97" -> "2437 fill__97"; -"2435 slice_321" -> "2436 slice_322"; -"2436 slice_322" -> "2437 fill__97"; -"2438 _tensor_constant142" -> "2439 lift_fresh_copy_98"; -"2439 lift_fresh_copy_98" -> "2442 fill__98"; -"2440 slice_323" -> "2441 slice_324"; -"2441 slice_324" -> "2442 fill__98"; -"2443 view_117" -> "2444 permute_98"; -"2444 permute_98" -> "2445 reshape_96"; -"2445 reshape_96" -> "2446 unsqueeze_62"; -"2445 reshape_96" -> "2447 unsqueeze_63"; -"2446 unsqueeze_62" -> "2448 sub_10"; -"2447 unsqueeze_63" -> "2448 sub_10"; -"2448 sub_10" -> "2449 ne_10"; -"2448 sub_10" -> "2450 masked_fill_20"; -"2448 sub_10" -> "2451 eq_10"; -"2449 ne_10" -> "2450 masked_fill_20"; -"2450 masked_fill_20" -> "2452 masked_fill_21"; -"2451 eq_10" -> "2452 masked_fill_21"; -"2452 masked_fill_21" -> "2454 unsqueeze_64"; -"2453 view_118" -> "2456 add_74"; -"2454 unsqueeze_64" -> "2455 unsqueeze_65"; -"2455 unsqueeze_65" -> "2456 add_74"; -"2456 add_74" -> "2457 view_119"; -"2457 view_119" -> "2458 softmax_21"; -"2458 softmax_21" -> "2459 dropout_84"; -"2459 dropout_84" -> "2460 matmul_43"; -"2460 matmul_43" -> "2461 transpose_43"; -"2461 transpose_43" -> "2462 reshape_97"; -"2462 reshape_97" -> "2465 linear_131"; -"2463 _param_constant352" -> "2465 linear_131"; -"2464 _param_constant353" -> "2465 linear_131"; -"2465 linear_131" -> "2466 dropout_85"; -"2466 dropout_85" -> "2467 view_120"; -"2467 view_120" -> "2468 permute_99"; -"2468 permute_99" -> "2469 reshape_98"; -"2469 reshape_98" -> "2470 roll_21"; -"2470 roll_21" -> "2471 slice_325"; -"2471 slice_325" -> "2472 slice_326"; -"2472 slice_326" -> "2473 slice_327"; -"2473 slice_327" -> "2474 slice_328"; -"2474 slice_328" -> "2475 contiguous_41"; -"2475 contiguous_41" -> "2478 layer_norm_45"; -"2476 _param_constant354" -> "2478 layer_norm_45"; -"2477 _param_constant355" -> "2478 layer_norm_45"; -"2478 layer_norm_45" -> "2479 add_75"; -"2479 add_75" -> "2482 linear_132"; -"2479 add_75" -> "2492 add_76"; -"2480 _param_constant356" -> "2482 linear_132"; -"2481 _param_constant357" -> "2482 linear_132"; -"2482 linear_132" -> "2483 gelu_21"; -"2483 gelu_21" -> "2484 dropout_86"; -"2484 dropout_86" -> "2487 linear_133"; -"2485 _param_constant358" -> "2487 linear_133"; -"2486 _param_constant359" -> "2487 linear_133"; -"2487 linear_133" -> "2488 dropout_87"; -"2488 dropout_87" -> "2491 layer_norm_46"; -"2489 _param_constant360" -> "2491 layer_norm_46"; -"2490 _param_constant361" -> "2491 layer_norm_46"; -"2491 layer_norm_46" -> "2492 add_76"; -"2492 add_76" -> "2493 pad_24"; -"2493 pad_24" -> "2494 slice_329"; -"2493 pad_24" -> "2497 slice_332"; -"2493 pad_24" -> "2500 slice_335"; -"2493 pad_24" -> "2503 slice_338"; -"2494 slice_329" -> "2495 slice_330"; -"2495 slice_330" -> "2496 slice_331"; -"2496 slice_331" -> "2506 cat_2"; -"2497 slice_332" -> "2498 slice_333"; -"2498 slice_333" -> "2499 slice_334"; -"2499 slice_334" -> "2506 cat_2"; -"2500 slice_335" -> "2501 slice_336"; -"2501 slice_336" -> "2502 slice_337"; -"2502 slice_337" -> "2506 cat_2"; -"2503 slice_338" -> "2504 slice_339"; -"2504 slice_339" -> "2505 slice_340"; -"2505 slice_340" -> "2506 cat_2"; -"2506 cat_2" -> "2508 linear_134"; -"2507 _param_constant362" -> "2508 linear_134"; -"2508 linear_134" -> "2511 layer_norm_47"; -"2509 _param_constant363" -> "2511 layer_norm_47"; -"2510 _param_constant364" -> "2511 layer_norm_47"; -"2511 layer_norm_47" -> "2528 pad_25"; -"2511 layer_norm_47" -> "2578 add_78"; -"2512 _tensor_constant143" -> "2515 linear_135"; -"2513 _param_constant365" -> "2515 linear_135"; -"2514 _param_constant366" -> "2515 linear_135"; -"2515 linear_135" -> "2516 relu__22"; -"2516 relu__22" -> "2518 linear_136"; -"2517 _param_constant367" -> "2518 linear_136"; -"2518 linear_136" -> "2519 view_121"; -"2519 view_121" -> "2521 index_22"; -"2520 _tensor_constant144" -> "2521 index_22"; -"2521 index_22" -> "2522 view_122"; -"2522 view_122" -> "2523 permute_100"; -"2523 permute_100" -> "2524 contiguous_42"; -"2524 contiguous_42" -> "2525 unsqueeze_66"; -"2525 unsqueeze_66" -> "2526 sigmoid_22"; -"2526 sigmoid_22" -> "2527 mul_44"; -"2527 mul_44" -> "2557 add_77"; -"2528 pad_25" -> "2529 view_123"; -"2529 view_123" -> "2530 permute_101"; -"2530 permute_101" -> "2531 reshape_99"; -"2531 reshape_99" -> "2537 linear_137"; -"2532 _param_constant368" -> "2533 clone_22"; -"2533 clone_22" -> "2534 slice_341"; -"2533 clone_22" -> "2537 linear_137"; -"2534 slice_341" -> "2535 zero__22"; -"2536 _param_constant369" -> "2537 linear_137"; -"2537 linear_137" -> "2538 reshape_100"; -"2538 reshape_100" -> "2539 permute_102"; -"2539 permute_102" -> "2540 select_66"; -"2539 permute_102" -> "2541 select_67"; -"2539 permute_102" -> "2542 select_68"; -"2540 select_66" -> "2543 linalg_vector_norm_44"; -"2540 select_66" -> "2545 expand_as_44"; -"2540 select_66" -> "2546 div_44"; -"2541 select_67" -> "2547 linalg_vector_norm_45"; -"2541 select_67" -> "2549 expand_as_45"; -"2541 select_67" -> "2550 div_45"; -"2542 select_68" -> "2560 matmul_45"; -"2543 linalg_vector_norm_44" -> "2544 clamp_min_44"; -"2544 clamp_min_44" -> "2545 expand_as_44"; -"2545 expand_as_44" -> "2546 div_44"; -"2546 div_44" -> "2552 matmul_44"; -"2547 linalg_vector_norm_45" -> "2548 clamp_min_45"; -"2548 clamp_min_45" -> "2549 expand_as_45"; -"2549 expand_as_45" -> "2550 div_45"; -"2550 div_45" -> "2551 transpose_44"; -"2551 transpose_44" -> "2552 matmul_44"; -"2552 matmul_44" -> "2556 mul_45"; -"2553 _param_constant370" -> "2554 clamp_22"; -"2554 clamp_22" -> "2555 exp_22"; -"2555 exp_22" -> "2556 mul_45"; -"2556 mul_45" -> "2557 add_77"; -"2557 add_77" -> "2558 softmax_22"; -"2558 softmax_22" -> "2559 dropout_88"; -"2559 dropout_88" -> "2560 matmul_45"; -"2560 matmul_45" -> "2561 transpose_45"; -"2561 transpose_45" -> "2562 reshape_101"; -"2562 reshape_101" -> "2565 linear_138"; -"2563 _param_constant371" -> "2565 linear_138"; -"2564 _param_constant372" -> "2565 linear_138"; -"2565 linear_138" -> "2566 dropout_89"; -"2566 dropout_89" -> "2567 view_124"; -"2567 view_124" -> "2568 permute_103"; -"2568 permute_103" -> "2569 reshape_102"; -"2569 reshape_102" -> "2570 slice_342"; -"2570 slice_342" -> "2571 slice_343"; -"2571 slice_343" -> "2572 slice_344"; -"2572 slice_344" -> "2573 slice_345"; -"2573 slice_345" -> "2574 contiguous_43"; -"2574 contiguous_43" -> "2577 layer_norm_48"; -"2575 _param_constant373" -> "2577 layer_norm_48"; -"2576 _param_constant374" -> "2577 layer_norm_48"; -"2577 layer_norm_48" -> "2578 add_78"; -"2578 add_78" -> "2581 linear_139"; -"2578 add_78" -> "2591 add_79"; -"2579 _param_constant375" -> "2581 linear_139"; -"2580 _param_constant376" -> "2581 linear_139"; -"2581 linear_139" -> "2582 gelu_22"; -"2582 gelu_22" -> "2583 dropout_90"; -"2583 dropout_90" -> "2586 linear_140"; -"2584 _param_constant377" -> "2586 linear_140"; -"2585 _param_constant378" -> "2586 linear_140"; -"2586 linear_140" -> "2587 dropout_91"; -"2587 dropout_91" -> "2590 layer_norm_49"; -"2588 _param_constant379" -> "2590 layer_norm_49"; -"2589 _param_constant380" -> "2590 layer_norm_49"; -"2590 layer_norm_49" -> "2591 add_79"; -"2591 add_79" -> "2608 pad_26"; -"2591 add_79" -> "2658 add_81"; -"2592 _tensor_constant145" -> "2595 linear_141"; -"2593 _param_constant381" -> "2595 linear_141"; -"2594 _param_constant382" -> "2595 linear_141"; -"2595 linear_141" -> "2596 relu__23"; -"2596 relu__23" -> "2598 linear_142"; -"2597 _param_constant383" -> "2598 linear_142"; -"2598 linear_142" -> "2599 view_125"; -"2599 view_125" -> "2601 index_23"; -"2600 _tensor_constant146" -> "2601 index_23"; -"2601 index_23" -> "2602 view_126"; -"2602 view_126" -> "2603 permute_104"; -"2603 permute_104" -> "2604 contiguous_44"; -"2604 contiguous_44" -> "2605 unsqueeze_67"; -"2605 unsqueeze_67" -> "2606 sigmoid_23"; -"2606 sigmoid_23" -> "2607 mul_46"; -"2607 mul_46" -> "2637 add_80"; -"2608 pad_26" -> "2609 view_127"; -"2609 view_127" -> "2610 permute_105"; -"2610 permute_105" -> "2611 reshape_103"; -"2611 reshape_103" -> "2617 linear_143"; -"2612 _param_constant384" -> "2613 clone_23"; -"2613 clone_23" -> "2614 slice_346"; -"2613 clone_23" -> "2617 linear_143"; -"2614 slice_346" -> "2615 zero__23"; -"2616 _param_constant385" -> "2617 linear_143"; -"2617 linear_143" -> "2618 reshape_104"; -"2618 reshape_104" -> "2619 permute_106"; -"2619 permute_106" -> "2620 select_69"; -"2619 permute_106" -> "2621 select_70"; -"2619 permute_106" -> "2622 select_71"; -"2620 select_69" -> "2623 linalg_vector_norm_46"; -"2620 select_69" -> "2625 expand_as_46"; -"2620 select_69" -> "2626 div_46"; -"2621 select_70" -> "2627 linalg_vector_norm_47"; -"2621 select_70" -> "2629 expand_as_47"; -"2621 select_70" -> "2630 div_47"; -"2622 select_71" -> "2640 matmul_47"; -"2623 linalg_vector_norm_46" -> "2624 clamp_min_46"; -"2624 clamp_min_46" -> "2625 expand_as_46"; -"2625 expand_as_46" -> "2626 div_46"; -"2626 div_46" -> "2632 matmul_46"; -"2627 linalg_vector_norm_47" -> "2628 clamp_min_47"; -"2628 clamp_min_47" -> "2629 expand_as_47"; -"2629 expand_as_47" -> "2630 div_47"; -"2630 div_47" -> "2631 transpose_46"; -"2631 transpose_46" -> "2632 matmul_46"; -"2632 matmul_46" -> "2636 mul_47"; -"2633 _param_constant386" -> "2634 clamp_23"; -"2634 clamp_23" -> "2635 exp_23"; -"2635 exp_23" -> "2636 mul_47"; -"2636 mul_47" -> "2637 add_80"; -"2637 add_80" -> "2638 softmax_23"; -"2638 softmax_23" -> "2639 dropout_92"; -"2639 dropout_92" -> "2640 matmul_47"; -"2640 matmul_47" -> "2641 transpose_47"; -"2641 transpose_47" -> "2642 reshape_105"; -"2642 reshape_105" -> "2645 linear_144"; -"2643 _param_constant387" -> "2645 linear_144"; -"2644 _param_constant388" -> "2645 linear_144"; -"2645 linear_144" -> "2646 dropout_93"; -"2646 dropout_93" -> "2647 view_128"; -"2647 view_128" -> "2648 permute_107"; -"2648 permute_107" -> "2649 reshape_106"; -"2649 reshape_106" -> "2650 slice_347"; -"2650 slice_347" -> "2651 slice_348"; -"2651 slice_348" -> "2652 slice_349"; -"2652 slice_349" -> "2653 slice_350"; -"2653 slice_350" -> "2654 contiguous_45"; -"2654 contiguous_45" -> "2657 layer_norm_50"; -"2655 _param_constant389" -> "2657 layer_norm_50"; -"2656 _param_constant390" -> "2657 layer_norm_50"; -"2657 layer_norm_50" -> "2658 add_81"; -"2658 add_81" -> "2661 linear_145"; -"2658 add_81" -> "2671 add_82"; -"2659 _param_constant391" -> "2661 linear_145"; -"2660 _param_constant392" -> "2661 linear_145"; -"2661 linear_145" -> "2662 gelu_23"; -"2662 gelu_23" -> "2663 dropout_94"; -"2663 dropout_94" -> "2666 linear_146"; -"2664 _param_constant393" -> "2666 linear_146"; -"2665 _param_constant394" -> "2666 linear_146"; -"2666 linear_146" -> "2667 dropout_95"; -"2667 dropout_95" -> "2670 layer_norm_51"; -"2668 _param_constant395" -> "2670 layer_norm_51"; -"2669 _param_constant396" -> "2670 layer_norm_51"; -"2670 layer_norm_51" -> "2671 add_82"; -"2671 add_82" -> "2674 layer_norm_52"; -"2672 _param_constant397" -> "2674 layer_norm_52"; -"2673 _param_constant398" -> "2674 layer_norm_52"; -"2674 layer_norm_52" -> "2675 permute_108"; -"2675 permute_108" -> "2676 adaptive_avg_pool2d"; -"2676 adaptive_avg_pool2d" -> "2677 flatten"; -"2677 flatten" -> "2680 linear_147"; -"2678 _param_constant399" -> "2680 linear_147"; -"2679 _param_constant400" -> "2680 linear_147"; -"2680 linear_147" -> "2681 output"; +"2681 output_1" [id=2681, type=output]; +"0 features_1_0_attn_qkv_weight" -> "570 linear_2" [label="(288, 96)", style=solid]; +"1 features_1_0_attn_proj_weight" -> "595 linear_3" [label="(96, 96)", style=solid]; +"2 features_1_0_attn_qkv_bias" -> "567 clone" [label="(288,)", style=solid]; +"3 features_1_0_attn_proj_bias" -> "595 linear_3" [label="(96,)", style=solid]; +"4 features_1_0_attn_logit_scale" -> "586 clamp" [label="(3, 1, 1)", style=solid]; +"5 features_1_1_attn_qkv_weight" -> "630 linear_8" [label="(288, 96)", style=solid]; +"6 features_1_1_attn_proj_weight" -> "707 linear_9" [label="(96, 96)", style=solid]; +"7 features_1_1_attn_qkv_bias" -> "627 clone_1" [label="(288,)", style=solid]; +"8 features_1_1_attn_proj_bias" -> "707 linear_9" [label="(96,)", style=solid]; +"9 features_1_1_attn_logit_scale" -> "646 clamp_1" [label="(3, 1, 1)", style=solid]; +"10 features_3_0_attn_qkv_weight" -> "758 linear_15" [label="(576, 192)", style=solid]; +"11 features_3_0_attn_proj_weight" -> "783 linear_16" [label="(192, 192)", style=solid]; +"12 features_3_0_attn_qkv_bias" -> "755 clone_2" [label="(576,)", style=solid]; +"13 features_3_0_attn_proj_bias" -> "783 linear_16" [label="(192,)", style=solid]; +"14 features_3_0_attn_logit_scale" -> "774 clamp_2" [label="(6, 1, 1)", style=solid]; +"15 features_3_1_attn_qkv_weight" -> "821 linear_21" [label="(576, 192)", style=solid]; +"16 features_3_1_attn_proj_weight" -> "898 linear_22" [label="(192, 192)", style=solid]; +"17 features_3_1_attn_qkv_bias" -> "818 clone_3" [label="(576,)", style=solid]; +"18 features_3_1_attn_proj_bias" -> "898 linear_22" [label="(192,)", style=solid]; +"19 features_3_1_attn_logit_scale" -> "837 clamp_3" [label="(6, 1, 1)", style=solid]; +"20 features_5_0_attn_qkv_weight" -> "952 linear_28" [label="(1152, 384)", style=solid]; +"21 features_5_0_attn_proj_weight" -> "977 linear_29" [label="(384, 384)", style=solid]; +"22 features_5_0_attn_qkv_bias" -> "949 clone_4" [label="(1152,)", style=solid]; +"23 features_5_0_attn_proj_bias" -> "977 linear_29" [label="(384,)", style=solid]; +"24 features_5_0_attn_logit_scale" -> "968 clamp_4" [label="(12, 1, 1)", style=solid]; +"25 features_5_1_attn_qkv_weight" -> "1015 linear_34" [label="(1152, 384)", style=solid]; +"26 features_5_1_attn_proj_weight" -> "1092 linear_35" [label="(384, 384)", style=solid]; +"27 features_5_1_attn_qkv_bias" -> "1012 clone_5" [label="(1152,)", style=solid]; +"28 features_5_1_attn_proj_bias" -> "1092 linear_35" [label="(384,)", style=solid]; +"29 features_5_1_attn_logit_scale" -> "1031 clamp_5" [label="(12, 1, 1)", style=solid]; +"30 features_5_2_attn_qkv_weight" -> "1130 linear_40" [label="(1152, 384)", style=solid]; +"31 features_5_2_attn_proj_weight" -> "1155 linear_41" [label="(384, 384)", style=solid]; +"32 features_5_2_attn_qkv_bias" -> "1127 clone_6" [label="(1152,)", style=solid]; +"33 features_5_2_attn_proj_bias" -> "1155 linear_41" [label="(384,)", style=solid]; +"34 features_5_2_attn_logit_scale" -> "1146 clamp_6" [label="(12, 1, 1)", style=solid]; +"35 features_5_3_attn_qkv_weight" -> "1193 linear_46" [label="(1152, 384)", style=solid]; +"36 features_5_3_attn_proj_weight" -> "1270 linear_47" [label="(384, 384)", style=solid]; +"37 features_5_3_attn_qkv_bias" -> "1190 clone_7" [label="(1152,)", style=solid]; +"38 features_5_3_attn_proj_bias" -> "1270 linear_47" [label="(384,)", style=solid]; +"39 features_5_3_attn_logit_scale" -> "1209 clamp_7" [label="(12, 1, 1)", style=solid]; +"40 features_5_4_attn_qkv_weight" -> "1308 linear_52" [label="(1152, 384)", style=solid]; +"41 features_5_4_attn_proj_weight" -> "1333 linear_53" [label="(384, 384)", style=solid]; +"42 features_5_4_attn_qkv_bias" -> "1305 clone_8" [label="(1152,)", style=solid]; +"43 features_5_4_attn_proj_bias" -> "1333 linear_53" [label="(384,)", style=solid]; +"44 features_5_4_attn_logit_scale" -> "1324 clamp_8" [label="(12, 1, 1)", style=solid]; +"45 features_5_5_attn_qkv_weight" -> "1371 linear_58" [label="(1152, 384)", style=solid]; +"46 features_5_5_attn_proj_weight" -> "1448 linear_59" [label="(384, 384)", style=solid]; +"47 features_5_5_attn_qkv_bias" -> "1368 clone_9" [label="(1152,)", style=solid]; +"48 features_5_5_attn_proj_bias" -> "1448 linear_59" [label="(384,)", style=solid]; +"49 features_5_5_attn_logit_scale" -> "1387 clamp_9" [label="(12, 1, 1)", style=solid]; +"50 features_5_6_attn_qkv_weight" -> "1486 linear_64" [label="(1152, 384)", style=solid]; +"51 features_5_6_attn_proj_weight" -> "1511 linear_65" [label="(384, 384)", style=solid]; +"52 features_5_6_attn_qkv_bias" -> "1483 clone_10" [label="(1152,)", style=solid]; +"53 features_5_6_attn_proj_bias" -> "1511 linear_65" [label="(384,)", style=solid]; +"54 features_5_6_attn_logit_scale" -> "1502 clamp_10" [label="(12, 1, 1)", style=solid]; +"55 features_5_7_attn_qkv_weight" -> "1549 linear_70" [label="(1152, 384)", style=solid]; +"56 features_5_7_attn_proj_weight" -> "1626 linear_71" [label="(384, 384)", style=solid]; +"57 features_5_7_attn_qkv_bias" -> "1546 clone_11" [label="(1152,)", style=solid]; +"58 features_5_7_attn_proj_bias" -> "1626 linear_71" [label="(384,)", style=solid]; +"59 features_5_7_attn_logit_scale" -> "1565 clamp_11" [label="(12, 1, 1)", style=solid]; +"60 features_5_8_attn_qkv_weight" -> "1664 linear_76" [label="(1152, 384)", style=solid]; +"61 features_5_8_attn_proj_weight" -> "1689 linear_77" [label="(384, 384)", style=solid]; +"62 features_5_8_attn_qkv_bias" -> "1661 clone_12" [label="(1152,)", style=solid]; +"63 features_5_8_attn_proj_bias" -> "1689 linear_77" [label="(384,)", style=solid]; +"64 features_5_8_attn_logit_scale" -> "1680 clamp_12" [label="(12, 1, 1)", style=solid]; +"65 features_5_9_attn_qkv_weight" -> "1727 linear_82" [label="(1152, 384)", style=solid]; +"66 features_5_9_attn_proj_weight" -> "1804 linear_83" [label="(384, 384)", style=solid]; +"67 features_5_9_attn_qkv_bias" -> "1724 clone_13" [label="(1152,)", style=solid]; +"68 features_5_9_attn_proj_bias" -> "1804 linear_83" [label="(384,)", style=solid]; +"69 features_5_9_attn_logit_scale" -> "1743 clamp_13" [label="(12, 1, 1)", style=solid]; +"70 features_5_10_attn_qkv_weight" -> "1842 linear_88" [label="(1152, 384)", style=solid]; +"71 features_5_10_attn_proj_weight" -> "1867 linear_89" [label="(384, 384)", style=solid]; +"72 features_5_10_attn_qkv_bias" -> "1839 clone_14" [label="(1152,)", style=solid]; +"73 features_5_10_attn_proj_bias" -> "1867 linear_89" [label="(384,)", style=solid]; +"74 features_5_10_attn_logit_scale" -> "1858 clamp_14" [label="(12, 1, 1)", style=solid]; +"75 features_5_11_attn_qkv_weight" -> "1905 linear_94" [label="(1152, 384)", style=solid]; +"76 features_5_11_attn_proj_weight" -> "1982 linear_95" [label="(384, 384)", style=solid]; +"77 features_5_11_attn_qkv_bias" -> "1902 clone_15" [label="(1152,)", style=solid]; +"78 features_5_11_attn_proj_bias" -> "1982 linear_95" [label="(384,)", style=solid]; +"79 features_5_11_attn_logit_scale" -> "1921 clamp_15" [label="(12, 1, 1)", style=solid]; +"80 features_5_12_attn_qkv_weight" -> "2020 linear_100" [label="(1152, 384)", style=solid]; +"81 features_5_12_attn_proj_weight" -> "2045 linear_101" [label="(384, 384)", style=solid]; +"82 features_5_12_attn_qkv_bias" -> "2017 clone_16" [label="(1152,)", style=solid]; +"83 features_5_12_attn_proj_bias" -> "2045 linear_101" [label="(384,)", style=solid]; +"84 features_5_12_attn_logit_scale" -> "2036 clamp_16" [label="(12, 1, 1)", style=solid]; +"85 features_5_13_attn_qkv_weight" -> "2083 linear_106" [label="(1152, 384)", style=solid]; +"86 features_5_13_attn_proj_weight" -> "2160 linear_107" [label="(384, 384)", style=solid]; +"87 features_5_13_attn_qkv_bias" -> "2080 clone_17" [label="(1152,)", style=solid]; +"88 features_5_13_attn_proj_bias" -> "2160 linear_107" [label="(384,)", style=solid]; +"89 features_5_13_attn_logit_scale" -> "2099 clamp_17" [label="(12, 1, 1)", style=solid]; +"90 features_5_14_attn_qkv_weight" -> "2198 linear_112" [label="(1152, 384)", style=solid]; +"91 features_5_14_attn_proj_weight" -> "2223 linear_113" [label="(384, 384)", style=solid]; +"92 features_5_14_attn_qkv_bias" -> "2195 clone_18" [label="(1152,)", style=solid]; +"93 features_5_14_attn_proj_bias" -> "2223 linear_113" [label="(384,)", style=solid]; +"94 features_5_14_attn_logit_scale" -> "2214 clamp_18" [label="(12, 1, 1)", style=solid]; +"95 features_5_15_attn_qkv_weight" -> "2261 linear_118" [label="(1152, 384)", style=solid]; +"96 features_5_15_attn_proj_weight" -> "2338 linear_119" [label="(384, 384)", style=solid]; +"97 features_5_15_attn_qkv_bias" -> "2258 clone_19" [label="(1152,)", style=solid]; +"98 features_5_15_attn_proj_bias" -> "2338 linear_119" [label="(384,)", style=solid]; +"99 features_5_15_attn_logit_scale" -> "2277 clamp_19" [label="(12, 1, 1)", style=solid]; +"100 features_5_16_attn_qkv_weight" -> "2376 linear_124" [label="(1152, 384)", style=solid]; +"101 features_5_16_attn_proj_weight" -> "2401 linear_125" [label="(384, 384)", style=solid]; +"102 features_5_16_attn_qkv_bias" -> "2373 clone_20" [label="(1152,)", style=solid]; +"103 features_5_16_attn_proj_bias" -> "2401 linear_125" [label="(384,)", style=solid]; +"104 features_5_16_attn_logit_scale" -> "2392 clamp_20" [label="(12, 1, 1)", style=solid]; +"105 features_5_17_attn_qkv_weight" -> "2439 linear_130" [label="(1152, 384)", style=solid]; +"106 features_5_17_attn_proj_weight" -> "2516 linear_131" [label="(384, 384)", style=solid]; +"107 features_5_17_attn_qkv_bias" -> "2436 clone_21" [label="(1152,)", style=solid]; +"108 features_5_17_attn_proj_bias" -> "2516 linear_131" [label="(384,)", style=solid]; +"109 features_5_17_attn_logit_scale" -> "2455 clamp_21" [label="(12, 1, 1)", style=solid]; +"110 features_7_0_attn_qkv_weight" -> "2570 linear_137" [label="(2304, 768)", style=solid]; +"111 features_7_0_attn_proj_weight" -> "2595 linear_138" [label="(768, 768)", style=solid]; +"112 features_7_0_attn_qkv_bias" -> "2567 clone_22" [label="(2304,)", style=solid]; +"113 features_7_0_attn_proj_bias" -> "2595 linear_138" [label="(768,)", style=solid]; +"114 features_7_0_attn_logit_scale" -> "2586 clamp_22" [label="(24, 1, 1)", style=solid]; +"115 features_7_1_attn_qkv_weight" -> "2632 linear_143" [label="(2304, 768)", style=solid]; +"116 features_7_1_attn_proj_weight" -> "2657 linear_144" [label="(768, 768)", style=solid]; +"117 features_7_1_attn_qkv_bias" -> "2629 clone_23" [label="(2304,)", style=solid]; +"118 features_7_1_attn_proj_bias" -> "2657 linear_144" [label="(768,)", style=solid]; +"119 features_7_1_attn_logit_scale" -> "2648 clamp_23" [label="(24, 1, 1)", style=solid]; +"120 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clamp_min_13" -> "1142 expand_as_13" [label="(4, 12, 64, 1)", style=solid]; +"1142 expand_as_13" -> "1143 div_13" [label="(4, 12, 64, 32)", style=solid]; +"1143 div_13" -> "1144 transpose_12" [label="(4, 12, 64, 32)", style=solid]; +"1144 transpose_12" -> "1145 matmul_12" [label="(4, 12, 32, 64)", style=solid]; +"1145 matmul_12" -> "1148 mul_13" [label="(4, 12, 64, 64)", style=solid]; +"1146 clamp_6" -> "1147 exp_6" [label="(12, 1, 1)", style=solid]; +"1147 exp_6" -> "1148 mul_13" [label="(12, 1, 1)", style=solid]; +"1148 mul_13" -> "1149 add_21" [label="(4, 12, 64, 64)", style=solid]; +"1149 add_21" -> "1150 softmax_6" [label="(4, 12, 64, 64)", style=solid]; +"1150 softmax_6" -> "1151 dropout_24" [label="(4, 12, 64, 64)", style=solid]; +"1151 dropout_24" -> "1152 matmul_13" [label="(4, 12, 64, 64)", style=solid]; +"1152 matmul_13" -> "1153 transpose_13" [label="(4, 12, 64, 32)", style=solid]; +"1153 transpose_13" -> "1154 reshape_29" [label="(4, 64, 12, 32)", style=solid]; +"1154 reshape_29" -> "1155 linear_41" [label="(4, 64, 384)", style=solid]; +"1155 linear_41" -> "1156 dropout_25" [label="(4, 64, 384)", style=solid]; +"1156 dropout_25" -> "1157 view_36" [label="(4, 64, 384)", style=solid]; +"1157 view_36" -> "1158 permute_31" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1158 permute_31" -> "1159 reshape_30" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1159 reshape_30" -> "1160 slice_106" [label="(1, 16, 16, 384)", style=solid]; +"1160 slice_106" -> "1161 slice_107" [label="(1, 16, 16, 384)", style=solid]; +"1161 slice_107" -> "1162 slice_108" [label="(1, 14, 16, 384)", style=solid]; +"1162 slice_108" -> "1163 slice_109" [label="(1, 14, 14, 384)", style=solid]; +"1163 slice_109" -> "1164 contiguous_11" [label="(1, 14, 14, 384)", style=solid]; +"1164 contiguous_11" -> "1165 layer_norm_15" [label="(1, 14, 14, 384)", style=solid]; +"1165 layer_norm_15" -> "1166 add_22" [label="(1, 14, 14, 384)", style=solid]; +"1166 add_22" -> "1167 linear_42" [label="(1, 14, 14, 384)", style=solid]; +"1166 add_22" -> "1173 add_23" [label="(1, 14, 14, 384)", style=solid]; +"1167 linear_42" -> "1168 gelu_6" [label="(1, 14, 14, 1536)", style=solid]; +"1168 gelu_6" -> "1169 dropout_26" [label="(1, 14, 14, 1536)", style=solid]; +"1169 dropout_26" -> "1170 linear_43" [label="(1, 14, 14, 1536)", style=solid]; +"1170 linear_43" -> "1171 dropout_27" [label="(1, 14, 14, 384)", style=solid]; +"1171 dropout_27" -> "1172 layer_norm_16" [label="(1, 14, 14, 384)", style=solid]; +"1172 layer_norm_16" -> "1173 add_23" [label="(1, 14, 14, 384)", style=solid]; +"1173 add_23" -> "1185 pad_9" [label="(1, 14, 14, 384)", style=solid]; +"1173 add_23" -> "1282 add_26" [label="(1, 14, 14, 384)", style=solid]; +"1174 linear_44" -> "1175 relu__7" [label="(1, 15, 15, 512)", style=solid]; +"1175 relu__7" -> "1176 linear_45" [label="(1, 15, 15, 512)", style=solid]; +"1176 linear_45" -> "1177 view_37" [label="(1, 15, 15, 12)", style=solid]; +"1177 view_37" -> "1178 index_7" [label="(225, 12)", style=solid]; +"1178 index_7" -> "1179 view_38" [label="(4096, 12)", style=solid]; +"1179 view_38" -> "1180 permute_32" [label="(64, 64, 12)", style=solid]; +"1180 permute_32" -> "1181 contiguous_12" [label="(12, 64, 64)", style=solid]; +"1181 contiguous_12" -> "1182 unsqueeze_19" [label="(12, 64, 64)", style=solid]; +"1182 unsqueeze_19" -> "1183 sigmoid_7" [label="(1, 12, 64, 64)", style=solid]; +"1183 sigmoid_7" -> "1184 mul_14" [label="(1, 12, 64, 64)", style=solid]; +"1184 mul_14" -> "1212 add_24" [label="(1, 12, 64, 64)", style=solid]; +"1185 pad_9" -> "1186 roll_6" [label="(1, 16, 16, 384)", style=solid]; +"1186 roll_6" -> "1187 view_39" [label="(1, 16, 16, 384)", style=solid]; +"1187 view_39" -> "1188 permute_33" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1188 permute_33" -> "1189 reshape_31" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1189 reshape_31" -> "1193 linear_46" [label="(4, 64, 384)", style=solid]; +"1189 reshape_31" -> "1213 new_zeros_3" [label="(4, 64, 384)", style=solid]; +"1190 clone_7" -> "1191 slice_110" [label="(1152,)", style=solid]; +"1190 clone_7" -> "1193 linear_46" [label="(1152,)", style=solid]; +"1191 slice_110" -> "1192 zero__7" [label="(384,)", style=solid]; +"1193 linear_46" -> "1194 reshape_32" [label="(4, 64, 1152)", style=solid]; +"1194 reshape_32" -> "1195 permute_34" [label="(4, 64, 3, 12, 32)", style=solid]; +"1195 permute_34" -> "1196 select_21" [label="(3, 4, 12, 64, 32)", style=solid]; +"1195 permute_34" -> "1197 select_22" [label="(3, 4, 12, 64, 32)", style=solid]; +"1195 permute_34" -> "1198 select_23" [label="(3, 4, 12, 64, 32)", style=solid]; +"1196 select_21" -> "1199 linalg_vector_norm_14" [label="(4, 12, 64, 32)", style=solid]; +"1196 select_21" -> "1201 expand_as_14" [label="(4, 12, 64, 32)", style=solid]; +"1196 select_21" -> "1202 div_14" [label="(4, 12, 64, 32)", style=solid]; +"1197 select_22" -> "1203 linalg_vector_norm_15" [label="(4, 12, 64, 32)", style=solid]; +"1197 select_22" -> "1205 expand_as_15" [label="(4, 12, 64, 32)", style=solid]; +"1197 select_22" -> "1206 div_15" [label="(4, 12, 64, 32)", style=solid]; +"1198 select_23" -> "1267 matmul_15" [label="(4, 12, 64, 32)", style=solid]; +"1199 linalg_vector_norm_14" -> "1200 clamp_min_14" [label="(4, 12, 64, 1)", style=solid]; +"1200 clamp_min_14" -> "1201 expand_as_14" [label="(4, 12, 64, 1)", style=solid]; +"1201 expand_as_14" -> "1202 div_14" [label="(4, 12, 64, 32)", style=solid]; +"1202 div_14" -> "1208 matmul_14" [label="(4, 12, 64, 32)", style=solid]; +"1203 linalg_vector_norm_15" -> "1204 clamp_min_15" [label="(4, 12, 64, 1)", style=solid]; +"1204 clamp_min_15" -> "1205 expand_as_15" [label="(4, 12, 64, 1)", style=solid]; +"1205 expand_as_15" -> "1206 div_15" [label="(4, 12, 64, 32)", style=solid]; +"1206 div_15" -> "1207 transpose_14" [label="(4, 12, 64, 32)", style=solid]; +"1207 transpose_14" -> "1208 matmul_14" [label="(4, 12, 32, 64)", style=solid]; +"1208 matmul_14" -> "1211 mul_15" [label="(4, 12, 64, 64)", style=solid]; +"1209 clamp_7" -> "1210 exp_7" [label="(12, 1, 1)", style=solid]; +"1210 exp_7" -> "1211 mul_15" [label="(12, 1, 1)", style=solid]; +"1211 mul_15" -> "1212 add_24" [label="(4, 12, 64, 64)", style=solid]; +"1212 add_24" -> "1260 view_41" [label="(4, 12, 64, 64)", style=solid]; +"1213 new_zeros_3" -> "1215 slice_111" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1219 slice_113" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1223 slice_115" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1227 slice_117" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1231 slice_119" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1235 slice_121" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1239 slice_123" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1243 slice_125" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1247 slice_127" [label="(16, 16)", style=solid]; +"1213 new_zeros_3" -> "1250 view_40" [label="(16, 16)", style=solid]; +"1214 lift_fresh_copy_27" -> "1217 fill__27" [label="()", style=solid]; +"1215 slice_111" -> "1216 slice_112" [label="(8, 16)", style=solid]; +"1216 slice_112" -> "1217 fill__27" [label="(8, 8)", style=solid]; +"1218 lift_fresh_copy_28" -> "1221 fill__28" [label="()", style=solid]; +"1219 slice_113" -> "1220 slice_114" [label="(8, 16)", style=solid]; +"1220 slice_114" -> "1221 fill__28" [label="(8, 4)", style=solid]; +"1222 lift_fresh_copy_29" -> "1225 fill__29" [label="()", style=solid]; +"1223 slice_115" -> "1224 slice_116" [label="(8, 16)", style=solid]; +"1224 slice_116" -> "1225 fill__29" [label="(8, 4)", style=solid]; +"1226 lift_fresh_copy_30" -> "1229 fill__30" [label="()", style=solid]; +"1227 slice_117" -> "1228 slice_118" [label="(4, 16)", style=solid]; +"1228 slice_118" -> "1229 fill__30" [label="(4, 8)", style=solid]; +"1230 lift_fresh_copy_31" -> "1233 fill__31" [label="()", style=solid]; +"1231 slice_119" -> "1232 slice_120" [label="(4, 16)", style=solid]; +"1232 slice_120" -> "1233 fill__31" [label="(4, 4)", style=solid]; +"1234 lift_fresh_copy_32" -> "1237 fill__32" [label="()", style=solid]; +"1235 slice_121" -> "1236 slice_122" [label="(4, 16)", style=solid]; +"1236 slice_122" -> "1237 fill__32" [label="(4, 4)", style=solid]; +"1238 lift_fresh_copy_33" -> "1241 fill__33" [label="()", style=solid]; +"1239 slice_123" -> "1240 slice_124" [label="(4, 16)", style=solid]; +"1240 slice_124" -> "1241 fill__33" [label="(4, 8)", style=solid]; +"1242 lift_fresh_copy_34" -> "1245 fill__34" [label="()", style=solid]; +"1243 slice_125" -> "1244 slice_126" [label="(4, 16)", style=solid]; +"1244 slice_126" -> "1245 fill__34" [label="(4, 4)", style=solid]; +"1246 lift_fresh_copy_35" -> "1249 fill__35" [label="()", style=solid]; +"1247 slice_127" -> "1248 slice_128" [label="(4, 16)", style=solid]; +"1248 slice_128" -> "1249 fill__35" [label="(4, 4)", style=solid]; +"1250 view_40" -> "1251 permute_35" [label="(2, 8, 2, 8)", style=solid]; +"1251 permute_35" -> "1252 reshape_33" [label="(2, 2, 8, 8)", style=solid]; +"1252 reshape_33" -> "1253 unsqueeze_20" [label="(4, 64)", style=solid]; +"1252 reshape_33" -> "1254 unsqueeze_21" [label="(4, 64)", style=solid]; +"1253 unsqueeze_20" -> "1255 sub_3" [label="(4, 1, 64)", style=solid]; +"1254 unsqueeze_21" -> "1255 sub_3" [label="(4, 64, 1)", style=solid]; +"1255 sub_3" -> "1256 ne_3" [label="(4, 64, 64)", style=solid]; +"1255 sub_3" -> "1257 masked_fill_6" [label="(4, 64, 64)", style=solid]; +"1255 sub_3" -> "1258 eq_3" [label="(4, 64, 64)", style=solid]; +"1256 ne_3" -> "1257 masked_fill_6" [label="(4, 64, 64)", style=solid]; +"1257 masked_fill_6" -> "1259 masked_fill_7" [label="(4, 64, 64)", style=solid]; +"1258 eq_3" -> "1259 masked_fill_7" [label="(4, 64, 64)", style=solid]; +"1259 masked_fill_7" -> "1261 unsqueeze_22" [label="(4, 64, 64)", style=solid]; +"1260 view_41" -> "1263 add_25" [label="(1, 4, 12, 64, 64)", style=solid]; +"1261 unsqueeze_22" -> "1262 unsqueeze_23" [label="(4, 1, 64, 64)", style=solid]; +"1262 unsqueeze_23" -> "1263 add_25" [label="(1, 4, 1, 64, 64)", style=solid]; +"1263 add_25" -> "1264 view_42" [label="(1, 4, 12, 64, 64)", style=solid]; +"1264 view_42" -> "1265 softmax_7" [label="(4, 12, 64, 64)", style=solid]; +"1265 softmax_7" -> "1266 dropout_28" [label="(4, 12, 64, 64)", style=solid]; +"1266 dropout_28" -> "1267 matmul_15" [label="(4, 12, 64, 64)", style=solid]; +"1267 matmul_15" -> "1268 transpose_15" [label="(4, 12, 64, 32)", style=solid]; +"1268 transpose_15" -> "1269 reshape_34" [label="(4, 64, 12, 32)", style=solid]; +"1269 reshape_34" -> "1270 linear_47" [label="(4, 64, 384)", style=solid]; +"1270 linear_47" -> "1271 dropout_29" [label="(4, 64, 384)", style=solid]; +"1271 dropout_29" -> "1272 view_43" [label="(4, 64, 384)", style=solid]; +"1272 view_43" -> "1273 permute_36" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1273 permute_36" -> "1274 reshape_35" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1274 reshape_35" -> "1275 roll_7" [label="(1, 16, 16, 384)", style=solid]; +"1275 roll_7" -> "1276 slice_129" [label="(1, 16, 16, 384)", style=solid]; +"1276 slice_129" -> "1277 slice_130" [label="(1, 16, 16, 384)", style=solid]; +"1277 slice_130" -> "1278 slice_131" [label="(1, 14, 16, 384)", style=solid]; +"1278 slice_131" -> "1279 slice_132" [label="(1, 14, 14, 384)", style=solid]; +"1279 slice_132" -> "1280 contiguous_13" [label="(1, 14, 14, 384)", style=solid]; +"1280 contiguous_13" -> "1281 layer_norm_17" [label="(1, 14, 14, 384)", style=solid]; +"1281 layer_norm_17" -> "1282 add_26" [label="(1, 14, 14, 384)", style=solid]; +"1282 add_26" -> "1283 linear_48" [label="(1, 14, 14, 384)", style=solid]; +"1282 add_26" -> "1289 add_27" [label="(1, 14, 14, 384)", style=solid]; +"1283 linear_48" -> "1284 gelu_7" [label="(1, 14, 14, 1536)", style=solid]; +"1284 gelu_7" -> "1285 dropout_30" [label="(1, 14, 14, 1536)", style=solid]; +"1285 dropout_30" -> "1286 linear_49" [label="(1, 14, 14, 1536)", style=solid]; +"1286 linear_49" -> "1287 dropout_31" [label="(1, 14, 14, 384)", style=solid]; +"1287 dropout_31" -> "1288 layer_norm_18" [label="(1, 14, 14, 384)", style=solid]; +"1288 layer_norm_18" -> "1289 add_27" [label="(1, 14, 14, 384)", style=solid]; +"1289 add_27" -> "1301 pad_10" [label="(1, 14, 14, 384)", style=solid]; +"1289 add_27" -> "1344 add_29" [label="(1, 14, 14, 384)", style=solid]; +"1290 linear_50" -> "1291 relu__8" [label="(1, 15, 15, 512)", style=solid]; +"1291 relu__8" -> "1292 linear_51" [label="(1, 15, 15, 512)", style=solid]; +"1292 linear_51" -> "1293 view_44" [label="(1, 15, 15, 12)", style=solid]; +"1293 view_44" -> "1294 index_8" [label="(225, 12)", style=solid]; +"1294 index_8" -> "1295 view_45" [label="(4096, 12)", style=solid]; +"1295 view_45" -> "1296 permute_37" [label="(64, 64, 12)", style=solid]; +"1296 permute_37" -> "1297 contiguous_14" [label="(12, 64, 64)", style=solid]; +"1297 contiguous_14" -> "1298 unsqueeze_24" [label="(12, 64, 64)", style=solid]; +"1298 unsqueeze_24" -> "1299 sigmoid_8" [label="(1, 12, 64, 64)", style=solid]; +"1299 sigmoid_8" -> "1300 mul_16" [label="(1, 12, 64, 64)", style=solid]; +"1300 mul_16" -> "1327 add_28" [label="(1, 12, 64, 64)", style=solid]; +"1301 pad_10" -> "1302 view_46" [label="(1, 16, 16, 384)", style=solid]; +"1302 view_46" -> "1303 permute_38" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1303 permute_38" -> "1304 reshape_36" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1304 reshape_36" -> "1308 linear_52" [label="(4, 64, 384)", style=solid]; +"1305 clone_8" -> "1306 slice_133" [label="(1152,)", style=solid]; +"1305 clone_8" -> "1308 linear_52" [label="(1152,)", style=solid]; +"1306 slice_133" -> "1307 zero__8" [label="(384,)", style=solid]; +"1308 linear_52" -> "1309 reshape_37" [label="(4, 64, 1152)", style=solid]; +"1309 reshape_37" -> "1310 permute_39" [label="(4, 64, 3, 12, 32)", style=solid]; +"1310 permute_39" -> "1311 select_24" [label="(3, 4, 12, 64, 32)", style=solid]; +"1310 permute_39" -> "1312 select_25" [label="(3, 4, 12, 64, 32)", style=solid]; +"1310 permute_39" -> "1313 select_26" [label="(3, 4, 12, 64, 32)", style=solid]; +"1311 select_24" -> "1314 linalg_vector_norm_16" [label="(4, 12, 64, 32)", style=solid]; +"1311 select_24" -> "1316 expand_as_16" [label="(4, 12, 64, 32)", style=solid]; +"1311 select_24" -> "1317 div_16" [label="(4, 12, 64, 32)", style=solid]; +"1312 select_25" -> "1318 linalg_vector_norm_17" [label="(4, 12, 64, 32)", style=solid]; +"1312 select_25" -> "1320 expand_as_17" [label="(4, 12, 64, 32)", style=solid]; +"1312 select_25" -> "1321 div_17" [label="(4, 12, 64, 32)", style=solid]; +"1313 select_26" -> "1330 matmul_17" [label="(4, 12, 64, 32)", style=solid]; +"1314 linalg_vector_norm_16" -> "1315 clamp_min_16" [label="(4, 12, 64, 1)", style=solid]; +"1315 clamp_min_16" -> "1316 expand_as_16" [label="(4, 12, 64, 1)", style=solid]; +"1316 expand_as_16" -> "1317 div_16" [label="(4, 12, 64, 32)", style=solid]; +"1317 div_16" -> "1323 matmul_16" [label="(4, 12, 64, 32)", style=solid]; +"1318 linalg_vector_norm_17" -> "1319 clamp_min_17" [label="(4, 12, 64, 1)", style=solid]; +"1319 clamp_min_17" -> "1320 expand_as_17" [label="(4, 12, 64, 1)", style=solid]; +"1320 expand_as_17" -> "1321 div_17" [label="(4, 12, 64, 32)", style=solid]; +"1321 div_17" -> "1322 transpose_16" [label="(4, 12, 64, 32)", style=solid]; +"1322 transpose_16" -> "1323 matmul_16" [label="(4, 12, 32, 64)", style=solid]; +"1323 matmul_16" -> "1326 mul_17" [label="(4, 12, 64, 64)", style=solid]; +"1324 clamp_8" -> "1325 exp_8" [label="(12, 1, 1)", style=solid]; +"1325 exp_8" -> "1326 mul_17" [label="(12, 1, 1)", style=solid]; +"1326 mul_17" -> "1327 add_28" [label="(4, 12, 64, 64)", style=solid]; +"1327 add_28" -> "1328 softmax_8" [label="(4, 12, 64, 64)", style=solid]; +"1328 softmax_8" -> "1329 dropout_32" [label="(4, 12, 64, 64)", style=solid]; +"1329 dropout_32" -> "1330 matmul_17" [label="(4, 12, 64, 64)", style=solid]; +"1330 matmul_17" -> "1331 transpose_17" [label="(4, 12, 64, 32)", style=solid]; +"1331 transpose_17" -> "1332 reshape_38" [label="(4, 64, 12, 32)", style=solid]; +"1332 reshape_38" -> "1333 linear_53" [label="(4, 64, 384)", style=solid]; +"1333 linear_53" -> "1334 dropout_33" [label="(4, 64, 384)", style=solid]; +"1334 dropout_33" -> "1335 view_47" [label="(4, 64, 384)", style=solid]; +"1335 view_47" -> "1336 permute_40" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1336 permute_40" -> "1337 reshape_39" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1337 reshape_39" -> "1338 slice_134" [label="(1, 16, 16, 384)", style=solid]; +"1338 slice_134" -> "1339 slice_135" [label="(1, 16, 16, 384)", style=solid]; +"1339 slice_135" -> "1340 slice_136" [label="(1, 14, 16, 384)", style=solid]; +"1340 slice_136" -> "1341 slice_137" [label="(1, 14, 14, 384)", style=solid]; +"1341 slice_137" -> "1342 contiguous_15" [label="(1, 14, 14, 384)", style=solid]; 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style=solid]; +"1366 permute_42" -> "1367 reshape_40" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1367 reshape_40" -> "1371 linear_58" [label="(4, 64, 384)", style=solid]; +"1367 reshape_40" -> "1391 new_zeros_4" [label="(4, 64, 384)", style=solid]; +"1368 clone_9" -> "1369 slice_138" [label="(1152,)", style=solid]; +"1368 clone_9" -> "1371 linear_58" [label="(1152,)", style=solid]; +"1369 slice_138" -> "1370 zero__9" [label="(384,)", style=solid]; +"1371 linear_58" -> "1372 reshape_41" [label="(4, 64, 1152)", style=solid]; +"1372 reshape_41" -> "1373 permute_43" [label="(4, 64, 3, 12, 32)", style=solid]; +"1373 permute_43" -> "1374 select_27" [label="(3, 4, 12, 64, 32)", style=solid]; +"1373 permute_43" -> "1375 select_28" [label="(3, 4, 12, 64, 32)", style=solid]; +"1373 permute_43" -> "1376 select_29" [label="(3, 4, 12, 64, 32)", style=solid]; +"1374 select_27" -> "1377 linalg_vector_norm_18" [label="(4, 12, 64, 32)", style=solid]; +"1374 select_27" -> "1379 expand_as_18" [label="(4, 12, 64, 32)", style=solid]; +"1374 select_27" -> "1380 div_18" [label="(4, 12, 64, 32)", style=solid]; +"1375 select_28" -> "1381 linalg_vector_norm_19" [label="(4, 12, 64, 32)", style=solid]; +"1375 select_28" -> "1383 expand_as_19" [label="(4, 12, 64, 32)", style=solid]; +"1375 select_28" -> "1384 div_19" [label="(4, 12, 64, 32)", style=solid]; +"1376 select_29" -> "1445 matmul_19" [label="(4, 12, 64, 32)", style=solid]; +"1377 linalg_vector_norm_18" -> "1378 clamp_min_18" [label="(4, 12, 64, 1)", style=solid]; +"1378 clamp_min_18" -> "1379 expand_as_18" [label="(4, 12, 64, 1)", style=solid]; +"1379 expand_as_18" -> "1380 div_18" [label="(4, 12, 64, 32)", style=solid]; +"1380 div_18" -> "1386 matmul_18" [label="(4, 12, 64, 32)", style=solid]; +"1381 linalg_vector_norm_19" -> "1382 clamp_min_19" [label="(4, 12, 64, 1)", style=solid]; +"1382 clamp_min_19" -> "1383 expand_as_19" [label="(4, 12, 64, 1)", style=solid]; +"1383 expand_as_19" -> "1384 div_19" [label="(4, 12, 64, 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slice_151" [label="(16, 16)", style=solid]; +"1391 new_zeros_4" -> "1421 slice_153" [label="(16, 16)", style=solid]; +"1391 new_zeros_4" -> "1425 slice_155" [label="(16, 16)", style=solid]; +"1391 new_zeros_4" -> "1428 view_51" [label="(16, 16)", style=solid]; +"1392 lift_fresh_copy_36" -> "1395 fill__36" [label="()", style=solid]; +"1393 slice_139" -> "1394 slice_140" [label="(8, 16)", style=solid]; +"1394 slice_140" -> "1395 fill__36" [label="(8, 8)", style=solid]; +"1396 lift_fresh_copy_37" -> "1399 fill__37" [label="()", style=solid]; +"1397 slice_141" -> "1398 slice_142" [label="(8, 16)", style=solid]; +"1398 slice_142" -> "1399 fill__37" [label="(8, 4)", style=solid]; +"1400 lift_fresh_copy_38" -> "1403 fill__38" [label="()", style=solid]; +"1401 slice_143" -> "1402 slice_144" [label="(8, 16)", style=solid]; +"1402 slice_144" -> "1403 fill__38" [label="(8, 4)", style=solid]; +"1404 lift_fresh_copy_39" -> "1407 fill__39" [label="()", style=solid]; +"1405 slice_145" -> "1406 slice_146" [label="(4, 16)", style=solid]; +"1406 slice_146" -> "1407 fill__39" [label="(4, 8)", style=solid]; +"1408 lift_fresh_copy_40" -> "1411 fill__40" [label="()", style=solid]; +"1409 slice_147" -> "1410 slice_148" [label="(4, 16)", style=solid]; +"1410 slice_148" -> "1411 fill__40" [label="(4, 4)", style=solid]; +"1412 lift_fresh_copy_41" -> "1415 fill__41" [label="()", style=solid]; +"1413 slice_149" -> "1414 slice_150" [label="(4, 16)", style=solid]; +"1414 slice_150" -> "1415 fill__41" [label="(4, 4)", style=solid]; +"1416 lift_fresh_copy_42" -> "1419 fill__42" [label="()", style=solid]; +"1417 slice_151" -> "1418 slice_152" [label="(4, 16)", style=solid]; +"1418 slice_152" -> "1419 fill__42" [label="(4, 8)", style=solid]; +"1420 lift_fresh_copy_43" -> "1423 fill__43" [label="()", style=solid]; +"1421 slice_153" -> "1422 slice_154" [label="(4, 16)", style=solid]; +"1422 slice_154" -> "1423 fill__43" [label="(4, 4)", style=solid]; +"1424 lift_fresh_copy_44" -> "1427 fill__44" 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masked_fill_9" [label="(4, 64, 64)", style=solid]; +"1437 masked_fill_9" -> "1439 unsqueeze_28" [label="(4, 64, 64)", style=solid]; +"1438 view_52" -> "1441 add_32" [label="(1, 4, 12, 64, 64)", style=solid]; +"1439 unsqueeze_28" -> "1440 unsqueeze_29" [label="(4, 1, 64, 64)", style=solid]; +"1440 unsqueeze_29" -> "1441 add_32" [label="(1, 4, 1, 64, 64)", style=solid]; +"1441 add_32" -> "1442 view_53" [label="(1, 4, 12, 64, 64)", style=solid]; +"1442 view_53" -> "1443 softmax_9" [label="(4, 12, 64, 64)", style=solid]; +"1443 softmax_9" -> "1444 dropout_36" [label="(4, 12, 64, 64)", style=solid]; +"1444 dropout_36" -> "1445 matmul_19" [label="(4, 12, 64, 64)", style=solid]; +"1445 matmul_19" -> "1446 transpose_19" [label="(4, 12, 64, 32)", style=solid]; +"1446 transpose_19" -> "1447 reshape_43" [label="(4, 64, 12, 32)", style=solid]; +"1447 reshape_43" -> "1448 linear_59" [label="(4, 64, 384)", style=solid]; +"1448 linear_59" -> "1449 dropout_37" [label="(4, 64, 384)", style=solid]; 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[label="(1, 14, 14, 1536)", style=solid]; +"1704 linear_79" -> "1705 dropout_51" [label="(1, 14, 14, 384)", style=solid]; +"1705 dropout_51" -> "1706 layer_norm_28" [label="(1, 14, 14, 384)", style=solid]; +"1706 layer_norm_28" -> "1707 add_44" [label="(1, 14, 14, 384)", style=solid]; +"1707 add_44" -> "1719 pad_15" [label="(1, 14, 14, 384)", style=solid]; +"1707 add_44" -> "1816 add_47" [label="(1, 14, 14, 384)", style=solid]; +"1708 linear_80" -> "1709 relu__13" [label="(1, 15, 15, 512)", style=solid]; +"1709 relu__13" -> "1710 linear_81" [label="(1, 15, 15, 512)", style=solid]; +"1710 linear_81" -> "1711 view_70" [label="(1, 15, 15, 12)", style=solid]; +"1711 view_70" -> "1712 index_13" [label="(225, 12)", style=solid]; +"1712 index_13" -> "1713 view_71" [label="(4096, 12)", style=solid]; +"1713 view_71" -> "1714 permute_59" [label="(64, 64, 12)", style=solid]; +"1714 permute_59" -> "1715 contiguous_24" [label="(12, 64, 64)", style=solid]; +"1715 contiguous_24" -> "1716 unsqueeze_37" [label="(12, 64, 64)", style=solid]; +"1716 unsqueeze_37" -> "1717 sigmoid_13" [label="(1, 12, 64, 64)", style=solid]; +"1717 sigmoid_13" -> "1718 mul_26" [label="(1, 12, 64, 64)", style=solid]; +"1718 mul_26" -> "1746 add_45" [label="(1, 12, 64, 64)", style=solid]; +"1719 pad_15" -> "1720 roll_12" [label="(1, 16, 16, 384)", style=solid]; +"1720 roll_12" -> "1721 view_72" [label="(1, 16, 16, 384)", style=solid]; +"1721 view_72" -> "1722 permute_60" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1722 permute_60" -> "1723 reshape_58" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1723 reshape_58" -> "1727 linear_82" [label="(4, 64, 384)", style=solid]; +"1723 reshape_58" -> "1747 new_zeros_6" [label="(4, 64, 384)", style=solid]; +"1724 clone_13" -> "1725 slice_194" [label="(1152,)", style=solid]; +"1724 clone_13" -> "1727 linear_82" [label="(1152,)", style=solid]; +"1725 slice_194" -> "1726 zero__13" [label="(384,)", style=solid]; +"1727 linear_82" -> "1728 reshape_59" [label="(4, 64, 1152)", style=solid]; +"1728 reshape_59" -> "1729 permute_61" [label="(4, 64, 3, 12, 32)", style=solid]; +"1729 permute_61" -> "1730 select_39" [label="(3, 4, 12, 64, 32)", style=solid]; +"1729 permute_61" -> "1731 select_40" [label="(3, 4, 12, 64, 32)", style=solid]; +"1729 permute_61" -> "1732 select_41" [label="(3, 4, 12, 64, 32)", style=solid]; +"1730 select_39" -> "1733 linalg_vector_norm_26" [label="(4, 12, 64, 32)", style=solid]; +"1730 select_39" -> "1735 expand_as_26" [label="(4, 12, 64, 32)", style=solid]; +"1730 select_39" -> "1736 div_26" [label="(4, 12, 64, 32)", style=solid]; +"1731 select_40" -> "1737 linalg_vector_norm_27" [label="(4, 12, 64, 32)", style=solid]; +"1731 select_40" -> "1739 expand_as_27" [label="(4, 12, 64, 32)", style=solid]; +"1731 select_40" -> "1740 div_27" [label="(4, 12, 64, 32)", style=solid]; +"1732 select_41" -> "1801 matmul_27" [label="(4, 12, 64, 32)", style=solid]; +"1733 linalg_vector_norm_26" -> "1734 clamp_min_26" [label="(4, 12, 64, 1)", style=solid]; +"1734 clamp_min_26" -> "1735 expand_as_26" [label="(4, 12, 64, 1)", style=solid]; +"1735 expand_as_26" -> "1736 div_26" [label="(4, 12, 64, 32)", style=solid]; +"1736 div_26" -> "1742 matmul_26" [label="(4, 12, 64, 32)", style=solid]; +"1737 linalg_vector_norm_27" -> "1738 clamp_min_27" [label="(4, 12, 64, 1)", style=solid]; +"1738 clamp_min_27" -> "1739 expand_as_27" [label="(4, 12, 64, 1)", style=solid]; +"1739 expand_as_27" -> "1740 div_27" [label="(4, 12, 64, 32)", style=solid]; +"1740 div_27" -> "1741 transpose_26" [label="(4, 12, 64, 32)", style=solid]; +"1741 transpose_26" -> "1742 matmul_26" [label="(4, 12, 32, 64)", style=solid]; +"1742 matmul_26" -> "1745 mul_27" [label="(4, 12, 64, 64)", style=solid]; +"1743 clamp_13" -> "1744 exp_13" [label="(12, 1, 1)", style=solid]; +"1744 exp_13" -> "1745 mul_27" [label="(12, 1, 1)", style=solid]; +"1745 mul_27" -> "1746 add_45" [label="(4, 12, 64, 64)", style=solid]; +"1746 add_45" -> "1794 view_74" [label="(4, 12, 64, 64)", style=solid]; +"1747 new_zeros_6" -> "1749 slice_195" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1753 slice_197" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1757 slice_199" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1761 slice_201" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1765 slice_203" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1769 slice_205" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1773 slice_207" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1777 slice_209" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1781 slice_211" [label="(16, 16)", style=solid]; +"1747 new_zeros_6" -> "1784 view_73" [label="(16, 16)", style=solid]; +"1748 lift_fresh_copy_54" -> "1751 fill__54" [label="()", style=solid]; +"1749 slice_195" -> "1750 slice_196" [label="(8, 16)", style=solid]; +"1750 slice_196" -> "1751 fill__54" [label="(8, 8)", style=solid]; +"1752 lift_fresh_copy_55" -> "1755 fill__55" [label="()", style=solid]; +"1753 slice_197" -> "1754 slice_198" [label="(8, 16)", style=solid]; +"1754 slice_198" -> "1755 fill__55" [label="(8, 4)", style=solid]; +"1756 lift_fresh_copy_56" -> "1759 fill__56" [label="()", style=solid]; +"1757 slice_199" -> "1758 slice_200" [label="(8, 16)", style=solid]; +"1758 slice_200" -> "1759 fill__56" [label="(8, 4)", style=solid]; +"1760 lift_fresh_copy_57" -> "1763 fill__57" [label="()", style=solid]; +"1761 slice_201" -> "1762 slice_202" [label="(4, 16)", style=solid]; +"1762 slice_202" -> "1763 fill__57" [label="(4, 8)", style=solid]; +"1764 lift_fresh_copy_58" -> "1767 fill__58" [label="()", style=solid]; +"1765 slice_203" -> "1766 slice_204" [label="(4, 16)", style=solid]; +"1766 slice_204" -> "1767 fill__58" [label="(4, 4)", style=solid]; +"1768 lift_fresh_copy_59" -> "1771 fill__59" [label="()", style=solid]; +"1769 slice_205" -> "1770 slice_206" [label="(4, 16)", style=solid]; +"1770 slice_206" -> "1771 fill__59" [label="(4, 4)", style=solid]; +"1772 lift_fresh_copy_60" -> "1775 fill__60" [label="()", style=solid]; +"1773 slice_207" -> "1774 slice_208" [label="(4, 16)", style=solid]; +"1774 slice_208" -> "1775 fill__60" [label="(4, 8)", style=solid]; +"1776 lift_fresh_copy_61" -> "1779 fill__61" [label="()", style=solid]; +"1777 slice_209" -> "1778 slice_210" [label="(4, 16)", style=solid]; +"1778 slice_210" -> "1779 fill__61" [label="(4, 4)", style=solid]; +"1780 lift_fresh_copy_62" -> "1783 fill__62" [label="()", style=solid]; +"1781 slice_211" -> "1782 slice_212" [label="(4, 16)", style=solid]; +"1782 slice_212" -> "1783 fill__62" [label="(4, 4)", style=solid]; +"1784 view_73" -> "1785 permute_62" [label="(2, 8, 2, 8)", style=solid]; +"1785 permute_62" -> "1786 reshape_60" [label="(2, 2, 8, 8)", style=solid]; +"1786 reshape_60" -> "1787 unsqueeze_38" [label="(4, 64)", style=solid]; +"1786 reshape_60" -> "1788 unsqueeze_39" [label="(4, 64)", style=solid]; +"1787 unsqueeze_38" -> "1789 sub_6" [label="(4, 1, 64)", style=solid]; +"1788 unsqueeze_39" -> "1789 sub_6" [label="(4, 64, 1)", style=solid]; +"1789 sub_6" -> "1790 ne_6" [label="(4, 64, 64)", style=solid]; +"1789 sub_6" -> "1791 masked_fill_12" [label="(4, 64, 64)", style=solid]; +"1789 sub_6" -> "1792 eq_6" [label="(4, 64, 64)", style=solid]; +"1790 ne_6" -> "1791 masked_fill_12" [label="(4, 64, 64)", style=solid]; +"1791 masked_fill_12" -> "1793 masked_fill_13" [label="(4, 64, 64)", style=solid]; +"1792 eq_6" -> "1793 masked_fill_13" [label="(4, 64, 64)", style=solid]; +"1793 masked_fill_13" -> "1795 unsqueeze_40" [label="(4, 64, 64)", style=solid]; +"1794 view_74" -> "1797 add_46" [label="(1, 4, 12, 64, 64)", style=solid]; +"1795 unsqueeze_40" -> "1796 unsqueeze_41" [label="(4, 1, 64, 64)", style=solid]; +"1796 unsqueeze_41" -> "1797 add_46" [label="(1, 4, 1, 64, 64)", style=solid]; +"1797 add_46" -> "1798 view_75" [label="(1, 4, 12, 64, 64)", style=solid]; +"1798 view_75" -> "1799 softmax_13" [label="(4, 12, 64, 64)", style=solid]; +"1799 softmax_13" -> "1800 dropout_52" [label="(4, 12, 64, 64)", style=solid]; +"1800 dropout_52" -> "1801 matmul_27" [label="(4, 12, 64, 64)", style=solid]; +"1801 matmul_27" -> "1802 transpose_27" [label="(4, 12, 64, 32)", style=solid]; +"1802 transpose_27" -> "1803 reshape_61" [label="(4, 64, 12, 32)", style=solid]; +"1803 reshape_61" -> "1804 linear_83" [label="(4, 64, 384)", style=solid]; +"1804 linear_83" -> "1805 dropout_53" [label="(4, 64, 384)", style=solid]; +"1805 dropout_53" -> "1806 view_76" [label="(4, 64, 384)", style=solid]; +"1806 view_76" -> "1807 permute_63" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1807 permute_63" -> "1808 reshape_62" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1808 reshape_62" -> "1809 roll_13" [label="(1, 16, 16, 384)", style=solid]; +"1809 roll_13" -> "1810 slice_213" [label="(1, 16, 16, 384)", style=solid]; +"1810 slice_213" -> "1811 slice_214" [label="(1, 16, 16, 384)", style=solid]; +"1811 slice_214" -> "1812 slice_215" [label="(1, 14, 16, 384)", style=solid]; +"1812 slice_215" -> "1813 slice_216" [label="(1, 14, 14, 384)", style=solid]; +"1813 slice_216" -> "1814 contiguous_25" [label="(1, 14, 14, 384)", style=solid]; +"1814 contiguous_25" -> "1815 layer_norm_29" [label="(1, 14, 14, 384)", style=solid]; +"1815 layer_norm_29" -> "1816 add_47" [label="(1, 14, 14, 384)", style=solid]; +"1816 add_47" -> "1817 linear_84" [label="(1, 14, 14, 384)", style=solid]; +"1816 add_47" -> "1823 add_48" [label="(1, 14, 14, 384)", style=solid]; +"1817 linear_84" -> "1818 gelu_13" [label="(1, 14, 14, 1536)", style=solid]; +"1818 gelu_13" -> "1819 dropout_54" [label="(1, 14, 14, 1536)", style=solid]; +"1819 dropout_54" -> "1820 linear_85" [label="(1, 14, 14, 1536)", style=solid]; +"1820 linear_85" -> "1821 dropout_55" [label="(1, 14, 14, 384)", style=solid]; +"1821 dropout_55" -> "1822 layer_norm_30" [label="(1, 14, 14, 384)", style=solid]; +"1822 layer_norm_30" -> "1823 add_48" [label="(1, 14, 14, 384)", style=solid]; +"1823 add_48" -> "1835 pad_16" [label="(1, 14, 14, 384)", style=solid]; +"1823 add_48" -> "1878 add_50" [label="(1, 14, 14, 384)", style=solid]; +"1824 linear_86" -> "1825 relu__14" [label="(1, 15, 15, 512)", style=solid]; +"1825 relu__14" -> "1826 linear_87" [label="(1, 15, 15, 512)", style=solid]; +"1826 linear_87" -> "1827 view_77" [label="(1, 15, 15, 12)", style=solid]; +"1827 view_77" -> "1828 index_14" [label="(225, 12)", style=solid]; +"1828 index_14" -> "1829 view_78" [label="(4096, 12)", style=solid]; +"1829 view_78" -> "1830 permute_64" [label="(64, 64, 12)", style=solid]; +"1830 permute_64" -> "1831 contiguous_26" [label="(12, 64, 64)", style=solid]; +"1831 contiguous_26" -> "1832 unsqueeze_42" [label="(12, 64, 64)", style=solid]; +"1832 unsqueeze_42" -> "1833 sigmoid_14" [label="(1, 12, 64, 64)", style=solid]; +"1833 sigmoid_14" -> "1834 mul_28" [label="(1, 12, 64, 64)", style=solid]; +"1834 mul_28" -> "1861 add_49" [label="(1, 12, 64, 64)", style=solid]; +"1835 pad_16" -> "1836 view_79" [label="(1, 16, 16, 384)", style=solid]; +"1836 view_79" -> "1837 permute_65" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1837 permute_65" -> "1838 reshape_63" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1838 reshape_63" -> "1842 linear_88" [label="(4, 64, 384)", style=solid]; +"1839 clone_14" -> "1840 slice_217" [label="(1152,)", style=solid]; +"1839 clone_14" -> "1842 linear_88" [label="(1152,)", style=solid]; +"1840 slice_217" -> "1841 zero__14" [label="(384,)", style=solid]; +"1842 linear_88" -> "1843 reshape_64" [label="(4, 64, 1152)", style=solid]; +"1843 reshape_64" -> "1844 permute_66" [label="(4, 64, 3, 12, 32)", style=solid]; +"1844 permute_66" -> "1845 select_42" [label="(3, 4, 12, 64, 32)", style=solid]; +"1844 permute_66" -> "1846 select_43" [label="(3, 4, 12, 64, 32)", style=solid]; +"1844 permute_66" -> "1847 select_44" [label="(3, 4, 12, 64, 32)", style=solid]; +"1845 select_42" -> "1848 linalg_vector_norm_28" [label="(4, 12, 64, 32)", style=solid]; +"1845 select_42" -> "1850 expand_as_28" [label="(4, 12, 64, 32)", style=solid]; +"1845 select_42" -> "1851 div_28" [label="(4, 12, 64, 32)", style=solid]; +"1846 select_43" -> "1852 linalg_vector_norm_29" [label="(4, 12, 64, 32)", style=solid]; +"1846 select_43" -> "1854 expand_as_29" [label="(4, 12, 64, 32)", style=solid]; +"1846 select_43" -> "1855 div_29" [label="(4, 12, 64, 32)", style=solid]; +"1847 select_44" -> "1864 matmul_29" [label="(4, 12, 64, 32)", style=solid]; +"1848 linalg_vector_norm_28" -> "1849 clamp_min_28" [label="(4, 12, 64, 1)", style=solid]; +"1849 clamp_min_28" -> "1850 expand_as_28" [label="(4, 12, 64, 1)", style=solid]; +"1850 expand_as_28" -> "1851 div_28" [label="(4, 12, 64, 32)", style=solid]; +"1851 div_28" -> "1857 matmul_28" [label="(4, 12, 64, 32)", style=solid]; +"1852 linalg_vector_norm_29" -> "1853 clamp_min_29" [label="(4, 12, 64, 1)", style=solid]; +"1853 clamp_min_29" -> "1854 expand_as_29" [label="(4, 12, 64, 1)", style=solid]; +"1854 expand_as_29" -> "1855 div_29" [label="(4, 12, 64, 32)", style=solid]; +"1855 div_29" -> "1856 transpose_28" [label="(4, 12, 64, 32)", style=solid]; +"1856 transpose_28" -> "1857 matmul_28" [label="(4, 12, 32, 64)", style=solid]; +"1857 matmul_28" -> "1860 mul_29" [label="(4, 12, 64, 64)", style=solid]; +"1858 clamp_14" -> "1859 exp_14" [label="(12, 1, 1)", style=solid]; +"1859 exp_14" -> "1860 mul_29" [label="(12, 1, 1)", style=solid]; +"1860 mul_29" -> "1861 add_49" [label="(4, 12, 64, 64)", style=solid]; +"1861 add_49" -> "1862 softmax_14" [label="(4, 12, 64, 64)", style=solid]; +"1862 softmax_14" -> "1863 dropout_56" [label="(4, 12, 64, 64)", style=solid]; +"1863 dropout_56" -> "1864 matmul_29" [label="(4, 12, 64, 64)", style=solid]; +"1864 matmul_29" -> "1865 transpose_29" [label="(4, 12, 64, 32)", style=solid]; +"1865 transpose_29" -> "1866 reshape_65" [label="(4, 64, 12, 32)", style=solid]; +"1866 reshape_65" -> "1867 linear_89" [label="(4, 64, 384)", style=solid]; +"1867 linear_89" -> "1868 dropout_57" [label="(4, 64, 384)", style=solid]; +"1868 dropout_57" -> "1869 view_80" [label="(4, 64, 384)", style=solid]; +"1869 view_80" -> "1870 permute_67" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1870 permute_67" -> "1871 reshape_66" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1871 reshape_66" -> "1872 slice_218" [label="(1, 16, 16, 384)", style=solid]; +"1872 slice_218" -> "1873 slice_219" [label="(1, 16, 16, 384)", style=solid]; +"1873 slice_219" -> "1874 slice_220" [label="(1, 14, 16, 384)", style=solid]; +"1874 slice_220" -> "1875 slice_221" [label="(1, 14, 14, 384)", style=solid]; +"1875 slice_221" -> "1876 contiguous_27" [label="(1, 14, 14, 384)", style=solid]; +"1876 contiguous_27" -> "1877 layer_norm_31" [label="(1, 14, 14, 384)", style=solid]; +"1877 layer_norm_31" -> "1878 add_50" [label="(1, 14, 14, 384)", style=solid]; +"1878 add_50" -> "1879 linear_90" [label="(1, 14, 14, 384)", style=solid]; +"1878 add_50" -> "1885 add_51" [label="(1, 14, 14, 384)", style=solid]; +"1879 linear_90" -> "1880 gelu_14" [label="(1, 14, 14, 1536)", style=solid]; +"1880 gelu_14" -> "1881 dropout_58" [label="(1, 14, 14, 1536)", style=solid]; +"1881 dropout_58" -> "1882 linear_91" [label="(1, 14, 14, 1536)", style=solid]; +"1882 linear_91" -> "1883 dropout_59" [label="(1, 14, 14, 384)", style=solid]; +"1883 dropout_59" -> "1884 layer_norm_32" [label="(1, 14, 14, 384)", style=solid]; +"1884 layer_norm_32" -> "1885 add_51" [label="(1, 14, 14, 384)", style=solid]; +"1885 add_51" -> "1897 pad_17" [label="(1, 14, 14, 384)", style=solid]; +"1885 add_51" -> "1994 add_54" [label="(1, 14, 14, 384)", style=solid]; +"1886 linear_92" -> "1887 relu__15" [label="(1, 15, 15, 512)", style=solid]; +"1887 relu__15" -> "1888 linear_93" [label="(1, 15, 15, 512)", style=solid]; +"1888 linear_93" -> "1889 view_81" [label="(1, 15, 15, 12)", style=solid]; +"1889 view_81" -> "1890 index_15" [label="(225, 12)", style=solid]; +"1890 index_15" -> "1891 view_82" [label="(4096, 12)", style=solid]; +"1891 view_82" -> "1892 permute_68" [label="(64, 64, 12)", style=solid]; +"1892 permute_68" -> "1893 contiguous_28" [label="(12, 64, 64)", style=solid]; +"1893 contiguous_28" -> "1894 unsqueeze_43" [label="(12, 64, 64)", style=solid]; +"1894 unsqueeze_43" -> "1895 sigmoid_15" [label="(1, 12, 64, 64)", style=solid]; +"1895 sigmoid_15" -> "1896 mul_30" [label="(1, 12, 64, 64)", style=solid]; +"1896 mul_30" -> "1924 add_52" [label="(1, 12, 64, 64)", style=solid]; +"1897 pad_17" -> "1898 roll_14" [label="(1, 16, 16, 384)", style=solid]; +"1898 roll_14" -> "1899 view_83" [label="(1, 16, 16, 384)", style=solid]; +"1899 view_83" -> "1900 permute_69" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1900 permute_69" -> "1901 reshape_67" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1901 reshape_67" -> "1905 linear_94" [label="(4, 64, 384)", style=solid]; +"1901 reshape_67" -> "1925 new_zeros_7" [label="(4, 64, 384)", style=solid]; +"1902 clone_15" -> "1903 slice_222" [label="(1152,)", style=solid]; +"1902 clone_15" -> "1905 linear_94" [label="(1152,)", style=solid]; +"1903 slice_222" -> "1904 zero__15" [label="(384,)", style=solid]; +"1905 linear_94" -> "1906 reshape_68" [label="(4, 64, 1152)", style=solid]; +"1906 reshape_68" -> "1907 permute_70" [label="(4, 64, 3, 12, 32)", style=solid]; +"1907 permute_70" -> "1908 select_45" [label="(3, 4, 12, 64, 32)", style=solid]; +"1907 permute_70" -> "1909 select_46" [label="(3, 4, 12, 64, 32)", style=solid]; +"1907 permute_70" -> "1910 select_47" [label="(3, 4, 12, 64, 32)", style=solid]; +"1908 select_45" -> "1911 linalg_vector_norm_30" [label="(4, 12, 64, 32)", style=solid]; +"1908 select_45" -> "1913 expand_as_30" [label="(4, 12, 64, 32)", style=solid]; +"1908 select_45" -> "1914 div_30" [label="(4, 12, 64, 32)", style=solid]; +"1909 select_46" -> "1915 linalg_vector_norm_31" [label="(4, 12, 64, 32)", style=solid]; +"1909 select_46" -> "1917 expand_as_31" [label="(4, 12, 64, 32)", style=solid]; +"1909 select_46" -> "1918 div_31" [label="(4, 12, 64, 32)", style=solid]; +"1910 select_47" -> "1979 matmul_31" [label="(4, 12, 64, 32)", style=solid]; +"1911 linalg_vector_norm_30" -> "1912 clamp_min_30" [label="(4, 12, 64, 1)", style=solid]; +"1912 clamp_min_30" -> "1913 expand_as_30" [label="(4, 12, 64, 1)", style=solid]; +"1913 expand_as_30" -> "1914 div_30" [label="(4, 12, 64, 32)", style=solid]; +"1914 div_30" -> "1920 matmul_30" [label="(4, 12, 64, 32)", style=solid]; +"1915 linalg_vector_norm_31" -> "1916 clamp_min_31" [label="(4, 12, 64, 1)", style=solid]; +"1916 clamp_min_31" -> "1917 expand_as_31" [label="(4, 12, 64, 1)", style=solid]; +"1917 expand_as_31" -> "1918 div_31" [label="(4, 12, 64, 32)", style=solid]; +"1918 div_31" -> "1919 transpose_30" [label="(4, 12, 64, 32)", style=solid]; +"1919 transpose_30" -> "1920 matmul_30" [label="(4, 12, 32, 64)", style=solid]; +"1920 matmul_30" -> "1923 mul_31" [label="(4, 12, 64, 64)", style=solid]; +"1921 clamp_15" -> "1922 exp_15" [label="(12, 1, 1)", style=solid]; +"1922 exp_15" -> "1923 mul_31" [label="(12, 1, 1)", style=solid]; +"1923 mul_31" -> "1924 add_52" [label="(4, 12, 64, 64)", style=solid]; +"1924 add_52" -> "1972 view_85" [label="(4, 12, 64, 64)", style=solid]; +"1925 new_zeros_7" -> "1927 slice_223" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1931 slice_225" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1935 slice_227" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1939 slice_229" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1943 slice_231" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1947 slice_233" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1951 slice_235" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1955 slice_237" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1959 slice_239" [label="(16, 16)", style=solid]; +"1925 new_zeros_7" -> "1962 view_84" [label="(16, 16)", style=solid]; +"1926 lift_fresh_copy_63" -> "1929 fill__63" [label="()", style=solid]; +"1927 slice_223" -> "1928 slice_224" [label="(8, 16)", style=solid]; +"1928 slice_224" -> "1929 fill__63" [label="(8, 8)", style=solid]; +"1930 lift_fresh_copy_64" -> "1933 fill__64" [label="()", style=solid]; +"1931 slice_225" -> "1932 slice_226" [label="(8, 16)", style=solid]; +"1932 slice_226" -> "1933 fill__64" [label="(8, 4)", style=solid]; +"1934 lift_fresh_copy_65" -> "1937 fill__65" [label="()", style=solid]; +"1935 slice_227" -> "1936 slice_228" [label="(8, 16)", style=solid]; +"1936 slice_228" -> "1937 fill__65" [label="(8, 4)", style=solid]; +"1938 lift_fresh_copy_66" -> "1941 fill__66" [label="()", style=solid]; +"1939 slice_229" -> "1940 slice_230" [label="(4, 16)", style=solid]; +"1940 slice_230" -> "1941 fill__66" [label="(4, 8)", style=solid]; +"1942 lift_fresh_copy_67" -> "1945 fill__67" [label="()", style=solid]; +"1943 slice_231" -> "1944 slice_232" [label="(4, 16)", style=solid]; +"1944 slice_232" -> "1945 fill__67" [label="(4, 4)", style=solid]; +"1946 lift_fresh_copy_68" -> "1949 fill__68" [label="()", style=solid]; +"1947 slice_233" -> "1948 slice_234" [label="(4, 16)", style=solid]; +"1948 slice_234" -> "1949 fill__68" [label="(4, 4)", style=solid]; +"1950 lift_fresh_copy_69" -> "1953 fill__69" [label="()", style=solid]; +"1951 slice_235" -> "1952 slice_236" [label="(4, 16)", style=solid]; +"1952 slice_236" -> "1953 fill__69" [label="(4, 8)", style=solid]; +"1954 lift_fresh_copy_70" -> "1957 fill__70" [label="()", style=solid]; +"1955 slice_237" -> "1956 slice_238" [label="(4, 16)", style=solid]; +"1956 slice_238" -> "1957 fill__70" [label="(4, 4)", style=solid]; +"1958 lift_fresh_copy_71" -> "1961 fill__71" [label="()", style=solid]; +"1959 slice_239" -> "1960 slice_240" [label="(4, 16)", style=solid]; +"1960 slice_240" -> "1961 fill__71" [label="(4, 4)", style=solid]; +"1962 view_84" -> "1963 permute_71" [label="(2, 8, 2, 8)", style=solid]; +"1963 permute_71" -> "1964 reshape_69" [label="(2, 2, 8, 8)", style=solid]; +"1964 reshape_69" -> "1965 unsqueeze_44" [label="(4, 64)", style=solid]; +"1964 reshape_69" -> "1966 unsqueeze_45" [label="(4, 64)", style=solid]; +"1965 unsqueeze_44" -> "1967 sub_7" [label="(4, 1, 64)", style=solid]; +"1966 unsqueeze_45" -> "1967 sub_7" [label="(4, 64, 1)", style=solid]; +"1967 sub_7" -> "1968 ne_7" [label="(4, 64, 64)", style=solid]; +"1967 sub_7" -> "1969 masked_fill_14" [label="(4, 64, 64)", style=solid]; +"1967 sub_7" -> "1970 eq_7" [label="(4, 64, 64)", style=solid]; +"1968 ne_7" -> "1969 masked_fill_14" [label="(4, 64, 64)", style=solid]; +"1969 masked_fill_14" -> "1971 masked_fill_15" [label="(4, 64, 64)", style=solid]; +"1970 eq_7" -> "1971 masked_fill_15" [label="(4, 64, 64)", style=solid]; +"1971 masked_fill_15" -> "1973 unsqueeze_46" [label="(4, 64, 64)", style=solid]; +"1972 view_85" -> "1975 add_53" [label="(1, 4, 12, 64, 64)", style=solid]; +"1973 unsqueeze_46" -> "1974 unsqueeze_47" [label="(4, 1, 64, 64)", style=solid]; +"1974 unsqueeze_47" -> "1975 add_53" [label="(1, 4, 1, 64, 64)", style=solid]; +"1975 add_53" -> "1976 view_86" [label="(1, 4, 12, 64, 64)", style=solid]; +"1976 view_86" -> "1977 softmax_15" [label="(4, 12, 64, 64)", style=solid]; +"1977 softmax_15" -> "1978 dropout_60" [label="(4, 12, 64, 64)", style=solid]; +"1978 dropout_60" -> "1979 matmul_31" [label="(4, 12, 64, 64)", style=solid]; +"1979 matmul_31" -> "1980 transpose_31" [label="(4, 12, 64, 32)", style=solid]; +"1980 transpose_31" -> "1981 reshape_70" [label="(4, 64, 12, 32)", style=solid]; +"1981 reshape_70" -> "1982 linear_95" [label="(4, 64, 384)", style=solid]; +"1982 linear_95" -> "1983 dropout_61" [label="(4, 64, 384)", style=solid]; +"1983 dropout_61" -> "1984 view_87" [label="(4, 64, 384)", style=solid]; +"1984 view_87" -> "1985 permute_72" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"1985 permute_72" -> "1986 reshape_71" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"1986 reshape_71" -> "1987 roll_15" [label="(1, 16, 16, 384)", style=solid]; +"1987 roll_15" -> "1988 slice_241" [label="(1, 16, 16, 384)", style=solid]; +"1988 slice_241" -> "1989 slice_242" [label="(1, 16, 16, 384)", style=solid]; +"1989 slice_242" -> "1990 slice_243" [label="(1, 14, 16, 384)", style=solid]; +"1990 slice_243" -> "1991 slice_244" [label="(1, 14, 14, 384)", style=solid]; +"1991 slice_244" -> "1992 contiguous_29" [label="(1, 14, 14, 384)", style=solid]; +"1992 contiguous_29" -> "1993 layer_norm_33" [label="(1, 14, 14, 384)", style=solid]; +"1993 layer_norm_33" -> "1994 add_54" [label="(1, 14, 14, 384)", style=solid]; +"1994 add_54" -> "1995 linear_96" [label="(1, 14, 14, 384)", style=solid]; +"1994 add_54" -> "2001 add_55" [label="(1, 14, 14, 384)", style=solid]; +"1995 linear_96" -> "1996 gelu_15" [label="(1, 14, 14, 1536)", style=solid]; +"1996 gelu_15" -> "1997 dropout_62" [label="(1, 14, 14, 1536)", style=solid]; +"1997 dropout_62" -> "1998 linear_97" [label="(1, 14, 14, 1536)", style=solid]; +"1998 linear_97" -> "1999 dropout_63" [label="(1, 14, 14, 384)", style=solid]; +"1999 dropout_63" -> "2000 layer_norm_34" [label="(1, 14, 14, 384)", style=solid]; +"2000 layer_norm_34" -> "2001 add_55" [label="(1, 14, 14, 384)", style=solid]; +"2001 add_55" -> "2013 pad_18" [label="(1, 14, 14, 384)", style=solid]; +"2001 add_55" -> "2056 add_57" [label="(1, 14, 14, 384)", style=solid]; +"2002 linear_98" -> "2003 relu__16" [label="(1, 15, 15, 512)", style=solid]; +"2003 relu__16" -> "2004 linear_99" [label="(1, 15, 15, 512)", style=solid]; +"2004 linear_99" -> "2005 view_88" [label="(1, 15, 15, 12)", style=solid]; +"2005 view_88" -> "2006 index_16" [label="(225, 12)", style=solid]; +"2006 index_16" -> "2007 view_89" [label="(4096, 12)", style=solid]; +"2007 view_89" -> "2008 permute_73" [label="(64, 64, 12)", style=solid]; +"2008 permute_73" -> "2009 contiguous_30" [label="(12, 64, 64)", style=solid]; +"2009 contiguous_30" -> "2010 unsqueeze_48" [label="(12, 64, 64)", style=solid]; +"2010 unsqueeze_48" -> "2011 sigmoid_16" [label="(1, 12, 64, 64)", style=solid]; +"2011 sigmoid_16" -> "2012 mul_32" [label="(1, 12, 64, 64)", style=solid]; +"2012 mul_32" -> "2039 add_56" [label="(1, 12, 64, 64)", style=solid]; +"2013 pad_18" -> "2014 view_90" [label="(1, 16, 16, 384)", style=solid]; +"2014 view_90" -> "2015 permute_74" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"2015 permute_74" -> "2016 reshape_72" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"2016 reshape_72" -> "2020 linear_100" [label="(4, 64, 384)", style=solid]; +"2017 clone_16" -> "2018 slice_245" [label="(1152,)", style=solid]; +"2017 clone_16" -> "2020 linear_100" [label="(1152,)", style=solid]; +"2018 slice_245" -> "2019 zero__16" [label="(384,)", style=solid]; +"2020 linear_100" -> "2021 reshape_73" [label="(4, 64, 1152)", style=solid]; +"2021 reshape_73" -> "2022 permute_75" [label="(4, 64, 3, 12, 32)", style=solid]; +"2022 permute_75" -> "2023 select_48" [label="(3, 4, 12, 64, 32)", style=solid]; +"2022 permute_75" -> "2024 select_49" [label="(3, 4, 12, 64, 32)", style=solid]; +"2022 permute_75" -> "2025 select_50" [label="(3, 4, 12, 64, 32)", style=solid]; +"2023 select_48" -> "2026 linalg_vector_norm_32" [label="(4, 12, 64, 32)", style=solid]; +"2023 select_48" -> "2028 expand_as_32" [label="(4, 12, 64, 32)", style=solid]; +"2023 select_48" -> "2029 div_32" [label="(4, 12, 64, 32)", style=solid]; +"2024 select_49" -> "2030 linalg_vector_norm_33" [label="(4, 12, 64, 32)", style=solid]; +"2024 select_49" -> "2032 expand_as_33" [label="(4, 12, 64, 32)", style=solid]; +"2024 select_49" -> "2033 div_33" [label="(4, 12, 64, 32)", style=solid]; +"2025 select_50" -> "2042 matmul_33" [label="(4, 12, 64, 32)", style=solid]; +"2026 linalg_vector_norm_32" -> "2027 clamp_min_32" [label="(4, 12, 64, 1)", style=solid]; +"2027 clamp_min_32" -> "2028 expand_as_32" [label="(4, 12, 64, 1)", style=solid]; +"2028 expand_as_32" -> "2029 div_32" [label="(4, 12, 64, 32)", style=solid]; +"2029 div_32" -> "2035 matmul_32" [label="(4, 12, 64, 32)", style=solid]; +"2030 linalg_vector_norm_33" -> "2031 clamp_min_33" [label="(4, 12, 64, 1)", style=solid]; +"2031 clamp_min_33" -> "2032 expand_as_33" [label="(4, 12, 64, 1)", style=solid]; +"2032 expand_as_33" -> "2033 div_33" [label="(4, 12, 64, 32)", style=solid]; +"2033 div_33" -> "2034 transpose_32" [label="(4, 12, 64, 32)", style=solid]; +"2034 transpose_32" -> "2035 matmul_32" [label="(4, 12, 32, 64)", style=solid]; +"2035 matmul_32" -> "2038 mul_33" [label="(4, 12, 64, 64)", style=solid]; +"2036 clamp_16" -> "2037 exp_16" [label="(12, 1, 1)", style=solid]; +"2037 exp_16" -> "2038 mul_33" [label="(12, 1, 1)", style=solid]; +"2038 mul_33" -> "2039 add_56" [label="(4, 12, 64, 64)", style=solid]; +"2039 add_56" -> "2040 softmax_16" [label="(4, 12, 64, 64)", style=solid]; +"2040 softmax_16" -> "2041 dropout_64" [label="(4, 12, 64, 64)", style=solid]; +"2041 dropout_64" -> "2042 matmul_33" [label="(4, 12, 64, 64)", style=solid]; +"2042 matmul_33" -> "2043 transpose_33" [label="(4, 12, 64, 32)", style=solid]; +"2043 transpose_33" -> "2044 reshape_74" [label="(4, 64, 12, 32)", style=solid]; +"2044 reshape_74" -> "2045 linear_101" [label="(4, 64, 384)", style=solid]; +"2045 linear_101" -> "2046 dropout_65" [label="(4, 64, 384)", style=solid]; +"2046 dropout_65" -> "2047 view_91" [label="(4, 64, 384)", style=solid]; +"2047 view_91" -> "2048 permute_76" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"2048 permute_76" -> "2049 reshape_75" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"2049 reshape_75" -> "2050 slice_246" [label="(1, 16, 16, 384)", style=solid]; +"2050 slice_246" -> "2051 slice_247" [label="(1, 16, 16, 384)", style=solid]; +"2051 slice_247" -> "2052 slice_248" [label="(1, 14, 16, 384)", style=solid]; +"2052 slice_248" -> "2053 slice_249" [label="(1, 14, 14, 384)", style=solid]; +"2053 slice_249" -> "2054 contiguous_31" [label="(1, 14, 14, 384)", style=solid]; +"2054 contiguous_31" -> "2055 layer_norm_35" [label="(1, 14, 14, 384)", style=solid]; +"2055 layer_norm_35" -> "2056 add_57" [label="(1, 14, 14, 384)", style=solid]; +"2056 add_57" -> "2057 linear_102" [label="(1, 14, 14, 384)", style=solid]; +"2056 add_57" -> "2063 add_58" [label="(1, 14, 14, 384)", style=solid]; +"2057 linear_102" -> "2058 gelu_16" [label="(1, 14, 14, 1536)", style=solid]; +"2058 gelu_16" -> "2059 dropout_66" [label="(1, 14, 14, 1536)", style=solid]; +"2059 dropout_66" -> "2060 linear_103" [label="(1, 14, 14, 1536)", style=solid]; +"2060 linear_103" -> "2061 dropout_67" [label="(1, 14, 14, 384)", style=solid]; +"2061 dropout_67" -> "2062 layer_norm_36" [label="(1, 14, 14, 384)", style=solid]; +"2062 layer_norm_36" -> "2063 add_58" [label="(1, 14, 14, 384)", style=solid]; +"2063 add_58" -> "2075 pad_19" [label="(1, 14, 14, 384)", style=solid]; +"2063 add_58" -> "2172 add_61" [label="(1, 14, 14, 384)", style=solid]; +"2064 linear_104" -> "2065 relu__17" [label="(1, 15, 15, 512)", style=solid]; +"2065 relu__17" -> "2066 linear_105" [label="(1, 15, 15, 512)", style=solid]; +"2066 linear_105" -> "2067 view_92" [label="(1, 15, 15, 12)", style=solid]; +"2067 view_92" -> "2068 index_17" [label="(225, 12)", style=solid]; +"2068 index_17" -> "2069 view_93" [label="(4096, 12)", style=solid]; +"2069 view_93" -> "2070 permute_77" [label="(64, 64, 12)", style=solid]; +"2070 permute_77" -> "2071 contiguous_32" [label="(12, 64, 64)", style=solid]; +"2071 contiguous_32" -> "2072 unsqueeze_49" [label="(12, 64, 64)", style=solid]; +"2072 unsqueeze_49" -> "2073 sigmoid_17" [label="(1, 12, 64, 64)", style=solid]; +"2073 sigmoid_17" -> "2074 mul_34" [label="(1, 12, 64, 64)", style=solid]; +"2074 mul_34" -> "2102 add_59" [label="(1, 12, 64, 64)", style=solid]; +"2075 pad_19" -> "2076 roll_16" [label="(1, 16, 16, 384)", style=solid]; +"2076 roll_16" -> "2077 view_94" [label="(1, 16, 16, 384)", style=solid]; +"2077 view_94" -> "2078 permute_78" [label="(1, 2, 8, 2, 8, 384)", style=solid]; +"2078 permute_78" -> "2079 reshape_76" [label="(1, 2, 2, 8, 8, 384)", style=solid]; +"2079 reshape_76" -> "2083 linear_106" [label="(4, 64, 384)", style=solid]; +"2079 reshape_76" -> "2103 new_zeros_8" [label="(4, 64, 384)", style=solid]; +"2080 clone_17" -> "2081 slice_250" [label="(1152,)", style=solid]; +"2080 clone_17" -> "2083 linear_106" [label="(1152,)", style=solid]; +"2081 slice_250" -> "2082 zero__17" [label="(384,)", style=solid]; +"2083 linear_106" -> "2084 reshape_77" [label="(4, 64, 1152)", style=solid]; +"2084 reshape_77" -> "2085 permute_79" [label="(4, 64, 3, 12, 32)", style=solid]; +"2085 permute_79" -> "2086 select_51" [label="(3, 4, 12, 64, 32)", style=solid]; +"2085 permute_79" -> "2087 select_52" [label="(3, 4, 12, 64, 32)", style=solid]; +"2085 permute_79" -> "2088 select_53" [label="(3, 4, 12, 64, 32)", style=solid]; +"2086 select_51" -> "2089 linalg_vector_norm_34" [label="(4, 12, 64, 32)", style=solid]; +"2086 select_51" -> "2091 expand_as_34" [label="(4, 12, 64, 32)", style=solid]; +"2086 select_51" -> "2092 div_34" [label="(4, 12, 64, 32)", style=solid]; +"2087 select_52" -> "2093 linalg_vector_norm_35" [label="(4, 12, 64, 32)", style=solid]; +"2087 select_52" -> "2095 expand_as_35" [label="(4, 12, 64, 32)", style=solid]; +"2087 select_52" -> "2096 div_35" [label="(4, 12, 64, 32)", style=solid]; +"2088 select_53" -> "2157 matmul_35" [label="(4, 12, 64, 32)", style=solid]; +"2089 linalg_vector_norm_34" -> "2090 clamp_min_34" [label="(4, 12, 64, 1)", style=solid]; +"2090 clamp_min_34" -> "2091 expand_as_34" [label="(4, 12, 64, 1)", style=solid]; +"2091 expand_as_34" -> "2092 div_34" [label="(4, 12, 64, 32)", style=solid]; +"2092 div_34" -> "2098 matmul_34" [label="(4, 12, 64, 32)", style=solid]; +"2093 linalg_vector_norm_35" -> "2094 clamp_min_35" [label="(4, 12, 64, 1)", style=solid]; +"2094 clamp_min_35" -> "2095 expand_as_35" [label="(4, 12, 64, 1)", style=solid]; +"2095 expand_as_35" -> "2096 div_35" [label="(4, 12, 64, 32)", style=solid]; +"2096 div_35" -> "2097 transpose_34" [label="(4, 12, 64, 32)", style=solid]; +"2097 transpose_34" -> "2098 matmul_34" [label="(4, 12, 32, 64)", style=solid]; +"2098 matmul_34" -> "2101 mul_35" [label="(4, 12, 64, 64)", style=solid]; +"2099 clamp_17" -> "2100 exp_17" [label="(12, 1, 1)", style=solid]; +"2100 exp_17" -> "2101 mul_35" [label="(12, 1, 1)", style=solid]; +"2101 mul_35" -> "2102 add_59" [label="(4, 12, 64, 64)", style=solid]; +"2102 add_59" -> "2150 view_96" [label="(4, 12, 64, 64)", style=solid]; +"2103 new_zeros_8" -> "2105 slice_251" [label="(16, 16)", style=solid]; +"2103 new_zeros_8" -> "2109 slice_253" [label="(16, 16)", style=solid]; +"2103 new_zeros_8" -> "2113 slice_255" [label="(16, 16)", style=solid]; +"2103 new_zeros_8" -> "2117 slice_257" [label="(16, 16)", style=solid]; +"2103 new_zeros_8" -> "2121 slice_259" [label="(16, 16)", style=solid]; +"2103 new_zeros_8" -> "2125 slice_261" [label="(16, 16)", style=solid]; +"2103 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slice_325" [label="(1, 16, 16, 384)", style=solid]; +"2522 slice_325" -> "2523 slice_326" [label="(1, 16, 16, 384)", style=solid]; +"2523 slice_326" -> "2524 slice_327" [label="(1, 14, 16, 384)", style=solid]; +"2524 slice_327" -> "2525 slice_328" [label="(1, 14, 14, 384)", style=solid]; +"2525 slice_328" -> "2526 contiguous_41" [label="(1, 14, 14, 384)", style=solid]; +"2526 contiguous_41" -> "2527 layer_norm_45" [label="(1, 14, 14, 384)", style=solid]; +"2527 layer_norm_45" -> "2528 add_75" [label="(1, 14, 14, 384)", style=solid]; +"2528 add_75" -> "2529 linear_132" [label="(1, 14, 14, 384)", style=solid]; +"2528 add_75" -> "2535 add_76" [label="(1, 14, 14, 384)", style=solid]; +"2529 linear_132" -> "2530 gelu_21" [label="(1, 14, 14, 1536)", style=solid]; +"2530 gelu_21" -> "2531 dropout_86" [label="(1, 14, 14, 1536)", style=solid]; +"2531 dropout_86" -> "2532 linear_133" [label="(1, 14, 14, 1536)", style=solid]; +"2532 linear_133" -> "2533 dropout_87" [label="(1, 14, 14, 384)", style=solid]; +"2533 dropout_87" -> "2534 layer_norm_46" [label="(1, 14, 14, 384)", style=solid]; +"2534 layer_norm_46" -> "2535 add_76" [label="(1, 14, 14, 384)", style=solid]; +"2535 add_76" -> "2536 pad_24" [label="(1, 14, 14, 384)", style=solid]; +"2536 pad_24" -> "2537 slice_329" [label="(1, 14, 14, 384)", style=solid]; +"2536 pad_24" -> "2540 slice_332" [label="(1, 14, 14, 384)", style=solid]; +"2536 pad_24" -> "2543 slice_335" [label="(1, 14, 14, 384)", style=solid]; +"2536 pad_24" -> "2546 slice_338" [label="(1, 14, 14, 384)", style=solid]; +"2537 slice_329" -> "2538 slice_330" [label="(1, 7, 14, 384)", style=solid]; +"2538 slice_330" -> "2539 slice_331" [label="(1, 7, 7, 384)", style=solid]; +"2539 slice_331" -> "2549 cat_2" [label="(1, 7, 7, 384)", style=solid]; +"2540 slice_332" -> "2541 slice_333" [label="(1, 7, 14, 384)", style=solid]; +"2541 slice_333" -> "2542 slice_334" [label="(1, 7, 7, 384)", style=solid]; +"2542 slice_334" -> "2549 cat_2" [label="(1, 7, 7, 384)", style=solid]; +"2543 slice_335" -> "2544 slice_336" [label="(1, 7, 14, 384)", style=solid]; +"2544 slice_336" -> "2545 slice_337" [label="(1, 7, 7, 384)", style=solid]; +"2545 slice_337" -> "2549 cat_2" [label="(1, 7, 7, 384)", style=solid]; +"2546 slice_338" -> "2547 slice_339" [label="(1, 7, 14, 384)", style=solid]; +"2547 slice_339" -> "2548 slice_340" [label="(1, 7, 7, 384)", style=solid]; +"2548 slice_340" -> "2549 cat_2" [label="(1, 7, 7, 384)", style=solid]; +"2549 cat_2" -> "2550 linear_134" [label="(1, 7, 7, 1536)", style=solid]; +"2550 linear_134" -> "2551 layer_norm_47" [label="(1, 7, 7, 768)", style=solid]; +"2551 layer_norm_47" -> "2563 pad_25" [label="(1, 7, 7, 768)", style=solid]; +"2551 layer_norm_47" -> "2606 add_78" [label="(1, 7, 7, 768)", style=solid]; +"2552 linear_135" -> "2553 relu__22" [label="(1, 15, 15, 512)", style=solid]; +"2553 relu__22" -> "2554 linear_136" [label="(1, 15, 15, 512)", style=solid]; +"2554 linear_136" -> "2555 view_121" [label="(1, 15, 15, 24)", style=solid]; +"2555 view_121" -> "2556 index_22" [label="(225, 24)", style=solid]; +"2556 index_22" -> "2557 view_122" [label="(4096, 24)", style=solid]; +"2557 view_122" -> "2558 permute_100" [label="(64, 64, 24)", style=solid]; +"2558 permute_100" -> "2559 contiguous_42" [label="(24, 64, 64)", style=solid]; +"2559 contiguous_42" -> "2560 unsqueeze_66" [label="(24, 64, 64)", style=solid]; +"2560 unsqueeze_66" -> "2561 sigmoid_22" [label="(1, 24, 64, 64)", style=solid]; +"2561 sigmoid_22" -> "2562 mul_44" [label="(1, 24, 64, 64)", style=solid]; +"2562 mul_44" -> "2589 add_77" [label="(1, 24, 64, 64)", style=solid]; +"2563 pad_25" -> "2564 view_123" [label="(1, 8, 8, 768)", style=solid]; +"2564 view_123" -> "2565 permute_101" [label="(1, 1, 8, 1, 8, 768)", style=solid]; +"2565 permute_101" -> "2566 reshape_99" [label="(1, 1, 1, 8, 8, 768)", style=solid]; +"2566 reshape_99" -> "2570 linear_137" [label="(1, 64, 768)", style=solid]; +"2567 clone_22" -> "2568 slice_341" [label="(2304,)", style=solid]; +"2567 clone_22" -> "2570 linear_137" [label="(2304,)", style=solid]; +"2568 slice_341" -> "2569 zero__22" [label="(768,)", style=solid]; +"2570 linear_137" -> "2571 reshape_100" [label="(1, 64, 2304)", style=solid]; +"2571 reshape_100" -> "2572 permute_102" [label="(1, 64, 3, 24, 32)", style=solid]; +"2572 permute_102" -> "2573 select_66" [label="(3, 1, 24, 64, 32)", style=solid]; +"2572 permute_102" -> "2574 select_67" [label="(3, 1, 24, 64, 32)", style=solid]; +"2572 permute_102" -> "2575 select_68" [label="(3, 1, 24, 64, 32)", style=solid]; +"2573 select_66" -> "2576 linalg_vector_norm_44" [label="(1, 24, 64, 32)", style=solid]; +"2573 select_66" -> "2578 expand_as_44" [label="(1, 24, 64, 32)", style=solid]; +"2573 select_66" -> "2579 div_44" [label="(1, 24, 64, 32)", style=solid]; +"2574 select_67" -> "2580 linalg_vector_norm_45" [label="(1, 24, 64, 32)", style=solid]; +"2574 select_67" -> "2582 expand_as_45" [label="(1, 24, 64, 32)", style=solid]; +"2574 select_67" -> "2583 div_45" [label="(1, 24, 64, 32)", style=solid]; +"2575 select_68" -> "2592 matmul_45" [label="(1, 24, 64, 32)", style=solid]; +"2576 linalg_vector_norm_44" -> "2577 clamp_min_44" [label="(1, 24, 64, 1)", style=solid]; +"2577 clamp_min_44" -> "2578 expand_as_44" [label="(1, 24, 64, 1)", style=solid]; +"2578 expand_as_44" -> "2579 div_44" [label="(1, 24, 64, 32)", style=solid]; +"2579 div_44" -> "2585 matmul_44" [label="(1, 24, 64, 32)", style=solid]; +"2580 linalg_vector_norm_45" -> "2581 clamp_min_45" [label="(1, 24, 64, 1)", style=solid]; +"2581 clamp_min_45" -> "2582 expand_as_45" [label="(1, 24, 64, 1)", style=solid]; +"2582 expand_as_45" -> "2583 div_45" [label="(1, 24, 64, 32)", style=solid]; +"2583 div_45" -> "2584 transpose_44" [label="(1, 24, 64, 32)", style=solid]; +"2584 transpose_44" -> "2585 matmul_44" [label="(1, 24, 32, 64)", style=solid]; +"2585 matmul_44" -> "2588 mul_45" [label="(1, 24, 64, 64)", style=solid]; +"2586 clamp_22" -> "2587 exp_22" [label="(24, 1, 1)", style=solid]; +"2587 exp_22" -> "2588 mul_45" [label="(24, 1, 1)", style=solid]; +"2588 mul_45" -> "2589 add_77" [label="(1, 24, 64, 64)", style=solid]; +"2589 add_77" -> "2590 softmax_22" [label="(1, 24, 64, 64)", style=solid]; +"2590 softmax_22" -> "2591 dropout_88" [label="(1, 24, 64, 64)", style=solid]; +"2591 dropout_88" -> "2592 matmul_45" [label="(1, 24, 64, 64)", style=solid]; +"2592 matmul_45" -> "2593 transpose_45" [label="(1, 24, 64, 32)", style=solid]; +"2593 transpose_45" -> "2594 reshape_101" [label="(1, 64, 24, 32)", style=solid]; +"2594 reshape_101" -> "2595 linear_138" [label="(1, 64, 768)", style=solid]; +"2595 linear_138" -> "2596 dropout_89" [label="(1, 64, 768)", style=solid]; +"2596 dropout_89" -> "2597 view_124" [label="(1, 64, 768)", style=solid]; +"2597 view_124" -> "2598 permute_103" [label="(1, 1, 1, 8, 8, 768)", style=solid]; +"2598 permute_103" -> "2599 reshape_102" [label="(1, 1, 8, 1, 8, 768)", style=solid]; +"2599 reshape_102" -> "2600 slice_342" [label="(1, 8, 8, 768)", style=solid]; +"2600 slice_342" -> "2601 slice_343" [label="(1, 8, 8, 768)", style=solid]; +"2601 slice_343" -> "2602 slice_344" [label="(1, 7, 8, 768)", style=solid]; +"2602 slice_344" -> "2603 slice_345" [label="(1, 7, 7, 768)", style=solid]; +"2603 slice_345" -> "2604 contiguous_43" [label="(1, 7, 7, 768)", style=solid]; +"2604 contiguous_43" -> "2605 layer_norm_48" [label="(1, 7, 7, 768)", style=solid]; +"2605 layer_norm_48" -> "2606 add_78" [label="(1, 7, 7, 768)", style=solid]; +"2606 add_78" -> "2607 linear_139" [label="(1, 7, 7, 768)", style=solid]; +"2606 add_78" -> "2613 add_79" [label="(1, 7, 7, 768)", style=solid]; +"2607 linear_139" -> "2608 gelu_22" [label="(1, 7, 7, 3072)", style=solid]; +"2608 gelu_22" -> "2609 dropout_90" [label="(1, 7, 7, 3072)", style=solid]; +"2609 dropout_90" -> "2610 linear_140" [label="(1, 7, 7, 3072)", style=solid]; +"2610 linear_140" -> "2611 dropout_91" [label="(1, 7, 7, 768)", style=solid]; +"2611 dropout_91" -> "2612 layer_norm_49" [label="(1, 7, 7, 768)", style=solid]; +"2612 layer_norm_49" -> "2613 add_79" [label="(1, 7, 7, 768)", style=solid]; +"2613 add_79" -> "2625 pad_26" [label="(1, 7, 7, 768)", style=solid]; +"2613 add_79" -> "2668 add_81" [label="(1, 7, 7, 768)", style=solid]; +"2614 linear_141" -> "2615 relu__23" [label="(1, 15, 15, 512)", style=solid]; +"2615 relu__23" -> "2616 linear_142" [label="(1, 15, 15, 512)", style=solid]; +"2616 linear_142" -> "2617 view_125" [label="(1, 15, 15, 24)", style=solid]; +"2617 view_125" -> "2618 index_23" [label="(225, 24)", style=solid]; +"2618 index_23" -> "2619 view_126" [label="(4096, 24)", style=solid]; +"2619 view_126" -> "2620 permute_104" [label="(64, 64, 24)", style=solid]; +"2620 permute_104" -> "2621 contiguous_44" [label="(24, 64, 64)", style=solid]; +"2621 contiguous_44" -> "2622 unsqueeze_67" [label="(24, 64, 64)", style=solid]; +"2622 unsqueeze_67" -> "2623 sigmoid_23" [label="(1, 24, 64, 64)", style=solid]; +"2623 sigmoid_23" -> "2624 mul_46" [label="(1, 24, 64, 64)", style=solid]; +"2624 mul_46" -> "2651 add_80" [label="(1, 24, 64, 64)", style=solid]; +"2625 pad_26" -> "2626 view_127" [label="(1, 8, 8, 768)", style=solid]; +"2626 view_127" -> "2627 permute_105" [label="(1, 1, 8, 1, 8, 768)", style=solid]; +"2627 permute_105" -> "2628 reshape_103" [label="(1, 1, 1, 8, 8, 768)", style=solid]; +"2628 reshape_103" -> "2632 linear_143" [label="(1, 64, 768)", style=solid]; +"2629 clone_23" -> "2630 slice_346" [label="(2304,)", style=solid]; +"2629 clone_23" -> "2632 linear_143" [label="(2304,)", style=solid]; +"2630 slice_346" -> "2631 zero__23" [label="(768,)", style=solid]; +"2632 linear_143" -> "2633 reshape_104" [label="(1, 64, 2304)", style=solid]; +"2633 reshape_104" -> "2634 permute_106" [label="(1, 64, 3, 24, 32)", style=solid]; +"2634 permute_106" -> "2635 select_69" [label="(3, 1, 24, 64, 32)", style=solid]; +"2634 permute_106" -> "2636 select_70" [label="(3, 1, 24, 64, 32)", style=solid]; +"2634 permute_106" -> "2637 select_71" [label="(3, 1, 24, 64, 32)", style=solid]; +"2635 select_69" -> "2638 linalg_vector_norm_46" [label="(1, 24, 64, 32)", style=solid]; +"2635 select_69" -> "2640 expand_as_46" [label="(1, 24, 64, 32)", style=solid]; +"2635 select_69" -> "2641 div_46" [label="(1, 24, 64, 32)", style=solid]; +"2636 select_70" -> "2642 linalg_vector_norm_47" [label="(1, 24, 64, 32)", style=solid]; +"2636 select_70" -> "2644 expand_as_47" [label="(1, 24, 64, 32)", style=solid]; +"2636 select_70" -> "2645 div_47" [label="(1, 24, 64, 32)", style=solid]; +"2637 select_71" -> "2654 matmul_47" [label="(1, 24, 64, 32)", style=solid]; +"2638 linalg_vector_norm_46" -> "2639 clamp_min_46" [label="(1, 24, 64, 1)", style=solid]; +"2639 clamp_min_46" -> "2640 expand_as_46" [label="(1, 24, 64, 1)", style=solid]; +"2640 expand_as_46" -> "2641 div_46" [label="(1, 24, 64, 32)", style=solid]; +"2641 div_46" -> "2647 matmul_46" [label="(1, 24, 64, 32)", style=solid]; +"2642 linalg_vector_norm_47" -> "2643 clamp_min_47" [label="(1, 24, 64, 1)", style=solid]; +"2643 clamp_min_47" -> "2644 expand_as_47" [label="(1, 24, 64, 1)", style=solid]; +"2644 expand_as_47" -> "2645 div_47" [label="(1, 24, 64, 32)", style=solid]; +"2645 div_47" -> "2646 transpose_46" [label="(1, 24, 64, 32)", style=solid]; +"2646 transpose_46" -> "2647 matmul_46" [label="(1, 24, 32, 64)", style=solid]; +"2647 matmul_46" -> "2650 mul_47" [label="(1, 24, 64, 64)", style=solid]; +"2648 clamp_23" -> "2649 exp_23" [label="(24, 1, 1)", style=solid]; +"2649 exp_23" -> "2650 mul_47" [label="(24, 1, 1)", style=solid]; +"2650 mul_47" -> "2651 add_80" [label="(1, 24, 64, 64)", style=solid]; +"2651 add_80" -> "2652 softmax_23" [label="(1, 24, 64, 64)", style=solid]; +"2652 softmax_23" -> "2653 dropout_92" [label="(1, 24, 64, 64)", style=solid]; +"2653 dropout_92" -> "2654 matmul_47" [label="(1, 24, 64, 64)", style=solid]; +"2654 matmul_47" -> "2655 transpose_47" [label="(1, 24, 64, 32)", style=solid]; +"2655 transpose_47" -> "2656 reshape_105" [label="(1, 64, 24, 32)", style=solid]; +"2656 reshape_105" -> "2657 linear_144" [label="(1, 64, 768)", style=solid]; +"2657 linear_144" -> "2658 dropout_93" [label="(1, 64, 768)", style=solid]; +"2658 dropout_93" -> "2659 view_128" [label="(1, 64, 768)", style=solid]; +"2659 view_128" -> "2660 permute_107" [label="(1, 1, 1, 8, 8, 768)", style=solid]; +"2660 permute_107" -> "2661 reshape_106" [label="(1, 1, 8, 1, 8, 768)", style=solid]; +"2661 reshape_106" -> "2662 slice_347" [label="(1, 8, 8, 768)", style=solid]; +"2662 slice_347" -> "2663 slice_348" [label="(1, 8, 8, 768)", style=solid]; +"2663 slice_348" -> "2664 slice_349" [label="(1, 7, 8, 768)", style=solid]; +"2664 slice_349" -> "2665 slice_350" [label="(1, 7, 7, 768)", style=solid]; +"2665 slice_350" -> "2666 contiguous_45" [label="(1, 7, 7, 768)", style=solid]; +"2666 contiguous_45" -> "2667 layer_norm_50" [label="(1, 7, 7, 768)", style=solid]; +"2667 layer_norm_50" -> "2668 add_81" [label="(1, 7, 7, 768)", style=solid]; +"2668 add_81" -> "2669 linear_145" [label="(1, 7, 7, 768)", style=solid]; +"2668 add_81" -> "2675 add_82" [label="(1, 7, 7, 768)", style=solid]; +"2669 linear_145" -> "2670 gelu_23" [label="(1, 7, 7, 3072)", style=solid]; +"2670 gelu_23" -> "2671 dropout_94" [label="(1, 7, 7, 3072)", style=solid]; +"2671 dropout_94" -> "2672 linear_146" [label="(1, 7, 7, 3072)", style=solid]; +"2672 linear_146" -> "2673 dropout_95" [label="(1, 7, 7, 768)", style=solid]; +"2673 dropout_95" -> "2674 layer_norm_51" [label="(1, 7, 7, 768)", style=solid]; +"2674 layer_norm_51" -> "2675 add_82" [label="(1, 7, 7, 768)", style=solid]; +"2675 add_82" -> "2676 layer_norm_52" [label="(1, 7, 7, 768)", style=solid]; +"2676 layer_norm_52" -> "2677 permute_108" [label="(1, 7, 7, 768)", style=solid]; +"2677 permute_108" -> "2678 adaptive_avg_pool2d" [label="(1, 768, 7, 7)", style=solid]; +"2678 adaptive_avg_pool2d" -> "2679 flatten" [label="(1, 768, 1, 1)", style=solid]; +"2679 flatten" -> "2680 linear_147" [label="(1, 768)", style=solid]; +"2680 linear_147" -> "2681 output_1" [label="(1, 1000)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/synthetic_transformer.dot b/tests/torch/data/reference_graphs/fx/synthetic_transformer.dot new file mode 100644 index 00000000000..41c895e3cc0 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/synthetic_transformer.dot @@ -0,0 +1,21 @@ +strict digraph { +"0 wte_weight" [id=0, type=get_attr]; +"1 linear_weight" [id=1, type=get_attr]; +"2 linear_bias" [id=2, type=get_attr]; +"3 lm_head_weight" [id=3, type=get_attr]; +"4 lm_head_bias" [id=4, type=get_attr]; +"5 input_ids" [id=5, type=input]; +"6 embedding" [id=6, type=embedding]; +"7 linear" [id=7, type=linear]; +"8 linear_1" [id=8, type=linear]; +"9 output_1" [id=9, type=output]; +"0 wte_weight" -> "6 embedding" [label="(10, 5)", style=solid]; +"1 linear_weight" -> "7 linear" [label="(5, 5)", style=solid]; +"2 linear_bias" -> "7 linear" [label="(5,)", style=solid]; +"3 lm_head_weight" -> "8 linear_1" [label="(10, 5)", style=solid]; +"4 lm_head_bias" -> "8 linear_1" [label="(10,)", style=solid]; +"5 input_ids" -> "6 embedding" [label="(5,)", style=solid]; +"6 embedding" -> "7 linear" [label="(5, 5)", style=solid]; +"7 linear" -> "8 linear_1" [label="(5, 5)", style=solid]; +"8 linear_1" -> "9 output_1" [label="(5, 10)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_channel_invalid.dot b/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_channel_invalid.dot new file mode 100644 index 00000000000..e311fcb3773 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_channel_invalid.dot @@ -0,0 +1,48 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 scale_node0" [id=8, type=get_attr]; +"9 weight_node0" [id=9, type=get_attr]; +"10 quantize_per_channel_default" [id=10, type=quantize_per_channel]; +"11 add_tensor" [id=11, type=add]; +"12 dequantize_per_channel_default" [id=12, type=dequantize_per_channel]; +"13 conv2d" [id=13, type=conv2d]; +"14 conv2d_1" [id=14, type=conv2d]; +"15 add_" [id=15, type=add_]; +"16 add__1" [id=16, type=add_]; +"17 add" [id=17, type=add]; +"18 conv2d_2" [id=18, type=conv2d]; +"19 add_1" [id=19, type=add]; +"20 output_1" [id=20, type=output]; +"0 conv_a_weight" -> "10 quantize_per_channel_default" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "13 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "14 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "14 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "18 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "18 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "15 add_" [label="(1,)", style=solid]; +"6 bias" -> "16 add__1" [label="(1,)", style=solid]; +"6 bias" -> "19 add_1" [label="(1,)", style=solid]; +"7 x" -> "13 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"8 scale_node0" -> "10 quantize_per_channel_default" [label="(3,)", style=solid]; +"8 scale_node0" -> "12 dequantize_per_channel_default" [label="(3,)", style=solid]; +"9 weight_node0" -> "10 quantize_per_channel_default" [label="(3,)", style=solid]; +"9 weight_node0" -> "12 dequantize_per_channel_default" [label="(3,)", style=solid]; +"10 quantize_per_channel_default" -> "11 add_tensor" [label="(3, 3, 1, 1)", style=solid]; +"11 add_tensor" -> "12 dequantize_per_channel_default" [label="(3, 3, 1, 1)", style=solid]; +"12 dequantize_per_channel_default" -> "13 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"13 conv2d" -> "14 conv2d_1" [label="(1, 3, 224, 224)", style=solid]; +"13 conv2d" -> "15 add_" [label="(1, 3, 224, 224)", style=solid]; +"14 conv2d_1" -> "16 add__1" [label="(1, 3, 224, 224)", style=solid]; +"15 add_" -> "17 add" [label="(1, 3, 224, 224)", style=solid]; +"16 add__1" -> "17 add" [label="(1, 3, 224, 224)", style=solid]; +"17 add" -> "18 conv2d_2" [label="(1, 3, 224, 224)", style=solid]; +"18 conv2d_2" -> "19 add_1" [label="(1, 3, 224, 224)", style=solid]; +"19 add_1" -> "20 output_1" [label="(1, 3, 224, 224)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_channel_valid.dot b/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_channel_valid.dot new file mode 100644 index 00000000000..8ed024a8e1f --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_channel_valid.dot @@ -0,0 +1,44 @@ +strict digraph { +"0 conv_a_bias" [id=0, type=get_attr]; +"1 conv_b_weight" [id=1, type=get_attr]; +"2 conv_b_bias" [id=2, type=get_attr]; +"3 conv_c_weight" [id=3, type=get_attr]; +"4 conv_c_bias" [id=4, type=get_attr]; +"5 bias" [id=5, type=get_attr]; +"6 x" [id=6, type=input]; +"7 scale_updated_constant0" [id=7, type=get_attr]; +"8 compressed_weight_updated_constant0" [id=8, type=get_attr]; +"9 mul_tensor" [id=9, type=mul]; +"10 zero_point_updated_constant0" [id=10, type=get_attr]; +"11 sub_tensor" [id=11, type=sub]; +"12 conv2d" [id=12, type=conv2d]; +"13 conv2d_1" [id=13, type=conv2d]; +"14 add_" [id=14, type=add_]; +"15 add__1" [id=15, type=add_]; +"16 add" [id=16, type=add]; +"17 conv2d_2" [id=17, type=conv2d]; +"18 add_1" [id=18, type=add]; +"19 output_1" [id=19, type=output]; +"0 conv_a_bias" -> "12 conv2d" [label="(3,)", style=solid]; +"1 conv_b_weight" -> "13 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"2 conv_b_bias" -> "13 conv2d_1" [label="(3,)", style=solid]; +"3 conv_c_weight" -> "17 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"4 conv_c_bias" -> "17 conv2d_2" [label="(3,)", style=solid]; +"5 bias" -> "14 add_" [label="(1,)", style=solid]; +"5 bias" -> "15 add__1" [label="(1,)", style=solid]; +"5 bias" -> "18 add_1" [label="(1,)", style=solid]; +"6 x" -> "12 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"7 scale_updated_constant0" -> "9 mul_tensor" [label="(3, 1, 1, 1)", style=solid]; +"8 compressed_weight_updated_constant0" -> "9 mul_tensor" [label="(3, 3, 1, 1)", style=solid]; +"9 mul_tensor" -> "11 sub_tensor" [label="(3, 3, 1, 1)", style=solid]; +"10 zero_point_updated_constant0" -> "11 sub_tensor" [label="(3, 1, 1, 1)", style=solid]; +"11 sub_tensor" -> "12 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"12 conv2d" -> "13 conv2d_1" [label="(1, 3, 224, 224)", style=solid]; +"12 conv2d" -> "14 add_" [label="(1, 3, 224, 224)", style=solid]; +"13 conv2d_1" -> "15 add__1" [label="(1, 3, 224, 224)", style=solid]; +"14 add_" -> "16 add" [label="(1, 3, 224, 224)", style=solid]; +"15 add__1" -> "16 add" [label="(1, 3, 224, 224)", style=solid]; +"16 add" -> "17 conv2d_2" [label="(1, 3, 224, 224)", style=solid]; +"17 conv2d_2" -> "18 add_1" [label="(1, 3, 224, 224)", style=solid]; +"18 add_1" -> "19 output_1" [label="(1, 3, 224, 224)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_tensor_invalid.dot b/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_tensor_invalid.dot new file mode 100644 index 00000000000..0e2b0d16eea --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_tensor_invalid.dot @@ -0,0 +1,42 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 quantize_per_tensor_default" [id=8, type=quantize_per_tensor]; +"9 add_tensor" [id=9, type=add]; +"10 dequantize_per_tensor_default" [id=10, type=dequantize_per_tensor]; +"11 conv2d" [id=11, type=conv2d]; +"12 conv2d_1" [id=12, type=conv2d]; +"13 add_" [id=13, type=add_]; +"14 add__1" [id=14, type=add_]; +"15 add" [id=15, type=add]; +"16 conv2d_2" [id=16, type=conv2d]; +"17 add_1" [id=17, type=add]; +"18 output_1" [id=18, type=output]; +"0 conv_a_weight" -> "8 quantize_per_tensor_default" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "11 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "12 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "12 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "16 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "16 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "13 add_" [label="(1,)", style=solid]; +"6 bias" -> "14 add__1" [label="(1,)", style=solid]; +"6 bias" -> "17 add_1" [label="(1,)", style=solid]; +"7 x" -> "11 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"8 quantize_per_tensor_default" -> "9 add_tensor" [label="(3, 3, 1, 1)", style=solid]; +"9 add_tensor" -> "10 dequantize_per_tensor_default" [label="(3, 3, 1, 1)", style=solid]; +"10 dequantize_per_tensor_default" -> "11 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"11 conv2d" -> "12 conv2d_1" [label="(1, 3, 224, 224)", style=solid]; +"11 conv2d" -> "13 add_" [label="(1, 3, 224, 224)", style=solid]; +"12 conv2d_1" -> "14 add__1" [label="(1, 3, 224, 224)", style=solid]; +"13 add_" -> "15 add" [label="(1, 3, 224, 224)", style=solid]; +"14 add__1" -> "15 add" [label="(1, 3, 224, 224)", style=solid]; +"15 add" -> "16 conv2d_2" [label="(1, 3, 224, 224)", style=solid]; +"16 conv2d_2" -> "17 add_1" [label="(1, 3, 224, 224)", style=solid]; +"17 add_1" -> "18 output_1" [label="(1, 3, 224, 224)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_tensor_valid.dot b/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_tensor_valid.dot new file mode 100644 index 00000000000..9d0e7e08f2c --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/compress_post_quantize_per_tensor_valid.dot @@ -0,0 +1,40 @@ +strict digraph { +"0 conv_a_bias" [id=0, type=get_attr]; +"1 conv_b_weight" [id=1, type=get_attr]; +"2 conv_b_bias" [id=2, type=get_attr]; +"3 conv_c_weight" [id=3, type=get_attr]; +"4 conv_c_bias" [id=4, type=get_attr]; +"5 bias" [id=5, type=get_attr]; +"6 x" [id=6, type=input]; +"7 compressed_weight_updated_constant0" [id=7, type=get_attr]; +"8 mul_tensor" [id=8, type=mul]; +"9 sub_tensor" [id=9, type=sub]; +"10 conv2d" [id=10, type=conv2d]; +"11 conv2d_1" [id=11, type=conv2d]; +"12 add_" [id=12, type=add_]; +"13 add__1" [id=13, type=add_]; +"14 add" [id=14, type=add]; +"15 conv2d_2" [id=15, type=conv2d]; +"16 add_1" [id=16, type=add]; +"17 output_1" [id=17, type=output]; +"0 conv_a_bias" -> "10 conv2d" [label="(3,)", style=solid]; +"1 conv_b_weight" -> "11 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"2 conv_b_bias" -> "11 conv2d_1" [label="(3,)", style=solid]; +"3 conv_c_weight" -> "15 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"4 conv_c_bias" -> "15 conv2d_2" [label="(3,)", style=solid]; +"5 bias" -> "12 add_" [label="(1,)", style=solid]; +"5 bias" -> "13 add__1" [label="(1,)", style=solid]; +"5 bias" -> "16 add_1" [label="(1,)", style=solid]; +"6 x" -> "10 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"7 compressed_weight_updated_constant0" -> "8 mul_tensor" [label="(3, 3, 1, 1)", style=solid]; +"8 mul_tensor" -> "9 sub_tensor" [label="(3, 3, 1, 1)", style=solid]; +"9 sub_tensor" -> "10 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"10 conv2d" -> "11 conv2d_1" [label="(1, 3, 224, 224)", style=solid]; +"10 conv2d" -> "12 add_" [label="(1, 3, 224, 224)", style=solid]; +"11 conv2d_1" -> "13 add__1" [label="(1, 3, 224, 224)", style=solid]; +"12 add_" -> "14 add" [label="(1, 3, 224, 224)", style=solid]; +"13 add__1" -> "14 add" [label="(1, 3, 224, 224)", style=solid]; +"14 add" -> "15 conv2d_2" [label="(1, 3, 224, 224)", style=solid]; +"15 conv2d_2" -> "16 add_1" [label="(1, 3, 224, 224)", style=solid]; +"16 add_1" -> "17 output_1" [label="(1, 3, 224, 224)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/constant_update.dot b/tests/torch/data/reference_graphs/fx/transformed/constant_update.dot new file mode 100644 index 00000000000..dde22a7ecba --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/constant_update.dot @@ -0,0 +1,36 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 x" [id=6, type=input]; +"7 conv2d" [id=7, type=conv2d]; +"8 conv2d_1" [id=8, type=conv2d]; +"9 add__updated_constant0" [id=9, type=get_attr]; +"10 add_" [id=10, type=add_]; +"11 add__1" [id=11, type=add_]; +"12 add" [id=12, type=add]; +"13 conv2d_2" [id=13, type=conv2d]; +"14 add_1" [id=14, type=add]; +"15 output_1" [id=15, type=output]; +"0 conv_a_weight" -> "7 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "7 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "8 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "8 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "13 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "13 conv2d_2" [label="(3,)", style=solid]; +"6 x" -> "7 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"7 conv2d" -> "8 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"7 conv2d" -> "10 add_" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d_1" -> "11 add__1" [label="(1, 3, 3, 3)", style=solid]; +"9 add__updated_constant0" -> "10 add_" [label="(1,)", style=solid]; +"9 add__updated_constant0" -> "11 add__1" [label="(1,)", style=solid]; +"9 add__updated_constant0" -> "14 add_1" [label="(1,)", style=solid]; +"10 add_" -> "12 add" [label="(1, 3, 3, 3)", style=solid]; +"11 add__1" -> "12 add" [label="(1, 3, 3, 3)", style=solid]; +"12 add" -> "13 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"13 conv2d_2" -> "14 add_1" [label="(1, 3, 3, 3)", style=solid]; +"14 add_1" -> "15 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/folded_model.dot b/tests/torch/data/reference_graphs/fx/transformed/folded_model.dot new file mode 100644 index 00000000000..2dea2e7bd93 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/folded_model.dot @@ -0,0 +1,15 @@ +strict digraph { +"0 linear_act_weight" [id=0, type=get_attr]; +"1 linear_act_bias" [id=1, type=get_attr]; +"2 x" [id=2, type=input]; +"3 _frozen_param0" [id=3, type=get_attr]; +"4 linear_1" [id=4, type=linear]; +"5 add" [id=5, type=add]; +"6 output" [id=6, type=output]; +"0 linear_act_weight" -> "4 linear_1" [label="(3, 3)", style=solid]; +"1 linear_act_bias" -> "4 linear_1" [label="(3,)", style=solid]; +"2 x" -> "4 linear_1" [label="(1, 3, 3, 3)", style=solid]; +"3 _frozen_param0" -> "5 add" [label="(3, 3)", style=solid]; +"4 linear_1" -> "5 add" [label="(1, 3, 3, 3)", style=solid]; +"5 add" -> "6 output" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/folded_model_with_constraints_per_channel.dot b/tests/torch/data/reference_graphs/fx/transformed/folded_model_with_constraints_per_channel.dot new file mode 100644 index 00000000000..43efd89ba6f --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/folded_model_with_constraints_per_channel.dot @@ -0,0 +1,25 @@ +strict digraph { +"0 linear_act_weight" [id=0, type=get_attr]; +"1 linear_act_bias" [id=1, type=get_attr]; +"2 x" [id=2, type=input]; +"3 _frozen_param0" [id=3, type=get_attr]; +"4 scale_node0" [id=4, type=get_attr]; +"5 weight_node0" [id=5, type=get_attr]; +"6 quantize_per_channel_default" [id=6, type=quantize_per_channel]; +"7 dequantize_per_channel_default" [id=7, type=dequantize_per_channel]; +"8 linear_1" [id=8, type=linear]; +"9 add" [id=9, type=add]; +"10 output_1" [id=10, type=output]; +"0 linear_act_weight" -> "6 quantize_per_channel_default" [label="(3, 3)", style=solid]; +"1 linear_act_bias" -> "8 linear_1" [label="(3,)", style=solid]; +"2 x" -> "8 linear_1" [label="(1, 3, 3, 3)", style=solid]; +"3 _frozen_param0" -> "9 add" [label="(3, 3)", style=solid]; +"4 scale_node0" -> "6 quantize_per_channel_default" [label="(3,)", style=solid]; +"4 scale_node0" -> "7 dequantize_per_channel_default" [label="(3,)", style=solid]; +"5 weight_node0" -> "6 quantize_per_channel_default" [label="(3,)", style=solid]; +"5 weight_node0" -> "7 dequantize_per_channel_default" [label="(3,)", style=solid]; +"6 quantize_per_channel_default" -> "7 dequantize_per_channel_default" [label="(3, 3)", style=solid]; +"7 dequantize_per_channel_default" -> "8 linear_1" [label="(3, 3)", style=solid]; +"8 linear_1" -> "9 add" [label="(1, 3, 3, 3)", style=solid]; +"9 add" -> "10 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/folded_model_with_constraints_per_tensor.dot b/tests/torch/data/reference_graphs/fx/transformed/folded_model_with_constraints_per_tensor.dot new file mode 100644 index 00000000000..16e7d368683 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/folded_model_with_constraints_per_tensor.dot @@ -0,0 +1,19 @@ +strict digraph { +"0 linear_act_weight" [id=0, type=get_attr]; +"1 linear_act_bias" [id=1, type=get_attr]; +"2 x" [id=2, type=input]; +"3 _frozen_param0" [id=3, type=get_attr]; +"4 quantize_per_tensor_default" [id=4, type=quantize_per_tensor]; +"5 dequantize_per_tensor_default" [id=5, type=dequantize_per_tensor]; +"6 linear_1" [id=6, type=linear]; +"7 add" [id=7, type=add]; +"8 output_1" [id=8, type=output]; +"0 linear_act_weight" -> "4 quantize_per_tensor_default" [label="(3, 3)", style=solid]; +"1 linear_act_bias" -> "6 linear_1" [label="(3,)", style=solid]; +"2 x" -> "6 linear_1" [label="(1, 3, 3, 3)", style=solid]; +"3 _frozen_param0" -> "7 add" [label="(3, 3)", style=solid]; +"4 quantize_per_tensor_default" -> "5 dequantize_per_tensor_default" [label="(3, 3)", style=solid]; +"5 dequantize_per_tensor_default" -> "6 linear_1" [label="(3, 3)", style=solid]; +"6 linear_1" -> "7 add" [label="(1, 3, 3, 3)", style=solid]; +"7 add" -> "8 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/model_insertion.dot b/tests/torch/data/reference_graphs/fx/transformed/model_insertion.dot new file mode 100644 index 00000000000..825f345d634 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/model_insertion.dot @@ -0,0 +1,44 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 TEST_MODULE_0" [id=8, type=call_module]; +"9 TEST_MODULE_1" [id=9, type=call_module]; +"10 conv2d" [id=10, type=conv2d]; +"11 TEST_MODULE_3" [id=11, type=call_module]; +"12 TEST_MODULE_2" [id=12, type=call_module]; +"13 conv2d_1" [id=13, type=conv2d]; +"14 add_" [id=14, type=add_]; +"15 add__1" [id=15, type=add_]; +"16 add" [id=16, type=add]; +"17 conv2d_2" [id=17, type=conv2d]; +"18 add_1" [id=18, type=add]; +"19 output_1" [id=19, type=output]; +"0 conv_a_weight" -> "9 TEST_MODULE_1" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "10 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "12 TEST_MODULE_2" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "13 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "17 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "17 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "14 add_" [label="(1,)", style=solid]; +"6 bias" -> "15 add__1" [label="(1,)", style=solid]; +"6 bias" -> "18 add_1" [label="(1,)", style=solid]; +"7 x" -> "8 TEST_MODULE_0" [label="(1, 3, 3, 3)", style=solid]; +"8 TEST_MODULE_0" -> "10 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"9 TEST_MODULE_1" -> "10 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"10 conv2d" -> "11 TEST_MODULE_3" [label="(1, 3, 3, 3)", style=solid]; +"11 TEST_MODULE_3" -> "13 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"11 TEST_MODULE_3" -> "14 add_" [label="(1, 3, 3, 3)", style=solid]; +"12 TEST_MODULE_2" -> "13 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"13 conv2d_1" -> "15 add__1" [label="(1, 3, 3, 3)", style=solid]; +"14 add_" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"15 add__1" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"16 add" -> "17 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"17 conv2d_2" -> "18 add_1" [label="(1, 3, 3, 3)", style=solid]; +"18 add_1" -> "19 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/model_insertion_leaf.dot b/tests/torch/data/reference_graphs/fx/transformed/model_insertion_leaf.dot new file mode 100644 index 00000000000..d4eccf226a0 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/model_insertion_leaf.dot @@ -0,0 +1,44 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 TEST_MODULE_0" [id=8, type=call_module]; +"9 TEST_MODULE_1" [id=9, type=call_module]; +"10 conv2d" [id=10, type=conv2d]; +"11 TEST_MODULE_3" [id=11, type=call_module]; +"12 TEST_MODULE_2" [id=12, type=call_module]; +"13 conv2d_1" [id=13, type=conv2d]; +"14 add_" [id=14, type=add_]; +"15 add__1" [id=15, type=add_]; +"16 add" [id=16, type=add]; +"17 conv2d_2" [id=17, type=conv2d]; +"18 add_1" [id=18, type=add]; +"19 output_1" [id=19, type=output]; +"0 conv_a_weight" -> "9 TEST_MODULE_1" [label="(3, 3, 1, 1)", style=solid]; +"0 conv_a_weight" -> "10 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "10 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "12 TEST_MODULE_2" [label="(3, 3, 1, 1)", style=solid]; +"2 conv_b_weight" -> "13 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "13 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "17 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "17 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "14 add_" [label="(1,)", style=solid]; +"6 bias" -> "15 add__1" [label="(1,)", style=solid]; +"6 bias" -> "18 add_1" [label="(1,)", style=solid]; +"7 x" -> "8 TEST_MODULE_0" [label="(1, 3, 3, 3)", style=solid]; +"7 x" -> "10 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d" -> "11 TEST_MODULE_3" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d" -> "13 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d" -> "14 add_" [label="(1, 3, 3, 3)", style=solid]; +"13 conv2d_1" -> "15 add__1" [label="(1, 3, 3, 3)", style=solid]; +"14 add_" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"15 add__1" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"16 add" -> "17 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"17 conv2d_2" -> "18 add_1" [label="(1, 3, 3, 3)", style=solid]; +"18 add_1" -> "19 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/node_removal_ref.dot b/tests/torch/data/reference_graphs/fx/transformed/node_removal_ref.dot new file mode 100644 index 00000000000..db8acf6419c --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/node_removal_ref.dot @@ -0,0 +1,30 @@ +strict digraph { +"0 conv_b_weight" [id=0, type=get_attr]; +"1 conv_b_bias" [id=1, type=get_attr]; +"2 conv_c_weight" [id=2, type=get_attr]; +"3 conv_c_bias" [id=3, type=get_attr]; +"4 bias" [id=4, type=get_attr]; +"5 x" [id=5, type=input]; +"6 conv2d_1" [id=6, type=conv2d]; +"7 add_" [id=7, type=add_]; +"8 add__1" [id=8, type=add_]; +"9 add" [id=9, type=add]; +"10 conv2d_2" [id=10, type=conv2d]; +"11 add_1" [id=11, type=add]; +"12 output_1" [id=12, type=output]; +"0 conv_b_weight" -> "6 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_b_bias" -> "6 conv2d_1" [label="(3,)", style=solid]; +"2 conv_c_weight" -> "10 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_c_bias" -> "10 conv2d_2" [label="(3,)", style=solid]; +"4 bias" -> "7 add_" [label="(1,)", style=solid]; +"4 bias" -> "8 add__1" [label="(1,)", style=solid]; +"4 bias" -> "11 add_1" [label="(1,)", style=solid]; +"5 x" -> "6 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"5 x" -> "7 add_" [label="(1, 3, 3, 3)", style=solid]; +"6 conv2d_1" -> "8 add__1" [label="(1, 3, 3, 3)", style=solid]; +"7 add_" -> "9 add" [label="(1, 3, 3, 3)", style=solid]; +"8 add__1" -> "9 add" [label="(1, 3, 3, 3)", style=solid]; +"9 add" -> "10 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d_2" -> "11 add_1" [label="(1, 3, 3, 3)", style=solid]; +"11 add_1" -> "12 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_1_5_1.dot b/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_1_5_1.dot new file mode 100644 index 00000000000..8d641d46d41 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_1_5_1.dot @@ -0,0 +1,39 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_weight_cloned" [id=3, type=clone]; +"4 conv_b_bias" [id=4, type=get_attr]; +"5 conv_c_weight" [id=5, type=get_attr]; +"6 conv_c_bias" [id=6, type=get_attr]; +"7 bias" [id=7, type=get_attr]; +"8 x" [id=8, type=input]; +"9 conv2d" [id=9, type=conv2d]; +"10 conv2d_1" [id=10, type=conv2d]; +"11 add_" [id=11, type=add_]; +"12 add__1" [id=12, type=add_]; +"13 add" [id=13, type=add]; +"14 conv2d_2" [id=14, type=conv2d]; +"15 add_1" [id=15, type=add]; +"16 output_1" [id=16, type=output]; +"0 conv_a_weight" -> "9 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "9 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "3 conv_b_weight_cloned" [label="(3, 3, 1, 1)", style=solid]; +"2 conv_b_weight" -> "10 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_weight_cloned" -> "16 output_1" [label="(3, 3, 1, 1)", style=solid]; +"4 conv_b_bias" -> "10 conv2d_1" [label="(3,)", style=solid]; +"5 conv_c_weight" -> "14 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"6 conv_c_bias" -> "14 conv2d_2" [label="(3,)", style=solid]; +"7 bias" -> "11 add_" [label="(1,)", style=solid]; +"7 bias" -> "12 add__1" [label="(1,)", style=solid]; +"7 bias" -> "15 add_1" [label="(1,)", style=solid]; +"8 x" -> "9 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d" -> "10 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d" -> "11 add_" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d_1" -> "12 add__1" [label="(1, 3, 3, 3)", style=solid]; +"11 add_" -> "13 add" [label="(1, 3, 3, 3)", style=solid]; +"12 add__1" -> "13 add" [label="(1, 3, 3, 3)", style=solid]; +"13 add" -> "14 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"14 conv2d_2" -> "15 add_1" [label="(1, 3, 3, 3)", style=solid]; +"15 add_1" -> "16 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_6_0.dot b/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_6_0.dot new file mode 100644 index 00000000000..3a35ddf39cf --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_6_0.dot @@ -0,0 +1,39 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 x_cloned" [id=8, type=clone]; +"9 conv2d" [id=9, type=conv2d]; +"10 conv2d_1" [id=10, type=conv2d]; +"11 add_" [id=11, type=add_]; +"12 add__1" [id=12, type=add_]; +"13 add" [id=13, type=add]; +"14 conv2d_2" [id=14, type=conv2d]; +"15 add_1" [id=15, type=add]; +"16 output_1" [id=16, type=output]; +"0 conv_a_weight" -> "9 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "9 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "10 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "10 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "14 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "14 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "11 add_" [label="(1,)", style=solid]; +"6 bias" -> "12 add__1" [label="(1,)", style=solid]; +"6 bias" -> "15 add_1" [label="(1,)", style=solid]; +"7 x" -> "8 x_cloned" [label="(1, 3, 3, 3)", style=solid]; +"7 x" -> "9 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 x_cloned" -> "16 output_1" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d" -> "10 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d" -> "11 add_" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d_1" -> "12 add__1" [label="(1, 3, 3, 3)", style=solid]; +"11 add_" -> "13 add" [label="(1, 3, 3, 3)", style=solid]; +"12 add__1" -> "13 add" [label="(1, 3, 3, 3)", style=solid]; +"13 add" -> "14 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"14 conv2d_2" -> "15 add_1" [label="(1, 3, 3, 3)", style=solid]; +"15 add_1" -> "16 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_6_1.dot b/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_6_1.dot new file mode 100644 index 00000000000..290db19728b --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_6_1.dot @@ -0,0 +1,39 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_weight_cloned" [id=1, type=clone]; +"2 conv_a_bias" [id=2, type=get_attr]; +"3 conv_b_weight" [id=3, type=get_attr]; +"4 conv_b_bias" [id=4, type=get_attr]; +"5 conv_c_weight" [id=5, type=get_attr]; +"6 conv_c_bias" [id=6, type=get_attr]; +"7 bias" [id=7, type=get_attr]; +"8 x" [id=8, type=input]; +"9 conv2d" [id=9, type=conv2d]; +"10 conv2d_1" [id=10, type=conv2d]; +"11 add_" [id=11, type=add_]; +"12 add__1" [id=12, type=add_]; +"13 add" [id=13, type=add]; +"14 conv2d_2" [id=14, type=conv2d]; +"15 add_1" [id=15, type=add]; +"16 output_1" [id=16, type=output]; +"0 conv_a_weight" -> "1 conv_a_weight_cloned" [label="(3, 3, 1, 1)", style=solid]; +"0 conv_a_weight" -> "9 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_weight_cloned" -> "16 output_1" [label="(3, 3, 1, 1)", style=solid]; +"2 conv_a_bias" -> "9 conv2d" [label="(3,)", style=solid]; +"3 conv_b_weight" -> "10 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"4 conv_b_bias" -> "10 conv2d_1" [label="(3,)", style=solid]; +"5 conv_c_weight" -> "14 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"6 conv_c_bias" -> "14 conv2d_2" [label="(3,)", style=solid]; +"7 bias" -> "11 add_" [label="(1,)", style=solid]; +"7 bias" -> "12 add__1" [label="(1,)", style=solid]; +"7 bias" -> "15 add_1" [label="(1,)", style=solid]; +"8 x" -> "9 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d" -> "10 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d" -> "11 add_" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d_1" -> "12 add__1" [label="(1, 3, 3, 3)", style=solid]; +"11 add_" -> "13 add" [label="(1, 3, 3, 3)", style=solid]; +"12 add__1" -> "13 add" [label="(1, 3, 3, 3)", style=solid]; +"13 add" -> "14 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"14 conv2d_2" -> "15 add_1" [label="(1, 3, 3, 3)", style=solid]; +"15 add_1" -> "16 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_7_None.dot b/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_7_None.dot new file mode 100644 index 00000000000..ff9313f1acc --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/output_insertion_conv2d_7_None.dot @@ -0,0 +1,39 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 conv2d" [id=8, type=conv2d]; +"9 conv2d_cloned" [id=9, type=clone]; +"10 conv2d_1" [id=10, type=conv2d]; +"11 add_" [id=11, type=add_]; +"12 add__1" [id=12, type=add_]; +"13 add" [id=13, type=add]; +"14 conv2d_2" [id=14, type=conv2d]; +"15 add_1" [id=15, type=add]; +"16 output_1" [id=16, type=output]; +"0 conv_a_weight" -> "8 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "8 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "10 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "10 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "14 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "14 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "11 add_" [label="(1,)", style=solid]; +"6 bias" -> "12 add__1" [label="(1,)", style=solid]; +"6 bias" -> "15 add_1" [label="(1,)", style=solid]; +"7 x" -> "8 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "9 conv2d_cloned" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "10 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "11 add_" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d_cloned" -> "16 output_1" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d_1" -> "12 add__1" [label="(1, 3, 3, 3)", style=solid]; +"11 add_" -> "13 add" [label="(1, 3, 3, 3)", style=solid]; +"12 add__1" -> "13 add" [label="(1, 3, 3, 3)", style=solid]; +"13 add" -> "14 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"14 conv2d_2" -> "15 add_1" [label="(1, 3, 3, 3)", style=solid]; +"15 add_1" -> "16 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_1_5_1_per_channel.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_1_5_1_per_channel.dot new file mode 100644 index 00000000000..1843f556984 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_1_5_1_per_channel.dot @@ -0,0 +1,46 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 conv2d" [id=8, type=conv2d]; +"9 conv2d_1_scale_0" [id=9, type=get_attr]; +"10 conv2d_1_zero_point_0" [id=10, type=get_attr]; +"11 quantize_per_channel_default" [id=11, type=quantize_per_channel]; +"12 dequantize_per_channel_default" [id=12, type=dequantize_per_channel]; +"13 conv2d_1" [id=13, type=conv2d]; +"14 add_" [id=14, type=add_]; +"15 add__1" [id=15, type=add_]; +"16 add" [id=16, type=add]; +"17 conv2d_2" [id=17, type=conv2d]; +"18 add_1" [id=18, type=add]; +"19 output_1" [id=19, type=output]; +"0 conv_a_weight" -> "8 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "8 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "11 quantize_per_channel_default" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "13 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "17 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "17 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "14 add_" [label="(1,)", style=solid]; +"6 bias" -> "15 add__1" [label="(1,)", style=solid]; +"6 bias" -> "18 add_1" [label="(1,)", style=solid]; +"7 x" -> "8 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "13 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "14 add_" [label="(1, 3, 3, 3)", style=solid]; +"9 conv2d_1_scale_0" -> "11 quantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_1_scale_0" -> "12 dequantize_per_channel_default" [label="(1,)", style=solid]; +"10 conv2d_1_zero_point_0" -> "11 quantize_per_channel_default" [label="(1,)", style=solid]; +"10 conv2d_1_zero_point_0" -> "12 dequantize_per_channel_default" [label="(1,)", style=solid]; +"11 quantize_per_channel_default" -> "12 dequantize_per_channel_default" [label="(3, 3, 1, 1)", style=solid]; +"12 dequantize_per_channel_default" -> "13 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"13 conv2d_1" -> "15 add__1" [label="(1, 3, 3, 3)", style=solid]; +"14 add_" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"15 add__1" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"16 add" -> "17 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"17 conv2d_2" -> "18 add_1" [label="(1, 3, 3, 3)", style=solid]; +"18 add_1" -> "19 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_1_5_1_per_tensor.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_1_5_1_per_tensor.dot new file mode 100644 index 00000000000..1d8a5e629c0 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_1_5_1_per_tensor.dot @@ -0,0 +1,40 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 conv2d" [id=8, type=conv2d]; +"9 quantize_per_tensor_default" [id=9, type=quantize_per_tensor]; +"10 dequantize_per_tensor_default" [id=10, type=dequantize_per_tensor]; +"11 conv2d_1" [id=11, type=conv2d]; +"12 add_" [id=12, type=add_]; +"13 add__1" [id=13, type=add_]; +"14 add" [id=14, type=add]; +"15 conv2d_2" [id=15, type=conv2d]; +"16 add_1" [id=16, type=add]; +"17 output_1" [id=17, type=output]; +"0 conv_a_weight" -> "8 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "8 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "9 quantize_per_tensor_default" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "11 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "15 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "15 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "12 add_" [label="(1,)", style=solid]; +"6 bias" -> "13 add__1" [label="(1,)", style=solid]; +"6 bias" -> "16 add_1" [label="(1,)", style=solid]; +"7 x" -> "8 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "11 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "12 add_" [label="(1, 3, 3, 3)", style=solid]; +"9 quantize_per_tensor_default" -> "10 dequantize_per_tensor_default" [label="(3, 3, 1, 1)", style=solid]; +"10 dequantize_per_tensor_default" -> "11 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"11 conv2d_1" -> "13 add__1" [label="(1, 3, 3, 3)", style=solid]; +"12 add_" -> "14 add" [label="(1, 3, 3, 3)", style=solid]; +"13 add__1" -> "14 add" [label="(1, 3, 3, 3)", style=solid]; +"14 add" -> "15 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"15 conv2d_2" -> "16 add_1" [label="(1, 3, 3, 3)", style=solid]; +"16 add_1" -> "17 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_0_per_channel.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_0_per_channel.dot new file mode 100644 index 00000000000..50e8261f594 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_0_per_channel.dot @@ -0,0 +1,46 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 conv2d_scale_0" [id=8, type=get_attr]; +"9 conv2d_zero_point_0" [id=9, type=get_attr]; +"10 quantize_per_channel_default" [id=10, type=quantize_per_channel]; +"11 dequantize_per_channel_default" [id=11, type=dequantize_per_channel]; +"12 conv2d" [id=12, type=conv2d]; +"13 conv2d_1" [id=13, type=conv2d]; +"14 add_" [id=14, type=add_]; +"15 add__1" [id=15, type=add_]; +"16 add" [id=16, type=add]; +"17 conv2d_2" [id=17, type=conv2d]; +"18 add_1" [id=18, type=add]; +"19 output_1" [id=19, type=output]; +"0 conv_a_weight" -> "12 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "12 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "13 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "13 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "17 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "17 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "14 add_" [label="(1,)", style=solid]; +"6 bias" -> "15 add__1" [label="(1,)", style=solid]; +"6 bias" -> "18 add_1" [label="(1,)", style=solid]; +"7 x" -> "10 quantize_per_channel_default" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d_scale_0" -> "10 quantize_per_channel_default" [label="(1,)", style=solid]; +"8 conv2d_scale_0" -> "11 dequantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "10 quantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "11 dequantize_per_channel_default" [label="(1,)", style=solid]; +"10 quantize_per_channel_default" -> "11 dequantize_per_channel_default" [label="(1, 3, 3, 3)", style=solid]; +"11 dequantize_per_channel_default" -> "12 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"12 conv2d" -> "13 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"12 conv2d" -> "14 add_" [label="(1, 3, 3, 3)", style=solid]; +"13 conv2d_1" -> "15 add__1" [label="(1, 3, 3, 3)", style=solid]; +"14 add_" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"15 add__1" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"16 add" -> "17 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"17 conv2d_2" -> "18 add_1" [label="(1, 3, 3, 3)", style=solid]; +"18 add_1" -> "19 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_0_per_tensor.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_0_per_tensor.dot new file mode 100644 index 00000000000..01bfb0da32b --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_0_per_tensor.dot @@ -0,0 +1,40 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 quantize_per_tensor_default" [id=8, type=quantize_per_tensor]; +"9 dequantize_per_tensor_default" [id=9, type=dequantize_per_tensor]; +"10 conv2d" [id=10, type=conv2d]; +"11 conv2d_1" [id=11, type=conv2d]; +"12 add_" [id=12, type=add_]; +"13 add__1" [id=13, type=add_]; +"14 add" [id=14, type=add]; +"15 conv2d_2" [id=15, type=conv2d]; +"16 add_1" [id=16, type=add]; +"17 output_1" [id=17, type=output]; +"0 conv_a_weight" -> "10 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "10 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "11 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "11 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "15 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "15 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "12 add_" [label="(1,)", style=solid]; +"6 bias" -> "13 add__1" [label="(1,)", style=solid]; +"6 bias" -> "16 add_1" [label="(1,)", style=solid]; +"7 x" -> "8 quantize_per_tensor_default" [label="(1, 3, 3, 3)", style=solid]; +"8 quantize_per_tensor_default" -> "9 dequantize_per_tensor_default" [label="(1, 3, 3, 3)", style=solid]; +"9 dequantize_per_tensor_default" -> "10 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d" -> "11 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d" -> "12 add_" [label="(1, 3, 3, 3)", style=solid]; +"11 conv2d_1" -> "13 add__1" [label="(1, 3, 3, 3)", style=solid]; +"12 add_" -> "14 add" [label="(1, 3, 3, 3)", style=solid]; +"13 add__1" -> "14 add" [label="(1, 3, 3, 3)", style=solid]; +"14 add" -> "15 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"15 conv2d_2" -> "16 add_1" [label="(1, 3, 3, 3)", style=solid]; +"16 add_1" -> "17 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_1_per_channel.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_1_per_channel.dot new file mode 100644 index 00000000000..1c8808cc8c6 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_1_per_channel.dot @@ -0,0 +1,46 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 conv2d_scale_0" [id=8, type=get_attr]; +"9 conv2d_zero_point_0" [id=9, type=get_attr]; +"10 quantize_per_channel_default" [id=10, type=quantize_per_channel]; +"11 dequantize_per_channel_default" [id=11, type=dequantize_per_channel]; +"12 conv2d" [id=12, type=conv2d]; +"13 conv2d_1" [id=13, type=conv2d]; +"14 add_" [id=14, type=add_]; +"15 add__1" [id=15, type=add_]; +"16 add" [id=16, type=add]; +"17 conv2d_2" [id=17, type=conv2d]; +"18 add_1" [id=18, type=add]; +"19 output_1" [id=19, type=output]; +"0 conv_a_weight" -> "10 quantize_per_channel_default" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "12 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "13 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "13 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "17 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "17 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "14 add_" [label="(1,)", style=solid]; +"6 bias" -> "15 add__1" [label="(1,)", style=solid]; +"6 bias" -> "18 add_1" [label="(1,)", style=solid]; +"7 x" -> "12 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d_scale_0" -> "10 quantize_per_channel_default" [label="(1,)", style=solid]; +"8 conv2d_scale_0" -> "11 dequantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "10 quantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "11 dequantize_per_channel_default" [label="(1,)", style=solid]; +"10 quantize_per_channel_default" -> "11 dequantize_per_channel_default" [label="(3, 3, 1, 1)", style=solid]; +"11 dequantize_per_channel_default" -> "12 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"12 conv2d" -> "13 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"12 conv2d" -> "14 add_" [label="(1, 3, 3, 3)", style=solid]; +"13 conv2d_1" -> "15 add__1" [label="(1, 3, 3, 3)", style=solid]; +"14 add_" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"15 add__1" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"16 add" -> "17 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"17 conv2d_2" -> "18 add_1" [label="(1, 3, 3, 3)", style=solid]; +"18 add_1" -> "19 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_1_per_tensor.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_1_per_tensor.dot new file mode 100644 index 00000000000..62ab139712c --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_6_1_per_tensor.dot @@ -0,0 +1,40 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 quantize_per_tensor_default" [id=8, type=quantize_per_tensor]; +"9 dequantize_per_tensor_default" [id=9, type=dequantize_per_tensor]; +"10 conv2d" [id=10, type=conv2d]; +"11 conv2d_1" [id=11, type=conv2d]; +"12 add_" [id=12, type=add_]; +"13 add__1" [id=13, type=add_]; +"14 add" [id=14, type=add]; +"15 conv2d_2" [id=15, type=conv2d]; +"16 add_1" [id=16, type=add]; +"17 output_1" [id=17, type=output]; +"0 conv_a_weight" -> "8 quantize_per_tensor_default" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "10 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "11 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "11 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "15 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "15 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "12 add_" [label="(1,)", style=solid]; +"6 bias" -> "13 add__1" [label="(1,)", style=solid]; +"6 bias" -> "16 add_1" [label="(1,)", style=solid]; +"7 x" -> "10 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 quantize_per_tensor_default" -> "9 dequantize_per_tensor_default" [label="(3, 3, 1, 1)", style=solid]; +"9 dequantize_per_tensor_default" -> "10 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"10 conv2d" -> "11 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d" -> "12 add_" [label="(1, 3, 3, 3)", style=solid]; +"11 conv2d_1" -> "13 add__1" [label="(1, 3, 3, 3)", style=solid]; +"12 add_" -> "14 add" [label="(1, 3, 3, 3)", style=solid]; +"13 add__1" -> "14 add" [label="(1, 3, 3, 3)", style=solid]; +"14 add" -> "15 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"15 conv2d_2" -> "16 add_1" [label="(1, 3, 3, 3)", style=solid]; +"16 add_1" -> "17 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_7_None_per_channel.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_7_None_per_channel.dot new file mode 100644 index 00000000000..5c9e516a456 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_7_None_per_channel.dot @@ -0,0 +1,50 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 conv2d_scale_0" [id=8, type=get_attr]; +"9 conv2d_zero_point_0" [id=9, type=get_attr]; +"10 conv2d" [id=10, type=conv2d]; +"11 quantize_per_channel_default" [id=11, type=quantize_per_channel]; +"12 dequantize_per_channel_default_1" [id=12, type=dequantize_per_channel]; +"13 dequantize_per_channel_default" [id=13, type=dequantize_per_channel]; +"14 conv2d_1" [id=14, type=conv2d]; +"15 add_" [id=15, type=add_]; +"16 add__1" [id=16, type=add_]; +"17 add" [id=17, type=add]; +"18 conv2d_2" [id=18, type=conv2d]; +"19 add_1" [id=19, type=add]; +"20 output_1" [id=20, type=output]; +"0 conv_a_weight" -> "10 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "10 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "14 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "14 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "18 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "18 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "15 add_" [label="(1,)", style=solid]; +"6 bias" -> "16 add__1" [label="(1,)", style=solid]; +"6 bias" -> "19 add_1" [label="(1,)", style=solid]; +"7 x" -> "10 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d_scale_0" -> "11 quantize_per_channel_default" [label="(1,)", style=solid]; +"8 conv2d_scale_0" -> "12 dequantize_per_channel_default_1" [label="(1,)", style=solid]; +"8 conv2d_scale_0" -> "13 dequantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "11 quantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "12 dequantize_per_channel_default_1" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "13 dequantize_per_channel_default" [label="(1,)", style=solid]; +"10 conv2d" -> "11 quantize_per_channel_default" [label="(1, 3, 3, 3)", style=solid]; +"11 quantize_per_channel_default" -> "12 dequantize_per_channel_default_1" [label="(1, 3, 3, 3)", style=solid]; +"11 quantize_per_channel_default" -> "13 dequantize_per_channel_default" [label="(1, 3, 3, 3)", style=solid]; +"12 dequantize_per_channel_default_1" -> "15 add_" [label="(1, 3, 3, 3)", style=solid]; +"13 dequantize_per_channel_default" -> "14 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"14 conv2d_1" -> "16 add__1" [label="(1, 3, 3, 3)", style=solid]; +"15 add_" -> "17 add" [label="(1, 3, 3, 3)", style=solid]; +"16 add__1" -> "17 add" [label="(1, 3, 3, 3)", style=solid]; +"17 add" -> "18 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"18 conv2d_2" -> "19 add_1" [label="(1, 3, 3, 3)", style=solid]; +"19 add_1" -> "20 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_7_None_per_tensor.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_7_None_per_tensor.dot new file mode 100644 index 00000000000..b0d96c62313 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_insert_conv2d_7_None_per_tensor.dot @@ -0,0 +1,42 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 conv2d" [id=8, type=conv2d]; +"9 quantize_per_tensor_default" [id=9, type=quantize_per_tensor]; +"10 dequantize_per_tensor_default_1" [id=10, type=dequantize_per_tensor]; +"11 dequantize_per_tensor_default" [id=11, type=dequantize_per_tensor]; +"12 conv2d_1" [id=12, type=conv2d]; +"13 add_" [id=13, type=add_]; +"14 add__1" [id=14, type=add_]; +"15 add" [id=15, type=add]; +"16 conv2d_2" [id=16, type=conv2d]; +"17 add_1" [id=17, type=add]; +"18 output_1" [id=18, type=output]; +"0 conv_a_weight" -> "8 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "8 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "12 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "12 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "16 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "16 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "13 add_" [label="(1,)", style=solid]; +"6 bias" -> "14 add__1" [label="(1,)", style=solid]; +"6 bias" -> "17 add_1" [label="(1,)", style=solid]; +"7 x" -> "8 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d" -> "9 quantize_per_tensor_default" [label="(1, 3, 3, 3)", style=solid]; +"9 quantize_per_tensor_default" -> "10 dequantize_per_tensor_default_1" [label="(1, 3, 3, 3)", style=solid]; +"9 quantize_per_tensor_default" -> "11 dequantize_per_tensor_default" [label="(1, 3, 3, 3)", style=solid]; +"10 dequantize_per_tensor_default_1" -> "13 add_" [label="(1, 3, 3, 3)", style=solid]; +"11 dequantize_per_tensor_default" -> "12 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"12 conv2d_1" -> "14 add__1" [label="(1, 3, 3, 3)", style=solid]; +"13 add_" -> "15 add" [label="(1, 3, 3, 3)", style=solid]; +"14 add__1" -> "15 add" [label="(1, 3, 3, 3)", style=solid]; +"15 add" -> "16 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"16 conv2d_2" -> "17 add_1" [label="(1, 3, 3, 3)", style=solid]; +"17 add_1" -> "18 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_shared_insert_weights_per_channel.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_shared_insert_weights_per_channel.dot new file mode 100644 index 00000000000..38f5b8a0c91 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_shared_insert_weights_per_channel.dot @@ -0,0 +1,56 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 conv2d_scale_0" [id=8, type=get_attr]; +"9 conv2d_zero_point_0" [id=9, type=get_attr]; +"10 quantize_per_channel_default" [id=10, type=quantize_per_channel]; +"11 dequantize_per_channel_default" [id=11, type=dequantize_per_channel]; +"12 conv2d" [id=12, type=conv2d]; +"13 conv2d_1_scale_0" [id=13, type=get_attr]; +"14 conv2d_1_zero_point_0" [id=14, type=get_attr]; +"15 quantize_per_channel_default_1" [id=15, type=quantize_per_channel]; +"16 dequantize_per_channel_default_1" [id=16, type=dequantize_per_channel]; +"17 conv2d_1" [id=17, type=conv2d]; +"18 add_" [id=18, type=add_]; +"19 add__1" [id=19, type=add_]; +"20 add" [id=20, type=add]; +"21 conv2d_2" [id=21, type=conv2d]; +"22 add_1" [id=22, type=add]; +"23 output_1" [id=23, type=output]; +"0 conv_a_weight" -> "10 quantize_per_channel_default" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "12 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "15 quantize_per_channel_default_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "17 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "21 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "21 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "18 add_" [label="(1,)", style=solid]; +"6 bias" -> "19 add__1" [label="(1,)", style=solid]; +"6 bias" -> "22 add_1" [label="(1,)", style=solid]; +"7 x" -> "12 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 conv2d_scale_0" -> "10 quantize_per_channel_default" [label="(1,)", style=solid]; +"8 conv2d_scale_0" -> "11 dequantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "10 quantize_per_channel_default" [label="(1,)", style=solid]; +"9 conv2d_zero_point_0" -> "11 dequantize_per_channel_default" [label="(1,)", style=solid]; +"10 quantize_per_channel_default" -> "11 dequantize_per_channel_default" [label="(3, 3, 1, 1)", style=solid]; +"11 dequantize_per_channel_default" -> "12 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"12 conv2d" -> "17 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"12 conv2d" -> "18 add_" [label="(1, 3, 3, 3)", style=solid]; +"13 conv2d_1_scale_0" -> "15 quantize_per_channel_default_1" [label="(1,)", style=solid]; +"13 conv2d_1_scale_0" -> "16 dequantize_per_channel_default_1" [label="(1,)", style=solid]; +"14 conv2d_1_zero_point_0" -> "15 quantize_per_channel_default_1" [label="(1,)", style=solid]; +"14 conv2d_1_zero_point_0" -> "16 dequantize_per_channel_default_1" [label="(1,)", style=solid]; +"15 quantize_per_channel_default_1" -> "16 dequantize_per_channel_default_1" [label="(3, 3, 1, 1)", style=solid]; +"16 dequantize_per_channel_default_1" -> "17 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"17 conv2d_1" -> "19 add__1" [label="(1, 3, 3, 3)", style=solid]; +"18 add_" -> "20 add" [label="(1, 3, 3, 3)", style=solid]; +"19 add__1" -> "20 add" [label="(1, 3, 3, 3)", style=solid]; +"20 add" -> "21 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"21 conv2d_2" -> "22 add_1" [label="(1, 3, 3, 3)", style=solid]; +"22 add_1" -> "23 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/transformed/qdq_shared_insert_weights_per_tensor.dot b/tests/torch/data/reference_graphs/fx/transformed/qdq_shared_insert_weights_per_tensor.dot new file mode 100644 index 00000000000..952b7087ef5 --- /dev/null +++ b/tests/torch/data/reference_graphs/fx/transformed/qdq_shared_insert_weights_per_tensor.dot @@ -0,0 +1,44 @@ +strict digraph { +"0 conv_a_weight" [id=0, type=get_attr]; +"1 conv_a_bias" [id=1, type=get_attr]; +"2 conv_b_weight" [id=2, type=get_attr]; +"3 conv_b_bias" [id=3, type=get_attr]; +"4 conv_c_weight" [id=4, type=get_attr]; +"5 conv_c_bias" [id=5, type=get_attr]; +"6 bias" [id=6, type=get_attr]; +"7 x" [id=7, type=input]; +"8 quantize_per_tensor_default" [id=8, type=quantize_per_tensor]; +"9 dequantize_per_tensor_default" [id=9, type=dequantize_per_tensor]; +"10 conv2d" [id=10, type=conv2d]; +"11 quantize_per_tensor_default_1" [id=11, type=quantize_per_tensor]; +"12 dequantize_per_tensor_default_1" [id=12, type=dequantize_per_tensor]; +"13 conv2d_1" [id=13, type=conv2d]; +"14 add_" [id=14, type=add_]; +"15 add__1" [id=15, type=add_]; +"16 add" [id=16, type=add]; +"17 conv2d_2" [id=17, type=conv2d]; +"18 add_1" [id=18, type=add]; +"19 output_1" [id=19, type=output]; +"0 conv_a_weight" -> "8 quantize_per_tensor_default" [label="(3, 3, 1, 1)", style=solid]; +"1 conv_a_bias" -> "10 conv2d" [label="(3,)", style=solid]; +"2 conv_b_weight" -> "11 quantize_per_tensor_default_1" [label="(3, 3, 1, 1)", style=solid]; +"3 conv_b_bias" -> "13 conv2d_1" [label="(3,)", style=solid]; +"4 conv_c_weight" -> "17 conv2d_2" [label="(3, 3, 1, 1)", style=solid]; +"5 conv_c_bias" -> "17 conv2d_2" [label="(3,)", style=solid]; +"6 bias" -> "14 add_" [label="(1,)", style=solid]; +"6 bias" -> "15 add__1" [label="(1,)", style=solid]; +"6 bias" -> "18 add_1" [label="(1,)", style=solid]; +"7 x" -> "10 conv2d" [label="(1, 3, 3, 3)", style=solid]; +"8 quantize_per_tensor_default" -> "9 dequantize_per_tensor_default" [label="(3, 3, 1, 1)", style=solid]; +"9 dequantize_per_tensor_default" -> "10 conv2d" [label="(3, 3, 1, 1)", style=solid]; +"10 conv2d" -> "13 conv2d_1" [label="(1, 3, 3, 3)", style=solid]; +"10 conv2d" -> "14 add_" [label="(1, 3, 3, 3)", style=solid]; +"11 quantize_per_tensor_default_1" -> "12 dequantize_per_tensor_default_1" [label="(3, 3, 1, 1)", style=solid]; +"12 dequantize_per_tensor_default_1" -> "13 conv2d_1" [label="(3, 3, 1, 1)", style=solid]; +"13 conv2d_1" -> "15 add__1" [label="(1, 3, 3, 3)", style=solid]; +"14 add_" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"15 add__1" -> "16 add" [label="(1, 3, 3, 3)", style=solid]; +"16 add" -> "17 conv2d_2" [label="(1, 3, 3, 3)", style=solid]; +"17 conv2d_2" -> "18 add_1" [label="(1, 3, 3, 3)", style=solid]; +"18 add_1" -> "19 output_1" [label="(1, 3, 3, 3)", style=solid]; +} diff --git a/tests/torch/data/reference_graphs/fx/unet.dot b/tests/torch/data/reference_graphs/fx/unet.dot index 6033f629f24..730441ca545 100644 --- a/tests/torch/data/reference_graphs/fx/unet.dot +++ b/tests/torch/data/reference_graphs/fx/unet.dot @@ -1,537 +1,429 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; -"1 _param_constant0" [id=1, type=get_attr]; -"2 _param_constant1" [id=2, type=get_attr]; -"3 conv2d" [id=3, type=conv2d]; -"4 empty" [id=4, type=empty]; -"5 _param_constant2" [id=5, type=get_attr]; -"6 _param_constant3" [id=6, type=get_attr]; -"7 _tensor_constant0" [id=7, type=get_attr]; -"8 _tensor_constant1" [id=8, type=get_attr]; -"9 _native_batch_norm_legit_no_training" [id=9, type=_native_batch_norm_legit_no_training]; -"10 getitem" [id=10, type=__getitem__]; -"11 getitem_1" [id=11, type=__getitem__]; -"12 getitem_2" [id=12, type=__getitem__]; -"13 relu" [id=13, type=relu]; -"14 _param_constant4" [id=14, type=get_attr]; -"15 _param_constant5" [id=15, type=get_attr]; -"16 conv2d_1" [id=16, type=conv2d]; -"17 empty_1" [id=17, type=empty]; -"18 _param_constant6" [id=18, type=get_attr]; -"19 _param_constant7" [id=19, type=get_attr]; -"20 _tensor_constant2" [id=20, type=get_attr]; -"21 _tensor_constant3" [id=21, type=get_attr]; -"22 _native_batch_norm_legit_no_training_1" [id=22, type=_native_batch_norm_legit_no_training]; -"23 getitem_3" [id=23, type=__getitem__]; -"24 getitem_4" [id=24, type=__getitem__]; -"25 getitem_5" [id=25, type=__getitem__]; -"26 relu_1" [id=26, type=relu]; -"27 max_pool2d" [id=27, type=max_pool2d]; -"28 _param_constant8" [id=28, type=get_attr]; -"29 _param_constant9" [id=29, type=get_attr]; -"30 conv2d_2" [id=30, type=conv2d]; -"31 empty_2" [id=31, type=empty]; -"32 _param_constant10" [id=32, type=get_attr]; -"33 _param_constant11" [id=33, type=get_attr]; -"34 _tensor_constant4" [id=34, type=get_attr]; -"35 _tensor_constant5" [id=35, type=get_attr]; -"36 _native_batch_norm_legit_no_training_2" [id=36, type=_native_batch_norm_legit_no_training]; -"37 getitem_6" [id=37, type=__getitem__]; -"38 getitem_7" [id=38, type=__getitem__]; -"39 getitem_8" [id=39, type=__getitem__]; -"40 relu_2" [id=40, type=relu]; -"41 _param_constant12" [id=41, type=get_attr]; -"42 _param_constant13" [id=42, type=get_attr]; -"43 conv2d_3" [id=43, type=conv2d]; -"44 empty_3" [id=44, type=empty]; -"45 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type=batch_norm]; +"191 relu_12" [id=191, type=relu]; "192 conv2d_13" [id=192, type=conv2d]; -"193 empty_13" [id=193, type=empty]; -"194 _param_constant58" [id=194, type=get_attr]; -"195 _param_constant59" [id=195, type=get_attr]; -"196 _tensor_constant26" [id=196, type=get_attr]; -"197 _tensor_constant27" [id=197, type=get_attr]; -"198 _native_batch_norm_legit_no_training_13" [id=198, type=_native_batch_norm_legit_no_training]; -"199 getitem_39" [id=199, type=__getitem__]; -"200 getitem_40" [id=200, type=__getitem__]; -"201 getitem_41" [id=201, type=__getitem__]; -"202 relu_13" [id=202, type=relu]; -"203 _param_constant60" [id=203, type=get_attr]; -"204 _param_constant61" [id=204, type=get_attr]; -"205 conv_transpose2d_2" [id=205, type=conv_transpose2d]; -"206 slice_9" [id=206, type=slice]; -"207 slice_10" [id=207, type=slice]; -"208 slice_11" [id=208, type=slice]; -"209 slice_12" [id=209, type=slice]; -"210 cat_2" [id=210, type=cat]; -"211 _param_constant62" [id=211, type=get_attr]; -"212 _param_constant63" [id=212, type=get_attr]; -"213 conv2d_14" [id=213, type=conv2d]; -"214 empty_14" [id=214, type=empty]; -"215 _param_constant64" [id=215, type=get_attr]; -"216 _param_constant65" [id=216, type=get_attr]; -"217 _tensor_constant28" [id=217, type=get_attr]; -"218 _tensor_constant29" [id=218, type=get_attr]; -"219 _native_batch_norm_legit_no_training_14" [id=219, type=_native_batch_norm_legit_no_training]; -"220 getitem_42" [id=220, type=__getitem__]; -"221 getitem_43" [id=221, type=__getitem__]; -"222 getitem_44" [id=222, type=__getitem__]; -"223 relu_14" [id=223, type=relu]; -"224 _param_constant66" [id=224, type=get_attr]; -"225 _param_constant67" [id=225, type=get_attr]; -"226 conv2d_15" [id=226, type=conv2d]; -"227 empty_15" [id=227, type=empty]; -"228 _param_constant68" [id=228, type=get_attr]; -"229 _param_constant69" [id=229, type=get_attr]; -"230 _tensor_constant30" [id=230, type=get_attr]; -"231 _tensor_constant31" [id=231, type=get_attr]; -"232 _native_batch_norm_legit_no_training_15" [id=232, type=_native_batch_norm_legit_no_training]; -"233 getitem_45" [id=233, type=__getitem__]; -"234 getitem_46" [id=234, type=__getitem__]; -"235 getitem_47" [id=235, type=__getitem__]; -"236 relu_15" [id=236, type=relu]; -"237 _param_constant70" [id=237, type=get_attr]; -"238 _param_constant71" [id=238, type=get_attr]; -"239 conv_transpose2d_3" [id=239, type=conv_transpose2d]; -"240 slice_13" [id=240, type=slice]; -"241 slice_14" [id=241, type=slice]; -"242 slice_15" [id=242, type=slice]; -"243 slice_16" [id=243, type=slice]; -"244 cat_3" [id=244, type=cat]; -"245 _param_constant72" [id=245, type=get_attr]; -"246 _param_constant73" [id=246, type=get_attr]; -"247 conv2d_16" [id=247, type=conv2d]; -"248 empty_16" [id=248, type=empty]; -"249 _param_constant74" [id=249, type=get_attr]; -"250 _param_constant75" [id=250, type=get_attr]; -"251 _tensor_constant32" [id=251, type=get_attr]; -"252 _tensor_constant33" [id=252, type=get_attr]; -"253 _native_batch_norm_legit_no_training_16" [id=253, type=_native_batch_norm_legit_no_training]; -"254 getitem_48" [id=254, type=__getitem__]; -"255 getitem_49" [id=255, type=__getitem__]; -"256 getitem_50" [id=256, type=__getitem__]; -"257 relu_16" [id=257, type=relu]; -"258 _param_constant76" [id=258, type=get_attr]; -"259 _param_constant77" [id=259, type=get_attr]; -"260 conv2d_17" [id=260, type=conv2d]; -"261 empty_17" [id=261, type=empty]; -"262 _param_constant78" [id=262, type=get_attr]; -"263 _param_constant79" [id=263, type=get_attr]; -"264 _tensor_constant34" [id=264, type=get_attr]; -"265 _tensor_constant35" [id=265, type=get_attr]; -"266 _native_batch_norm_legit_no_training_17" [id=266, type=_native_batch_norm_legit_no_training]; -"267 getitem_51" [id=267, type=__getitem__]; -"268 getitem_52" [id=268, type=__getitem__]; -"269 getitem_53" [id=269, type=__getitem__]; -"270 relu_17" [id=270, type=relu]; -"271 _param_constant80" [id=271, type=get_attr]; -"272 _param_constant81" [id=272, type=get_attr]; -"273 conv2d_18" [id=273, type=conv2d]; -"274 output" [id=274, type=output]; -"0 arg0_1" -> "3 conv2d"; -"1 _param_constant0" -> "3 conv2d"; -"2 _param_constant1" -> "3 conv2d"; -"3 conv2d" -> "9 _native_batch_norm_legit_no_training"; -"5 _param_constant2" -> "9 _native_batch_norm_legit_no_training"; -"6 _param_constant3" -> "9 _native_batch_norm_legit_no_training"; -"7 _tensor_constant0" -> "9 _native_batch_norm_legit_no_training"; -"8 _tensor_constant1" -> "9 _native_batch_norm_legit_no_training"; -"9 _native_batch_norm_legit_no_training" -> "10 getitem"; -"9 _native_batch_norm_legit_no_training" -> "11 getitem_1"; -"9 _native_batch_norm_legit_no_training" -> "12 getitem_2"; -"10 getitem" -> "13 relu"; -"13 relu" -> "16 conv2d_1"; -"14 _param_constant4" -> "16 conv2d_1"; -"15 _param_constant5" -> "16 conv2d_1"; -"16 conv2d_1" -> "22 _native_batch_norm_legit_no_training_1"; -"18 _param_constant6" -> "22 _native_batch_norm_legit_no_training_1"; -"19 _param_constant7" -> "22 _native_batch_norm_legit_no_training_1"; -"20 _tensor_constant2" -> "22 _native_batch_norm_legit_no_training_1"; -"21 _tensor_constant3" -> "22 _native_batch_norm_legit_no_training_1"; -"22 _native_batch_norm_legit_no_training_1" -> "23 getitem_3"; -"22 _native_batch_norm_legit_no_training_1" -> "24 getitem_4"; -"22 _native_batch_norm_legit_no_training_1" -> "25 getitem_5"; -"23 getitem_3" -> "26 relu_1"; -"26 relu_1" -> "27 max_pool2d"; -"26 relu_1" -> "240 slice_13"; -"27 max_pool2d" -> "30 conv2d_2"; -"28 _param_constant8" -> "30 conv2d_2"; -"29 _param_constant9" -> "30 conv2d_2"; -"30 conv2d_2" -> "36 _native_batch_norm_legit_no_training_2"; -"32 _param_constant10" -> "36 _native_batch_norm_legit_no_training_2"; -"33 _param_constant11" -> "36 _native_batch_norm_legit_no_training_2"; -"34 _tensor_constant4" -> "36 _native_batch_norm_legit_no_training_2"; -"35 _tensor_constant5" -> "36 _native_batch_norm_legit_no_training_2"; -"36 _native_batch_norm_legit_no_training_2" -> "37 getitem_6"; -"36 _native_batch_norm_legit_no_training_2" -> "38 getitem_7"; -"36 _native_batch_norm_legit_no_training_2" -> "39 getitem_8"; -"37 getitem_6" -> "40 relu_2"; -"40 relu_2" -> "43 conv2d_3"; -"41 _param_constant12" -> "43 conv2d_3"; -"42 _param_constant13" -> "43 conv2d_3"; -"43 conv2d_3" -> "49 _native_batch_norm_legit_no_training_3"; -"45 _param_constant14" -> "49 _native_batch_norm_legit_no_training_3"; -"46 _param_constant15" -> "49 _native_batch_norm_legit_no_training_3"; -"47 _tensor_constant6" -> "49 _native_batch_norm_legit_no_training_3"; -"48 _tensor_constant7" -> "49 _native_batch_norm_legit_no_training_3"; -"49 _native_batch_norm_legit_no_training_3" -> "50 getitem_9"; -"49 _native_batch_norm_legit_no_training_3" -> "51 getitem_10"; -"49 _native_batch_norm_legit_no_training_3" -> "52 getitem_11"; -"50 getitem_9" -> "53 relu_3"; -"53 relu_3" -> "54 max_pool2d_1"; -"53 relu_3" -> "206 slice_9"; -"54 max_pool2d_1" -> "57 conv2d_4"; -"55 _param_constant16" -> "57 conv2d_4"; -"56 _param_constant17" -> "57 conv2d_4"; -"57 conv2d_4" -> "63 _native_batch_norm_legit_no_training_4"; -"59 _param_constant18" -> "63 _native_batch_norm_legit_no_training_4"; -"60 _param_constant19" -> "63 _native_batch_norm_legit_no_training_4"; -"61 _tensor_constant8" -> "63 _native_batch_norm_legit_no_training_4"; -"62 _tensor_constant9" -> "63 _native_batch_norm_legit_no_training_4"; -"63 _native_batch_norm_legit_no_training_4" -> "64 getitem_12"; -"63 _native_batch_norm_legit_no_training_4" -> "65 getitem_13"; -"63 _native_batch_norm_legit_no_training_4" -> "66 getitem_14"; -"64 getitem_12" -> "67 relu_4"; -"67 relu_4" -> "70 conv2d_5"; -"68 _param_constant20" -> "70 conv2d_5"; -"69 _param_constant21" -> "70 conv2d_5"; -"70 conv2d_5" -> "76 _native_batch_norm_legit_no_training_5"; -"72 _param_constant22" -> "76 _native_batch_norm_legit_no_training_5"; -"73 _param_constant23" -> "76 _native_batch_norm_legit_no_training_5"; -"74 _tensor_constant10" -> "76 _native_batch_norm_legit_no_training_5"; -"75 _tensor_constant11" -> "76 _native_batch_norm_legit_no_training_5"; -"76 _native_batch_norm_legit_no_training_5" -> "77 getitem_15"; -"76 _native_batch_norm_legit_no_training_5" -> "78 getitem_16"; -"76 _native_batch_norm_legit_no_training_5" -> "79 getitem_17"; -"77 getitem_15" -> "80 relu_5"; -"80 relu_5" -> "81 max_pool2d_2"; -"80 relu_5" -> "172 slice_5"; -"81 max_pool2d_2" -> "84 conv2d_6"; -"82 _param_constant24" -> "84 conv2d_6"; -"83 _param_constant25" -> "84 conv2d_6"; -"84 conv2d_6" -> "90 _native_batch_norm_legit_no_training_6"; -"86 _param_constant26" -> "90 _native_batch_norm_legit_no_training_6"; -"87 _param_constant27" -> "90 _native_batch_norm_legit_no_training_6"; -"88 _tensor_constant12" -> "90 _native_batch_norm_legit_no_training_6"; -"89 _tensor_constant13" -> "90 _native_batch_norm_legit_no_training_6"; -"90 _native_batch_norm_legit_no_training_6" -> "91 getitem_18"; -"90 _native_batch_norm_legit_no_training_6" -> "92 getitem_19"; -"90 _native_batch_norm_legit_no_training_6" -> "93 getitem_20"; -"91 getitem_18" -> "94 relu_6"; -"94 relu_6" -> "97 conv2d_7"; -"95 _param_constant28" -> "97 conv2d_7"; -"96 _param_constant29" -> "97 conv2d_7"; -"97 conv2d_7" -> "103 _native_batch_norm_legit_no_training_7"; -"99 _param_constant30" -> "103 _native_batch_norm_legit_no_training_7"; -"100 _param_constant31" -> "103 _native_batch_norm_legit_no_training_7"; -"101 _tensor_constant14" -> "103 _native_batch_norm_legit_no_training_7"; -"102 _tensor_constant15" -> "103 _native_batch_norm_legit_no_training_7"; -"103 _native_batch_norm_legit_no_training_7" -> "104 getitem_21"; -"103 _native_batch_norm_legit_no_training_7" -> "105 getitem_22"; -"103 _native_batch_norm_legit_no_training_7" -> "106 getitem_23"; -"104 getitem_21" -> "107 relu_7"; -"107 relu_7" -> "108 max_pool2d_3"; -"107 relu_7" -> "138 slice_1"; -"108 max_pool2d_3" -> "111 conv2d_8"; -"109 _param_constant32" -> "111 conv2d_8"; -"110 _param_constant33" -> "111 conv2d_8"; -"111 conv2d_8" -> "117 _native_batch_norm_legit_no_training_8"; -"113 _param_constant34" -> "117 _native_batch_norm_legit_no_training_8"; -"114 _param_constant35" -> "117 _native_batch_norm_legit_no_training_8"; -"115 _tensor_constant16" -> "117 _native_batch_norm_legit_no_training_8"; -"116 _tensor_constant17" -> "117 _native_batch_norm_legit_no_training_8"; -"117 _native_batch_norm_legit_no_training_8" -> "118 getitem_24"; -"117 _native_batch_norm_legit_no_training_8" -> "119 getitem_25"; -"117 _native_batch_norm_legit_no_training_8" -> "120 getitem_26"; -"118 getitem_24" -> "121 relu_8"; -"121 relu_8" -> "124 conv2d_9"; -"122 _param_constant36" -> "124 conv2d_9"; -"123 _param_constant37" -> "124 conv2d_9"; -"124 conv2d_9" -> "130 _native_batch_norm_legit_no_training_9"; -"126 _param_constant38" -> "130 _native_batch_norm_legit_no_training_9"; -"127 _param_constant39" -> "130 _native_batch_norm_legit_no_training_9"; -"128 _tensor_constant18" -> "130 _native_batch_norm_legit_no_training_9"; -"129 _tensor_constant19" -> "130 _native_batch_norm_legit_no_training_9"; -"130 _native_batch_norm_legit_no_training_9" -> "131 getitem_27"; -"130 _native_batch_norm_legit_no_training_9" -> "132 getitem_28"; -"130 _native_batch_norm_legit_no_training_9" -> "133 getitem_29"; -"131 getitem_27" -> "134 relu_9"; -"134 relu_9" -> "137 conv_transpose2d"; -"135 _param_constant40" -> "137 conv_transpose2d"; -"136 _param_constant41" -> "137 conv_transpose2d"; -"137 conv_transpose2d" -> "142 cat"; -"138 slice_1" -> "139 slice_2"; -"139 slice_2" -> "140 slice_3"; -"140 slice_3" -> "141 slice_4"; -"141 slice_4" -> "142 cat"; -"142 cat" -> "145 conv2d_10"; -"143 _param_constant42" -> "145 conv2d_10"; -"144 _param_constant43" -> "145 conv2d_10"; -"145 conv2d_10" -> "151 _native_batch_norm_legit_no_training_10"; -"147 _param_constant44" -> "151 _native_batch_norm_legit_no_training_10"; -"148 _param_constant45" -> "151 _native_batch_norm_legit_no_training_10"; -"149 _tensor_constant20" -> "151 _native_batch_norm_legit_no_training_10"; -"150 _tensor_constant21" -> "151 _native_batch_norm_legit_no_training_10"; -"151 _native_batch_norm_legit_no_training_10" -> "152 getitem_30"; -"151 _native_batch_norm_legit_no_training_10" -> "153 getitem_31"; -"151 _native_batch_norm_legit_no_training_10" -> "154 getitem_32"; -"152 getitem_30" -> "155 relu_10"; -"155 relu_10" -> "158 conv2d_11"; -"156 _param_constant46" -> "158 conv2d_11"; -"157 _param_constant47" -> "158 conv2d_11"; -"158 conv2d_11" -> "164 _native_batch_norm_legit_no_training_11"; -"160 _param_constant48" -> "164 _native_batch_norm_legit_no_training_11"; -"161 _param_constant49" -> "164 _native_batch_norm_legit_no_training_11"; -"162 _tensor_constant22" -> "164 _native_batch_norm_legit_no_training_11"; -"163 _tensor_constant23" -> "164 _native_batch_norm_legit_no_training_11"; -"164 _native_batch_norm_legit_no_training_11" -> "165 getitem_33"; -"164 _native_batch_norm_legit_no_training_11" -> "166 getitem_34"; -"164 _native_batch_norm_legit_no_training_11" -> "167 getitem_35"; -"165 getitem_33" -> "168 relu_11"; -"168 relu_11" -> "171 conv_transpose2d_1"; -"169 _param_constant50" -> "171 conv_transpose2d_1"; -"170 _param_constant51" -> "171 conv_transpose2d_1"; -"171 conv_transpose2d_1" -> "176 cat_1"; -"172 slice_5" -> "173 slice_6"; -"173 slice_6" -> "174 slice_7"; -"174 slice_7" -> "175 slice_8"; -"175 slice_8" -> "176 cat_1"; -"176 cat_1" -> "179 conv2d_12"; -"177 _param_constant52" -> "179 conv2d_12"; -"178 _param_constant53" -> "179 conv2d_12"; -"179 conv2d_12" -> "185 _native_batch_norm_legit_no_training_12"; -"181 _param_constant54" -> "185 _native_batch_norm_legit_no_training_12"; -"182 _param_constant55" -> "185 _native_batch_norm_legit_no_training_12"; -"183 _tensor_constant24" -> "185 _native_batch_norm_legit_no_training_12"; -"184 _tensor_constant25" -> "185 _native_batch_norm_legit_no_training_12"; -"185 _native_batch_norm_legit_no_training_12" -> "186 getitem_36"; -"185 _native_batch_norm_legit_no_training_12" -> "187 getitem_37"; -"185 _native_batch_norm_legit_no_training_12" -> "188 getitem_38"; -"186 getitem_36" -> "189 relu_12"; -"189 relu_12" -> "192 conv2d_13"; -"190 _param_constant56" -> "192 conv2d_13"; -"191 _param_constant57" -> "192 conv2d_13"; -"192 conv2d_13" -> "198 _native_batch_norm_legit_no_training_13"; -"194 _param_constant58" -> "198 _native_batch_norm_legit_no_training_13"; -"195 _param_constant59" -> "198 _native_batch_norm_legit_no_training_13"; -"196 _tensor_constant26" -> "198 _native_batch_norm_legit_no_training_13"; -"197 _tensor_constant27" -> "198 _native_batch_norm_legit_no_training_13"; -"198 _native_batch_norm_legit_no_training_13" -> "199 getitem_39"; -"198 _native_batch_norm_legit_no_training_13" -> "200 getitem_40"; -"198 _native_batch_norm_legit_no_training_13" -> "201 getitem_41"; -"199 getitem_39" -> "202 relu_13"; -"202 relu_13" -> "205 conv_transpose2d_2"; -"203 _param_constant60" -> "205 conv_transpose2d_2"; -"204 _param_constant61" -> "205 conv_transpose2d_2"; -"205 conv_transpose2d_2" -> "210 cat_2"; -"206 slice_9" -> "207 slice_10"; -"207 slice_10" -> "208 slice_11"; -"208 slice_11" -> "209 slice_12"; -"209 slice_12" -> "210 cat_2"; -"210 cat_2" -> "213 conv2d_14"; -"211 _param_constant62" -> "213 conv2d_14"; -"212 _param_constant63" -> "213 conv2d_14"; -"213 conv2d_14" -> "219 _native_batch_norm_legit_no_training_14"; -"215 _param_constant64" -> "219 _native_batch_norm_legit_no_training_14"; -"216 _param_constant65" -> "219 _native_batch_norm_legit_no_training_14"; -"217 _tensor_constant28" -> "219 _native_batch_norm_legit_no_training_14"; -"218 _tensor_constant29" -> "219 _native_batch_norm_legit_no_training_14"; -"219 _native_batch_norm_legit_no_training_14" -> "220 getitem_42"; -"219 _native_batch_norm_legit_no_training_14" -> "221 getitem_43"; -"219 _native_batch_norm_legit_no_training_14" -> "222 getitem_44"; -"220 getitem_42" -> "223 relu_14"; -"223 relu_14" -> "226 conv2d_15"; -"224 _param_constant66" -> "226 conv2d_15"; -"225 _param_constant67" -> "226 conv2d_15"; -"226 conv2d_15" -> "232 _native_batch_norm_legit_no_training_15"; -"228 _param_constant68" -> "232 _native_batch_norm_legit_no_training_15"; -"229 _param_constant69" -> "232 _native_batch_norm_legit_no_training_15"; -"230 _tensor_constant30" -> "232 _native_batch_norm_legit_no_training_15"; -"231 _tensor_constant31" -> "232 _native_batch_norm_legit_no_training_15"; -"232 _native_batch_norm_legit_no_training_15" -> "233 getitem_45"; -"232 _native_batch_norm_legit_no_training_15" -> "234 getitem_46"; -"232 _native_batch_norm_legit_no_training_15" -> "235 getitem_47"; -"233 getitem_45" -> "236 relu_15"; -"236 relu_15" -> "239 conv_transpose2d_3"; -"237 _param_constant70" -> "239 conv_transpose2d_3"; -"238 _param_constant71" -> "239 conv_transpose2d_3"; -"239 conv_transpose2d_3" -> "244 cat_3"; -"240 slice_13" -> "241 slice_14"; -"241 slice_14" -> "242 slice_15"; -"242 slice_15" -> "243 slice_16"; -"243 slice_16" -> "244 cat_3"; -"244 cat_3" -> "247 conv2d_16"; -"245 _param_constant72" -> "247 conv2d_16"; -"246 _param_constant73" -> "247 conv2d_16"; -"247 conv2d_16" -> "253 _native_batch_norm_legit_no_training_16"; -"249 _param_constant74" -> "253 _native_batch_norm_legit_no_training_16"; -"250 _param_constant75" -> "253 _native_batch_norm_legit_no_training_16"; -"251 _tensor_constant32" -> "253 _native_batch_norm_legit_no_training_16"; -"252 _tensor_constant33" -> "253 _native_batch_norm_legit_no_training_16"; -"253 _native_batch_norm_legit_no_training_16" -> "254 getitem_48"; -"253 _native_batch_norm_legit_no_training_16" -> "255 getitem_49"; -"253 _native_batch_norm_legit_no_training_16" -> "256 getitem_50"; -"254 getitem_48" -> "257 relu_16"; -"257 relu_16" -> "260 conv2d_17"; -"258 _param_constant76" -> "260 conv2d_17"; -"259 _param_constant77" -> "260 conv2d_17"; -"260 conv2d_17" -> "266 _native_batch_norm_legit_no_training_17"; -"262 _param_constant78" -> "266 _native_batch_norm_legit_no_training_17"; -"263 _param_constant79" -> "266 _native_batch_norm_legit_no_training_17"; -"264 _tensor_constant34" -> "266 _native_batch_norm_legit_no_training_17"; -"265 _tensor_constant35" -> "266 _native_batch_norm_legit_no_training_17"; -"266 _native_batch_norm_legit_no_training_17" -> "267 getitem_51"; -"266 _native_batch_norm_legit_no_training_17" -> "268 getitem_52"; -"266 _native_batch_norm_legit_no_training_17" -> "269 getitem_53"; -"267 getitem_51" -> "270 relu_17"; -"270 relu_17" -> "273 conv2d_18"; -"271 _param_constant80" -> "273 conv2d_18"; -"272 _param_constant81" -> "273 conv2d_18"; -"273 conv2d_18" -> "274 output"; +"193 batch_norm_13" [id=193, type=batch_norm]; +"194 relu_13" [id=194, type=relu]; +"195 conv_transpose2d_2" [id=195, type=conv_transpose2d]; +"196 slice_9" [id=196, type=slice]; +"197 slice_10" [id=197, type=slice]; +"198 slice_11" [id=198, type=slice]; +"199 slice_12" [id=199, type=slice]; +"200 cat_2" [id=200, type=cat]; +"201 conv2d_14" [id=201, type=conv2d]; +"202 batch_norm_14" [id=202, type=batch_norm]; +"203 relu_14" [id=203, type=relu]; +"204 conv2d_15" [id=204, type=conv2d]; +"205 batch_norm_15" [id=205, type=batch_norm]; +"206 relu_15" [id=206, type=relu]; +"207 conv_transpose2d_3" [id=207, type=conv_transpose2d]; +"208 slice_13" [id=208, type=slice]; +"209 slice_14" [id=209, type=slice]; +"210 slice_15" [id=210, type=slice]; +"211 slice_16" [id=211, type=slice]; +"212 cat_3" [id=212, type=cat]; +"213 conv2d_16" [id=213, type=conv2d]; +"214 batch_norm_16" [id=214, type=batch_norm]; +"215 relu_16" [id=215, type=relu]; +"216 conv2d_17" [id=216, type=conv2d]; +"217 batch_norm_17" [id=217, type=batch_norm]; +"218 relu_17" [id=218, type=relu]; +"219 conv2d_18" [id=219, type=conv2d]; +"220 output_1" [id=220, type=output]; +"0 down_path_0_block_0_weight" -> "137 conv2d" [label="(64, 3, 3, 3)", style=solid]; +"1 down_path_0_block_0_bias" -> "137 conv2d" [label="(64,)", style=solid]; +"2 down_path_0_block_1_weight" -> "138 batch_norm" [label="(64,)", style=solid]; +"3 down_path_0_block_1_bias" -> "138 batch_norm" [label="(64,)", style=solid]; +"4 down_path_0_block_3_weight" -> "140 conv2d_1" [label="(64, 64, 3, 3)", style=solid]; +"5 down_path_0_block_3_bias" -> "140 conv2d_1" [label="(64,)", style=solid]; +"6 down_path_0_block_4_weight" -> "141 batch_norm_1" [label="(64,)", style=solid]; +"7 down_path_0_block_4_bias" -> "141 batch_norm_1" [label="(64,)", style=solid]; +"8 down_path_1_block_0_weight" -> "144 conv2d_2" [label="(128, 64, 3, 3)", style=solid]; +"9 down_path_1_block_0_bias" -> "144 conv2d_2" [label="(128,)", style=solid]; +"10 down_path_1_block_1_weight" -> "145 batch_norm_2" [label="(128,)", style=solid]; +"11 down_path_1_block_1_bias" -> "145 batch_norm_2" [label="(128,)", style=solid]; +"12 down_path_1_block_3_weight" -> "147 conv2d_3" [label="(128, 128, 3, 3)", style=solid]; +"13 down_path_1_block_3_bias" -> "147 conv2d_3" [label="(128,)", style=solid]; +"14 down_path_1_block_4_weight" -> "148 batch_norm_3" [label="(128,)", style=solid]; +"15 down_path_1_block_4_bias" -> "148 batch_norm_3" [label="(128,)", style=solid]; +"16 down_path_2_block_0_weight" -> "151 conv2d_4" [label="(256, 128, 3, 3)", style=solid]; +"17 down_path_2_block_0_bias" -> "151 conv2d_4" [label="(256,)", style=solid]; +"18 down_path_2_block_1_weight" -> "152 batch_norm_4" [label="(256,)", style=solid]; +"19 down_path_2_block_1_bias" -> "152 batch_norm_4" [label="(256,)", style=solid]; +"20 down_path_2_block_3_weight" -> "154 conv2d_5" [label="(256, 256, 3, 3)", style=solid]; +"21 down_path_2_block_3_bias" -> "154 conv2d_5" [label="(256,)", style=solid]; +"22 down_path_2_block_4_weight" -> "155 batch_norm_5" [label="(256,)", style=solid]; +"23 down_path_2_block_4_bias" -> "155 batch_norm_5" [label="(256,)", style=solid]; +"24 down_path_3_block_0_weight" -> "158 conv2d_6" [label="(512, 256, 3, 3)", style=solid]; +"25 down_path_3_block_0_bias" -> "158 conv2d_6" [label="(512,)", style=solid]; +"26 down_path_3_block_1_weight" -> "159 batch_norm_6" [label="(512,)", style=solid]; +"27 down_path_3_block_1_bias" -> "159 batch_norm_6" [label="(512,)", style=solid]; +"28 down_path_3_block_3_weight" -> "161 conv2d_7" [label="(512, 512, 3, 3)", style=solid]; +"29 down_path_3_block_3_bias" -> "161 conv2d_7" [label="(512,)", style=solid]; +"30 down_path_3_block_4_weight" -> "162 batch_norm_7" [label="(512,)", style=solid]; +"31 down_path_3_block_4_bias" -> "162 batch_norm_7" [label="(512,)", style=solid]; +"32 down_path_4_block_0_weight" -> "165 conv2d_8" [label="(1024, 512, 3, 3)", style=solid]; +"33 down_path_4_block_0_bias" -> "165 conv2d_8" [label="(1024,)", style=solid]; +"34 down_path_4_block_1_weight" -> "166 batch_norm_8" [label="(1024,)", style=solid]; +"35 down_path_4_block_1_bias" -> "166 batch_norm_8" [label="(1024,)", style=solid]; +"36 down_path_4_block_3_weight" -> "168 conv2d_9" [label="(1024, 1024, 3, 3)", style=solid]; +"37 down_path_4_block_3_bias" -> "168 conv2d_9" [label="(1024,)", style=solid]; +"38 down_path_4_block_4_weight" -> "169 batch_norm_9" [label="(1024,)", style=solid]; +"39 down_path_4_block_4_bias" -> "169 batch_norm_9" [label="(1024,)", style=solid]; +"40 up_path_0_up_weight" -> "171 conv_transpose2d" [label="(1024, 512, 2, 2)", style=solid]; +"41 up_path_0_up_bias" -> "171 conv_transpose2d" [label="(512,)", style=solid]; +"42 up_path_0_conv_block_block_0_weight" -> "177 conv2d_10" [label="(512, 1024, 3, 3)", style=solid]; +"43 up_path_0_conv_block_block_0_bias" -> "177 conv2d_10" [label="(512,)", style=solid]; +"44 up_path_0_conv_block_block_1_weight" -> "178 batch_norm_10" [label="(512,)", style=solid]; +"45 up_path_0_conv_block_block_1_bias" -> "178 batch_norm_10" [label="(512,)", style=solid]; +"46 up_path_0_conv_block_block_3_weight" -> "180 conv2d_11" [label="(512, 512, 3, 3)", style=solid]; +"47 up_path_0_conv_block_block_3_bias" -> "180 conv2d_11" [label="(512,)", style=solid]; +"48 up_path_0_conv_block_block_4_weight" -> "181 batch_norm_11" [label="(512,)", style=solid]; +"49 up_path_0_conv_block_block_4_bias" -> "181 batch_norm_11" [label="(512,)", style=solid]; +"50 up_path_1_up_weight" -> "183 conv_transpose2d_1" [label="(512, 256, 2, 2)", style=solid]; +"51 up_path_1_up_bias" -> "183 conv_transpose2d_1" [label="(256,)", style=solid]; +"52 up_path_1_conv_block_block_0_weight" -> "189 conv2d_12" [label="(256, 512, 3, 3)", style=solid]; +"53 up_path_1_conv_block_block_0_bias" -> "189 conv2d_12" [label="(256,)", style=solid]; +"54 up_path_1_conv_block_block_1_weight" -> "190 batch_norm_12" [label="(256,)", style=solid]; +"55 up_path_1_conv_block_block_1_bias" -> "190 batch_norm_12" [label="(256,)", style=solid]; +"56 up_path_1_conv_block_block_3_weight" -> "192 conv2d_13" [label="(256, 256, 3, 3)", style=solid]; +"57 up_path_1_conv_block_block_3_bias" -> "192 conv2d_13" [label="(256,)", style=solid]; +"58 up_path_1_conv_block_block_4_weight" -> "193 batch_norm_13" [label="(256,)", style=solid]; +"59 up_path_1_conv_block_block_4_bias" -> "193 batch_norm_13" [label="(256,)", style=solid]; +"60 up_path_2_up_weight" -> "195 conv_transpose2d_2" [label="(256, 128, 2, 2)", style=solid]; +"61 up_path_2_up_bias" -> "195 conv_transpose2d_2" [label="(128,)", style=solid]; +"62 up_path_2_conv_block_block_0_weight" -> "201 conv2d_14" [label="(128, 256, 3, 3)", style=solid]; +"63 up_path_2_conv_block_block_0_bias" -> "201 conv2d_14" [label="(128,)", style=solid]; +"64 up_path_2_conv_block_block_1_weight" -> "202 batch_norm_14" [label="(128,)", style=solid]; +"65 up_path_2_conv_block_block_1_bias" -> "202 batch_norm_14" [label="(128,)", style=solid]; +"66 up_path_2_conv_block_block_3_weight" -> "204 conv2d_15" [label="(128, 128, 3, 3)", style=solid]; +"67 up_path_2_conv_block_block_3_bias" -> "204 conv2d_15" [label="(128,)", style=solid]; +"68 up_path_2_conv_block_block_4_weight" -> "205 batch_norm_15" [label="(128,)", style=solid]; +"69 up_path_2_conv_block_block_4_bias" -> "205 batch_norm_15" [label="(128,)", style=solid]; +"70 up_path_3_up_weight" -> "207 conv_transpose2d_3" [label="(128, 64, 2, 2)", style=solid]; +"71 up_path_3_up_bias" -> "207 conv_transpose2d_3" [label="(64,)", style=solid]; +"72 up_path_3_conv_block_block_0_weight" -> "213 conv2d_16" [label="(64, 128, 3, 3)", style=solid]; +"73 up_path_3_conv_block_block_0_bias" -> "213 conv2d_16" [label="(64,)", style=solid]; +"74 up_path_3_conv_block_block_1_weight" -> "214 batch_norm_16" [label="(64,)", style=solid]; +"75 up_path_3_conv_block_block_1_bias" -> "214 batch_norm_16" [label="(64,)", style=solid]; +"76 up_path_3_conv_block_block_3_weight" -> "216 conv2d_17" [label="(64, 64, 3, 3)", style=solid]; +"77 up_path_3_conv_block_block_3_bias" -> "216 conv2d_17" [label="(64,)", style=solid]; +"78 up_path_3_conv_block_block_4_weight" -> "217 batch_norm_17" [label="(64,)", style=solid]; +"79 up_path_3_conv_block_block_4_bias" -> "217 batch_norm_17" [label="(64,)", style=solid]; +"80 last_weight" -> "219 conv2d_18" [label="(12, 64, 1, 1)", style=solid]; +"81 last_bias" -> "219 conv2d_18" [label="(12,)", style=solid]; +"82 down_path_0_block_1_running_mean" -> "138 batch_norm" [label="(64,)", style=solid]; +"83 down_path_0_block_1_running_var" -> "138 batch_norm" [label="(64,)", style=solid]; +"85 down_path_0_block_4_running_mean" -> "141 batch_norm_1" [label="(64,)", style=solid]; +"86 down_path_0_block_4_running_var" -> "141 batch_norm_1" [label="(64,)", style=solid]; +"88 down_path_1_block_1_running_mean" -> "145 batch_norm_2" [label="(128,)", style=solid]; +"89 down_path_1_block_1_running_var" -> "145 batch_norm_2" [label="(128,)", style=solid]; +"91 down_path_1_block_4_running_mean" -> "148 batch_norm_3" [label="(128,)", style=solid]; +"92 down_path_1_block_4_running_var" -> "148 batch_norm_3" [label="(128,)", style=solid]; +"94 down_path_2_block_1_running_mean" -> "152 batch_norm_4" [label="(256,)", style=solid]; +"95 down_path_2_block_1_running_var" -> "152 batch_norm_4" [label="(256,)", style=solid]; +"97 down_path_2_block_4_running_mean" -> "155 batch_norm_5" [label="(256,)", style=solid]; +"98 down_path_2_block_4_running_var" -> "155 batch_norm_5" [label="(256,)", style=solid]; +"100 down_path_3_block_1_running_mean" -> "159 batch_norm_6" [label="(512,)", style=solid]; +"101 down_path_3_block_1_running_var" -> "159 batch_norm_6" [label="(512,)", style=solid]; +"103 down_path_3_block_4_running_mean" -> "162 batch_norm_7" [label="(512,)", style=solid]; +"104 down_path_3_block_4_running_var" -> "162 batch_norm_7" [label="(512,)", style=solid]; +"106 down_path_4_block_1_running_mean" -> "166 batch_norm_8" [label="(1024,)", style=solid]; +"107 down_path_4_block_1_running_var" -> "166 batch_norm_8" [label="(1024,)", style=solid]; +"109 down_path_4_block_4_running_mean" -> "169 batch_norm_9" [label="(1024,)", style=solid]; +"110 down_path_4_block_4_running_var" -> "169 batch_norm_9" [label="(1024,)", style=solid]; +"112 up_path_0_conv_block_block_1_running_mean" -> "178 batch_norm_10" [label="(512,)", style=solid]; +"113 up_path_0_conv_block_block_1_running_var" -> "178 batch_norm_10" [label="(512,)", style=solid]; +"115 up_path_0_conv_block_block_4_running_mean" -> "181 batch_norm_11" [label="(512,)", style=solid]; +"116 up_path_0_conv_block_block_4_running_var" -> "181 batch_norm_11" [label="(512,)", style=solid]; +"118 up_path_1_conv_block_block_1_running_mean" -> "190 batch_norm_12" [label="(256,)", style=solid]; +"119 up_path_1_conv_block_block_1_running_var" -> "190 batch_norm_12" [label="(256,)", style=solid]; +"121 up_path_1_conv_block_block_4_running_mean" -> "193 batch_norm_13" [label="(256,)", style=solid]; +"122 up_path_1_conv_block_block_4_running_var" -> "193 batch_norm_13" [label="(256,)", style=solid]; +"124 up_path_2_conv_block_block_1_running_mean" -> "202 batch_norm_14" [label="(128,)", style=solid]; +"125 up_path_2_conv_block_block_1_running_var" -> "202 batch_norm_14" [label="(128,)", style=solid]; +"127 up_path_2_conv_block_block_4_running_mean" -> "205 batch_norm_15" [label="(128,)", style=solid]; +"128 up_path_2_conv_block_block_4_running_var" -> "205 batch_norm_15" [label="(128,)", style=solid]; +"130 up_path_3_conv_block_block_1_running_mean" -> "214 batch_norm_16" [label="(64,)", style=solid]; +"131 up_path_3_conv_block_block_1_running_var" -> "214 batch_norm_16" [label="(64,)", style=solid]; +"133 up_path_3_conv_block_block_4_running_mean" -> "217 batch_norm_17" [label="(64,)", style=solid]; +"134 up_path_3_conv_block_block_4_running_var" -> "217 batch_norm_17" [label="(64,)", style=solid]; +"136 x" -> "137 conv2d" [label="(1, 3, 224, 224)", style=solid]; +"137 conv2d" -> "138 batch_norm" [label="(1, 64, 222, 222)", style=solid]; +"138 batch_norm" -> "139 relu" [label="(1, 64, 222, 222)", style=solid]; +"139 relu" -> "140 conv2d_1" [label="(1, 64, 222, 222)", style=solid]; +"140 conv2d_1" -> "141 batch_norm_1" [label="(1, 64, 220, 220)", style=solid]; +"141 batch_norm_1" -> "142 relu_1" [label="(1, 64, 220, 220)", style=solid]; +"142 relu_1" -> "143 max_pool2d" [label="(1, 64, 220, 220)", style=solid]; +"142 relu_1" -> "208 slice_13" [label="(1, 64, 220, 220)", style=solid]; +"143 max_pool2d" -> "144 conv2d_2" [label="(1, 64, 110, 110)", style=solid]; +"144 conv2d_2" -> "145 batch_norm_2" [label="(1, 128, 108, 108)", style=solid]; +"145 batch_norm_2" -> "146 relu_2" [label="(1, 128, 108, 108)", style=solid]; +"146 relu_2" -> "147 conv2d_3" [label="(1, 128, 108, 108)", style=solid]; +"147 conv2d_3" -> "148 batch_norm_3" [label="(1, 128, 106, 106)", style=solid]; +"148 batch_norm_3" -> "149 relu_3" [label="(1, 128, 106, 106)", style=solid]; +"149 relu_3" -> "150 max_pool2d_1" [label="(1, 128, 106, 106)", style=solid]; +"149 relu_3" -> "196 slice_9" [label="(1, 128, 106, 106)", style=solid]; +"150 max_pool2d_1" -> "151 conv2d_4" [label="(1, 128, 53, 53)", style=solid]; +"151 conv2d_4" -> "152 batch_norm_4" [label="(1, 256, 51, 51)", style=solid]; +"152 batch_norm_4" -> "153 relu_4" [label="(1, 256, 51, 51)", style=solid]; +"153 relu_4" -> "154 conv2d_5" [label="(1, 256, 51, 51)", style=solid]; +"154 conv2d_5" -> "155 batch_norm_5" [label="(1, 256, 49, 49)", style=solid]; +"155 batch_norm_5" -> "156 relu_5" [label="(1, 256, 49, 49)", style=solid]; +"156 relu_5" -> "157 max_pool2d_2" [label="(1, 256, 49, 49)", style=solid]; +"156 relu_5" -> "184 slice_5" [label="(1, 256, 49, 49)", style=solid]; +"157 max_pool2d_2" -> "158 conv2d_6" [label="(1, 256, 24, 24)", style=solid]; +"158 conv2d_6" -> "159 batch_norm_6" [label="(1, 512, 22, 22)", style=solid]; +"159 batch_norm_6" -> "160 relu_6" [label="(1, 512, 22, 22)", style=solid]; +"160 relu_6" -> "161 conv2d_7" [label="(1, 512, 22, 22)", style=solid]; +"161 conv2d_7" -> "162 batch_norm_7" [label="(1, 512, 20, 20)", style=solid]; +"162 batch_norm_7" -> "163 relu_7" [label="(1, 512, 20, 20)", style=solid]; +"163 relu_7" -> "164 max_pool2d_3" [label="(1, 512, 20, 20)", style=solid]; +"163 relu_7" -> "172 slice_1" [label="(1, 512, 20, 20)", style=solid]; +"164 max_pool2d_3" -> "165 conv2d_8" [label="(1, 512, 10, 10)", style=solid]; +"165 conv2d_8" -> "166 batch_norm_8" [label="(1, 1024, 8, 8)", style=solid]; +"166 batch_norm_8" -> "167 relu_8" [label="(1, 1024, 8, 8)", style=solid]; +"167 relu_8" -> "168 conv2d_9" [label="(1, 1024, 8, 8)", style=solid]; +"168 conv2d_9" -> "169 batch_norm_9" [label="(1, 1024, 6, 6)", style=solid]; +"169 batch_norm_9" -> "170 relu_9" [label="(1, 1024, 6, 6)", style=solid]; +"170 relu_9" -> "171 conv_transpose2d" [label="(1, 1024, 6, 6)", style=solid]; +"171 conv_transpose2d" -> "176 cat" [label="(1, 512, 12, 12)", style=solid]; +"172 slice_1" -> "173 slice_2" [label="(1, 512, 20, 20)", style=solid]; +"173 slice_2" -> "174 slice_3" [label="(1, 512, 20, 20)", style=solid]; +"174 slice_3" -> "175 slice_4" [label="(1, 512, 12, 20)", style=solid]; +"175 slice_4" -> "176 cat" [label="(1, 512, 12, 12)", style=solid]; +"176 cat" -> "177 conv2d_10" [label="(1, 1024, 12, 12)", style=solid]; +"177 conv2d_10" -> "178 batch_norm_10" [label="(1, 512, 10, 10)", style=solid]; +"178 batch_norm_10" -> "179 relu_10" [label="(1, 512, 10, 10)", style=solid]; +"179 relu_10" -> "180 conv2d_11" [label="(1, 512, 10, 10)", style=solid]; +"180 conv2d_11" -> "181 batch_norm_11" [label="(1, 512, 8, 8)", style=solid]; +"181 batch_norm_11" -> "182 relu_11" [label="(1, 512, 8, 8)", style=solid]; +"182 relu_11" -> "183 conv_transpose2d_1" [label="(1, 512, 8, 8)", style=solid]; +"183 conv_transpose2d_1" -> "188 cat_1" [label="(1, 256, 16, 16)", style=solid]; +"184 slice_5" -> "185 slice_6" [label="(1, 256, 49, 49)", style=solid]; +"185 slice_6" -> "186 slice_7" [label="(1, 256, 49, 49)", style=solid]; +"186 slice_7" -> "187 slice_8" [label="(1, 256, 16, 49)", style=solid]; +"187 slice_8" -> "188 cat_1" [label="(1, 256, 16, 16)", style=solid]; +"188 cat_1" -> "189 conv2d_12" [label="(1, 512, 16, 16)", style=solid]; +"189 conv2d_12" -> "190 batch_norm_12" [label="(1, 256, 14, 14)", style=solid]; +"190 batch_norm_12" -> "191 relu_12" [label="(1, 256, 14, 14)", style=solid]; +"191 relu_12" -> "192 conv2d_13" [label="(1, 256, 14, 14)", style=solid]; +"192 conv2d_13" -> "193 batch_norm_13" [label="(1, 256, 12, 12)", style=solid]; +"193 batch_norm_13" -> "194 relu_13" [label="(1, 256, 12, 12)", style=solid]; +"194 relu_13" -> "195 conv_transpose2d_2" [label="(1, 256, 12, 12)", style=solid]; +"195 conv_transpose2d_2" -> "200 cat_2" [label="(1, 128, 24, 24)", style=solid]; +"196 slice_9" -> "197 slice_10" [label="(1, 128, 106, 106)", style=solid]; +"197 slice_10" -> "198 slice_11" [label="(1, 128, 106, 106)", style=solid]; +"198 slice_11" -> "199 slice_12" [label="(1, 128, 24, 106)", style=solid]; +"199 slice_12" -> "200 cat_2" [label="(1, 128, 24, 24)", style=solid]; +"200 cat_2" -> "201 conv2d_14" [label="(1, 256, 24, 24)", style=solid]; +"201 conv2d_14" -> "202 batch_norm_14" [label="(1, 128, 22, 22)", style=solid]; +"202 batch_norm_14" -> "203 relu_14" [label="(1, 128, 22, 22)", style=solid]; +"203 relu_14" -> "204 conv2d_15" [label="(1, 128, 22, 22)", style=solid]; +"204 conv2d_15" -> "205 batch_norm_15" [label="(1, 128, 20, 20)", style=solid]; +"205 batch_norm_15" -> "206 relu_15" [label="(1, 128, 20, 20)", style=solid]; +"206 relu_15" -> "207 conv_transpose2d_3" [label="(1, 128, 20, 20)", style=solid]; +"207 conv_transpose2d_3" -> "212 cat_3" [label="(1, 64, 40, 40)", style=solid]; +"208 slice_13" -> "209 slice_14" [label="(1, 64, 220, 220)", style=solid]; +"209 slice_14" -> "210 slice_15" [label="(1, 64, 220, 220)", style=solid]; +"210 slice_15" -> "211 slice_16" [label="(1, 64, 40, 220)", style=solid]; +"211 slice_16" -> "212 cat_3" [label="(1, 64, 40, 40)", style=solid]; +"212 cat_3" -> "213 conv2d_16" [label="(1, 128, 40, 40)", style=solid]; +"213 conv2d_16" -> "214 batch_norm_16" [label="(1, 64, 38, 38)", style=solid]; +"214 batch_norm_16" -> "215 relu_16" [label="(1, 64, 38, 38)", style=solid]; +"215 relu_16" -> "216 conv2d_17" [label="(1, 64, 38, 38)", style=solid]; +"216 conv2d_17" -> "217 batch_norm_17" [label="(1, 64, 36, 36)", style=solid]; +"217 batch_norm_17" -> "218 relu_17" [label="(1, 64, 36, 36)", style=solid]; +"218 relu_17" -> "219 conv2d_18" [label="(1, 64, 36, 36)", style=solid]; +"219 conv2d_18" -> "220 output_1" [label="(1, 12, 36, 36)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/fx/vit_b_16.dot b/tests/torch/data/reference_graphs/fx/vit_b_16.dot index 70e71d5e8ce..b29787ce0c4 100644 --- a/tests/torch/data/reference_graphs/fx/vit_b_16.dot +++ b/tests/torch/data/reference_graphs/fx/vit_b_16.dot @@ -1,1219 +1,1219 @@ strict digraph { -"0 arg0_1" [id=0, type=input]; -"1 _param_constant0" [id=1, type=get_attr]; -"2 _param_constant1" [id=2, type=get_attr]; -"3 conv2d" [id=3, type=conv2d]; -"4 reshape" [id=4, type=reshape]; -"5 permute" [id=5, type=permute]; -"6 _param_constant2" [id=6, type=get_attr]; -"7 expand" [id=7, type=expand]; -"8 cat" [id=8, type=cat]; -"9 _param_constant3" [id=9, type=get_attr]; -"10 add" [id=10, type=add]; -"11 dropout" [id=11, type=dropout]; -"12 _param_constant4" [id=12, type=get_attr]; -"13 _param_constant5" [id=13, type=get_attr]; -"14 layer_norm" [id=14, type=layer_norm]; -"15 transpose" [id=15, type=transpose]; -"16 _param_constant6" [id=16, type=get_attr]; -"17 _param_constant7" [id=17, type=get_attr]; -"18 linear" [id=18, type=linear]; -"19 unflatten" [id=19, type=unflatten]; -"20 unsqueeze" [id=20, type=unsqueeze]; -"21 transpose_1" [id=21, type=transpose]; -"22 squeeze" [id=22, type=squeeze]; -"23 contiguous" [id=23, type=contiguous]; -"24 select" [id=24, type=select]; -"25 select_1" [id=25, type=select]; -"26 select_2" [id=26, type=select]; -"27 view" [id=27, type=view]; -"28 transpose_2" [id=28, type=transpose]; -"29 view_1" [id=29, type=view]; -"30 transpose_3" [id=30, type=transpose]; -"31 view_2" [id=31, type=view]; -"32 transpose_4" [id=32, type=transpose]; -"33 view_3" [id=33, type=view]; -"34 view_4" [id=34, type=view]; -"35 view_5" [id=35, type=view]; -"36 scaled_dot_product_attention" [id=36, type=scaled_dot_product_attention]; -"37 permute_1" [id=37, type=permute]; -"38 view_6" [id=38, type=view]; -"39 _param_constant8" [id=39, type=get_attr]; -"40 _param_constant9" [id=40, type=get_attr]; -"41 linear_1" [id=41, type=linear]; -"42 view_7" [id=42, type=view]; -"43 transpose_5" [id=43, type=transpose]; -"44 dropout_1" [id=44, type=dropout]; -"45 add_1" [id=45, type=add]; -"46 _param_constant10" [id=46, type=get_attr]; -"47 _param_constant11" [id=47, type=get_attr]; -"48 layer_norm_1" [id=48, type=layer_norm]; -"49 _param_constant12" [id=49, type=get_attr]; -"50 _param_constant13" [id=50, type=get_attr]; -"51 linear_2" [id=51, type=linear]; -"52 gelu" [id=52, type=gelu]; -"53 dropout_2" [id=53, type=dropout]; -"54 _param_constant14" [id=54, type=get_attr]; -"55 _param_constant15" [id=55, type=get_attr]; -"56 linear_3" [id=56, type=linear]; -"57 dropout_3" [id=57, type=dropout]; -"58 add_2" [id=58, type=add]; -"59 _param_constant16" [id=59, type=get_attr]; -"60 _param_constant17" [id=60, type=get_attr]; -"61 layer_norm_2" [id=61, type=layer_norm]; -"62 transpose_6" [id=62, type=transpose]; -"63 _param_constant18" [id=63, type=get_attr]; -"64 _param_constant19" [id=64, type=get_attr]; -"65 linear_4" [id=65, type=linear]; -"66 unflatten_1" [id=66, type=unflatten]; -"67 unsqueeze_1" [id=67, type=unsqueeze]; -"68 transpose_7" [id=68, type=transpose]; -"69 squeeze_1" [id=69, type=squeeze]; -"70 contiguous_1" [id=70, type=contiguous]; -"71 select_3" [id=71, type=select]; -"72 select_4" [id=72, type=select]; -"73 select_5" [id=73, type=select]; -"74 view_8" [id=74, type=view]; -"75 transpose_8" [id=75, type=transpose]; -"76 view_9" [id=76, type=view]; -"77 transpose_9" [id=77, type=transpose]; -"78 view_10" [id=78, type=view]; -"79 transpose_10" [id=79, type=transpose]; -"80 view_11" [id=80, type=view]; -"81 view_12" [id=81, type=view]; -"82 view_13" [id=82, type=view]; -"83 scaled_dot_product_attention_1" [id=83, type=scaled_dot_product_attention]; -"84 permute_2" [id=84, type=permute]; -"85 view_14" [id=85, type=view]; -"86 _param_constant20" [id=86, type=get_attr]; -"87 _param_constant21" [id=87, type=get_attr]; -"88 linear_5" [id=88, type=linear]; -"89 view_15" [id=89, type=view]; -"90 transpose_11" [id=90, type=transpose]; -"91 dropout_4" [id=91, type=dropout]; -"92 add_3" [id=92, type=add]; -"93 _param_constant22" [id=93, type=get_attr]; -"94 _param_constant23" [id=94, type=get_attr]; -"95 layer_norm_3" [id=95, type=layer_norm]; -"96 _param_constant24" [id=96, type=get_attr]; -"97 _param_constant25" [id=97, type=get_attr]; -"98 linear_6" [id=98, type=linear]; -"99 gelu_1" [id=99, type=gelu]; -"100 dropout_5" [id=100, type=dropout]; -"101 _param_constant26" [id=101, type=get_attr]; -"102 _param_constant27" [id=102, type=get_attr]; -"103 linear_7" [id=103, type=linear]; -"104 dropout_6" [id=104, type=dropout]; -"105 add_4" [id=105, type=add]; -"106 _param_constant28" [id=106, type=get_attr]; -"107 _param_constant29" [id=107, type=get_attr]; -"108 layer_norm_4" [id=108, type=layer_norm]; -"109 transpose_12" [id=109, type=transpose]; -"110 _param_constant30" [id=110, type=get_attr]; -"111 _param_constant31" [id=111, type=get_attr]; -"112 linear_8" [id=112, type=linear]; -"113 unflatten_2" [id=113, type=unflatten]; -"114 unsqueeze_2" [id=114, type=unsqueeze]; -"115 transpose_13" [id=115, type=transpose]; -"116 squeeze_2" [id=116, type=squeeze]; -"117 contiguous_2" [id=117, type=contiguous]; -"118 select_6" [id=118, type=select]; -"119 select_7" [id=119, type=select]; -"120 select_8" [id=120, type=select]; -"121 view_16" [id=121, type=view]; -"122 transpose_14" [id=122, type=transpose]; -"123 view_17" [id=123, type=view]; -"124 transpose_15" [id=124, type=transpose]; -"125 view_18" [id=125, type=view]; -"126 transpose_16" [id=126, type=transpose]; -"127 view_19" [id=127, type=view]; -"128 view_20" [id=128, type=view]; -"129 view_21" [id=129, type=view]; -"130 scaled_dot_product_attention_2" [id=130, type=scaled_dot_product_attention]; -"131 permute_3" [id=131, type=permute]; -"132 view_22" [id=132, type=view]; -"133 _param_constant32" [id=133, type=get_attr]; -"134 _param_constant33" [id=134, type=get_attr]; -"135 linear_9" [id=135, type=linear]; -"136 view_23" [id=136, type=view]; -"137 transpose_17" [id=137, type=transpose]; -"138 dropout_7" [id=138, type=dropout]; -"139 add_5" [id=139, type=add]; -"140 _param_constant34" [id=140, type=get_attr]; -"141 _param_constant35" [id=141, type=get_attr]; -"142 layer_norm_5" [id=142, type=layer_norm]; -"143 _param_constant36" [id=143, type=get_attr]; -"144 _param_constant37" [id=144, type=get_attr]; -"145 linear_10" [id=145, type=linear]; -"146 gelu_2" [id=146, type=gelu]; -"147 dropout_8" [id=147, type=dropout]; -"148 _param_constant38" [id=148, type=get_attr]; -"149 _param_constant39" [id=149, type=get_attr]; -"150 linear_11" [id=150, type=linear]; -"151 dropout_9" [id=151, type=dropout]; -"152 add_6" [id=152, type=add]; -"153 _param_constant40" [id=153, type=get_attr]; -"154 _param_constant41" [id=154, type=get_attr]; -"155 layer_norm_6" [id=155, type=layer_norm]; -"156 transpose_18" [id=156, type=transpose]; -"157 _param_constant42" [id=157, type=get_attr]; -"158 _param_constant43" [id=158, type=get_attr]; -"159 linear_12" [id=159, type=linear]; -"160 unflatten_3" [id=160, type=unflatten]; -"161 unsqueeze_3" [id=161, type=unsqueeze]; -"162 transpose_19" [id=162, type=transpose]; -"163 squeeze_3" [id=163, type=squeeze]; -"164 contiguous_3" [id=164, type=contiguous]; -"165 select_9" [id=165, type=select]; -"166 select_10" [id=166, type=select]; -"167 select_11" [id=167, type=select]; -"168 view_24" [id=168, type=view]; -"169 transpose_20" [id=169, type=transpose]; -"170 view_25" [id=170, type=view]; -"171 transpose_21" [id=171, type=transpose]; -"172 view_26" [id=172, type=view]; -"173 transpose_22" [id=173, type=transpose]; -"174 view_27" [id=174, type=view]; -"175 view_28" [id=175, type=view]; -"176 view_29" [id=176, type=view]; -"177 scaled_dot_product_attention_3" [id=177, type=scaled_dot_product_attention]; -"178 permute_4" [id=178, type=permute]; -"179 view_30" [id=179, type=view]; -"180 _param_constant44" [id=180, type=get_attr]; -"181 _param_constant45" [id=181, type=get_attr]; -"182 linear_13" [id=182, type=linear]; -"183 view_31" [id=183, type=view]; -"184 transpose_23" [id=184, type=transpose]; -"185 dropout_10" [id=185, type=dropout]; -"186 add_7" [id=186, type=add]; -"187 _param_constant46" [id=187, type=get_attr]; -"188 _param_constant47" [id=188, type=get_attr]; -"189 layer_norm_7" [id=189, type=layer_norm]; -"190 _param_constant48" [id=190, type=get_attr]; -"191 _param_constant49" [id=191, type=get_attr]; -"192 linear_14" [id=192, type=linear]; -"193 gelu_3" [id=193, type=gelu]; -"194 dropout_11" [id=194, type=dropout]; -"195 _param_constant50" [id=195, type=get_attr]; -"196 _param_constant51" [id=196, type=get_attr]; -"197 linear_15" [id=197, type=linear]; -"198 dropout_12" [id=198, type=dropout]; -"199 add_8" [id=199, type=add]; -"200 _param_constant52" [id=200, type=get_attr]; -"201 _param_constant53" [id=201, type=get_attr]; -"202 layer_norm_8" [id=202, type=layer_norm]; -"203 transpose_24" [id=203, type=transpose]; -"204 _param_constant54" [id=204, type=get_attr]; -"205 _param_constant55" [id=205, type=get_attr]; -"206 linear_16" [id=206, type=linear]; -"207 unflatten_4" [id=207, type=unflatten]; -"208 unsqueeze_4" [id=208, type=unsqueeze]; -"209 transpose_25" [id=209, type=transpose]; -"210 squeeze_4" [id=210, type=squeeze]; -"211 contiguous_4" [id=211, type=contiguous]; -"212 select_12" [id=212, type=select]; -"213 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type=transpose]; +"422 view_59" [id=422, type=view]; +"423 view_60" [id=423, type=view]; +"424 view_61" [id=424, type=view]; +"425 scaled_dot_product_attention_7" [id=425, type=scaled_dot_product_attention]; +"426 permute_8" [id=426, type=permute]; +"427 view_62" [id=427, type=view]; +"428 linear_29" [id=428, type=linear]; +"429 view_63" [id=429, type=view]; +"430 transpose_47" [id=430, type=transpose]; +"431 dropout_22" [id=431, type=dropout]; +"432 add_15" [id=432, type=add]; +"433 layer_norm_15" [id=433, type=layer_norm]; +"434 linear_30" [id=434, type=linear]; +"435 gelu_7" [id=435, type=gelu]; +"436 dropout_23" [id=436, type=dropout]; +"437 linear_31" [id=437, type=linear]; +"438 dropout_24" [id=438, type=dropout]; +"439 add_16" [id=439, type=add]; +"440 layer_norm_16" [id=440, type=layer_norm]; +"441 transpose_48" [id=441, type=transpose]; +"442 linear_32" [id=442, type=linear]; +"443 unflatten_8" [id=443, type=unflatten]; +"444 unsqueeze_8" [id=444, type=unsqueeze]; +"445 transpose_49" [id=445, type=transpose]; +"446 squeeze_8" [id=446, type=squeeze]; +"447 contiguous_8" [id=447, type=contiguous]; +"448 select_24" [id=448, type=select]; +"449 select_25" [id=449, type=select]; +"450 select_26" [id=450, type=select]; +"451 view_64" [id=451, type=view]; +"452 transpose_50" [id=452, type=transpose]; +"453 view_65" [id=453, type=view]; +"454 transpose_51" [id=454, type=transpose]; +"455 view_66" [id=455, type=view]; +"456 transpose_52" [id=456, type=transpose]; +"457 view_67" [id=457, type=view]; +"458 view_68" [id=458, type=view]; +"459 view_69" [id=459, type=view]; +"460 scaled_dot_product_attention_8" [id=460, type=scaled_dot_product_attention]; +"461 permute_9" [id=461, type=permute]; +"462 view_70" [id=462, type=view]; +"463 linear_33" [id=463, type=linear]; +"464 view_71" [id=464, type=view]; +"465 transpose_53" [id=465, type=transpose]; +"466 dropout_25" [id=466, type=dropout]; +"467 add_17" [id=467, type=add]; +"468 layer_norm_17" [id=468, type=layer_norm]; +"469 linear_34" [id=469, type=linear]; +"470 gelu_8" [id=470, type=gelu]; +"471 dropout_26" [id=471, type=dropout]; +"472 linear_35" [id=472, type=linear]; +"473 dropout_27" [id=473, type=dropout]; +"474 add_18" [id=474, type=add]; +"475 layer_norm_18" [id=475, type=layer_norm]; +"476 transpose_54" [id=476, type=transpose]; +"477 linear_36" [id=477, type=linear]; +"478 unflatten_9" [id=478, type=unflatten]; +"479 unsqueeze_9" [id=479, type=unsqueeze]; +"480 transpose_55" [id=480, type=transpose]; +"481 squeeze_9" [id=481, type=squeeze]; +"482 contiguous_9" [id=482, type=contiguous]; +"483 select_27" [id=483, type=select]; +"484 select_28" [id=484, type=select]; +"485 select_29" [id=485, type=select]; +"486 view_72" [id=486, type=view]; +"487 transpose_56" [id=487, type=transpose]; +"488 view_73" [id=488, type=view]; +"489 transpose_57" [id=489, type=transpose]; +"490 view_74" [id=490, type=view]; +"491 transpose_58" [id=491, type=transpose]; +"492 view_75" [id=492, type=view]; +"493 view_76" [id=493, type=view]; +"494 view_77" [id=494, type=view]; +"495 scaled_dot_product_attention_9" [id=495, type=scaled_dot_product_attention]; +"496 permute_10" [id=496, type=permute]; +"497 view_78" [id=497, type=view]; +"498 linear_37" [id=498, type=linear]; +"499 view_79" [id=499, type=view]; +"500 transpose_59" [id=500, type=transpose]; +"501 dropout_28" [id=501, type=dropout]; +"502 add_19" [id=502, type=add]; +"503 layer_norm_19" [id=503, type=layer_norm]; +"504 linear_38" [id=504, type=linear]; +"505 gelu_9" [id=505, type=gelu]; +"506 dropout_29" [id=506, type=dropout]; +"507 linear_39" [id=507, type=linear]; +"508 dropout_30" [id=508, type=dropout]; +"509 add_20" [id=509, type=add]; +"510 layer_norm_20" [id=510, type=layer_norm]; +"511 transpose_60" [id=511, type=transpose]; +"512 linear_40" [id=512, type=linear]; +"513 unflatten_10" [id=513, type=unflatten]; +"514 unsqueeze_10" [id=514, type=unsqueeze]; +"515 transpose_61" [id=515, type=transpose]; +"516 squeeze_10" [id=516, type=squeeze]; +"517 contiguous_10" [id=517, type=contiguous]; +"518 select_30" [id=518, type=select]; +"519 select_31" [id=519, type=select]; +"520 select_32" [id=520, type=select]; +"521 view_80" [id=521, type=view]; +"522 transpose_62" [id=522, type=transpose]; +"523 view_81" [id=523, type=view]; +"524 transpose_63" [id=524, type=transpose]; +"525 view_82" [id=525, type=view]; +"526 transpose_64" [id=526, type=transpose]; +"527 view_83" [id=527, type=view]; +"528 view_84" [id=528, type=view]; +"529 view_85" [id=529, type=view]; +"530 scaled_dot_product_attention_10" [id=530, type=scaled_dot_product_attention]; +"531 permute_11" [id=531, type=permute]; +"532 view_86" [id=532, type=view]; +"533 linear_41" [id=533, type=linear]; +"534 view_87" [id=534, type=view]; +"535 transpose_65" [id=535, type=transpose]; +"536 dropout_31" [id=536, type=dropout]; +"537 add_21" [id=537, type=add]; +"538 layer_norm_21" [id=538, type=layer_norm]; +"539 linear_42" [id=539, type=linear]; +"540 gelu_10" [id=540, type=gelu]; +"541 dropout_32" [id=541, type=dropout]; +"542 linear_43" [id=542, type=linear]; +"543 dropout_33" [id=543, type=dropout]; +"544 add_22" [id=544, type=add]; +"545 layer_norm_22" [id=545, type=layer_norm]; +"546 transpose_66" [id=546, type=transpose]; +"547 linear_44" [id=547, type=linear]; +"548 unflatten_11" [id=548, type=unflatten]; +"549 unsqueeze_11" [id=549, type=unsqueeze]; +"550 transpose_67" [id=550, type=transpose]; +"551 squeeze_11" [id=551, type=squeeze]; +"552 contiguous_11" [id=552, type=contiguous]; +"553 select_33" [id=553, type=select]; +"554 select_34" [id=554, type=select]; +"555 select_35" [id=555, type=select]; +"556 view_88" [id=556, type=view]; +"557 transpose_68" [id=557, type=transpose]; +"558 view_89" [id=558, type=view]; +"559 transpose_69" [id=559, type=transpose]; +"560 view_90" [id=560, type=view]; +"561 transpose_70" [id=561, type=transpose]; +"562 view_91" [id=562, type=view]; +"563 view_92" [id=563, type=view]; +"564 view_93" [id=564, type=view]; +"565 scaled_dot_product_attention_11" [id=565, type=scaled_dot_product_attention]; +"566 permute_12" [id=566, type=permute]; +"567 view_94" [id=567, type=view]; +"568 linear_45" [id=568, type=linear]; +"569 view_95" [id=569, type=view]; +"570 transpose_71" [id=570, type=transpose]; +"571 dropout_34" [id=571, type=dropout]; +"572 add_23" [id=572, type=add]; +"573 layer_norm_23" [id=573, type=layer_norm]; +"574 linear_46" [id=574, type=linear]; +"575 gelu_11" [id=575, type=gelu]; +"576 dropout_35" [id=576, type=dropout]; +"577 linear_47" [id=577, type=linear]; +"578 dropout_36" [id=578, type=dropout]; +"579 add_24" [id=579, type=add]; +"580 layer_norm_24" [id=580, type=layer_norm]; +"581 slice_1" [id=581, type=slice]; +"582 select_36" [id=582, type=select]; "583 linear_48" [id=583, type=linear]; -"584 output" [id=584, type=output]; -"0 arg0_1" -> "3 conv2d"; -"1 _param_constant0" -> "3 conv2d"; -"2 _param_constant1" -> "3 conv2d"; -"3 conv2d" -> "4 reshape"; -"4 reshape" -> "5 permute"; -"5 permute" -> "8 cat"; -"6 _param_constant2" -> "7 expand"; -"7 expand" -> "8 cat"; -"8 cat" -> "10 add"; -"9 _param_constant3" -> "10 add"; -"10 add" -> "11 dropout"; -"11 dropout" -> "14 layer_norm"; -"11 dropout" -> "45 add_1"; -"12 _param_constant4" -> "14 layer_norm"; -"13 _param_constant5" -> "14 layer_norm"; -"14 layer_norm" -> "15 transpose"; -"15 transpose" -> "18 linear"; -"16 _param_constant6" -> "18 linear"; -"17 _param_constant7" -> "18 linear"; -"18 linear" -> "19 unflatten"; -"19 unflatten" -> "20 unsqueeze"; -"20 unsqueeze" -> "21 transpose_1"; -"21 transpose_1" -> "22 squeeze"; -"22 squeeze" -> "23 contiguous"; -"23 contiguous" -> "24 select"; -"23 contiguous" -> "25 select_1"; -"23 contiguous" -> "26 select_2"; -"24 select" -> "27 view"; -"25 select_1" -> "29 view_1"; -"26 select_2" -> "31 view_2"; -"27 view" -> "28 transpose_2"; -"28 transpose_2" -> "33 view_3"; -"29 view_1" -> "30 transpose_3"; -"30 transpose_3" -> "34 view_4"; -"31 view_2" -> "32 transpose_4"; -"32 transpose_4" -> "35 view_5"; -"33 view_3" -> "36 scaled_dot_product_attention"; -"34 view_4" -> "36 scaled_dot_product_attention"; -"35 view_5" -> "36 scaled_dot_product_attention"; -"36 scaled_dot_product_attention" -> "37 permute_1"; -"37 permute_1" -> "38 view_6"; -"38 view_6" -> "41 linear_1"; -"39 _param_constant8" -> "41 linear_1"; -"40 _param_constant9" -> "41 linear_1"; -"41 linear_1" -> "42 view_7"; -"42 view_7" -> "43 transpose_5"; -"43 transpose_5" -> "44 dropout_1"; -"44 dropout_1" -> "45 add_1"; -"45 add_1" -> "48 layer_norm_1"; -"45 add_1" -> "58 add_2"; -"46 _param_constant10" -> "48 layer_norm_1"; -"47 _param_constant11" -> "48 layer_norm_1"; -"48 layer_norm_1" -> "51 linear_2"; -"49 _param_constant12" -> "51 linear_2"; -"50 _param_constant13" -> "51 linear_2"; -"51 linear_2" -> "52 gelu"; -"52 gelu" -> "53 dropout_2"; -"53 dropout_2" -> "56 linear_3"; -"54 _param_constant14" -> "56 linear_3"; -"55 _param_constant15" -> "56 linear_3"; -"56 linear_3" -> "57 dropout_3"; -"57 dropout_3" -> "58 add_2"; -"58 add_2" -> "61 layer_norm_2"; -"58 add_2" -> "92 add_3"; -"59 _param_constant16" -> "61 layer_norm_2"; -"60 _param_constant17" -> "61 layer_norm_2"; -"61 layer_norm_2" -> "62 transpose_6"; -"62 transpose_6" -> "65 linear_4"; -"63 _param_constant18" -> "65 linear_4"; -"64 _param_constant19" -> "65 linear_4"; -"65 linear_4" -> "66 unflatten_1"; -"66 unflatten_1" -> "67 unsqueeze_1"; -"67 unsqueeze_1" -> "68 transpose_7"; -"68 transpose_7" -> "69 squeeze_1"; -"69 squeeze_1" -> "70 contiguous_1"; -"70 contiguous_1" -> "71 select_3"; -"70 contiguous_1" -> "72 select_4"; -"70 contiguous_1" -> "73 select_5"; -"71 select_3" -> "74 view_8"; -"72 select_4" -> "76 view_9"; -"73 select_5" -> "78 view_10"; -"74 view_8" -> "75 transpose_8"; -"75 transpose_8" -> "80 view_11"; -"76 view_9" -> "77 transpose_9"; -"77 transpose_9" -> "81 view_12"; -"78 view_10" -> "79 transpose_10"; -"79 transpose_10" -> "82 view_13"; -"80 view_11" -> "83 scaled_dot_product_attention_1"; -"81 view_12" -> "83 scaled_dot_product_attention_1"; -"82 view_13" -> "83 scaled_dot_product_attention_1"; -"83 scaled_dot_product_attention_1" -> "84 permute_2"; -"84 permute_2" -> "85 view_14"; -"85 view_14" -> "88 linear_5"; -"86 _param_constant20" -> "88 linear_5"; -"87 _param_constant21" -> "88 linear_5"; -"88 linear_5" -> "89 view_15"; -"89 view_15" -> "90 transpose_11"; -"90 transpose_11" -> "91 dropout_4"; -"91 dropout_4" -> "92 add_3"; -"92 add_3" -> "95 layer_norm_3"; -"92 add_3" -> "105 add_4"; -"93 _param_constant22" -> "95 layer_norm_3"; -"94 _param_constant23" -> "95 layer_norm_3"; -"95 layer_norm_3" -> "98 linear_6"; -"96 _param_constant24" -> "98 linear_6"; -"97 _param_constant25" -> "98 linear_6"; -"98 linear_6" -> "99 gelu_1"; -"99 gelu_1" -> "100 dropout_5"; -"100 dropout_5" -> "103 linear_7"; -"101 _param_constant26" -> "103 linear_7"; -"102 _param_constant27" -> "103 linear_7"; -"103 linear_7" -> "104 dropout_6"; -"104 dropout_6" -> "105 add_4"; -"105 add_4" -> "108 layer_norm_4"; -"105 add_4" -> "139 add_5"; -"106 _param_constant28" -> "108 layer_norm_4"; -"107 _param_constant29" -> "108 layer_norm_4"; -"108 layer_norm_4" -> "109 transpose_12"; -"109 transpose_12" -> "112 linear_8"; -"110 _param_constant30" -> "112 linear_8"; -"111 _param_constant31" -> "112 linear_8"; -"112 linear_8" -> "113 unflatten_2"; -"113 unflatten_2" -> "114 unsqueeze_2"; -"114 unsqueeze_2" -> "115 transpose_13"; -"115 transpose_13" -> "116 squeeze_2"; -"116 squeeze_2" -> "117 contiguous_2"; -"117 contiguous_2" -> "118 select_6"; -"117 contiguous_2" -> "119 select_7"; -"117 contiguous_2" -> "120 select_8"; -"118 select_6" -> "121 view_16"; -"119 select_7" -> "123 view_17"; -"120 select_8" -> "125 view_18"; -"121 view_16" -> "122 transpose_14"; -"122 transpose_14" -> "127 view_19"; -"123 view_17" -> "124 transpose_15"; -"124 transpose_15" -> "128 view_20"; -"125 view_18" -> "126 transpose_16"; -"126 transpose_16" -> "129 view_21"; -"127 view_19" -> "130 scaled_dot_product_attention_2"; -"128 view_20" -> "130 scaled_dot_product_attention_2"; -"129 view_21" -> "130 scaled_dot_product_attention_2"; -"130 scaled_dot_product_attention_2" -> "131 permute_3"; -"131 permute_3" -> "132 view_22"; -"132 view_22" -> "135 linear_9"; -"133 _param_constant32" -> "135 linear_9"; -"134 _param_constant33" -> "135 linear_9"; -"135 linear_9" -> "136 view_23"; -"136 view_23" -> "137 transpose_17"; -"137 transpose_17" -> "138 dropout_7"; -"138 dropout_7" -> "139 add_5"; -"139 add_5" -> "142 layer_norm_5"; -"139 add_5" -> "152 add_6"; -"140 _param_constant34" -> "142 layer_norm_5"; -"141 _param_constant35" -> "142 layer_norm_5"; -"142 layer_norm_5" -> "145 linear_10"; -"143 _param_constant36" -> "145 linear_10"; -"144 _param_constant37" -> "145 linear_10"; -"145 linear_10" -> "146 gelu_2"; -"146 gelu_2" -> "147 dropout_8"; -"147 dropout_8" -> "150 linear_11"; -"148 _param_constant38" -> "150 linear_11"; -"149 _param_constant39" -> "150 linear_11"; -"150 linear_11" -> "151 dropout_9"; -"151 dropout_9" -> "152 add_6"; -"152 add_6" -> "155 layer_norm_6"; -"152 add_6" -> "186 add_7"; -"153 _param_constant40" -> "155 layer_norm_6"; -"154 _param_constant41" -> "155 layer_norm_6"; -"155 layer_norm_6" -> "156 transpose_18"; -"156 transpose_18" -> "159 linear_12"; -"157 _param_constant42" -> "159 linear_12"; -"158 _param_constant43" -> "159 linear_12"; -"159 linear_12" -> "160 unflatten_3"; -"160 unflatten_3" -> "161 unsqueeze_3"; -"161 unsqueeze_3" -> "162 transpose_19"; -"162 transpose_19" -> "163 squeeze_3"; -"163 squeeze_3" -> "164 contiguous_3"; -"164 contiguous_3" -> "165 select_9"; -"164 contiguous_3" -> "166 select_10"; -"164 contiguous_3" -> "167 select_11"; -"165 select_9" -> "168 view_24"; -"166 select_10" -> "170 view_25"; -"167 select_11" -> "172 view_26"; -"168 view_24" -> "169 transpose_20"; -"169 transpose_20" -> "174 view_27"; -"170 view_25" -> "171 transpose_21"; -"171 transpose_21" -> "175 view_28"; -"172 view_26" -> "173 transpose_22"; -"173 transpose_22" -> "176 view_29"; -"174 view_27" -> "177 scaled_dot_product_attention_3"; -"175 view_28" -> "177 scaled_dot_product_attention_3"; -"176 view_29" -> "177 scaled_dot_product_attention_3"; -"177 scaled_dot_product_attention_3" -> "178 permute_4"; -"178 permute_4" -> "179 view_30"; -"179 view_30" -> "182 linear_13"; -"180 _param_constant44" -> "182 linear_13"; -"181 _param_constant45" -> "182 linear_13"; -"182 linear_13" -> "183 view_31"; -"183 view_31" -> "184 transpose_23"; -"184 transpose_23" -> "185 dropout_10"; -"185 dropout_10" -> "186 add_7"; -"186 add_7" -> "189 layer_norm_7"; -"186 add_7" -> "199 add_8"; -"187 _param_constant46" -> "189 layer_norm_7"; -"188 _param_constant47" -> "189 layer_norm_7"; -"189 layer_norm_7" -> "192 linear_14"; -"190 _param_constant48" -> "192 linear_14"; -"191 _param_constant49" -> "192 linear_14"; -"192 linear_14" -> "193 gelu_3"; -"193 gelu_3" -> "194 dropout_11"; -"194 dropout_11" -> "197 linear_15"; -"195 _param_constant50" -> "197 linear_15"; -"196 _param_constant51" -> "197 linear_15"; -"197 linear_15" -> "198 dropout_12"; -"198 dropout_12" -> "199 add_8"; -"199 add_8" -> "202 layer_norm_8"; -"199 add_8" -> "233 add_9"; -"200 _param_constant52" -> "202 layer_norm_8"; -"201 _param_constant53" -> "202 layer_norm_8"; -"202 layer_norm_8" -> "203 transpose_24"; -"203 transpose_24" -> "206 linear_16"; -"204 _param_constant54" -> "206 linear_16"; -"205 _param_constant55" -> "206 linear_16"; -"206 linear_16" -> "207 unflatten_4"; -"207 unflatten_4" -> "208 unsqueeze_4"; -"208 unsqueeze_4" -> "209 transpose_25"; -"209 transpose_25" -> "210 squeeze_4"; -"210 squeeze_4" -> "211 contiguous_4"; -"211 contiguous_4" -> "212 select_12"; -"211 contiguous_4" -> "213 select_13"; -"211 contiguous_4" -> "214 select_14"; -"212 select_12" -> "215 view_32"; -"213 select_13" -> "217 view_33"; -"214 select_14" -> "219 view_34"; -"215 view_32" -> "216 transpose_26"; -"216 transpose_26" -> "221 view_35"; -"217 view_33" -> "218 transpose_27"; -"218 transpose_27" -> "222 view_36"; -"219 view_34" -> "220 transpose_28"; -"220 transpose_28" -> "223 view_37"; -"221 view_35" -> "224 scaled_dot_product_attention_4"; -"222 view_36" -> "224 scaled_dot_product_attention_4"; -"223 view_37" -> "224 scaled_dot_product_attention_4"; -"224 scaled_dot_product_attention_4" -> "225 permute_5"; -"225 permute_5" -> "226 view_38"; -"226 view_38" -> "229 linear_17"; -"227 _param_constant56" -> "229 linear_17"; -"228 _param_constant57" -> "229 linear_17"; -"229 linear_17" -> "230 view_39"; -"230 view_39" -> "231 transpose_29"; -"231 transpose_29" -> "232 dropout_13"; -"232 dropout_13" -> "233 add_9"; -"233 add_9" -> "236 layer_norm_9"; -"233 add_9" -> "246 add_10"; -"234 _param_constant58" -> "236 layer_norm_9"; -"235 _param_constant59" -> "236 layer_norm_9"; -"236 layer_norm_9" -> "239 linear_18"; -"237 _param_constant60" -> "239 linear_18"; -"238 _param_constant61" -> "239 linear_18"; -"239 linear_18" -> "240 gelu_4"; -"240 gelu_4" -> "241 dropout_14"; -"241 dropout_14" -> "244 linear_19"; -"242 _param_constant62" -> "244 linear_19"; -"243 _param_constant63" -> "244 linear_19"; -"244 linear_19" -> "245 dropout_15"; -"245 dropout_15" -> "246 add_10"; -"246 add_10" -> "249 layer_norm_10"; -"246 add_10" -> "280 add_11"; -"247 _param_constant64" -> "249 layer_norm_10"; -"248 _param_constant65" -> "249 layer_norm_10"; -"249 layer_norm_10" -> "250 transpose_30"; -"250 transpose_30" -> "253 linear_20"; -"251 _param_constant66" -> "253 linear_20"; -"252 _param_constant67" -> "253 linear_20"; -"253 linear_20" -> "254 unflatten_5"; -"254 unflatten_5" -> "255 unsqueeze_5"; -"255 unsqueeze_5" -> "256 transpose_31"; -"256 transpose_31" -> "257 squeeze_5"; -"257 squeeze_5" -> "258 contiguous_5"; -"258 contiguous_5" -> "259 select_15"; -"258 contiguous_5" -> "260 select_16"; -"258 contiguous_5" -> "261 select_17"; -"259 select_15" -> "262 view_40"; -"260 select_16" -> "264 view_41"; -"261 select_17" -> "266 view_42"; -"262 view_40" -> "263 transpose_32"; -"263 transpose_32" -> "268 view_43"; -"264 view_41" -> "265 transpose_33"; -"265 transpose_33" -> "269 view_44"; -"266 view_42" -> "267 transpose_34"; -"267 transpose_34" -> "270 view_45"; -"268 view_43" -> "271 scaled_dot_product_attention_5"; -"269 view_44" -> "271 scaled_dot_product_attention_5"; -"270 view_45" -> "271 scaled_dot_product_attention_5"; -"271 scaled_dot_product_attention_5" -> "272 permute_6"; -"272 permute_6" -> "273 view_46"; -"273 view_46" -> "276 linear_21"; -"274 _param_constant68" -> "276 linear_21"; -"275 _param_constant69" -> "276 linear_21"; -"276 linear_21" -> "277 view_47"; -"277 view_47" -> "278 transpose_35"; -"278 transpose_35" -> "279 dropout_16"; -"279 dropout_16" -> "280 add_11"; -"280 add_11" -> "283 layer_norm_11"; -"280 add_11" -> "293 add_12"; -"281 _param_constant70" -> "283 layer_norm_11"; -"282 _param_constant71" -> "283 layer_norm_11"; -"283 layer_norm_11" -> "286 linear_22"; -"284 _param_constant72" -> "286 linear_22"; -"285 _param_constant73" -> "286 linear_22"; -"286 linear_22" -> "287 gelu_5"; -"287 gelu_5" -> "288 dropout_17"; -"288 dropout_17" -> "291 linear_23"; -"289 _param_constant74" -> "291 linear_23"; -"290 _param_constant75" -> "291 linear_23"; -"291 linear_23" -> "292 dropout_18"; -"292 dropout_18" -> "293 add_12"; -"293 add_12" -> "296 layer_norm_12"; -"293 add_12" -> "327 add_13"; -"294 _param_constant76" -> "296 layer_norm_12"; -"295 _param_constant77" -> "296 layer_norm_12"; -"296 layer_norm_12" -> "297 transpose_36"; -"297 transpose_36" -> "300 linear_24"; -"298 _param_constant78" -> "300 linear_24"; -"299 _param_constant79" -> "300 linear_24"; -"300 linear_24" -> "301 unflatten_6"; -"301 unflatten_6" -> "302 unsqueeze_6"; -"302 unsqueeze_6" -> "303 transpose_37"; -"303 transpose_37" -> "304 squeeze_6"; -"304 squeeze_6" -> "305 contiguous_6"; -"305 contiguous_6" -> "306 select_18"; -"305 contiguous_6" -> "307 select_19"; -"305 contiguous_6" -> "308 select_20"; -"306 select_18" -> "309 view_48"; -"307 select_19" -> "311 view_49"; -"308 select_20" -> "313 view_50"; -"309 view_48" -> "310 transpose_38"; -"310 transpose_38" -> "315 view_51"; -"311 view_49" -> "312 transpose_39"; -"312 transpose_39" -> "316 view_52"; -"313 view_50" -> "314 transpose_40"; -"314 transpose_40" -> "317 view_53"; -"315 view_51" -> "318 scaled_dot_product_attention_6"; -"316 view_52" -> "318 scaled_dot_product_attention_6"; -"317 view_53" -> "318 scaled_dot_product_attention_6"; -"318 scaled_dot_product_attention_6" -> "319 permute_7"; -"319 permute_7" -> "320 view_54"; -"320 view_54" -> "323 linear_25"; -"321 _param_constant80" -> "323 linear_25"; -"322 _param_constant81" -> "323 linear_25"; -"323 linear_25" -> "324 view_55"; -"324 view_55" -> "325 transpose_41"; -"325 transpose_41" -> "326 dropout_19"; -"326 dropout_19" -> "327 add_13"; -"327 add_13" -> "330 layer_norm_13"; -"327 add_13" -> "340 add_14"; -"328 _param_constant82" -> "330 layer_norm_13"; -"329 _param_constant83" -> "330 layer_norm_13"; -"330 layer_norm_13" -> "333 linear_26"; -"331 _param_constant84" -> "333 linear_26"; -"332 _param_constant85" -> "333 linear_26"; -"333 linear_26" -> "334 gelu_6"; -"334 gelu_6" -> "335 dropout_20"; -"335 dropout_20" -> "338 linear_27"; -"336 _param_constant86" -> "338 linear_27"; -"337 _param_constant87" -> "338 linear_27"; -"338 linear_27" -> "339 dropout_21"; -"339 dropout_21" -> "340 add_14"; -"340 add_14" -> "343 layer_norm_14"; -"340 add_14" -> "374 add_15"; -"341 _param_constant88" -> "343 layer_norm_14"; -"342 _param_constant89" -> "343 layer_norm_14"; -"343 layer_norm_14" -> "344 transpose_42"; -"344 transpose_42" -> "347 linear_28"; -"345 _param_constant90" -> "347 linear_28"; -"346 _param_constant91" -> "347 linear_28"; -"347 linear_28" -> "348 unflatten_7"; -"348 unflatten_7" -> "349 unsqueeze_7"; -"349 unsqueeze_7" -> "350 transpose_43"; -"350 transpose_43" -> "351 squeeze_7"; -"351 squeeze_7" -> "352 contiguous_7"; -"352 contiguous_7" -> "353 select_21"; -"352 contiguous_7" -> "354 select_22"; -"352 contiguous_7" -> "355 select_23"; -"353 select_21" -> "356 view_56"; -"354 select_22" -> "358 view_57"; -"355 select_23" -> "360 view_58"; -"356 view_56" -> "357 transpose_44"; -"357 transpose_44" -> "362 view_59"; -"358 view_57" -> "359 transpose_45"; -"359 transpose_45" -> "363 view_60"; -"360 view_58" -> "361 transpose_46"; -"361 transpose_46" -> "364 view_61"; -"362 view_59" -> "365 scaled_dot_product_attention_7"; -"363 view_60" -> "365 scaled_dot_product_attention_7"; -"364 view_61" -> "365 scaled_dot_product_attention_7"; -"365 scaled_dot_product_attention_7" -> "366 permute_8"; -"366 permute_8" -> "367 view_62"; -"367 view_62" -> "370 linear_29"; -"368 _param_constant92" -> "370 linear_29"; -"369 _param_constant93" -> "370 linear_29"; -"370 linear_29" -> "371 view_63"; -"371 view_63" -> "372 transpose_47"; -"372 transpose_47" -> "373 dropout_22"; -"373 dropout_22" -> "374 add_15"; -"374 add_15" -> "377 layer_norm_15"; -"374 add_15" -> "387 add_16"; -"375 _param_constant94" -> "377 layer_norm_15"; -"376 _param_constant95" -> "377 layer_norm_15"; -"377 layer_norm_15" -> "380 linear_30"; -"378 _param_constant96" -> "380 linear_30"; -"379 _param_constant97" -> "380 linear_30"; -"380 linear_30" -> "381 gelu_7"; -"381 gelu_7" -> "382 dropout_23"; -"382 dropout_23" -> "385 linear_31"; -"383 _param_constant98" -> "385 linear_31"; -"384 _param_constant99" -> "385 linear_31"; -"385 linear_31" -> "386 dropout_24"; -"386 dropout_24" -> "387 add_16"; -"387 add_16" -> "390 layer_norm_16"; -"387 add_16" -> "421 add_17"; -"388 _param_constant100" -> "390 layer_norm_16"; -"389 _param_constant101" -> "390 layer_norm_16"; -"390 layer_norm_16" -> "391 transpose_48"; -"391 transpose_48" -> "394 linear_32"; -"392 _param_constant102" -> "394 linear_32"; -"393 _param_constant103" -> "394 linear_32"; -"394 linear_32" -> "395 unflatten_8"; -"395 unflatten_8" -> "396 unsqueeze_8"; -"396 unsqueeze_8" -> "397 transpose_49"; -"397 transpose_49" -> "398 squeeze_8"; -"398 squeeze_8" -> "399 contiguous_8"; -"399 contiguous_8" -> "400 select_24"; -"399 contiguous_8" -> "401 select_25"; -"399 contiguous_8" -> "402 select_26"; -"400 select_24" -> "403 view_64"; -"401 select_25" -> "405 view_65"; -"402 select_26" -> "407 view_66"; -"403 view_64" -> "404 transpose_50"; -"404 transpose_50" -> "409 view_67"; -"405 view_65" -> "406 transpose_51"; -"406 transpose_51" -> "410 view_68"; -"407 view_66" -> "408 transpose_52"; -"408 transpose_52" -> "411 view_69"; -"409 view_67" -> "412 scaled_dot_product_attention_8"; -"410 view_68" -> "412 scaled_dot_product_attention_8"; -"411 view_69" -> "412 scaled_dot_product_attention_8"; -"412 scaled_dot_product_attention_8" -> "413 permute_9"; -"413 permute_9" -> "414 view_70"; -"414 view_70" -> "417 linear_33"; -"415 _param_constant104" -> "417 linear_33"; -"416 _param_constant105" -> "417 linear_33"; -"417 linear_33" -> "418 view_71"; -"418 view_71" -> "419 transpose_53"; -"419 transpose_53" -> "420 dropout_25"; -"420 dropout_25" -> "421 add_17"; -"421 add_17" -> "424 layer_norm_17"; -"421 add_17" -> "434 add_18"; -"422 _param_constant106" -> "424 layer_norm_17"; -"423 _param_constant107" -> "424 layer_norm_17"; -"424 layer_norm_17" -> "427 linear_34"; -"425 _param_constant108" -> "427 linear_34"; -"426 _param_constant109" -> "427 linear_34"; -"427 linear_34" -> "428 gelu_8"; -"428 gelu_8" -> "429 dropout_26"; -"429 dropout_26" -> "432 linear_35"; -"430 _param_constant110" -> "432 linear_35"; -"431 _param_constant111" -> "432 linear_35"; -"432 linear_35" -> "433 dropout_27"; -"433 dropout_27" -> "434 add_18"; -"434 add_18" -> "437 layer_norm_18"; -"434 add_18" -> "468 add_19"; -"435 _param_constant112" -> "437 layer_norm_18"; -"436 _param_constant113" -> "437 layer_norm_18"; -"437 layer_norm_18" -> "438 transpose_54"; -"438 transpose_54" -> "441 linear_36"; -"439 _param_constant114" -> "441 linear_36"; -"440 _param_constant115" -> "441 linear_36"; -"441 linear_36" -> "442 unflatten_9"; -"442 unflatten_9" -> "443 unsqueeze_9"; -"443 unsqueeze_9" -> "444 transpose_55"; -"444 transpose_55" -> "445 squeeze_9"; -"445 squeeze_9" -> "446 contiguous_9"; -"446 contiguous_9" -> "447 select_27"; -"446 contiguous_9" -> "448 select_28"; -"446 contiguous_9" -> "449 select_29"; -"447 select_27" -> "450 view_72"; -"448 select_28" -> "452 view_73"; -"449 select_29" -> "454 view_74"; -"450 view_72" -> "451 transpose_56"; -"451 transpose_56" -> "456 view_75"; -"452 view_73" -> "453 transpose_57"; -"453 transpose_57" -> "457 view_76"; -"454 view_74" -> "455 transpose_58"; -"455 transpose_58" -> "458 view_77"; -"456 view_75" -> "459 scaled_dot_product_attention_9"; -"457 view_76" -> "459 scaled_dot_product_attention_9"; -"458 view_77" -> "459 scaled_dot_product_attention_9"; -"459 scaled_dot_product_attention_9" -> "460 permute_10"; -"460 permute_10" -> "461 view_78"; -"461 view_78" -> "464 linear_37"; -"462 _param_constant116" -> "464 linear_37"; -"463 _param_constant117" -> "464 linear_37"; -"464 linear_37" -> "465 view_79"; -"465 view_79" -> "466 transpose_59"; -"466 transpose_59" -> "467 dropout_28"; -"467 dropout_28" -> "468 add_19"; -"468 add_19" -> "471 layer_norm_19"; -"468 add_19" -> "481 add_20"; -"469 _param_constant118" -> "471 layer_norm_19"; -"470 _param_constant119" -> "471 layer_norm_19"; -"471 layer_norm_19" -> "474 linear_38"; -"472 _param_constant120" -> "474 linear_38"; -"473 _param_constant121" -> "474 linear_38"; -"474 linear_38" -> "475 gelu_9"; -"475 gelu_9" -> "476 dropout_29"; -"476 dropout_29" -> "479 linear_39"; -"477 _param_constant122" -> "479 linear_39"; -"478 _param_constant123" -> "479 linear_39"; -"479 linear_39" -> "480 dropout_30"; -"480 dropout_30" -> "481 add_20"; -"481 add_20" -> "484 layer_norm_20"; -"481 add_20" -> "515 add_21"; -"482 _param_constant124" -> "484 layer_norm_20"; -"483 _param_constant125" -> "484 layer_norm_20"; -"484 layer_norm_20" -> "485 transpose_60"; -"485 transpose_60" -> "488 linear_40"; -"486 _param_constant126" -> "488 linear_40"; -"487 _param_constant127" -> "488 linear_40"; -"488 linear_40" -> "489 unflatten_10"; -"489 unflatten_10" -> "490 unsqueeze_10"; -"490 unsqueeze_10" -> "491 transpose_61"; -"491 transpose_61" -> "492 squeeze_10"; -"492 squeeze_10" -> "493 contiguous_10"; -"493 contiguous_10" -> "494 select_30"; -"493 contiguous_10" -> "495 select_31"; -"493 contiguous_10" -> "496 select_32"; -"494 select_30" -> "497 view_80"; -"495 select_31" -> "499 view_81"; -"496 select_32" -> "501 view_82"; -"497 view_80" -> "498 transpose_62"; -"498 transpose_62" -> "503 view_83"; -"499 view_81" -> "500 transpose_63"; -"500 transpose_63" -> "504 view_84"; -"501 view_82" -> "502 transpose_64"; -"502 transpose_64" -> "505 view_85"; -"503 view_83" -> "506 scaled_dot_product_attention_10"; -"504 view_84" -> "506 scaled_dot_product_attention_10"; -"505 view_85" -> "506 scaled_dot_product_attention_10"; -"506 scaled_dot_product_attention_10" -> "507 permute_11"; -"507 permute_11" -> "508 view_86"; -"508 view_86" -> "511 linear_41"; -"509 _param_constant128" -> "511 linear_41"; -"510 _param_constant129" -> "511 linear_41"; -"511 linear_41" -> "512 view_87"; -"512 view_87" -> "513 transpose_65"; -"513 transpose_65" -> "514 dropout_31"; -"514 dropout_31" -> "515 add_21"; -"515 add_21" -> "518 layer_norm_21"; -"515 add_21" -> "528 add_22"; -"516 _param_constant130" -> "518 layer_norm_21"; -"517 _param_constant131" -> "518 layer_norm_21"; -"518 layer_norm_21" -> "521 linear_42"; -"519 _param_constant132" -> "521 linear_42"; -"520 _param_constant133" -> "521 linear_42"; -"521 linear_42" -> "522 gelu_10"; -"522 gelu_10" -> "523 dropout_32"; -"523 dropout_32" -> "526 linear_43"; -"524 _param_constant134" -> "526 linear_43"; -"525 _param_constant135" -> "526 linear_43"; -"526 linear_43" -> "527 dropout_33"; -"527 dropout_33" -> "528 add_22"; -"528 add_22" -> "531 layer_norm_22"; -"528 add_22" -> "562 add_23"; -"529 _param_constant136" -> "531 layer_norm_22"; -"530 _param_constant137" -> "531 layer_norm_22"; -"531 layer_norm_22" -> "532 transpose_66"; -"532 transpose_66" -> "535 linear_44"; -"533 _param_constant138" -> "535 linear_44"; -"534 _param_constant139" -> "535 linear_44"; -"535 linear_44" -> "536 unflatten_11"; -"536 unflatten_11" -> "537 unsqueeze_11"; -"537 unsqueeze_11" -> "538 transpose_67"; -"538 transpose_67" -> "539 squeeze_11"; -"539 squeeze_11" -> "540 contiguous_11"; -"540 contiguous_11" -> "541 select_33"; -"540 contiguous_11" -> "542 select_34"; -"540 contiguous_11" -> "543 select_35"; -"541 select_33" -> "544 view_88"; -"542 select_34" -> "546 view_89"; -"543 select_35" -> "548 view_90"; -"544 view_88" -> "545 transpose_68"; -"545 transpose_68" -> "550 view_91"; -"546 view_89" -> "547 transpose_69"; -"547 transpose_69" -> "551 view_92"; -"548 view_90" -> "549 transpose_70"; -"549 transpose_70" -> "552 view_93"; -"550 view_91" -> "553 scaled_dot_product_attention_11"; -"551 view_92" -> "553 scaled_dot_product_attention_11"; -"552 view_93" -> "553 scaled_dot_product_attention_11"; -"553 scaled_dot_product_attention_11" -> "554 permute_12"; -"554 permute_12" -> "555 view_94"; -"555 view_94" -> "558 linear_45"; -"556 _param_constant140" -> "558 linear_45"; -"557 _param_constant141" -> "558 linear_45"; -"558 linear_45" -> "559 view_95"; -"559 view_95" -> "560 transpose_71"; -"560 transpose_71" -> "561 dropout_34"; -"561 dropout_34" -> "562 add_23"; -"562 add_23" -> "565 layer_norm_23"; -"562 add_23" -> "575 add_24"; -"563 _param_constant142" -> "565 layer_norm_23"; -"564 _param_constant143" -> "565 layer_norm_23"; -"565 layer_norm_23" -> "568 linear_46"; -"566 _param_constant144" -> "568 linear_46"; -"567 _param_constant145" -> "568 linear_46"; -"568 linear_46" -> "569 gelu_11"; -"569 gelu_11" -> "570 dropout_35"; -"570 dropout_35" -> "573 linear_47"; -"571 _param_constant146" -> "573 linear_47"; -"572 _param_constant147" -> "573 linear_47"; -"573 linear_47" -> "574 dropout_36"; -"574 dropout_36" -> "575 add_24"; -"575 add_24" -> "578 layer_norm_24"; -"576 _param_constant148" -> "578 layer_norm_24"; -"577 _param_constant149" -> "578 layer_norm_24"; -"578 layer_norm_24" -> "579 slice_1"; -"579 slice_1" -> "580 select_36"; -"580 select_36" -> "583 linear_48"; -"581 _param_constant150" -> "583 linear_48"; -"582 _param_constant151" -> "583 linear_48"; -"583 linear_48" -> "584 output"; +"584 output_1" [id=584, type=output]; +"0 class_token" -> "156 expand" [label="(1, 1, 768)", style=solid]; +"1 encoder_pos_embedding" -> "158 add" [label="(1, 197, 768)", style=solid]; +"2 conv_proj_weight" -> "153 conv2d" [label="(768, 3, 16, 16)", style=solid]; +"3 conv_proj_bias" -> "153 conv2d" [label="(768,)", style=solid]; +"4 encoder_layers_encoder_layer_0_ln_1_weight" -> "160 layer_norm" [label="(768,)", style=solid]; +"5 encoder_layers_encoder_layer_0_ln_1_bias" -> "160 layer_norm" [label="(768,)", style=solid]; +"6 encoder_layers_encoder_layer_0_self_attention_in_proj_weight" -> "162 linear" [label="(2304, 768)", style=solid]; +"7 encoder_layers_encoder_layer_0_self_attention_in_proj_bias" -> "162 linear" [label="(2304,)", style=solid]; +"8 encoder_layers_encoder_layer_0_self_attention_out_proj_weight" -> "183 linear_1" [label="(768, 768)", style=solid]; +"9 encoder_layers_encoder_layer_0_self_attention_out_proj_bias" -> "183 linear_1" [label="(768,)", style=solid]; +"10 encoder_layers_encoder_layer_0_ln_2_weight" -> "188 layer_norm_1" [label="(768,)", style=solid]; +"11 encoder_layers_encoder_layer_0_ln_2_bias" -> "188 layer_norm_1" [label="(768,)", style=solid]; +"12 encoder_layers_encoder_layer_0_mlp_0_weight" -> "189 linear_2" [label="(3072, 768)", style=solid]; +"13 encoder_layers_encoder_layer_0_mlp_0_bias" -> "189 linear_2" [label="(3072,)", style=solid]; +"14 encoder_layers_encoder_layer_0_mlp_3_weight" -> "192 linear_3" [label="(768, 3072)", style=solid]; +"15 encoder_layers_encoder_layer_0_mlp_3_bias" -> "192 linear_3" [label="(768,)", style=solid]; +"16 encoder_layers_encoder_layer_1_ln_1_weight" -> "195 layer_norm_2" [label="(768,)", style=solid]; +"17 encoder_layers_encoder_layer_1_ln_1_bias" -> "195 layer_norm_2" [label="(768,)", style=solid]; +"18 encoder_layers_encoder_layer_1_self_attention_in_proj_weight" -> "197 linear_4" [label="(2304, 768)", style=solid]; +"19 encoder_layers_encoder_layer_1_self_attention_in_proj_bias" -> "197 linear_4" [label="(2304,)", style=solid]; +"20 encoder_layers_encoder_layer_1_self_attention_out_proj_weight" -> "218 linear_5" [label="(768, 768)", style=solid]; +"21 encoder_layers_encoder_layer_1_self_attention_out_proj_bias" -> "218 linear_5" [label="(768,)", style=solid]; +"22 encoder_layers_encoder_layer_1_ln_2_weight" -> "223 layer_norm_3" [label="(768,)", style=solid]; +"23 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style=solid]; +"237 contiguous_2" -> "238 select_6" [label="(3, 197, 1, 768)", style=solid]; +"237 contiguous_2" -> "239 select_7" [label="(3, 197, 1, 768)", style=solid]; +"237 contiguous_2" -> "240 select_8" [label="(3, 197, 1, 768)", style=solid]; +"238 select_6" -> "241 view_16" [label="(197, 1, 768)", style=solid]; +"239 select_7" -> "243 view_17" [label="(197, 1, 768)", style=solid]; +"240 select_8" -> "245 view_18" [label="(197, 1, 768)", style=solid]; +"241 view_16" -> "242 transpose_14" [label="(197, 12, 64)", style=solid]; +"242 transpose_14" -> "247 view_19" [label="(12, 197, 64)", style=solid]; +"243 view_17" -> "244 transpose_15" [label="(197, 12, 64)", style=solid]; +"244 transpose_15" -> "248 view_20" [label="(12, 197, 64)", style=solid]; +"245 view_18" -> "246 transpose_16" [label="(197, 12, 64)", style=solid]; +"246 transpose_16" -> "249 view_21" [label="(12, 197, 64)", style=solid]; +"247 view_19" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"248 view_20" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"249 view_21" -> "250 scaled_dot_product_attention_2" [label="(1, 12, 197, 64)", style=solid]; +"250 scaled_dot_product_attention_2" -> "251 permute_3" [label="(1, 12, 197, 64)", style=solid]; +"251 permute_3" -> "252 view_22" [label="(197, 1, 12, 64)", style=solid]; +"252 view_22" -> "253 linear_9" [label="(197, 768)", style=solid]; +"253 linear_9" -> "254 view_23" [label="(197, 768)", style=solid]; +"254 view_23" -> "255 transpose_17" [label="(197, 1, 768)", style=solid]; +"255 transpose_17" -> "256 dropout_7" [label="(1, 197, 768)", style=solid]; +"256 dropout_7" -> "257 add_5" [label="(1, 197, 768)", style=solid]; +"257 add_5" -> "258 layer_norm_5" [label="(1, 197, 768)", style=solid]; +"257 add_5" -> "264 add_6" [label="(1, 197, 768)", style=solid]; +"258 layer_norm_5" -> "259 linear_10" [label="(1, 197, 768)", style=solid]; +"259 linear_10" -> "260 gelu_2" [label="(1, 197, 3072)", style=solid]; +"260 gelu_2" -> "261 dropout_8" [label="(1, 197, 3072)", style=solid]; +"261 dropout_8" -> "262 linear_11" [label="(1, 197, 3072)", style=solid]; +"262 linear_11" -> "263 dropout_9" [label="(1, 197, 768)", style=solid]; +"263 dropout_9" -> "264 add_6" [label="(1, 197, 768)", style=solid]; +"264 add_6" -> "265 layer_norm_6" [label="(1, 197, 768)", style=solid]; +"264 add_6" -> "292 add_7" [label="(1, 197, 768)", style=solid]; +"265 layer_norm_6" -> "266 transpose_18" [label="(1, 197, 768)", style=solid]; +"266 transpose_18" -> "267 linear_12" [label="(197, 1, 768)", style=solid]; +"267 linear_12" -> "268 unflatten_3" [label="(197, 1, 2304)", style=solid]; +"268 unflatten_3" -> "269 unsqueeze_3" [label="(197, 1, 3, 768)", style=solid]; +"269 unsqueeze_3" -> "270 transpose_19" [label="(1, 197, 1, 3, 768)", style=solid]; +"270 transpose_19" -> "271 squeeze_3" [label="(3, 197, 1, 1, 768)", style=solid]; +"271 squeeze_3" -> "272 contiguous_3" [label="(3, 197, 1, 768)", style=solid]; +"272 contiguous_3" -> "273 select_9" [label="(3, 197, 1, 768)", style=solid]; +"272 contiguous_3" -> "274 select_10" [label="(3, 197, 1, 768)", style=solid]; +"272 contiguous_3" -> "275 select_11" [label="(3, 197, 1, 768)", style=solid]; +"273 select_9" -> "276 view_24" [label="(197, 1, 768)", style=solid]; +"274 select_10" -> "278 view_25" [label="(197, 1, 768)", style=solid]; +"275 select_11" -> "280 view_26" [label="(197, 1, 768)", style=solid]; +"276 view_24" -> "277 transpose_20" [label="(197, 12, 64)", style=solid]; +"277 transpose_20" -> "282 view_27" [label="(12, 197, 64)", style=solid]; +"278 view_25" -> "279 transpose_21" [label="(197, 12, 64)", style=solid]; +"279 transpose_21" -> "283 view_28" [label="(12, 197, 64)", style=solid]; +"280 view_26" -> "281 transpose_22" [label="(197, 12, 64)", style=solid]; +"281 transpose_22" -> "284 view_29" [label="(12, 197, 64)", style=solid]; +"282 view_27" -> "285 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"283 view_28" -> "285 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"284 view_29" -> "285 scaled_dot_product_attention_3" [label="(1, 12, 197, 64)", style=solid]; +"285 scaled_dot_product_attention_3" -> "286 permute_4" [label="(1, 12, 197, 64)", style=solid]; +"286 permute_4" -> "287 view_30" [label="(197, 1, 12, 64)", style=solid]; +"287 view_30" -> "288 linear_13" [label="(197, 768)", style=solid]; +"288 linear_13" -> "289 view_31" [label="(197, 768)", style=solid]; +"289 view_31" -> "290 transpose_23" [label="(197, 1, 768)", style=solid]; +"290 transpose_23" -> "291 dropout_10" [label="(1, 197, 768)", style=solid]; +"291 dropout_10" -> "292 add_7" [label="(1, 197, 768)", style=solid]; +"292 add_7" -> "293 layer_norm_7" [label="(1, 197, 768)", style=solid]; +"292 add_7" -> "299 add_8" [label="(1, 197, 768)", style=solid]; +"293 layer_norm_7" -> "294 linear_14" [label="(1, 197, 768)", style=solid]; +"294 linear_14" -> "295 gelu_3" [label="(1, 197, 3072)", style=solid]; +"295 gelu_3" -> "296 dropout_11" [label="(1, 197, 3072)", style=solid]; +"296 dropout_11" -> "297 linear_15" [label="(1, 197, 3072)", style=solid]; +"297 linear_15" -> "298 dropout_12" [label="(1, 197, 768)", style=solid]; +"298 dropout_12" -> "299 add_8" [label="(1, 197, 768)", style=solid]; +"299 add_8" -> "300 layer_norm_8" [label="(1, 197, 768)", style=solid]; +"299 add_8" -> "327 add_9" [label="(1, 197, 768)", style=solid]; +"300 layer_norm_8" -> "301 transpose_24" [label="(1, 197, 768)", style=solid]; +"301 transpose_24" -> "302 linear_16" [label="(197, 1, 768)", style=solid]; +"302 linear_16" -> "303 unflatten_4" [label="(197, 1, 2304)", style=solid]; +"303 unflatten_4" -> "304 unsqueeze_4" [label="(197, 1, 3, 768)", style=solid]; +"304 unsqueeze_4" -> "305 transpose_25" [label="(1, 197, 1, 3, 768)", style=solid]; +"305 transpose_25" -> "306 squeeze_4" [label="(3, 197, 1, 1, 768)", style=solid]; +"306 squeeze_4" -> "307 contiguous_4" [label="(3, 197, 1, 768)", style=solid]; +"307 contiguous_4" -> "308 select_12" [label="(3, 197, 1, 768)", style=solid]; +"307 contiguous_4" -> "309 select_13" [label="(3, 197, 1, 768)", style=solid]; +"307 contiguous_4" -> "310 select_14" [label="(3, 197, 1, 768)", style=solid]; +"308 select_12" -> "311 view_32" [label="(197, 1, 768)", style=solid]; +"309 select_13" -> "313 view_33" [label="(197, 1, 768)", style=solid]; +"310 select_14" -> "315 view_34" [label="(197, 1, 768)", style=solid]; +"311 view_32" -> "312 transpose_26" [label="(197, 12, 64)", style=solid]; +"312 transpose_26" -> "317 view_35" [label="(12, 197, 64)", style=solid]; +"313 view_33" -> "314 transpose_27" [label="(197, 12, 64)", style=solid]; +"314 transpose_27" -> "318 view_36" [label="(12, 197, 64)", style=solid]; +"315 view_34" -> "316 transpose_28" [label="(197, 12, 64)", style=solid]; +"316 transpose_28" -> "319 view_37" [label="(12, 197, 64)", style=solid]; +"317 view_35" -> "320 scaled_dot_product_attention_4" [label="(1, 12, 197, 64)", style=solid]; +"318 view_36" -> "320 scaled_dot_product_attention_4" [label="(1, 12, 197, 64)", style=solid]; +"319 view_37" -> "320 scaled_dot_product_attention_4" [label="(1, 12, 197, 64)", style=solid]; +"320 scaled_dot_product_attention_4" -> "321 permute_5" [label="(1, 12, 197, 64)", style=solid]; +"321 permute_5" -> "322 view_38" [label="(197, 1, 12, 64)", style=solid]; +"322 view_38" -> "323 linear_17" [label="(197, 768)", style=solid]; +"323 linear_17" -> "324 view_39" [label="(197, 768)", style=solid]; +"324 view_39" -> "325 transpose_29" [label="(197, 1, 768)", style=solid]; +"325 transpose_29" -> "326 dropout_13" [label="(1, 197, 768)", style=solid]; +"326 dropout_13" -> "327 add_9" [label="(1, 197, 768)", style=solid]; +"327 add_9" -> "328 layer_norm_9" [label="(1, 197, 768)", style=solid]; +"327 add_9" -> "334 add_10" [label="(1, 197, 768)", style=solid]; +"328 layer_norm_9" -> "329 linear_18" [label="(1, 197, 768)", style=solid]; +"329 linear_18" -> "330 gelu_4" [label="(1, 197, 3072)", style=solid]; +"330 gelu_4" -> "331 dropout_14" [label="(1, 197, 3072)", style=solid]; +"331 dropout_14" -> "332 linear_19" [label="(1, 197, 3072)", style=solid]; +"332 linear_19" -> "333 dropout_15" [label="(1, 197, 768)", style=solid]; +"333 dropout_15" -> "334 add_10" [label="(1, 197, 768)", style=solid]; +"334 add_10" -> "335 layer_norm_10" [label="(1, 197, 768)", style=solid]; +"334 add_10" -> "362 add_11" [label="(1, 197, 768)", style=solid]; +"335 layer_norm_10" -> "336 transpose_30" [label="(1, 197, 768)", style=solid]; +"336 transpose_30" -> "337 linear_20" [label="(197, 1, 768)", style=solid]; +"337 linear_20" -> "338 unflatten_5" [label="(197, 1, 2304)", style=solid]; +"338 unflatten_5" -> "339 unsqueeze_5" [label="(197, 1, 3, 768)", style=solid]; +"339 unsqueeze_5" -> "340 transpose_31" [label="(1, 197, 1, 3, 768)", style=solid]; +"340 transpose_31" -> "341 squeeze_5" [label="(3, 197, 1, 1, 768)", style=solid]; +"341 squeeze_5" -> "342 contiguous_5" [label="(3, 197, 1, 768)", style=solid]; +"342 contiguous_5" -> "343 select_15" [label="(3, 197, 1, 768)", style=solid]; +"342 contiguous_5" -> "344 select_16" [label="(3, 197, 1, 768)", style=solid]; +"342 contiguous_5" -> "345 select_17" [label="(3, 197, 1, 768)", style=solid]; +"343 select_15" -> "346 view_40" [label="(197, 1, 768)", style=solid]; +"344 select_16" -> "348 view_41" [label="(197, 1, 768)", style=solid]; +"345 select_17" -> "350 view_42" [label="(197, 1, 768)", style=solid]; +"346 view_40" -> "347 transpose_32" [label="(197, 12, 64)", style=solid]; +"347 transpose_32" -> "352 view_43" [label="(12, 197, 64)", style=solid]; +"348 view_41" -> "349 transpose_33" [label="(197, 12, 64)", style=solid]; +"349 transpose_33" -> "353 view_44" [label="(12, 197, 64)", style=solid]; +"350 view_42" -> "351 transpose_34" [label="(197, 12, 64)", style=solid]; +"351 transpose_34" -> "354 view_45" [label="(12, 197, 64)", style=solid]; +"352 view_43" -> "355 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"353 view_44" -> "355 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"354 view_45" -> "355 scaled_dot_product_attention_5" [label="(1, 12, 197, 64)", style=solid]; +"355 scaled_dot_product_attention_5" -> "356 permute_6" [label="(1, 12, 197, 64)", style=solid]; +"356 permute_6" -> "357 view_46" [label="(197, 1, 12, 64)", style=solid]; +"357 view_46" -> "358 linear_21" [label="(197, 768)", style=solid]; +"358 linear_21" -> "359 view_47" [label="(197, 768)", style=solid]; +"359 view_47" -> "360 transpose_35" [label="(197, 1, 768)", style=solid]; +"360 transpose_35" -> "361 dropout_16" [label="(1, 197, 768)", style=solid]; +"361 dropout_16" -> "362 add_11" [label="(1, 197, 768)", style=solid]; +"362 add_11" -> "363 layer_norm_11" [label="(1, 197, 768)", style=solid]; +"362 add_11" -> "369 add_12" [label="(1, 197, 768)", style=solid]; +"363 layer_norm_11" -> "364 linear_22" [label="(1, 197, 768)", style=solid]; +"364 linear_22" -> "365 gelu_5" [label="(1, 197, 3072)", style=solid]; +"365 gelu_5" -> "366 dropout_17" [label="(1, 197, 3072)", style=solid]; +"366 dropout_17" -> "367 linear_23" [label="(1, 197, 3072)", style=solid]; +"367 linear_23" -> "368 dropout_18" [label="(1, 197, 768)", style=solid]; +"368 dropout_18" -> "369 add_12" [label="(1, 197, 768)", style=solid]; +"369 add_12" -> "370 layer_norm_12" [label="(1, 197, 768)", style=solid]; +"369 add_12" -> "397 add_13" [label="(1, 197, 768)", style=solid]; +"370 layer_norm_12" -> "371 transpose_36" [label="(1, 197, 768)", style=solid]; +"371 transpose_36" -> "372 linear_24" [label="(197, 1, 768)", style=solid]; +"372 linear_24" -> "373 unflatten_6" [label="(197, 1, 2304)", style=solid]; +"373 unflatten_6" -> "374 unsqueeze_6" [label="(197, 1, 3, 768)", style=solid]; +"374 unsqueeze_6" -> "375 transpose_37" [label="(1, 197, 1, 3, 768)", style=solid]; +"375 transpose_37" -> "376 squeeze_6" [label="(3, 197, 1, 1, 768)", style=solid]; +"376 squeeze_6" -> "377 contiguous_6" [label="(3, 197, 1, 768)", style=solid]; +"377 contiguous_6" -> "378 select_18" [label="(3, 197, 1, 768)", style=solid]; +"377 contiguous_6" -> "379 select_19" [label="(3, 197, 1, 768)", style=solid]; +"377 contiguous_6" -> "380 select_20" [label="(3, 197, 1, 768)", style=solid]; +"378 select_18" -> "381 view_48" [label="(197, 1, 768)", style=solid]; +"379 select_19" -> "383 view_49" [label="(197, 1, 768)", style=solid]; +"380 select_20" -> "385 view_50" [label="(197, 1, 768)", style=solid]; +"381 view_48" -> "382 transpose_38" [label="(197, 12, 64)", style=solid]; +"382 transpose_38" -> "387 view_51" [label="(12, 197, 64)", style=solid]; +"383 view_49" -> "384 transpose_39" [label="(197, 12, 64)", style=solid]; +"384 transpose_39" -> "388 view_52" [label="(12, 197, 64)", style=solid]; +"385 view_50" -> "386 transpose_40" [label="(197, 12, 64)", style=solid]; +"386 transpose_40" -> "389 view_53" [label="(12, 197, 64)", style=solid]; +"387 view_51" -> "390 scaled_dot_product_attention_6" [label="(1, 12, 197, 64)", style=solid]; +"388 view_52" -> "390 scaled_dot_product_attention_6" [label="(1, 12, 197, 64)", style=solid]; +"389 view_53" -> "390 scaled_dot_product_attention_6" [label="(1, 12, 197, 64)", style=solid]; +"390 scaled_dot_product_attention_6" -> "391 permute_7" [label="(1, 12, 197, 64)", style=solid]; +"391 permute_7" -> "392 view_54" [label="(197, 1, 12, 64)", style=solid]; +"392 view_54" -> "393 linear_25" [label="(197, 768)", style=solid]; +"393 linear_25" -> "394 view_55" [label="(197, 768)", style=solid]; +"394 view_55" -> "395 transpose_41" [label="(197, 1, 768)", style=solid]; +"395 transpose_41" -> "396 dropout_19" [label="(1, 197, 768)", style=solid]; +"396 dropout_19" -> "397 add_13" [label="(1, 197, 768)", style=solid]; +"397 add_13" -> "398 layer_norm_13" [label="(1, 197, 768)", style=solid]; +"397 add_13" -> "404 add_14" [label="(1, 197, 768)", style=solid]; +"398 layer_norm_13" -> "399 linear_26" [label="(1, 197, 768)", style=solid]; +"399 linear_26" -> "400 gelu_6" [label="(1, 197, 3072)", style=solid]; +"400 gelu_6" -> "401 dropout_20" [label="(1, 197, 3072)", style=solid]; +"401 dropout_20" -> "402 linear_27" [label="(1, 197, 3072)", style=solid]; +"402 linear_27" -> "403 dropout_21" [label="(1, 197, 768)", style=solid]; +"403 dropout_21" -> "404 add_14" [label="(1, 197, 768)", style=solid]; +"404 add_14" -> "405 layer_norm_14" [label="(1, 197, 768)", style=solid]; +"404 add_14" -> "432 add_15" [label="(1, 197, 768)", style=solid]; +"405 layer_norm_14" -> "406 transpose_42" [label="(1, 197, 768)", style=solid]; +"406 transpose_42" -> "407 linear_28" [label="(197, 1, 768)", style=solid]; +"407 linear_28" -> "408 unflatten_7" [label="(197, 1, 2304)", style=solid]; +"408 unflatten_7" -> "409 unsqueeze_7" [label="(197, 1, 3, 768)", style=solid]; +"409 unsqueeze_7" -> "410 transpose_43" [label="(1, 197, 1, 3, 768)", style=solid]; +"410 transpose_43" -> "411 squeeze_7" [label="(3, 197, 1, 1, 768)", style=solid]; +"411 squeeze_7" -> "412 contiguous_7" [label="(3, 197, 1, 768)", style=solid]; +"412 contiguous_7" -> "413 select_21" [label="(3, 197, 1, 768)", style=solid]; +"412 contiguous_7" -> "414 select_22" [label="(3, 197, 1, 768)", style=solid]; +"412 contiguous_7" -> "415 select_23" [label="(3, 197, 1, 768)", style=solid]; +"413 select_21" -> "416 view_56" [label="(197, 1, 768)", style=solid]; +"414 select_22" -> "418 view_57" [label="(197, 1, 768)", style=solid]; +"415 select_23" -> "420 view_58" [label="(197, 1, 768)", style=solid]; +"416 view_56" -> "417 transpose_44" [label="(197, 12, 64)", style=solid]; +"417 transpose_44" -> "422 view_59" [label="(12, 197, 64)", style=solid]; +"418 view_57" -> "419 transpose_45" [label="(197, 12, 64)", style=solid]; +"419 transpose_45" -> "423 view_60" [label="(12, 197, 64)", style=solid]; +"420 view_58" -> "421 transpose_46" [label="(197, 12, 64)", style=solid]; +"421 transpose_46" -> "424 view_61" [label="(12, 197, 64)", style=solid]; +"422 view_59" -> "425 scaled_dot_product_attention_7" [label="(1, 12, 197, 64)", style=solid]; +"423 view_60" -> "425 scaled_dot_product_attention_7" [label="(1, 12, 197, 64)", style=solid]; +"424 view_61" -> "425 scaled_dot_product_attention_7" [label="(1, 12, 197, 64)", style=solid]; +"425 scaled_dot_product_attention_7" -> "426 permute_8" [label="(1, 12, 197, 64)", style=solid]; +"426 permute_8" -> "427 view_62" [label="(197, 1, 12, 64)", style=solid]; +"427 view_62" -> "428 linear_29" [label="(197, 768)", style=solid]; +"428 linear_29" -> "429 view_63" [label="(197, 768)", style=solid]; +"429 view_63" -> "430 transpose_47" [label="(197, 1, 768)", style=solid]; +"430 transpose_47" -> "431 dropout_22" [label="(1, 197, 768)", style=solid]; +"431 dropout_22" -> "432 add_15" [label="(1, 197, 768)", style=solid]; +"432 add_15" -> "433 layer_norm_15" [label="(1, 197, 768)", style=solid]; +"432 add_15" -> "439 add_16" [label="(1, 197, 768)", style=solid]; +"433 layer_norm_15" -> "434 linear_30" [label="(1, 197, 768)", style=solid]; +"434 linear_30" -> "435 gelu_7" [label="(1, 197, 3072)", style=solid]; +"435 gelu_7" -> "436 dropout_23" [label="(1, 197, 3072)", style=solid]; +"436 dropout_23" -> "437 linear_31" [label="(1, 197, 3072)", style=solid]; +"437 linear_31" -> "438 dropout_24" [label="(1, 197, 768)", style=solid]; +"438 dropout_24" -> "439 add_16" [label="(1, 197, 768)", style=solid]; +"439 add_16" -> "440 layer_norm_16" [label="(1, 197, 768)", style=solid]; +"439 add_16" -> "467 add_17" [label="(1, 197, 768)", style=solid]; +"440 layer_norm_16" -> "441 transpose_48" [label="(1, 197, 768)", style=solid]; +"441 transpose_48" -> "442 linear_32" [label="(197, 1, 768)", style=solid]; +"442 linear_32" -> "443 unflatten_8" [label="(197, 1, 2304)", style=solid]; +"443 unflatten_8" -> "444 unsqueeze_8" [label="(197, 1, 3, 768)", style=solid]; +"444 unsqueeze_8" -> "445 transpose_49" [label="(1, 197, 1, 3, 768)", style=solid]; +"445 transpose_49" -> "446 squeeze_8" [label="(3, 197, 1, 1, 768)", style=solid]; +"446 squeeze_8" -> "447 contiguous_8" [label="(3, 197, 1, 768)", style=solid]; +"447 contiguous_8" -> "448 select_24" [label="(3, 197, 1, 768)", style=solid]; +"447 contiguous_8" -> "449 select_25" [label="(3, 197, 1, 768)", style=solid]; +"447 contiguous_8" -> "450 select_26" [label="(3, 197, 1, 768)", style=solid]; +"448 select_24" -> "451 view_64" [label="(197, 1, 768)", style=solid]; +"449 select_25" -> "453 view_65" [label="(197, 1, 768)", style=solid]; +"450 select_26" -> "455 view_66" [label="(197, 1, 768)", style=solid]; +"451 view_64" -> "452 transpose_50" [label="(197, 12, 64)", style=solid]; +"452 transpose_50" -> "457 view_67" [label="(12, 197, 64)", style=solid]; +"453 view_65" -> "454 transpose_51" [label="(197, 12, 64)", style=solid]; +"454 transpose_51" -> "458 view_68" [label="(12, 197, 64)", style=solid]; +"455 view_66" -> "456 transpose_52" [label="(197, 12, 64)", style=solid]; +"456 transpose_52" -> "459 view_69" [label="(12, 197, 64)", style=solid]; +"457 view_67" -> "460 scaled_dot_product_attention_8" [label="(1, 12, 197, 64)", style=solid]; +"458 view_68" -> "460 scaled_dot_product_attention_8" [label="(1, 12, 197, 64)", style=solid]; +"459 view_69" -> "460 scaled_dot_product_attention_8" [label="(1, 12, 197, 64)", style=solid]; +"460 scaled_dot_product_attention_8" -> "461 permute_9" [label="(1, 12, 197, 64)", style=solid]; +"461 permute_9" -> "462 view_70" [label="(197, 1, 12, 64)", style=solid]; +"462 view_70" -> "463 linear_33" [label="(197, 768)", style=solid]; +"463 linear_33" -> "464 view_71" [label="(197, 768)", style=solid]; +"464 view_71" -> "465 transpose_53" [label="(197, 1, 768)", style=solid]; +"465 transpose_53" -> "466 dropout_25" [label="(1, 197, 768)", style=solid]; +"466 dropout_25" -> "467 add_17" [label="(1, 197, 768)", style=solid]; +"467 add_17" -> "468 layer_norm_17" [label="(1, 197, 768)", style=solid]; +"467 add_17" -> "474 add_18" [label="(1, 197, 768)", style=solid]; +"468 layer_norm_17" -> "469 linear_34" [label="(1, 197, 768)", style=solid]; +"469 linear_34" -> "470 gelu_8" [label="(1, 197, 3072)", style=solid]; +"470 gelu_8" -> "471 dropout_26" [label="(1, 197, 3072)", style=solid]; +"471 dropout_26" -> "472 linear_35" [label="(1, 197, 3072)", style=solid]; +"472 linear_35" -> "473 dropout_27" [label="(1, 197, 768)", style=solid]; +"473 dropout_27" -> "474 add_18" [label="(1, 197, 768)", style=solid]; +"474 add_18" -> "475 layer_norm_18" [label="(1, 197, 768)", style=solid]; +"474 add_18" -> "502 add_19" [label="(1, 197, 768)", style=solid]; +"475 layer_norm_18" -> "476 transpose_54" [label="(1, 197, 768)", style=solid]; +"476 transpose_54" -> "477 linear_36" [label="(197, 1, 768)", style=solid]; +"477 linear_36" -> "478 unflatten_9" [label="(197, 1, 2304)", style=solid]; +"478 unflatten_9" -> "479 unsqueeze_9" [label="(197, 1, 3, 768)", style=solid]; +"479 unsqueeze_9" -> "480 transpose_55" [label="(1, 197, 1, 3, 768)", style=solid]; +"480 transpose_55" -> "481 squeeze_9" [label="(3, 197, 1, 1, 768)", style=solid]; +"481 squeeze_9" -> "482 contiguous_9" [label="(3, 197, 1, 768)", style=solid]; +"482 contiguous_9" -> "483 select_27" [label="(3, 197, 1, 768)", style=solid]; +"482 contiguous_9" -> "484 select_28" [label="(3, 197, 1, 768)", style=solid]; +"482 contiguous_9" -> "485 select_29" [label="(3, 197, 1, 768)", style=solid]; +"483 select_27" -> "486 view_72" [label="(197, 1, 768)", style=solid]; +"484 select_28" -> "488 view_73" [label="(197, 1, 768)", style=solid]; +"485 select_29" -> "490 view_74" [label="(197, 1, 768)", style=solid]; +"486 view_72" -> "487 transpose_56" [label="(197, 12, 64)", style=solid]; +"487 transpose_56" -> "492 view_75" [label="(12, 197, 64)", style=solid]; +"488 view_73" -> "489 transpose_57" [label="(197, 12, 64)", style=solid]; +"489 transpose_57" -> "493 view_76" [label="(12, 197, 64)", style=solid]; +"490 view_74" -> "491 transpose_58" [label="(197, 12, 64)", style=solid]; +"491 transpose_58" -> "494 view_77" [label="(12, 197, 64)", style=solid]; +"492 view_75" -> "495 scaled_dot_product_attention_9" [label="(1, 12, 197, 64)", style=solid]; +"493 view_76" -> "495 scaled_dot_product_attention_9" [label="(1, 12, 197, 64)", style=solid]; +"494 view_77" -> "495 scaled_dot_product_attention_9" [label="(1, 12, 197, 64)", style=solid]; +"495 scaled_dot_product_attention_9" -> "496 permute_10" [label="(1, 12, 197, 64)", style=solid]; +"496 permute_10" -> "497 view_78" [label="(197, 1, 12, 64)", style=solid]; +"497 view_78" -> "498 linear_37" [label="(197, 768)", style=solid]; +"498 linear_37" -> "499 view_79" [label="(197, 768)", style=solid]; +"499 view_79" -> "500 transpose_59" [label="(197, 1, 768)", style=solid]; +"500 transpose_59" -> "501 dropout_28" [label="(1, 197, 768)", style=solid]; +"501 dropout_28" -> "502 add_19" [label="(1, 197, 768)", style=solid]; +"502 add_19" -> "503 layer_norm_19" [label="(1, 197, 768)", style=solid]; +"502 add_19" -> "509 add_20" [label="(1, 197, 768)", style=solid]; +"503 layer_norm_19" -> "504 linear_38" [label="(1, 197, 768)", style=solid]; +"504 linear_38" -> "505 gelu_9" [label="(1, 197, 3072)", style=solid]; +"505 gelu_9" -> "506 dropout_29" [label="(1, 197, 3072)", style=solid]; +"506 dropout_29" -> "507 linear_39" [label="(1, 197, 3072)", style=solid]; +"507 linear_39" -> "508 dropout_30" [label="(1, 197, 768)", style=solid]; +"508 dropout_30" -> "509 add_20" [label="(1, 197, 768)", style=solid]; +"509 add_20" -> "510 layer_norm_20" [label="(1, 197, 768)", style=solid]; +"509 add_20" -> "537 add_21" [label="(1, 197, 768)", style=solid]; +"510 layer_norm_20" -> "511 transpose_60" [label="(1, 197, 768)", style=solid]; +"511 transpose_60" -> "512 linear_40" [label="(197, 1, 768)", style=solid]; +"512 linear_40" -> "513 unflatten_10" [label="(197, 1, 2304)", style=solid]; +"513 unflatten_10" -> "514 unsqueeze_10" [label="(197, 1, 3, 768)", style=solid]; +"514 unsqueeze_10" -> "515 transpose_61" [label="(1, 197, 1, 3, 768)", style=solid]; +"515 transpose_61" -> "516 squeeze_10" [label="(3, 197, 1, 1, 768)", style=solid]; +"516 squeeze_10" -> "517 contiguous_10" [label="(3, 197, 1, 768)", style=solid]; +"517 contiguous_10" -> "518 select_30" [label="(3, 197, 1, 768)", style=solid]; +"517 contiguous_10" -> "519 select_31" [label="(3, 197, 1, 768)", style=solid]; +"517 contiguous_10" -> "520 select_32" [label="(3, 197, 1, 768)", style=solid]; +"518 select_30" -> "521 view_80" [label="(197, 1, 768)", style=solid]; +"519 select_31" -> "523 view_81" [label="(197, 1, 768)", style=solid]; +"520 select_32" -> "525 view_82" [label="(197, 1, 768)", style=solid]; +"521 view_80" -> "522 transpose_62" [label="(197, 12, 64)", style=solid]; +"522 transpose_62" -> "527 view_83" [label="(12, 197, 64)", style=solid]; +"523 view_81" -> "524 transpose_63" [label="(197, 12, 64)", style=solid]; +"524 transpose_63" -> "528 view_84" [label="(12, 197, 64)", style=solid]; +"525 view_82" -> "526 transpose_64" [label="(197, 12, 64)", style=solid]; +"526 transpose_64" -> "529 view_85" [label="(12, 197, 64)", style=solid]; +"527 view_83" -> "530 scaled_dot_product_attention_10" [label="(1, 12, 197, 64)", style=solid]; +"528 view_84" -> "530 scaled_dot_product_attention_10" [label="(1, 12, 197, 64)", style=solid]; +"529 view_85" -> "530 scaled_dot_product_attention_10" [label="(1, 12, 197, 64)", style=solid]; +"530 scaled_dot_product_attention_10" -> "531 permute_11" [label="(1, 12, 197, 64)", style=solid]; +"531 permute_11" -> "532 view_86" [label="(197, 1, 12, 64)", style=solid]; +"532 view_86" -> "533 linear_41" [label="(197, 768)", style=solid]; +"533 linear_41" -> "534 view_87" [label="(197, 768)", style=solid]; +"534 view_87" -> "535 transpose_65" [label="(197, 1, 768)", style=solid]; +"535 transpose_65" -> "536 dropout_31" [label="(1, 197, 768)", style=solid]; +"536 dropout_31" -> "537 add_21" [label="(1, 197, 768)", style=solid]; +"537 add_21" -> "538 layer_norm_21" [label="(1, 197, 768)", style=solid]; +"537 add_21" -> "544 add_22" [label="(1, 197, 768)", style=solid]; +"538 layer_norm_21" -> "539 linear_42" [label="(1, 197, 768)", style=solid]; +"539 linear_42" -> "540 gelu_10" [label="(1, 197, 3072)", style=solid]; +"540 gelu_10" -> "541 dropout_32" [label="(1, 197, 3072)", style=solid]; +"541 dropout_32" -> "542 linear_43" [label="(1, 197, 3072)", style=solid]; +"542 linear_43" -> "543 dropout_33" [label="(1, 197, 768)", style=solid]; +"543 dropout_33" -> "544 add_22" [label="(1, 197, 768)", style=solid]; +"544 add_22" -> "545 layer_norm_22" [label="(1, 197, 768)", style=solid]; +"544 add_22" -> "572 add_23" [label="(1, 197, 768)", style=solid]; +"545 layer_norm_22" -> "546 transpose_66" [label="(1, 197, 768)", style=solid]; +"546 transpose_66" -> "547 linear_44" [label="(197, 1, 768)", style=solid]; +"547 linear_44" -> "548 unflatten_11" [label="(197, 1, 2304)", style=solid]; +"548 unflatten_11" -> "549 unsqueeze_11" [label="(197, 1, 3, 768)", style=solid]; +"549 unsqueeze_11" -> "550 transpose_67" [label="(1, 197, 1, 3, 768)", style=solid]; +"550 transpose_67" -> "551 squeeze_11" [label="(3, 197, 1, 1, 768)", style=solid]; +"551 squeeze_11" -> "552 contiguous_11" [label="(3, 197, 1, 768)", style=solid]; +"552 contiguous_11" -> "553 select_33" [label="(3, 197, 1, 768)", style=solid]; +"552 contiguous_11" -> "554 select_34" [label="(3, 197, 1, 768)", style=solid]; +"552 contiguous_11" -> "555 select_35" [label="(3, 197, 1, 768)", style=solid]; +"553 select_33" -> "556 view_88" [label="(197, 1, 768)", style=solid]; +"554 select_34" -> "558 view_89" [label="(197, 1, 768)", style=solid]; +"555 select_35" -> "560 view_90" [label="(197, 1, 768)", style=solid]; +"556 view_88" -> "557 transpose_68" [label="(197, 12, 64)", style=solid]; +"557 transpose_68" -> "562 view_91" [label="(12, 197, 64)", style=solid]; +"558 view_89" -> "559 transpose_69" [label="(197, 12, 64)", style=solid]; +"559 transpose_69" -> "563 view_92" [label="(12, 197, 64)", style=solid]; +"560 view_90" -> "561 transpose_70" [label="(197, 12, 64)", style=solid]; +"561 transpose_70" -> "564 view_93" [label="(12, 197, 64)", style=solid]; +"562 view_91" -> "565 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"563 view_92" -> "565 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"564 view_93" -> "565 scaled_dot_product_attention_11" [label="(1, 12, 197, 64)", style=solid]; +"565 scaled_dot_product_attention_11" -> "566 permute_12" [label="(1, 12, 197, 64)", style=solid]; +"566 permute_12" -> "567 view_94" [label="(197, 1, 12, 64)", style=solid]; +"567 view_94" -> "568 linear_45" [label="(197, 768)", style=solid]; +"568 linear_45" -> "569 view_95" [label="(197, 768)", style=solid]; +"569 view_95" -> "570 transpose_71" [label="(197, 1, 768)", style=solid]; +"570 transpose_71" -> "571 dropout_34" [label="(1, 197, 768)", style=solid]; +"571 dropout_34" -> "572 add_23" [label="(1, 197, 768)", style=solid]; +"572 add_23" -> "573 layer_norm_23" [label="(1, 197, 768)", style=solid]; +"572 add_23" -> "579 add_24" [label="(1, 197, 768)", style=solid]; +"573 layer_norm_23" -> "574 linear_46" [label="(1, 197, 768)", style=solid]; +"574 linear_46" -> "575 gelu_11" [label="(1, 197, 3072)", style=solid]; +"575 gelu_11" -> "576 dropout_35" [label="(1, 197, 3072)", style=solid]; +"576 dropout_35" -> "577 linear_47" [label="(1, 197, 3072)", style=solid]; +"577 linear_47" -> "578 dropout_36" [label="(1, 197, 768)", style=solid]; +"578 dropout_36" -> "579 add_24" [label="(1, 197, 768)", style=solid]; +"579 add_24" -> "580 layer_norm_24" [label="(1, 197, 768)", style=solid]; +"580 layer_norm_24" -> "581 slice_1" [label="(1, 197, 768)", style=solid]; +"581 slice_1" -> "582 select_36" [label="(1, 197, 768)", style=solid]; +"582 select_36" -> "583 linear_48" [label="(1, 768)", style=solid]; +"583 linear_48" -> "584 output_1" [label="(1, 1000)", style=solid]; } diff --git a/tests/torch/data/reference_graphs/quantized/asymmetric/inception_v3.dot b/tests/torch/data/reference_graphs/quantized/asymmetric/inception_v3.dot index 6500c205a23..7d941f0d9b5 100644 --- a/tests/torch/data/reference_graphs/quantized/asymmetric/inception_v3.dot +++ b/tests/torch/data/reference_graphs/quantized/asymmetric/inception_v3.dot @@ -4,1105 +4,1099 @@ strict digraph { "2 Inception3/__getitem___0" [id=2, type=__getitem__]; "3 Inception3/unsqueeze_0" [id=3, type=unsqueeze]; "4 Inception3/__mul___0" [id=4, type=__mul__]; -"5 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__mul___0|OUTPUT]/asymmetric_quantize_0" [id=5, type=asymmetric_quantize]; -"6 Inception3/__add___0" [id=6, type=__add__]; -"7 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_0" [id=7, type=asymmetric_quantize]; -"8 Inception3/__getitem___1" [id=8, type=__getitem__]; -"9 Inception3/unsqueeze_1" [id=9, type=unsqueeze]; -"10 Inception3/__mul___1" [id=10, type=__mul__]; -"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__mul___1|OUTPUT]/asymmetric_quantize_0" [id=11, type=asymmetric_quantize]; -"12 Inception3/__add___1" [id=12, type=__add__]; -"13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_1" [id=13, type=asymmetric_quantize]; -"14 Inception3/__getitem___2" [id=14, type=__getitem__]; -"15 Inception3/unsqueeze_2" [id=15, type=unsqueeze]; -"16 Inception3/__mul___2" [id=16, type=__mul__]; -"17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__mul___2|OUTPUT]/asymmetric_quantize_0" [id=17, type=asymmetric_quantize]; -"18 Inception3/__add___2" [id=18, type=__add__]; -"19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_2" [id=19, type=asymmetric_quantize]; -"20 Inception3/cat_0" [id=20, type=cat]; -"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=21, type=asymmetric_quantize]; -"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=22, type=conv2d]; -"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=23, type=batch_norm]; -"24 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" [id=24, type=relu_]; -"25 Inception3/BasicConv2d[Conv2d_1a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=25, type=asymmetric_quantize]; -"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=26, type=asymmetric_quantize]; -"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=27, type=conv2d]; -"28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=28, type=batch_norm]; -"29 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" [id=29, type=relu_]; -"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=30, type=asymmetric_quantize]; -"31 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=31, type=asymmetric_quantize]; -"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" [id=32, type=conv2d]; -"33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=33, type=batch_norm]; -"34 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" [id=34, type=relu_]; -"35 Inception3/BasicConv2d[Conv2d_2b_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=35, type=asymmetric_quantize]; -"36 Inception3/max_pool2d_0" [id=36, type=max_pool2d]; -"37 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=37, type=asymmetric_quantize]; -"38 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" [id=38, type=conv2d]; -"39 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=39, type=batch_norm]; -"40 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" [id=40, type=relu_]; -"41 Inception3/BasicConv2d[Conv2d_3b_1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=41, type=asymmetric_quantize]; -"42 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=42, type=asymmetric_quantize]; -"43 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=43, type=conv2d]; -"44 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=44, type=batch_norm]; -"45 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" [id=45, type=relu_]; -"46 Inception3/BasicConv2d[Conv2d_4a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=46, type=asymmetric_quantize]; -"47 Inception3/max_pool2d_1" [id=47, type=max_pool2d]; -"48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=48, type=asymmetric_quantize]; -"49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=49, type=conv2d]; -"50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=50, type=batch_norm]; -"51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" [id=51, type=relu_]; -"52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=52, type=asymmetric_quantize]; -"53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=53, type=asymmetric_quantize]; -"54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=54, type=conv2d]; -"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=55, type=batch_norm]; -"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" [id=56, type=relu_]; -"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=57, type=asymmetric_quantize]; -"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=58, type=asymmetric_quantize]; -"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=59, type=conv2d]; -"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=60, type=batch_norm]; -"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" [id=61, type=relu_]; -"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=62, type=asymmetric_quantize]; -"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=63, type=asymmetric_quantize]; -"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=64, type=conv2d]; -"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=65, type=batch_norm]; -"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=66, type=relu_]; -"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=67, type=asymmetric_quantize]; -"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=68, type=asymmetric_quantize]; -"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=69, type=conv2d]; -"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=70, type=batch_norm]; -"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=71, type=relu_]; -"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=72, type=asymmetric_quantize]; -"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=73, type=asymmetric_quantize]; -"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=74, type=conv2d]; -"75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=75, type=batch_norm]; -"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=76, type=relu_]; -"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=77, type=asymmetric_quantize]; -"78 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" [id=78, type=avg_pool2d]; -"79 Inception3/InceptionA[Mixed_5b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=79, type=asymmetric_quantize]; -"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=80, type=asymmetric_quantize]; -"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=81, type=conv2d]; -"82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=82, type=batch_norm]; -"83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" [id=83, type=relu_]; -"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=84, type=asymmetric_quantize]; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" [id=85, type=cat]; -"86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=86, type=asymmetric_quantize]; -"87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=87, type=conv2d]; -"88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=88, type=batch_norm]; -"89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" [id=89, type=relu_]; -"90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=90, type=asymmetric_quantize]; -"91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=91, type=asymmetric_quantize]; -"92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=92, type=conv2d]; -"93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=93, type=batch_norm]; -"94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" [id=94, type=relu_]; -"95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=95, type=asymmetric_quantize]; -"96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=96, type=asymmetric_quantize]; -"97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=97, type=conv2d]; -"98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=98, type=batch_norm]; -"99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" [id=99, type=relu_]; -"100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=100, type=asymmetric_quantize]; -"101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=101, type=asymmetric_quantize]; -"102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=102, type=conv2d]; -"103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=103, type=batch_norm]; -"104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=104, type=relu_]; -"105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=105, type=asymmetric_quantize]; -"106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=106, type=asymmetric_quantize]; -"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=107, type=conv2d]; -"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=108, type=batch_norm]; -"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=109, type=relu_]; -"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=110, type=asymmetric_quantize]; -"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=111, type=asymmetric_quantize]; -"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=112, type=conv2d]; -"113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=113, type=batch_norm]; -"114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=114, type=relu_]; -"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=115, type=asymmetric_quantize]; -"116 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" [id=116, type=avg_pool2d]; -"117 Inception3/InceptionA[Mixed_5c]/AsymmetricQuantizer/asymmetric_quantize_0" [id=117, type=asymmetric_quantize]; -"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=118, type=asymmetric_quantize]; -"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=119, type=conv2d]; -"120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=120, type=batch_norm]; -"121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" [id=121, type=relu_]; -"122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=122, type=asymmetric_quantize]; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" [id=123, type=cat]; -"124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=124, type=asymmetric_quantize]; -"125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=125, type=conv2d]; -"126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=126, type=batch_norm]; -"127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" [id=127, type=relu_]; -"128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=128, type=asymmetric_quantize]; -"129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=129, type=asymmetric_quantize]; -"130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=130, type=conv2d]; -"131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=131, type=batch_norm]; -"132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" [id=132, type=relu_]; -"133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=133, type=asymmetric_quantize]; -"134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=134, type=asymmetric_quantize]; -"135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=135, type=conv2d]; -"136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=136, type=batch_norm]; -"137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" [id=137, type=relu_]; -"138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=138, type=asymmetric_quantize]; -"139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=139, type=asymmetric_quantize]; -"140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=140, type=conv2d]; -"141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=141, type=batch_norm]; -"142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=142, type=relu_]; -"143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=143, type=asymmetric_quantize]; -"144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=144, type=asymmetric_quantize]; -"145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=145, type=conv2d]; -"146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=146, type=batch_norm]; -"147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=147, type=relu_]; -"148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=148, type=asymmetric_quantize]; -"149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=149, type=asymmetric_quantize]; -"150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=150, type=conv2d]; -"151 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=151, type=batch_norm]; -"152 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=152, type=relu_]; -"153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=153, type=asymmetric_quantize]; -"154 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" [id=154, type=avg_pool2d]; -"155 Inception3/InceptionA[Mixed_5d]/AsymmetricQuantizer/asymmetric_quantize_0" [id=155, type=asymmetric_quantize]; -"156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=156, type=asymmetric_quantize]; -"157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=157, type=conv2d]; -"158 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=158, type=batch_norm]; -"159 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" [id=159, type=relu_]; -"160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=160, type=asymmetric_quantize]; -"161 Inception3/InceptionA[Mixed_5d]/cat_0" [id=161, type=cat]; -"162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=162, type=asymmetric_quantize]; -"163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" [id=163, type=conv2d]; -"164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=164, type=batch_norm]; -"165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" [id=165, type=relu_]; -"166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=166, type=asymmetric_quantize]; -"167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=167, type=asymmetric_quantize]; -"168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=168, type=conv2d]; -"169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=169, type=batch_norm]; -"170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=170, type=relu_]; -"171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=171, type=asymmetric_quantize]; -"172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=172, type=asymmetric_quantize]; -"173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=173, type=conv2d]; -"174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=174, type=batch_norm]; -"175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=175, type=relu_]; -"176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=176, type=asymmetric_quantize]; -"177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=177, type=asymmetric_quantize]; -"178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=178, type=conv2d]; -"179 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=179, type=batch_norm]; -"180 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=180, type=relu_]; -"181 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=181, type=asymmetric_quantize]; -"182 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" [id=182, type=max_pool2d]; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" [id=183, type=cat]; -"184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=184, type=asymmetric_quantize]; -"185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=185, type=conv2d]; -"186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=186, type=batch_norm]; -"187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" [id=187, type=relu_]; -"188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=188, type=asymmetric_quantize]; -"189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=189, type=asymmetric_quantize]; -"190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=190, type=conv2d]; -"191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=191, type=batch_norm]; -"192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" [id=192, type=relu_]; -"193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=193, type=asymmetric_quantize]; -"194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=194, type=asymmetric_quantize]; -"195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=195, type=conv2d]; -"196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=196, type=batch_norm]; -"197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" [id=197, type=relu_]; -"198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=198, type=asymmetric_quantize]; -"199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=199, type=asymmetric_quantize]; -"200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=200, type=conv2d]; -"201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=201, type=batch_norm]; -"202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" [id=202, type=relu_]; -"203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=203, type=asymmetric_quantize]; -"204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=204, type=asymmetric_quantize]; -"205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=205, type=conv2d]; -"206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=206, type=batch_norm]; -"207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=207, type=relu_]; -"208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=208, type=asymmetric_quantize]; -"209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=209, type=asymmetric_quantize]; -"210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=210, type=conv2d]; -"211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=211, type=batch_norm]; -"212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=212, type=relu_]; -"213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=213, type=asymmetric_quantize]; -"214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=214, type=asymmetric_quantize]; -"215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=215, type=conv2d]; -"216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=216, type=batch_norm]; -"217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=217, type=relu_]; -"218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=218, type=asymmetric_quantize]; -"219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=219, type=asymmetric_quantize]; -"220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=220, type=conv2d]; -"221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=221, type=batch_norm]; -"222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=222, type=relu_]; -"223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=223, type=asymmetric_quantize]; -"224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=224, type=asymmetric_quantize]; -"225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=225, type=conv2d]; -"226 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=226, type=batch_norm]; -"227 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=227, type=relu_]; -"228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" [id=228, type=asymmetric_quantize]; -"229 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" [id=229, type=avg_pool2d]; -"230 Inception3/InceptionC[Mixed_6b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=230, type=asymmetric_quantize]; -"231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=231, type=asymmetric_quantize]; -"232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=232, type=conv2d]; -"233 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=233, type=batch_norm]; -"234 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" [id=234, type=relu_]; -"235 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=235, type=asymmetric_quantize]; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" [id=236, type=cat]; -"237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=237, type=asymmetric_quantize]; -"238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=238, type=conv2d]; -"239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=239, type=batch_norm]; -"240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" [id=240, type=relu_]; -"241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=241, type=asymmetric_quantize]; -"242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=242, type=asymmetric_quantize]; -"243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=243, type=conv2d]; -"244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=244, type=batch_norm]; -"245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" [id=245, type=relu_]; -"246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=246, type=asymmetric_quantize]; -"247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=247, type=asymmetric_quantize]; -"248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=248, type=conv2d]; -"249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=249, type=batch_norm]; -"250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" [id=250, type=relu_]; -"251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=251, type=asymmetric_quantize]; -"252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=252, type=asymmetric_quantize]; -"253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=253, type=conv2d]; -"254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=254, type=batch_norm]; -"255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" [id=255, type=relu_]; -"256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=256, type=asymmetric_quantize]; -"257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=257, type=asymmetric_quantize]; -"258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=258, type=conv2d]; -"259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=259, type=batch_norm]; -"260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=260, type=relu_]; -"261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=261, type=asymmetric_quantize]; -"262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=262, type=asymmetric_quantize]; -"263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=263, type=conv2d]; -"264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=264, type=batch_norm]; -"265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=265, type=relu_]; -"266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=266, type=asymmetric_quantize]; -"267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=267, type=asymmetric_quantize]; -"268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=268, type=conv2d]; -"269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=269, type=batch_norm]; -"270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=270, type=relu_]; -"271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=271, type=asymmetric_quantize]; -"272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=272, type=asymmetric_quantize]; -"273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=273, type=conv2d]; -"274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=274, type=batch_norm]; -"275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=275, type=relu_]; -"276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=276, type=asymmetric_quantize]; -"277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=277, type=asymmetric_quantize]; -"278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=278, type=conv2d]; -"279 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=279, type=batch_norm]; -"280 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=280, type=relu_]; -"281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" [id=281, type=asymmetric_quantize]; -"282 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" [id=282, type=avg_pool2d]; -"283 Inception3/InceptionC[Mixed_6c]/AsymmetricQuantizer/asymmetric_quantize_0" [id=283, type=asymmetric_quantize]; -"284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=284, type=asymmetric_quantize]; -"285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=285, type=conv2d]; -"286 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=286, type=batch_norm]; -"287 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" [id=287, type=relu_]; -"288 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=288, type=asymmetric_quantize]; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" [id=289, type=cat]; -"290 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=290, type=asymmetric_quantize]; -"291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=291, type=conv2d]; -"292 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=292, type=batch_norm]; -"293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" [id=293, type=relu_]; -"294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=294, type=asymmetric_quantize]; -"295 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=295, type=asymmetric_quantize]; -"296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=296, type=conv2d]; -"297 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=297, type=batch_norm]; -"298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" [id=298, type=relu_]; -"299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=299, type=asymmetric_quantize]; -"300 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=300, type=asymmetric_quantize]; -"301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=301, type=conv2d]; -"302 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=302, type=batch_norm]; -"303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" [id=303, type=relu_]; -"304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=304, type=asymmetric_quantize]; -"305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=305, type=asymmetric_quantize]; -"306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=306, type=conv2d]; -"307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=307, type=batch_norm]; -"308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" [id=308, type=relu_]; -"309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=309, type=asymmetric_quantize]; -"310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=310, type=asymmetric_quantize]; -"311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=311, type=conv2d]; -"312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=312, type=batch_norm]; -"313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=313, type=relu_]; -"314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=314, type=asymmetric_quantize]; -"315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=315, type=asymmetric_quantize]; -"316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=316, type=conv2d]; -"317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=317, type=batch_norm]; -"318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=318, type=relu_]; -"319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=319, type=asymmetric_quantize]; -"320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=320, type=asymmetric_quantize]; -"321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=321, type=conv2d]; -"322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=322, type=batch_norm]; -"323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=323, type=relu_]; -"324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=324, type=asymmetric_quantize]; -"325 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=325, type=asymmetric_quantize]; -"326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=326, type=conv2d]; -"327 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=327, type=batch_norm]; -"328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=328, type=relu_]; -"329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=329, type=asymmetric_quantize]; -"330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=330, type=asymmetric_quantize]; -"331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=331, type=conv2d]; -"332 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=332, type=batch_norm]; -"333 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=333, type=relu_]; -"334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" [id=334, type=asymmetric_quantize]; -"335 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" [id=335, type=avg_pool2d]; -"336 Inception3/InceptionC[Mixed_6d]/AsymmetricQuantizer/asymmetric_quantize_0" [id=336, type=asymmetric_quantize]; -"337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=337, type=asymmetric_quantize]; -"338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=338, type=conv2d]; -"339 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=339, type=batch_norm]; -"340 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" [id=340, type=relu_]; -"341 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=341, type=asymmetric_quantize]; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" [id=342, type=cat]; -"343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=343, type=asymmetric_quantize]; -"344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=344, type=conv2d]; -"345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=345, type=batch_norm]; -"346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" [id=346, type=relu_]; -"347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=347, type=asymmetric_quantize]; -"348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=348, type=asymmetric_quantize]; -"349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=349, type=conv2d]; -"350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=350, type=batch_norm]; -"351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" [id=351, type=relu_]; -"352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=352, type=asymmetric_quantize]; -"353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=353, type=asymmetric_quantize]; -"354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=354, type=conv2d]; -"355 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=355, type=batch_norm]; -"356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" [id=356, type=relu_]; -"357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=357, type=asymmetric_quantize]; -"358 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=358, type=asymmetric_quantize]; -"359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=359, type=conv2d]; -"360 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=360, type=batch_norm]; -"361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" [id=361, type=relu_]; -"362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=362, type=asymmetric_quantize]; -"363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=363, type=asymmetric_quantize]; -"364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=364, type=conv2d]; -"365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=365, type=batch_norm]; -"366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=366, type=relu_]; -"367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=367, type=asymmetric_quantize]; -"368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=368, type=asymmetric_quantize]; -"369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=369, type=conv2d]; -"370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=370, type=batch_norm]; -"371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=371, type=relu_]; -"372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=372, type=asymmetric_quantize]; -"373 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=373, type=asymmetric_quantize]; -"374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=374, type=conv2d]; -"375 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=375, type=batch_norm]; -"376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=376, type=relu_]; -"377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=377, type=asymmetric_quantize]; -"378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=378, type=asymmetric_quantize]; -"379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=379, type=conv2d]; -"380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=380, type=batch_norm]; -"381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=381, type=relu_]; -"382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=382, type=asymmetric_quantize]; -"383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=383, type=asymmetric_quantize]; -"384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=384, type=conv2d]; -"385 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=385, type=batch_norm]; -"386 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=386, type=relu_]; -"387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" [id=387, type=asymmetric_quantize]; -"388 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" [id=388, type=avg_pool2d]; -"389 Inception3/InceptionC[Mixed_6e]/AsymmetricQuantizer/asymmetric_quantize_0" [id=389, type=asymmetric_quantize]; -"390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=390, type=asymmetric_quantize]; -"391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=391, type=conv2d]; -"392 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=392, type=batch_norm]; -"393 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" [id=393, type=relu_]; -"394 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=394, type=asymmetric_quantize]; -"395 Inception3/InceptionC[Mixed_6e]/cat_0" [id=395, type=cat]; -"396 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=396, type=asymmetric_quantize]; -"397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=397, type=conv2d]; -"398 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=398, type=batch_norm]; -"399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" [id=399, type=relu_]; -"400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=400, type=asymmetric_quantize]; -"401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=401, type=asymmetric_quantize]; -"402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" [id=402, type=conv2d]; -"403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=403, type=batch_norm]; -"404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" [id=404, type=relu_]; -"405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=405, type=asymmetric_quantize]; -"406 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=406, type=asymmetric_quantize]; -"407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" [id=407, type=conv2d]; -"408 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=408, type=batch_norm]; -"409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" [id=409, type=relu_]; -"410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=410, type=asymmetric_quantize]; -"411 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=411, type=asymmetric_quantize]; -"412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" [id=412, type=conv2d]; -"413 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=413, type=batch_norm]; -"414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" [id=414, type=relu_]; -"415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=415, type=asymmetric_quantize]; -"416 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=416, type=asymmetric_quantize]; -"417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" [id=417, type=conv2d]; -"418 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=418, type=batch_norm]; -"419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" [id=419, type=relu_]; -"420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=420, type=asymmetric_quantize]; -"421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=421, type=asymmetric_quantize]; -"422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" [id=422, type=conv2d]; -"423 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=423, type=batch_norm]; -"424 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" [id=424, type=relu_]; -"425 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=425, type=asymmetric_quantize]; -"426 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" [id=426, type=max_pool2d]; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" [id=427, type=cat]; -"428 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=428, type=asymmetric_quantize]; -"429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=429, type=conv2d]; -"430 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=430, type=batch_norm]; -"431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" [id=431, type=relu_]; -"432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=432, type=asymmetric_quantize]; -"433 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=433, type=asymmetric_quantize]; -"434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=434, type=conv2d]; -"435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=435, type=batch_norm]; -"436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" [id=436, type=relu_]; -"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=437, type=asymmetric_quantize]; -"438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=438, type=asymmetric_quantize]; -"439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" [id=439, type=conv2d]; -"440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=440, type=batch_norm]; -"441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" [id=441, type=relu_]; -"442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0" [id=442, type=asymmetric_quantize]; -"443 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=443, type=asymmetric_quantize]; -"444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" [id=444, type=conv2d]; -"445 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=445, type=batch_norm]; -"446 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" [id=446, type=relu_]; -"447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=447, type=asymmetric_quantize]; -"448 Inception3/InceptionE[Mixed_7b]/cat_0" [id=448, type=cat]; -"449 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=449, type=asymmetric_quantize]; -"450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=450, type=conv2d]; -"451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=451, type=batch_norm]; -"452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=452, type=relu_]; -"453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=453, type=asymmetric_quantize]; -"454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=454, type=asymmetric_quantize]; -"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=455, type=conv2d]; -"456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=456, type=batch_norm]; -"457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=457, type=relu_]; -"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=458, type=asymmetric_quantize]; -"459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=459, type=asymmetric_quantize]; -"460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=460, type=conv2d]; -"461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=461, type=batch_norm]; -"462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=462, type=relu_]; -"463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0" [id=463, type=asymmetric_quantize]; -"464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=464, type=asymmetric_quantize]; -"465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=465, type=conv2d]; -"466 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=466, type=batch_norm]; -"467 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=467, type=relu_]; -"468 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=468, type=asymmetric_quantize]; -"469 Inception3/InceptionE[Mixed_7b]/cat_1" [id=469, type=cat]; -"470 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" [id=470, type=avg_pool2d]; -"471 Inception3/InceptionE[Mixed_7b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=471, type=asymmetric_quantize]; -"472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=472, type=asymmetric_quantize]; -"473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=473, type=conv2d]; -"474 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=474, type=batch_norm]; -"475 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" [id=475, type=relu_]; -"476 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=476, type=asymmetric_quantize]; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" [id=477, type=cat]; -"478 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=478, type=asymmetric_quantize]; -"479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=479, type=conv2d]; -"480 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=480, type=batch_norm]; -"481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" [id=481, type=relu_]; -"482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=482, type=asymmetric_quantize]; -"483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=483, type=asymmetric_quantize]; -"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=484, type=conv2d]; -"485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=485, type=batch_norm]; -"486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" [id=486, type=relu_]; -"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=487, type=asymmetric_quantize]; -"488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=488, type=asymmetric_quantize]; -"489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" [id=489, type=conv2d]; -"490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=490, type=batch_norm]; -"491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" [id=491, type=relu_]; -"492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0" [id=492, type=asymmetric_quantize]; -"493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=493, type=asymmetric_quantize]; -"494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" [id=494, type=conv2d]; -"495 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=495, type=batch_norm]; -"496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" [id=496, type=relu_]; -"497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=497, type=asymmetric_quantize]; -"498 Inception3/InceptionE[Mixed_7c]/cat_0" [id=498, type=cat]; -"499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=499, type=asymmetric_quantize]; -"500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=500, type=conv2d]; -"501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=501, type=batch_norm]; -"502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=502, type=relu_]; -"503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=503, type=asymmetric_quantize]; -"504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=504, type=asymmetric_quantize]; -"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=505, type=conv2d]; -"506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=506, type=batch_norm]; -"507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=507, type=relu_]; -"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=508, type=asymmetric_quantize]; -"509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=509, type=asymmetric_quantize]; -"510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=510, type=conv2d]; -"511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=511, type=batch_norm]; -"512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=512, type=relu_]; -"513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0" [id=513, type=asymmetric_quantize]; -"514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=514, type=asymmetric_quantize]; -"515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=515, type=conv2d]; -"516 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=516, type=batch_norm]; -"517 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=517, type=relu_]; -"518 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=518, type=asymmetric_quantize]; -"519 Inception3/InceptionE[Mixed_7c]/cat_1" [id=519, type=cat]; -"520 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" [id=520, type=avg_pool2d]; -"521 Inception3/InceptionE[Mixed_7c]/AsymmetricQuantizer/asymmetric_quantize_0" [id=521, type=asymmetric_quantize]; -"522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=522, type=asymmetric_quantize]; -"523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=523, type=conv2d]; -"524 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=524, type=batch_norm]; -"525 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" [id=525, type=relu_]; -"526 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=526, type=asymmetric_quantize]; -"527 Inception3/InceptionE[Mixed_7c]/cat_2" [id=527, type=cat]; -"528 Inception3/adaptive_avg_pool2d_0" [id=528, type=adaptive_avg_pool2d]; -"529 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/asymmetric_quantize_0" [id=529, type=asymmetric_quantize]; -"530 Inception3/dropout_0" [id=530, type=dropout]; -"531 Inception3/view_0" [id=531, type=view]; -"532 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=532, type=asymmetric_quantize]; -"533 Inception3/NNCFLinear[fc]/linear_0" [id=533, type=linear]; -"534 /nncf_model_output_0" [id=534, type=nncf_model_output]; +"5 Inception3/__add___0" [id=5, type=__add__]; +"6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_0" [id=6, type=asymmetric_quantize]; +"7 Inception3/__getitem___1" [id=7, type=__getitem__]; +"8 Inception3/unsqueeze_1" [id=8, type=unsqueeze]; +"9 Inception3/__mul___1" [id=9, type=__mul__]; +"10 Inception3/__add___1" [id=10, type=__add__]; +"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_1" [id=11, type=asymmetric_quantize]; +"12 Inception3/__getitem___2" [id=12, type=__getitem__]; +"13 Inception3/unsqueeze_2" [id=13, type=unsqueeze]; +"14 Inception3/__mul___2" [id=14, type=__mul__]; +"15 Inception3/__add___2" [id=15, type=__add__]; +"16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_2" [id=16, type=asymmetric_quantize]; +"17 Inception3/cat_0" [id=17, type=cat]; +"18 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=18, type=asymmetric_quantize]; +"19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=19, type=conv2d]; +"20 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=20, type=batch_norm]; +"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" [id=21, type=relu_]; +"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=22, type=asymmetric_quantize]; +"23 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=23, type=asymmetric_quantize]; +"24 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=24, type=conv2d]; +"25 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=25, type=batch_norm]; +"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" [id=26, type=relu_]; +"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=27, type=asymmetric_quantize]; +"28 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=28, type=asymmetric_quantize]; +"29 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" [id=29, type=conv2d]; +"30 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=30, type=batch_norm]; +"31 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" [id=31, type=relu_]; +"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=32, type=asymmetric_quantize]; +"33 Inception3/max_pool2d_0" [id=33, type=max_pool2d]; +"34 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=34, type=asymmetric_quantize]; +"35 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" [id=35, type=conv2d]; +"36 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=36, type=batch_norm]; +"37 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" [id=37, type=relu_]; +"38 Inception3/BasicConv2d[Conv2d_3b_1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=38, type=asymmetric_quantize]; +"39 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=39, type=asymmetric_quantize]; +"40 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=40, type=conv2d]; +"41 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=41, type=batch_norm]; +"42 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" [id=42, type=relu_]; +"43 Inception3/BasicConv2d[Conv2d_4a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=43, type=asymmetric_quantize]; +"44 Inception3/max_pool2d_1" [id=44, type=max_pool2d]; +"45 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=45, type=asymmetric_quantize]; +"46 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=46, type=conv2d]; +"47 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=47, type=batch_norm]; +"48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" [id=48, type=relu_]; +"49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=49, type=asymmetric_quantize]; +"50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=50, type=asymmetric_quantize]; +"51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=51, type=conv2d]; +"52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=52, type=batch_norm]; +"53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" [id=53, type=relu_]; +"54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=54, type=asymmetric_quantize]; +"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=55, type=asymmetric_quantize]; +"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=56, type=conv2d]; +"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=57, type=batch_norm]; +"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" [id=58, type=relu_]; +"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=59, type=asymmetric_quantize]; +"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=60, type=asymmetric_quantize]; +"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=61, type=conv2d]; +"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=62, type=batch_norm]; +"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=63, type=relu_]; +"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=64, type=asymmetric_quantize]; +"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=65, type=asymmetric_quantize]; +"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=66, type=conv2d]; +"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=67, type=batch_norm]; +"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=68, type=relu_]; +"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=69, type=asymmetric_quantize]; +"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=70, type=asymmetric_quantize]; +"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=71, type=conv2d]; +"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=72, type=batch_norm]; +"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=73, type=relu_]; +"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=74, type=asymmetric_quantize]; +"75 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" [id=75, type=avg_pool2d]; +"76 Inception3/InceptionA[Mixed_5b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=76, type=asymmetric_quantize]; +"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=77, type=asymmetric_quantize]; +"78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=78, type=conv2d]; +"79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=79, type=batch_norm]; +"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" [id=80, type=relu_]; +"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=81, type=asymmetric_quantize]; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" [id=82, type=cat]; +"83 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=83, type=asymmetric_quantize]; +"84 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=84, type=conv2d]; +"85 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=85, type=batch_norm]; +"86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" [id=86, type=relu_]; +"87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=87, type=asymmetric_quantize]; +"88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=88, type=asymmetric_quantize]; +"89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=89, type=conv2d]; +"90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=90, type=batch_norm]; +"91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" [id=91, type=relu_]; +"92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=92, type=asymmetric_quantize]; +"93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=93, type=asymmetric_quantize]; +"94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=94, type=conv2d]; +"95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=95, type=batch_norm]; +"96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" [id=96, type=relu_]; +"97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=97, type=asymmetric_quantize]; +"98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=98, type=asymmetric_quantize]; +"99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=99, type=conv2d]; +"100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=100, type=batch_norm]; +"101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=101, type=relu_]; +"102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=102, type=asymmetric_quantize]; +"103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=103, type=asymmetric_quantize]; +"104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=104, type=conv2d]; +"105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=105, type=batch_norm]; +"106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=106, type=relu_]; +"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=107, type=asymmetric_quantize]; +"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=108, type=asymmetric_quantize]; +"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=109, type=conv2d]; +"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=110, type=batch_norm]; +"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=111, type=relu_]; +"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=112, type=asymmetric_quantize]; +"113 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" [id=113, type=avg_pool2d]; +"114 Inception3/InceptionA[Mixed_5c]/AsymmetricQuantizer/asymmetric_quantize_0" [id=114, type=asymmetric_quantize]; +"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=115, type=asymmetric_quantize]; +"116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=116, type=conv2d]; +"117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=117, type=batch_norm]; +"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" [id=118, type=relu_]; +"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=119, type=asymmetric_quantize]; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" [id=120, type=cat]; +"121 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=121, type=asymmetric_quantize]; +"122 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=122, type=conv2d]; +"123 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=123, type=batch_norm]; +"124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" [id=124, type=relu_]; +"125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=125, type=asymmetric_quantize]; +"126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=126, type=asymmetric_quantize]; +"127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=127, type=conv2d]; +"128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=128, type=batch_norm]; +"129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" [id=129, type=relu_]; +"130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=130, type=asymmetric_quantize]; +"131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=131, type=asymmetric_quantize]; +"132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=132, type=conv2d]; +"133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=133, type=batch_norm]; +"134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" [id=134, type=relu_]; +"135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=135, type=asymmetric_quantize]; +"136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=136, type=asymmetric_quantize]; +"137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=137, type=conv2d]; +"138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=138, type=batch_norm]; +"139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=139, type=relu_]; +"140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=140, type=asymmetric_quantize]; +"141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=141, type=asymmetric_quantize]; +"142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=142, type=conv2d]; +"143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=143, type=batch_norm]; +"144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=144, type=relu_]; +"145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=145, type=asymmetric_quantize]; +"146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=146, type=asymmetric_quantize]; +"147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=147, type=conv2d]; +"148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=148, type=batch_norm]; +"149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=149, type=relu_]; +"150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=150, type=asymmetric_quantize]; +"151 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" [id=151, type=avg_pool2d]; +"152 Inception3/InceptionA[Mixed_5d]/AsymmetricQuantizer/asymmetric_quantize_0" [id=152, type=asymmetric_quantize]; +"153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=153, type=asymmetric_quantize]; +"154 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=154, type=conv2d]; +"155 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=155, type=batch_norm]; +"156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" [id=156, type=relu_]; +"157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=157, type=asymmetric_quantize]; +"158 Inception3/InceptionA[Mixed_5d]/cat_0" [id=158, type=cat]; +"159 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=159, type=asymmetric_quantize]; +"160 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" [id=160, type=conv2d]; +"161 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=161, type=batch_norm]; +"162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" [id=162, type=relu_]; +"163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=163, type=asymmetric_quantize]; +"164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=164, type=asymmetric_quantize]; +"165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=165, type=conv2d]; +"166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=166, type=batch_norm]; +"167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=167, type=relu_]; +"168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=168, type=asymmetric_quantize]; +"169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=169, type=asymmetric_quantize]; +"170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=170, type=conv2d]; +"171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=171, type=batch_norm]; +"172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=172, type=relu_]; +"173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=173, type=asymmetric_quantize]; +"174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=174, type=asymmetric_quantize]; +"175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=175, type=conv2d]; +"176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=176, type=batch_norm]; +"177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=177, type=relu_]; +"178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=178, type=asymmetric_quantize]; +"179 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" [id=179, type=max_pool2d]; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" [id=180, type=cat]; 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=188, type=batch_norm]; +"189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" [id=189, type=relu_]; +"190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=190, type=asymmetric_quantize]; +"191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=191, type=asymmetric_quantize]; +"192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=192, type=conv2d]; +"193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=193, type=batch_norm]; +"194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" [id=194, type=relu_]; +"195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=195, type=asymmetric_quantize]; +"196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=196, type=asymmetric_quantize]; +"197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=197, type=conv2d]; +"198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=198, type=batch_norm]; +"199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" [id=199, type=relu_]; +"200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=200, type=asymmetric_quantize]; +"201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=201, type=asymmetric_quantize]; +"202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=202, type=conv2d]; +"203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=203, type=batch_norm]; +"204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=204, type=relu_]; +"205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=205, type=asymmetric_quantize]; +"206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=206, type=asymmetric_quantize]; +"207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=207, type=conv2d]; +"208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=208, type=batch_norm]; +"209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=209, type=relu_]; +"210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=210, type=asymmetric_quantize]; +"211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=211, type=asymmetric_quantize]; +"212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=212, type=conv2d]; +"213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=213, type=batch_norm]; +"214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=214, type=relu_]; +"215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=215, type=asymmetric_quantize]; +"216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=216, type=asymmetric_quantize]; +"217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=217, type=conv2d]; +"218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=218, type=batch_norm]; +"219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=219, type=relu_]; +"220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=220, type=asymmetric_quantize]; +"221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=221, type=asymmetric_quantize]; +"222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=222, type=conv2d]; +"223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=223, type=batch_norm]; +"224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=224, type=relu_]; +"225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" [id=225, type=asymmetric_quantize]; +"226 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" [id=226, type=avg_pool2d]; +"227 Inception3/InceptionC[Mixed_6b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=227, type=asymmetric_quantize]; +"228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=228, type=asymmetric_quantize]; +"229 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=229, type=conv2d]; +"230 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=230, type=batch_norm]; +"231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" [id=231, type=relu_]; +"232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=232, type=asymmetric_quantize]; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" [id=233, type=cat]; +"234 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=234, type=asymmetric_quantize]; +"235 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=235, type=conv2d]; +"236 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=236, type=batch_norm]; +"237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" [id=237, type=relu_]; +"238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=238, type=asymmetric_quantize]; +"239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=239, type=asymmetric_quantize]; +"240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=240, type=conv2d]; +"241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=241, type=batch_norm]; +"242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" [id=242, type=relu_]; +"243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=243, type=asymmetric_quantize]; +"244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=244, type=asymmetric_quantize]; +"245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=245, type=conv2d]; +"246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=246, type=batch_norm]; +"247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" [id=247, type=relu_]; +"248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=248, type=asymmetric_quantize]; +"249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=249, type=asymmetric_quantize]; +"250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=250, type=conv2d]; +"251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=251, type=batch_norm]; +"252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" [id=252, type=relu_]; +"253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=253, type=asymmetric_quantize]; +"254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=254, type=asymmetric_quantize]; +"255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=255, type=conv2d]; +"256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=256, type=batch_norm]; +"257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=257, type=relu_]; +"258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=258, type=asymmetric_quantize]; +"259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=259, type=asymmetric_quantize]; +"260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=260, type=conv2d]; +"261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=261, type=batch_norm]; +"262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=262, type=relu_]; +"263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=263, type=asymmetric_quantize]; +"264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=264, type=asymmetric_quantize]; +"265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=265, type=conv2d]; +"266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=266, type=batch_norm]; +"267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=267, type=relu_]; +"268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=268, type=asymmetric_quantize]; +"269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=269, type=asymmetric_quantize]; +"270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=270, type=conv2d]; +"271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=271, type=batch_norm]; +"272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=272, type=relu_]; +"273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=273, type=asymmetric_quantize]; +"274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=274, type=asymmetric_quantize]; +"275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=275, type=conv2d]; +"276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=276, type=batch_norm]; +"277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=277, type=relu_]; +"278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" [id=278, type=asymmetric_quantize]; +"279 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" [id=279, type=avg_pool2d]; +"280 Inception3/InceptionC[Mixed_6c]/AsymmetricQuantizer/asymmetric_quantize_0" [id=280, type=asymmetric_quantize]; +"281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=281, type=asymmetric_quantize]; +"282 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=282, type=conv2d]; +"283 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=283, type=batch_norm]; +"284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" [id=284, type=relu_]; +"285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" [id=285, type=asymmetric_quantize]; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" [id=286, type=cat]; +"287 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=287, type=asymmetric_quantize]; +"288 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type=asymmetric_quantize]; +"303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=303, type=conv2d]; +"304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=304, type=batch_norm]; +"305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" [id=305, type=relu_]; +"306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=306, type=asymmetric_quantize]; +"307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=307, type=asymmetric_quantize]; +"308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=308, type=conv2d]; +"309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=309, type=batch_norm]; +"310 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Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=317, type=asymmetric_quantize]; +"318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=318, type=conv2d]; +"319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=319, type=batch_norm]; +"320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=320, type=relu_]; +"321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" [id=321, type=asymmetric_quantize]; +"322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=322, type=asymmetric_quantize]; +"323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=323, type=conv2d]; 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type=asymmetric_quantize]; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" [id=339, type=cat]; +"340 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=340, type=asymmetric_quantize]; +"341 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=341, type=conv2d]; +"342 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=342, type=batch_norm]; +"343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" [id=343, type=relu_]; +"344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=344, type=asymmetric_quantize]; +"345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=345, type=asymmetric_quantize]; +"346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=346, type=conv2d]; +"347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=347, type=batch_norm]; +"348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" [id=348, type=relu_]; +"349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=349, type=asymmetric_quantize]; +"350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=350, type=asymmetric_quantize]; +"351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=351, type=conv2d]; +"352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=352, type=batch_norm]; +"353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" [id=353, type=relu_]; 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type=asymmetric_quantize]; +"361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=361, type=conv2d]; +"362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=362, type=batch_norm]; +"363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=363, type=relu_]; +"364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=364, type=asymmetric_quantize]; +"365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=365, type=asymmetric_quantize]; +"366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=366, type=conv2d]; +"367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=367, type=batch_norm]; +"368 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Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=375, type=asymmetric_quantize]; +"376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=376, type=conv2d]; +"377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=377, type=batch_norm]; +"378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=378, type=relu_]; +"379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=379, type=asymmetric_quantize]; +"380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=380, type=asymmetric_quantize]; +"381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=381, type=conv2d]; 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Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=398, type=asymmetric_quantize]; +"399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" [id=399, type=conv2d]; +"400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=400, type=batch_norm]; +"401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" [id=401, type=relu_]; +"402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=402, type=asymmetric_quantize]; +"403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=403, type=asymmetric_quantize]; +"404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" [id=404, type=conv2d]; +"405 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type=asymmetric_quantize]; +"419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" [id=419, type=conv2d]; +"420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=420, type=batch_norm]; +"421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" [id=421, type=relu_]; +"422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/AsymmetricQuantizer/asymmetric_quantize_0" [id=422, type=asymmetric_quantize]; +"423 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" [id=423, type=max_pool2d]; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" [id=424, type=cat]; +"425 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=425, type=asymmetric_quantize]; +"426 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=426, type=conv2d]; +"427 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type=asymmetric_quantize]; +"435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=435, type=asymmetric_quantize]; +"436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" [id=436, type=conv2d]; +"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=437, type=batch_norm]; +"438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" [id=438, type=relu_]; +"439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0" [id=439, type=asymmetric_quantize]; +"440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=440, type=asymmetric_quantize]; +"441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" 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Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=450, type=asymmetric_quantize]; +"451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=451, type=asymmetric_quantize]; +"452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=452, type=conv2d]; +"453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=453, type=batch_norm]; +"454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=454, type=relu_]; +"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=455, type=asymmetric_quantize]; +"456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=456, type=asymmetric_quantize]; +"457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=457, type=conv2d]; +"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=458, type=batch_norm]; +"459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=459, type=relu_]; +"460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0" [id=460, type=asymmetric_quantize]; +"461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=461, type=asymmetric_quantize]; +"462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=462, type=conv2d]; +"463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=463, type=batch_norm]; +"464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=464, type=relu_]; +"465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=465, type=asymmetric_quantize]; +"466 Inception3/InceptionE[Mixed_7b]/cat_1" [id=466, type=cat]; +"467 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" [id=467, type=avg_pool2d]; +"468 Inception3/InceptionE[Mixed_7b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=468, type=asymmetric_quantize]; +"469 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=469, type=asymmetric_quantize]; +"470 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=470, type=conv2d]; +"471 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=471, type=batch_norm]; +"472 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Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=480, type=asymmetric_quantize]; +"481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=481, type=conv2d]; +"482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=482, type=batch_norm]; +"483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" [id=483, type=relu_]; +"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=484, type=asymmetric_quantize]; +"485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=485, type=asymmetric_quantize]; +"486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" [id=486, type=conv2d]; +"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=487, type=batch_norm]; +"488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" [id=488, type=relu_]; +"489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0" [id=489, type=asymmetric_quantize]; +"490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=490, type=asymmetric_quantize]; +"491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" [id=491, type=conv2d]; +"492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=492, type=batch_norm]; +"493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" [id=493, type=relu_]; +"494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=494, type=asymmetric_quantize]; +"495 Inception3/InceptionE[Mixed_7c]/cat_0" [id=495, type=cat]; +"496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=496, type=asymmetric_quantize]; +"497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=497, type=conv2d]; +"498 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=498, type=batch_norm]; +"499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=499, type=relu_]; +"500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" [id=500, type=asymmetric_quantize]; +"501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=501, type=asymmetric_quantize]; +"502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=502, type=conv2d]; +"503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=503, type=batch_norm]; +"504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=504, type=relu_]; +"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" [id=505, type=asymmetric_quantize]; +"506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=506, type=asymmetric_quantize]; +"507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=507, type=conv2d]; +"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=508, type=batch_norm]; +"509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=509, type=relu_]; +"510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0" [id=510, type=asymmetric_quantize]; +"511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=511, type=asymmetric_quantize]; +"512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=512, type=conv2d]; +"513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=513, type=batch_norm]; +"514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=514, type=relu_]; +"515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0" [id=515, type=asymmetric_quantize]; +"516 Inception3/InceptionE[Mixed_7c]/cat_1" [id=516, type=cat]; +"517 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Inception3/cat_0"; -"8 Inception3/__getitem___1" -> "9 Inception3/unsqueeze_1"; -"9 Inception3/unsqueeze_1" -> "10 Inception3/__mul___1"; -"10 Inception3/__mul___1" -> "11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__mul___1|OUTPUT]/asymmetric_quantize_0"; -"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__mul___1|OUTPUT]/asymmetric_quantize_0" -> "12 Inception3/__add___1"; -"12 Inception3/__add___1" -> "13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_1"; -"13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_1" -> "20 Inception3/cat_0"; -"14 Inception3/__getitem___2" -> "15 Inception3/unsqueeze_2"; -"15 Inception3/unsqueeze_2" -> "16 Inception3/__mul___2"; -"16 Inception3/__mul___2" -> "17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__mul___2|OUTPUT]/asymmetric_quantize_0"; -"17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__mul___2|OUTPUT]/asymmetric_quantize_0" -> "18 Inception3/__add___2"; -"18 Inception3/__add___2" -> "19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_2"; -"19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_2" -> "20 Inception3/cat_0"; -"20 Inception3/cat_0" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "24 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0"; -"24 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" -> "25 Inception3/BasicConv2d[Conv2d_1a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"25 Inception3/BasicConv2d[Conv2d_1a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "29 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0"; -"29 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" -> "30 Inception3/BasicConv2d[Conv2d_2a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; -"31 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; -"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" -> "33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "34 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0"; -"34 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" -> "35 Inception3/BasicConv2d[Conv2d_2b_3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"35 Inception3/BasicConv2d[Conv2d_2b_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "36 Inception3/max_pool2d_0"; -"36 Inception3/max_pool2d_0" -> "38 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; -"37 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "38 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; -"38 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" -> "39 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"39 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "40 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0"; -"40 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" -> "41 Inception3/BasicConv2d[Conv2d_3b_1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"41 Inception3/BasicConv2d[Conv2d_3b_1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "43 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"42 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "43 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"43 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "44 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"44 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "45 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0"; -"45 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" -> "46 Inception3/BasicConv2d[Conv2d_4a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"46 Inception3/BasicConv2d[Conv2d_4a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "47 Inception3/max_pool2d_1"; -"47 Inception3/max_pool2d_1" -> "49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"47 Inception3/max_pool2d_1" -> "54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"47 Inception3/max_pool2d_1" -> "64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"47 Inception3/max_pool2d_1" -> "78 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0"; -"48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0"; -"51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" -> "52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "85 Inception3/InceptionA[Mixed_5b]/cat_0"; -"53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0"; -"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" -> "57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0"; -"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" -> "62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "85 Inception3/InceptionA[Mixed_5b]/cat_0"; -"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "85 Inception3/InceptionA[Mixed_5b]/cat_0"; -"78 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" -> "79 Inception3/InceptionA[Mixed_5b]/AsymmetricQuantizer/asymmetric_quantize_0"; -"79 Inception3/InceptionA[Mixed_5b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0"; -"83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" -> "84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "85 Inception3/InceptionA[Mixed_5b]/cat_0"; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" -> "87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" -> "92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" -> "102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" -> "116 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0"; -"86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0"; -"89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" -> "90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "123 Inception3/InceptionA[Mixed_5c]/cat_0"; -"91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0"; -"94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" -> "95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0"; -"99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" -> "100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "123 Inception3/InceptionA[Mixed_5c]/cat_0"; -"101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "123 Inception3/InceptionA[Mixed_5c]/cat_0"; -"116 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" -> "117 Inception3/InceptionA[Mixed_5c]/AsymmetricQuantizer/asymmetric_quantize_0"; -"117 Inception3/InceptionA[Mixed_5c]/AsymmetricQuantizer/asymmetric_quantize_0" -> "119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0"; -"121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" -> "122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "123 Inception3/InceptionA[Mixed_5c]/cat_0"; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" -> "125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" -> "130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" -> "140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" -> "154 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0"; -"124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0"; -"127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" -> "128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5d]/cat_0"; -"129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0"; -"132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" -> "133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0"; -"137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" -> "138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5d]/cat_0"; -"139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "151 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"151 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "152 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"152 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5d]/cat_0"; -"154 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" -> "155 Inception3/InceptionA[Mixed_5d]/AsymmetricQuantizer/asymmetric_quantize_0"; -"155 Inception3/InceptionA[Mixed_5d]/AsymmetricQuantizer/asymmetric_quantize_0" -> "157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "158 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"158 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "159 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0"; -"159 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" -> "160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5d]/cat_0"; -"161 Inception3/InceptionA[Mixed_5d]/cat_0" -> "163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; -"161 Inception3/InceptionA[Mixed_5d]/cat_0" -> "168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"161 Inception3/InceptionA[Mixed_5d]/cat_0" -> "182 Inception3/InceptionB[Mixed_6a]/max_pool2d_0"; -"162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; -"163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" -> "164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0"; -"165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" -> "166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "183 Inception3/InceptionB[Mixed_6a]/cat_0"; -"167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "179 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"179 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "180 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"180 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "181 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"181 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "183 Inception3/InceptionB[Mixed_6a]/cat_0"; -"182 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" -> "183 Inception3/InceptionB[Mixed_6a]/cat_0"; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" -> "185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" -> "190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" -> "205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" -> "229 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0"; -"184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0"; -"187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" -> "188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "236 Inception3/InceptionC[Mixed_6b]/cat_0"; -"189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0"; -"192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" -> "193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0"; -"197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" -> "198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0"; -"202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" -> "203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "236 Inception3/InceptionC[Mixed_6b]/cat_0"; -"204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0"; -"223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "226 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"226 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "227 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"227 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0"; -"228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" -> "236 Inception3/InceptionC[Mixed_6b]/cat_0"; -"229 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" -> "230 Inception3/InceptionC[Mixed_6b]/AsymmetricQuantizer/asymmetric_quantize_0"; -"230 Inception3/InceptionC[Mixed_6b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "233 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"233 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "234 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0"; -"234 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" -> "235 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"235 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "236 Inception3/InceptionC[Mixed_6b]/cat_0"; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" -> "238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" -> "243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" -> "258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" -> "282 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0"; -"237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0"; -"240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" -> "241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6c]/cat_0"; -"242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0"; -"245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" -> "246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0"; -"250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" -> "251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0"; -"255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" -> "256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6c]/cat_0"; -"257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0"; -"276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "279 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"279 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "280 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"280 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0"; -"281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6c]/cat_0"; -"282 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" -> "283 Inception3/InceptionC[Mixed_6c]/AsymmetricQuantizer/asymmetric_quantize_0"; -"283 Inception3/InceptionC[Mixed_6c]/AsymmetricQuantizer/asymmetric_quantize_0" -> "285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "286 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"286 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "287 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0"; -"287 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" -> "288 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"288 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6c]/cat_0"; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" -> "291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" -> "296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" -> "311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" -> "335 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0"; -"290 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "292 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"292 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0"; -"293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" -> "294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "342 Inception3/InceptionC[Mixed_6d]/cat_0"; -"295 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "297 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"297 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0"; -"298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" -> "299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"300 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "302 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"302 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0"; -"303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" -> "304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0"; -"308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" -> "309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "342 Inception3/InceptionC[Mixed_6d]/cat_0"; -"310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"325 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "327 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"327 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0"; -"329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "332 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"332 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "333 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"333 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0"; -"334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" -> "342 Inception3/InceptionC[Mixed_6d]/cat_0"; -"335 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" -> "336 Inception3/InceptionC[Mixed_6d]/AsymmetricQuantizer/asymmetric_quantize_0"; -"336 Inception3/InceptionC[Mixed_6d]/AsymmetricQuantizer/asymmetric_quantize_0" -> "338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "339 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"339 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "340 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0"; -"340 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" -> "341 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"341 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "342 Inception3/InceptionC[Mixed_6d]/cat_0"; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" -> "344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" -> "349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" -> "364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" -> "388 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0"; -"343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0"; -"346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" -> "347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "395 Inception3/InceptionC[Mixed_6e]/cat_0"; -"348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0"; -"351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" -> "352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "355 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"355 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0"; -"356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" -> "357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"358 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "360 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"360 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0"; -"361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" -> "362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "395 Inception3/InceptionC[Mixed_6e]/cat_0"; -"363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"373 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "375 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"375 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0"; -"382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "385 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"385 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "386 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"386 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0"; -"387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" -> "395 Inception3/InceptionC[Mixed_6e]/cat_0"; -"388 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" -> "389 Inception3/InceptionC[Mixed_6e]/AsymmetricQuantizer/asymmetric_quantize_0"; -"389 Inception3/InceptionC[Mixed_6e]/AsymmetricQuantizer/asymmetric_quantize_0" -> "391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "392 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"392 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "393 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0"; -"393 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" -> "394 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"394 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "395 Inception3/InceptionC[Mixed_6e]/cat_0"; -"395 Inception3/InceptionC[Mixed_6e]/cat_0" -> "397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"395 Inception3/InceptionC[Mixed_6e]/cat_0" -> "407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; -"395 Inception3/InceptionC[Mixed_6e]/cat_0" -> "426 Inception3/InceptionD[Mixed_7a]/max_pool2d_0"; -"396 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "398 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"398 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0"; -"399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" -> "400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; -"401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; -"402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" -> "403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0"; -"404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" -> "405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "427 Inception3/InceptionD[Mixed_7a]/cat_0"; -"406 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; -"407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" -> "408 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"408 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0"; -"409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" -> "410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; -"411 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; -"412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" -> "413 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"413 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0"; -"414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" -> "415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; -"416 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; -"417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" -> "418 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"418 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0"; -"419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" -> "420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/AsymmetricQuantizer/asymmetric_quantize_0"; -"420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; -"421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; -"422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" -> "423 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"423 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "424 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0"; -"424 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" -> "425 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/AsymmetricQuantizer/asymmetric_quantize_0"; -"425 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "427 Inception3/InceptionD[Mixed_7a]/cat_0"; -"426 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" -> "427 Inception3/InceptionD[Mixed_7a]/cat_0"; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" -> "429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" -> "434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" -> "450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" -> "470 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0"; -"428 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "430 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"430 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0"; -"431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" -> "432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "477 Inception3/InceptionE[Mixed_7b]/cat_2"; -"433 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0"; -"436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" -> "437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0"; -"441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" -> "442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0"; -"442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0" -> "448 Inception3/InceptionE[Mixed_7b]/cat_0"; -"443 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "445 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"445 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "446 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0"; -"446 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" -> "447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0"; -"447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "448 Inception3/InceptionE[Mixed_7b]/cat_0"; -"448 Inception3/InceptionE[Mixed_7b]/cat_0" -> "477 Inception3/InceptionE[Mixed_7b]/cat_2"; -"449 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0"; -"462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0"; -"463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0" -> "469 Inception3/InceptionE[Mixed_7b]/cat_1"; -"464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "466 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"466 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "467 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0"; -"467 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "468 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0"; -"468 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "469 Inception3/InceptionE[Mixed_7b]/cat_1"; -"469 Inception3/InceptionE[Mixed_7b]/cat_1" -> "477 Inception3/InceptionE[Mixed_7b]/cat_2"; -"470 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" -> "471 Inception3/InceptionE[Mixed_7b]/AsymmetricQuantizer/asymmetric_quantize_0"; -"471 Inception3/InceptionE[Mixed_7b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "474 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"474 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "475 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0"; -"475 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" -> "476 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"476 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "477 Inception3/InceptionE[Mixed_7b]/cat_2"; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" -> "479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" -> "484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" -> "500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" -> "520 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0"; -"478 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "480 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"480 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0"; -"481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" -> "482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "527 Inception3/InceptionE[Mixed_7c]/cat_2"; -"483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0"; -"486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" -> "487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0"; -"491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" -> "492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0"; -"492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0" -> "498 Inception3/InceptionE[Mixed_7c]/cat_0"; -"493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "495 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"495 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0"; -"496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" -> "497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0"; -"497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "498 Inception3/InceptionE[Mixed_7c]/cat_0"; -"498 Inception3/InceptionE[Mixed_7c]/cat_0" -> "527 Inception3/InceptionE[Mixed_7c]/cat_2"; -"499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; -"503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; -"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0"; -"512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0"; -"513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0" -> "519 Inception3/InceptionE[Mixed_7c]/cat_1"; -"514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "516 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"516 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "517 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0"; -"517 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "518 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0"; -"518 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "519 Inception3/InceptionE[Mixed_7c]/cat_1"; -"519 Inception3/InceptionE[Mixed_7c]/cat_1" -> "527 Inception3/InceptionE[Mixed_7c]/cat_2"; -"520 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" -> "521 Inception3/InceptionE[Mixed_7c]/AsymmetricQuantizer/asymmetric_quantize_0"; -"521 Inception3/InceptionE[Mixed_7c]/AsymmetricQuantizer/asymmetric_quantize_0" -> "523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "524 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"524 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "525 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0"; -"525 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" -> "526 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; -"526 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "527 Inception3/InceptionE[Mixed_7c]/cat_2"; -"527 Inception3/InceptionE[Mixed_7c]/cat_2" -> "528 Inception3/adaptive_avg_pool2d_0"; -"528 Inception3/adaptive_avg_pool2d_0" -> "529 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/asymmetric_quantize_0"; -"529 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/asymmetric_quantize_0" -> "530 Inception3/dropout_0"; -"530 Inception3/dropout_0" -> "531 Inception3/view_0"; -"531 Inception3/view_0" -> "533 Inception3/NNCFLinear[fc]/linear_0"; -"532 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "533 Inception3/NNCFLinear[fc]/linear_0"; -"533 Inception3/NNCFLinear[fc]/linear_0" -> "534 /nncf_model_output_0"; +"4 Inception3/__mul___0" -> "5 Inception3/__add___0"; +"5 Inception3/__add___0" -> "6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_0"; +"6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_0" -> "17 Inception3/cat_0"; +"7 Inception3/__getitem___1" -> "8 Inception3/unsqueeze_1"; +"8 Inception3/unsqueeze_1" -> "9 Inception3/__mul___1"; +"9 Inception3/__mul___1" -> "10 Inception3/__add___1"; +"10 Inception3/__add___1" -> "11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_1"; +"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_1" -> "17 Inception3/cat_0"; +"12 Inception3/__getitem___2" -> "13 Inception3/unsqueeze_2"; +"13 Inception3/unsqueeze_2" -> "14 Inception3/__mul___2"; +"14 Inception3/__mul___2" -> "15 Inception3/__add___2"; +"15 Inception3/__add___2" -> "16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_2"; +"16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/asymmetric_quantize_2" -> "17 Inception3/cat_0"; +"17 Inception3/cat_0" -> "19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"18 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "20 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"20 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "21 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0"; +"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "24 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"23 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "24 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"24 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "25 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"25 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "26 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0"; +"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" -> "27 Inception3/BasicConv2d[Conv2d_2a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "29 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; +"28 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "29 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; +"29 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" -> "30 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"30 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "31 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0"; +"31 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" -> "32 Inception3/BasicConv2d[Conv2d_2b_3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "33 Inception3/max_pool2d_0"; +"33 Inception3/max_pool2d_0" -> "35 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; +"34 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "35 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; +"35 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" -> "36 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"36 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "37 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0"; +"37 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" -> "38 Inception3/BasicConv2d[Conv2d_3b_1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"38 Inception3/BasicConv2d[Conv2d_3b_1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "40 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"39 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "40 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"40 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "41 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"41 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "42 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0"; +"42 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" -> "43 Inception3/BasicConv2d[Conv2d_4a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"43 Inception3/BasicConv2d[Conv2d_4a_3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "44 Inception3/max_pool2d_1"; +"44 Inception3/max_pool2d_1" -> "46 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"44 Inception3/max_pool2d_1" -> "51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"44 Inception3/max_pool2d_1" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"44 Inception3/max_pool2d_1" -> "75 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0"; +"45 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "46 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"46 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "47 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"47 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0"; +"48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" -> "49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/cat_0"; +"50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0"; +"53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" -> "54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0"; +"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" -> "59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/cat_0"; +"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/cat_0"; +"75 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" -> "76 Inception3/InceptionA[Mixed_5b]/AsymmetricQuantizer/asymmetric_quantize_0"; +"76 Inception3/InceptionA[Mixed_5b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0"; +"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" -> "81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/cat_0"; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" -> "84 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" -> "89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" -> "99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" -> "113 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0"; +"83 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "84 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"84 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "85 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"85 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0"; +"86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" -> "87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/cat_0"; +"88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0"; +"91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" -> "92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0"; +"96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" -> "97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/cat_0"; +"98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/cat_0"; +"113 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" -> "114 Inception3/InceptionA[Mixed_5c]/AsymmetricQuantizer/asymmetric_quantize_0"; +"114 Inception3/InceptionA[Mixed_5c]/AsymmetricQuantizer/asymmetric_quantize_0" -> "116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0"; +"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" -> "119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/cat_0"; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" -> "122 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" -> "127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" -> "137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" -> "151 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0"; +"121 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "122 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"122 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "123 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"123 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0"; +"124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" -> "125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5d]/cat_0"; +"126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0"; +"129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" -> "130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0"; +"134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" -> "135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5d]/cat_0"; +"136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5d]/cat_0"; +"151 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" -> "152 Inception3/InceptionA[Mixed_5d]/AsymmetricQuantizer/asymmetric_quantize_0"; +"152 Inception3/InceptionA[Mixed_5d]/AsymmetricQuantizer/asymmetric_quantize_0" -> "154 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "154 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"154 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "155 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"155 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0"; +"156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" -> "157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5d]/cat_0"; +"158 Inception3/InceptionA[Mixed_5d]/cat_0" -> "160 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; +"158 Inception3/InceptionA[Mixed_5d]/cat_0" -> "165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"158 Inception3/InceptionA[Mixed_5d]/cat_0" -> "179 Inception3/InceptionB[Mixed_6a]/max_pool2d_0"; +"159 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "160 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; +"160 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" -> "161 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"161 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0"; +"162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" -> "163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "180 Inception3/InceptionB[Mixed_6a]/cat_0"; +"164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "180 Inception3/InceptionB[Mixed_6a]/cat_0"; +"179 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" -> "180 Inception3/InceptionB[Mixed_6a]/cat_0"; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" -> "182 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" -> "187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" -> "202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" -> "226 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0"; +"181 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "182 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"182 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "183 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"183 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0"; +"184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" -> "185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "233 Inception3/InceptionC[Mixed_6b]/cat_0"; +"186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0"; +"189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" -> "190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0"; +"194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" -> "195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0"; +"199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" -> "200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "233 Inception3/InceptionC[Mixed_6b]/cat_0"; +"201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0"; +"220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0"; +"225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" -> "233 Inception3/InceptionC[Mixed_6b]/cat_0"; +"226 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" -> "227 Inception3/InceptionC[Mixed_6b]/AsymmetricQuantizer/asymmetric_quantize_0"; +"227 Inception3/InceptionC[Mixed_6b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "229 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "229 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"229 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "230 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"230 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0"; +"231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" -> "232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "233 Inception3/InceptionC[Mixed_6b]/cat_0"; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" -> "235 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" -> "240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" -> "255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" -> "279 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0"; +"234 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "235 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"235 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "236 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"236 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0"; +"237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" -> "238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6c]/cat_0"; +"239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0"; +"242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" -> "243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0"; +"247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" -> "248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0"; +"252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" -> "253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6c]/cat_0"; +"254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0"; +"273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0"; +"278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6c]/cat_0"; +"279 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" -> "280 Inception3/InceptionC[Mixed_6c]/AsymmetricQuantizer/asymmetric_quantize_0"; +"280 Inception3/InceptionC[Mixed_6c]/AsymmetricQuantizer/asymmetric_quantize_0" -> "282 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "282 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"282 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "283 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"283 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0"; +"284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" -> "285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6c]/cat_0"; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" -> "288 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" -> "293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" -> "308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" -> "332 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0"; +"287 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "288 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"288 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "289 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"289 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "290 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0"; +"290 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" -> "291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "339 Inception3/InceptionC[Mixed_6d]/cat_0"; +"292 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "295 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0"; +"295 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" -> "296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"297 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "300 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0"; +"300 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" -> "301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"302 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0"; +"305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" -> "306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "339 Inception3/InceptionC[Mixed_6d]/cat_0"; +"307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "325 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"325 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0"; +"326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"327 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0"; +"331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" -> "339 Inception3/InceptionC[Mixed_6d]/cat_0"; +"332 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" -> "333 Inception3/InceptionC[Mixed_6d]/AsymmetricQuantizer/asymmetric_quantize_0"; +"333 Inception3/InceptionC[Mixed_6d]/AsymmetricQuantizer/asymmetric_quantize_0" -> "335 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "335 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"335 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "336 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"336 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0"; +"337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" -> "338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "339 Inception3/InceptionC[Mixed_6d]/cat_0"; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" -> "341 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" -> "346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" -> "361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" -> "385 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0"; +"340 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "341 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"341 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "342 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"342 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0"; +"343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" -> "344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "392 Inception3/InceptionC[Mixed_6e]/cat_0"; +"345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0"; +"348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" -> "349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0"; +"353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" -> "354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"355 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "358 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0"; +"358 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" -> "359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "392 Inception3/InceptionC[Mixed_6e]/cat_0"; +"360 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "373 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"373 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"375 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0"; +"379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0"; +"384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/AsymmetricQuantizer/asymmetric_quantize_0" -> "392 Inception3/InceptionC[Mixed_6e]/cat_0"; +"385 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" -> "386 Inception3/InceptionC[Mixed_6e]/AsymmetricQuantizer/asymmetric_quantize_0"; +"386 Inception3/InceptionC[Mixed_6e]/AsymmetricQuantizer/asymmetric_quantize_0" -> "388 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "388 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"388 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "389 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"389 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0"; +"390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" -> "391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "392 Inception3/InceptionC[Mixed_6e]/cat_0"; +"392 Inception3/InceptionC[Mixed_6e]/cat_0" -> "394 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"392 Inception3/InceptionC[Mixed_6e]/cat_0" -> "404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; +"392 Inception3/InceptionC[Mixed_6e]/cat_0" -> "423 Inception3/InceptionD[Mixed_7a]/max_pool2d_0"; +"393 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "394 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"394 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "395 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"395 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "396 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0"; +"396 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" -> "397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; +"398 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; +"399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" -> "400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0"; +"401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" -> "402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "424 Inception3/InceptionD[Mixed_7a]/cat_0"; +"403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; +"404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" -> "405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "406 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0"; +"406 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" -> "407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; +"408 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; +"409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" -> "410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "411 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0"; +"411 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" -> "412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; +"413 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; +"414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" -> "415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "416 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0"; +"416 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" -> "417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/AsymmetricQuantizer/asymmetric_quantize_0"; +"417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/AsymmetricQuantizer/asymmetric_quantize_0" -> "419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; +"418 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; +"419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" -> "420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0"; +"421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" -> "422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/AsymmetricQuantizer/asymmetric_quantize_0"; +"422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/AsymmetricQuantizer/asymmetric_quantize_0" -> "424 Inception3/InceptionD[Mixed_7a]/cat_0"; +"423 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" -> "424 Inception3/InceptionD[Mixed_7a]/cat_0"; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" -> "426 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" -> "431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" -> "447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" -> "467 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0"; +"425 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "426 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"426 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "427 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"427 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "428 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0"; +"428 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" -> "429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "474 Inception3/InceptionE[Mixed_7b]/cat_2"; +"430 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "433 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0"; +"433 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" -> "434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0"; +"438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" -> "439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0"; +"439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0" -> "445 Inception3/InceptionE[Mixed_7b]/cat_0"; +"440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "443 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0"; +"443 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" -> "444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0"; +"444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "445 Inception3/InceptionE[Mixed_7b]/cat_0"; +"445 Inception3/InceptionE[Mixed_7b]/cat_0" -> "474 Inception3/InceptionE[Mixed_7b]/cat_2"; +"446 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "448 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"448 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "449 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"449 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0"; +"459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0"; +"460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0" -> "466 Inception3/InceptionE[Mixed_7b]/cat_1"; +"461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0"; +"464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0"; +"465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "466 Inception3/InceptionE[Mixed_7b]/cat_1"; +"466 Inception3/InceptionE[Mixed_7b]/cat_1" -> "474 Inception3/InceptionE[Mixed_7b]/cat_2"; +"467 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" -> "468 Inception3/InceptionE[Mixed_7b]/AsymmetricQuantizer/asymmetric_quantize_0"; +"468 Inception3/InceptionE[Mixed_7b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "470 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"469 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "470 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"470 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "471 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"471 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0"; +"472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" -> "473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "474 Inception3/InceptionE[Mixed_7b]/cat_2"; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" -> "476 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" -> "481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" -> "497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" -> "517 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0"; +"475 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "476 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"476 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "477 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"477 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "478 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0"; +"478 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" -> "479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "524 Inception3/InceptionE[Mixed_7c]/cat_2"; +"480 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0"; +"483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" -> "484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0"; +"488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" -> "489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0"; +"489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/AsymmetricQuantizer/asymmetric_quantize_0" -> "495 Inception3/InceptionE[Mixed_7c]/cat_0"; +"490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0"; +"493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" -> "494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0"; +"494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "495 Inception3/InceptionE[Mixed_7c]/cat_0"; +"495 Inception3/InceptionE[Mixed_7c]/cat_0" -> "524 Inception3/InceptionE[Mixed_7c]/cat_2"; +"496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "498 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"498 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0"; +"500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/AsymmetricQuantizer/asymmetric_quantize_0" -> "502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0"; +"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/AsymmetricQuantizer/asymmetric_quantize_0" -> "512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0"; +"509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0"; +"510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/AsymmetricQuantizer/asymmetric_quantize_0" -> "516 Inception3/InceptionE[Mixed_7c]/cat_1"; +"511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0"; +"514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0"; +"515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/AsymmetricQuantizer/asymmetric_quantize_0" -> "516 Inception3/InceptionE[Mixed_7c]/cat_1"; +"516 Inception3/InceptionE[Mixed_7c]/cat_1" -> "524 Inception3/InceptionE[Mixed_7c]/cat_2"; +"517 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" -> "518 Inception3/InceptionE[Mixed_7c]/AsymmetricQuantizer/asymmetric_quantize_0"; +"518 Inception3/InceptionE[Mixed_7c]/AsymmetricQuantizer/asymmetric_quantize_0" -> "520 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"519 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "520 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"520 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "521 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"521 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0"; +"522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" -> "523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0"; +"523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/AsymmetricQuantizer/asymmetric_quantize_0" -> "524 Inception3/InceptionE[Mixed_7c]/cat_2"; +"524 Inception3/InceptionE[Mixed_7c]/cat_2" -> "525 Inception3/adaptive_avg_pool2d_0"; +"525 Inception3/adaptive_avg_pool2d_0" -> "526 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/asymmetric_quantize_0"; +"526 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/AsymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/asymmetric_quantize_0" -> "527 Inception3/dropout_0"; +"527 Inception3/dropout_0" -> "528 Inception3/view_0"; +"528 Inception3/view_0" -> "530 Inception3/NNCFLinear[fc]/linear_0"; +"529 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "530 Inception3/NNCFLinear[fc]/linear_0"; +"530 Inception3/NNCFLinear[fc]/linear_0" -> "531 /nncf_model_output_0"; } diff --git a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_bi_seq.dot b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_bi_seq.dot index 1cec7bf6341..abeff76408b 100644 --- a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_bi_seq.dot +++ b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_bi_seq.dot @@ -12,133 +12,129 @@ strict digraph { "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=11, type=asymmetric_quantize]; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=13, type=asymmetric_quantize]; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=15, type=asymmetric_quantize]; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=17, type=asymmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=19, type=asymmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=21, type=asymmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" [id=24, type=asymmetric_quantize]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=26, type=linear]; -"27 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"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6"; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "65 /nncf_model_output_2"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "63 /nncf_model_output_0"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "64 /nncf_model_output_1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "63 /nncf_model_output_2"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "61 /nncf_model_output_0"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "62 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_bi_stacked.dot b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_bi_stacked.dot index 2b72621c2b4..cad352b14ab 100644 --- a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_bi_stacked.dot +++ b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_bi_stacked.dot @@ -12,262 +12,254 @@ strict digraph { "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=11, type=asymmetric_quantize]; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=13, type=asymmetric_quantize]; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=15, type=asymmetric_quantize]; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=17, type=asymmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=19, type=asymmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=21, type=asymmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" [id=24, type=asymmetric_quantize]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=26, type=linear]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=27, type=asymmetric_quantize]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=28, type=cat]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=29, type=view]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=30, type=asymmetric_quantize]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=31, type=linear]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=32, type=asymmetric_quantize]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=33, type=asymmetric_quantize]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=34, type=linear]; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=35, type=asymmetric_quantize]; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=36, type=__add__]; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=37, type=chunk]; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=38, type=sigmoid]; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=39, type=asymmetric_quantize]; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=40, type=sigmoid]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=13, type=tanh]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=14, type=asymmetric_quantize]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=15, type=sigmoid]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=16, type=asymmetric_quantize]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=17, type=__mul__]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=18, type=asymmetric_quantize]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=19, type=__mul__]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=20, type=asymmetric_quantize]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=21, type=__add__]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=22, type=tanh]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=23, type=asymmetric_quantize]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=24, type=__mul__]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=25, type=linear]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=26, type=asymmetric_quantize]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=27, type=cat]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=28, type=view]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=29, type=asymmetric_quantize]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=30, type=linear]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=31, type=asymmetric_quantize]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=32, type=asymmetric_quantize]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=33, type=linear]; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=34, type=asymmetric_quantize]; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=35, type=__add__]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=36, type=chunk]; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=37, type=sigmoid]; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=38, type=asymmetric_quantize]; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=39, type=sigmoid]; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=40, type=tanh]; "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=41, type=asymmetric_quantize]; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=42, type=tanh]; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=42, type=sigmoid]; "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=43, type=asymmetric_quantize]; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=44, type=sigmoid]; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" [id=44, type=__mul__]; "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=45, type=asymmetric_quantize]; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" [id=46, type=__mul__]; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" [id=46, type=__mul__]; "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=47, type=asymmetric_quantize]; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" [id=48, type=__mul__]; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=49, type=asymmetric_quantize]; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" [id=50, type=__add__]; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" [id=51, type=tanh]; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" [id=52, type=asymmetric_quantize]; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" [id=53, type=__mul__]; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=54, type=linear]; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=55, type=asymmetric_quantize]; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [id=56, type=cat]; -"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" [id=57, type=view]; -"58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=58, type=cat]; -"59 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0" [id=59, type=asymmetric_quantize]; -"60 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NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "119 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "117 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; -"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; -"69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; -"70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; -"71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0"; -"72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0" -> "73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; -"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; -"74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; -"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; -"76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; -"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; -"78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1" -> "79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; -"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; -"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1"; -"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "119 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6"; -"82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; -"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "117 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "85 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" -> "87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0"; -"87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" -> "116 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; -"91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; -"94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0" -> "95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; -"97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; -"98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; -"99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0"; -"100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0" -> "101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; -"101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; -"102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; -"103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; -"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" -> "105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; -"105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; -"106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; -"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; -"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1"; -"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "119 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" -> "110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6"; -"110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" -> "111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "117 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0"; -"115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" -> "116 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"116 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "123 /nncf_model_output_2"; -"117 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "118 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"118 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "121 /nncf_model_output_0"; -"119 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "120 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"120 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "122 /nncf_model_output_1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0" -> "86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; +"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; +"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; +"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0"; +"69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; +"71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; +"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0" -> "74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; +"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1" -> "76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" -> "79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0"; +"84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"85 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; +"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; +"91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0" -> "92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; +"94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; +"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0"; +"96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0" -> "97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; +"98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; +"100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" -> "101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; +"102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" -> "103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; +"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1"; +"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" -> "106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; +"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" -> "111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0"; +"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"112 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "119 /nncf_model_output_2"; +"113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "114 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"114 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "117 /nncf_model_output_0"; +"115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "116 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"116 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "118 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_cell.dot b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_cell.dot index c3c0b51ffe8..8de873db389 100644 --- a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_cell.dot +++ b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_cell.dot @@ -12,21 +12,20 @@ strict digraph { "10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=11, type=asymmetric_quantize]; "12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=13, type=asymmetric_quantize]; -"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=15, type=asymmetric_quantize]; -"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=17, type=asymmetric_quantize]; -"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=19, type=asymmetric_quantize]; -"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=21, type=asymmetric_quantize]; -"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" [id=24, type=asymmetric_quantize]; -"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 /nncf_model_output_0" [id=26, type=nncf_model_output]; -"27 /nncf_model_output_1" [id=27, type=nncf_model_output]; +"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" [id=13, type=tanh]; +"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=14, type=asymmetric_quantize]; +"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=15, type=sigmoid]; +"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=16, type=asymmetric_quantize]; +"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" [id=17, type=__mul__]; +"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=18, type=asymmetric_quantize]; +"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" [id=19, type=__mul__]; +"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=20, type=asymmetric_quantize]; +"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" [id=21, type=__add__]; +"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" [id=22, type=tanh]; +"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=23, type=asymmetric_quantize]; +"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" [id=24, type=__mul__]; +"25 /nncf_model_output_0" [id=25, type=nncf_model_output]; +"26 /nncf_model_output_1" [id=26, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 AsymmetricQuantizer/asymmetric_quantize_0"; "1 AsymmetricQuantizer/asymmetric_quantize_0" -> "3 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "3 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; @@ -38,23 +37,22 @@ strict digraph { "8 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___0" -> "9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0"; "9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0"; "9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0"; -"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0"; +"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2"; "10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; -"11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; -"12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; -"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0"; -"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" -> "15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; -"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; -"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; -"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" -> "19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; -"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; -"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" -> "21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; -"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; -"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1"; -"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "27 /nncf_model_output_1"; -"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6"; -"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" -> "25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; -"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" -> "26 /nncf_model_output_0"; +"11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; +"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; +"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "26 /nncf_model_output_1"; +"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 /nncf_model_output_0"; } diff --git a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_uni_seq.dot b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_uni_seq.dot index b572562e32f..77f7b3f28f1 100644 --- a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_uni_seq.dot +++ b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_uni_seq.dot @@ -12,70 +12,68 @@ strict digraph { "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=11, type=asymmetric_quantize]; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=13, type=asymmetric_quantize]; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=15, type=asymmetric_quantize]; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=17, type=asymmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=19, type=asymmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=21, type=asymmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" [id=24, type=asymmetric_quantize]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=26, type=linear]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=27, type=asymmetric_quantize]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=28, type=cat]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=29, type=view]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=30, type=cat]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=31, type=cat]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=32, type=view]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=33, type=cat]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=34, type=view]; -"35 /nncf_model_output_0" [id=35, type=nncf_model_output]; -"36 /nncf_model_output_1" [id=36, type=nncf_model_output]; -"37 /nncf_model_output_2" [id=37, type=nncf_model_output]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=13, type=tanh]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=14, type=asymmetric_quantize]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=15, type=sigmoid]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=16, type=asymmetric_quantize]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=17, type=__mul__]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=18, type=asymmetric_quantize]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=19, type=__mul__]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=20, type=asymmetric_quantize]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=21, type=__add__]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=22, type=tanh]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=23, type=asymmetric_quantize]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=24, type=__mul__]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=25, type=linear]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=26, type=asymmetric_quantize]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=27, type=cat]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=28, type=view]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=29, type=cat]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=30, type=cat]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=31, type=view]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=32, type=cat]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=33, type=view]; +"34 /nncf_model_output_0" [id=34, type=nncf_model_output]; +"35 /nncf_model_output_1" [id=35, type=nncf_model_output]; +"36 /nncf_model_output_2" [id=36, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 AsymmetricQuantizer/asymmetric_quantize_0"; "1 AsymmetricQuantizer/asymmetric_quantize_0" -> "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "4 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; "4 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; -"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; -"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6"; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "37 /nncf_model_output_2"; -"31 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"32 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "35 /nncf_model_output_0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"34 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "36 /nncf_model_output_1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "36 /nncf_model_output_2"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "34 /nncf_model_output_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "35 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_uni_stacked.dot b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_uni_stacked.dot index 9424d07d13e..668084a48eb 100644 --- a/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_uni_stacked.dot +++ b/tests/torch/data/reference_graphs/quantized/asymmetric/lstm_uni_stacked.dot @@ -12,136 +12,132 @@ strict digraph { "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=11, type=asymmetric_quantize]; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=13, type=asymmetric_quantize]; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=15, type=asymmetric_quantize]; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=17, type=asymmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=19, type=asymmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=21, type=asymmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" [id=24, type=asymmetric_quantize]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=26, type=linear]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=27, type=asymmetric_quantize]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=28, type=cat]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=29, type=view]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=30, type=cat]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0" [id=31, type=asymmetric_quantize]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=32, type=asymmetric_quantize]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=33, type=linear]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=34, type=asymmetric_quantize]; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=35, type=asymmetric_quantize]; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=36, type=linear]; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=37, type=asymmetric_quantize]; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=38, type=__add__]; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=39, type=chunk]; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=40, type=sigmoid]; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=41, type=asymmetric_quantize]; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=42, type=sigmoid]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=13, type=tanh]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=14, type=asymmetric_quantize]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=15, type=sigmoid]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" [id=16, type=asymmetric_quantize]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=17, type=__mul__]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" [id=18, type=asymmetric_quantize]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=19, type=__mul__]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" [id=20, type=asymmetric_quantize]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=21, type=__add__]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=22, type=tanh]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" [id=23, type=asymmetric_quantize]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=24, type=__mul__]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=25, type=linear]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=26, type=asymmetric_quantize]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=27, type=cat]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=28, type=view]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=29, type=cat]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0" [id=30, type=asymmetric_quantize]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=31, type=asymmetric_quantize]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=32, type=linear]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=33, type=asymmetric_quantize]; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" [id=34, type=asymmetric_quantize]; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=35, type=linear]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" [id=36, type=asymmetric_quantize]; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=37, type=__add__]; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=38, type=chunk]; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=39, type=sigmoid]; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" [id=40, type=asymmetric_quantize]; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=41, type=sigmoid]; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=42, type=tanh]; "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" [id=43, type=asymmetric_quantize]; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=44, type=tanh]; +"44 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NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; -"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "18 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"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_6" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "67 /nncf_model_output_2"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "65 /nncf_model_output_0"; -"63 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"64 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "66 /nncf_model_output_1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/AsymmetricQuantizer/asymmetric_quantize_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/AsymmetricQuantizer[op]/asymmetric_quantize_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_1" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_2" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_3" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_4" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/AsymmetricQuantizer/asymmetric_quantize_5" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/AsymmetricQuantizer/asymmetric_quantize_0"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "65 /nncf_model_output_2"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "63 /nncf_model_output_0"; +"61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"62 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "64 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized/hw/CPU/inception_v3.dot b/tests/torch/data/reference_graphs/quantized/hw/CPU/inception_v3.dot index d95673c08dd..f1b0199bdea 100644 --- a/tests/torch/data/reference_graphs/quantized/hw/CPU/inception_v3.dot +++ b/tests/torch/data/reference_graphs/quantized/hw/CPU/inception_v3.dot @@ -329,11 +329,8 @@ strict digraph { "327 Inception3/NNCFLinear[fc]/linear_0" [id=327, type=linear]; "328 /nncf_model_output_0" [id=328, type=nncf_model_output]; "/nncf_model_input_0|OUT" [color=purple, id=0, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___0|OUT" [color=purple, id=3, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___0|OUT" [color=purple, id=4, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___1|OUT" [color=purple, id=7, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___1|OUT" [color=purple, id=8, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___2|OUT" [color=purple, id=11, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___2|OUT" [color=purple, id=12, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0|WEIGHT" [color=purple, id=14, label="Quantizer: B:8 M:S SGN:S PC:Y"]; "Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0|OUT" [color=purple, id=16, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; @@ -537,15 +534,15 @@ strict digraph { "0 /nncf_model_input_0" -> "/nncf_model_input_0|OUT"; "1 Inception3/__getitem___0" -> "2 Inception3/unsqueeze_0"; "2 Inception3/unsqueeze_0" -> "3 Inception3/__mul___0"; -"3 Inception3/__mul___0" -> "Inception3/__mul___0|OUT"; +"3 Inception3/__mul___0" -> "4 Inception3/__add___0"; "4 Inception3/__add___0" -> "Inception3/__add___0|OUT"; "5 Inception3/__getitem___1" -> "6 Inception3/unsqueeze_1"; "6 Inception3/unsqueeze_1" -> "7 Inception3/__mul___1"; -"7 Inception3/__mul___1" -> "Inception3/__mul___1|OUT"; +"7 Inception3/__mul___1" -> "8 Inception3/__add___1"; "8 Inception3/__add___1" -> "Inception3/__add___1|OUT"; "9 Inception3/__getitem___2" -> "10 Inception3/unsqueeze_2"; "10 Inception3/unsqueeze_2" -> "11 Inception3/__mul___2"; -"11 Inception3/__mul___2" -> "Inception3/__mul___2|OUT"; +"11 Inception3/__mul___2" -> "12 Inception3/__add___2"; "12 Inception3/__add___2" -> "Inception3/__add___2|OUT"; "13 Inception3/cat_0" -> "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "15 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; @@ -896,11 +893,8 @@ strict digraph { "/nncf_model_input_0|OUT" -> "1 Inception3/__getitem___0"; "/nncf_model_input_0|OUT" -> "5 Inception3/__getitem___1"; "/nncf_model_input_0|OUT" -> "9 Inception3/__getitem___2"; -"Inception3/__mul___0|OUT" -> "4 Inception3/__add___0"; "Inception3/__add___0|OUT" -> "13 Inception3/cat_0"; -"Inception3/__mul___1|OUT" -> "8 Inception3/__add___1"; "Inception3/__add___1|OUT" -> "13 Inception3/cat_0"; -"Inception3/__mul___2|OUT" -> "12 Inception3/__add___2"; "Inception3/__add___2|OUT" -> "13 Inception3/cat_0"; "Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0|WEIGHT" -> "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; "Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0|OUT" -> "17 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; diff --git a/tests/torch/data/reference_graphs/quantized/hw/GPU/inception_v3.dot b/tests/torch/data/reference_graphs/quantized/hw/GPU/inception_v3.dot index d95673c08dd..f1b0199bdea 100644 --- a/tests/torch/data/reference_graphs/quantized/hw/GPU/inception_v3.dot +++ b/tests/torch/data/reference_graphs/quantized/hw/GPU/inception_v3.dot @@ -329,11 +329,8 @@ strict digraph { "327 Inception3/NNCFLinear[fc]/linear_0" [id=327, type=linear]; "328 /nncf_model_output_0" [id=328, type=nncf_model_output]; "/nncf_model_input_0|OUT" [color=purple, id=0, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___0|OUT" [color=purple, id=3, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___0|OUT" [color=purple, id=4, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___1|OUT" [color=purple, id=7, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___1|OUT" [color=purple, id=8, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___2|OUT" [color=purple, id=11, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___2|OUT" [color=purple, id=12, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0|WEIGHT" [color=purple, id=14, label="Quantizer: B:8 M:S SGN:S PC:Y"]; "Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0|OUT" [color=purple, id=16, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; @@ -537,15 +534,15 @@ strict digraph { "0 /nncf_model_input_0" -> "/nncf_model_input_0|OUT"; "1 Inception3/__getitem___0" -> "2 Inception3/unsqueeze_0"; "2 Inception3/unsqueeze_0" -> "3 Inception3/__mul___0"; -"3 Inception3/__mul___0" -> "Inception3/__mul___0|OUT"; +"3 Inception3/__mul___0" -> "4 Inception3/__add___0"; "4 Inception3/__add___0" -> "Inception3/__add___0|OUT"; "5 Inception3/__getitem___1" -> "6 Inception3/unsqueeze_1"; "6 Inception3/unsqueeze_1" -> "7 Inception3/__mul___1"; -"7 Inception3/__mul___1" -> "Inception3/__mul___1|OUT"; +"7 Inception3/__mul___1" -> "8 Inception3/__add___1"; "8 Inception3/__add___1" -> "Inception3/__add___1|OUT"; "9 Inception3/__getitem___2" -> "10 Inception3/unsqueeze_2"; "10 Inception3/unsqueeze_2" -> "11 Inception3/__mul___2"; -"11 Inception3/__mul___2" -> "Inception3/__mul___2|OUT"; +"11 Inception3/__mul___2" -> "12 Inception3/__add___2"; "12 Inception3/__add___2" -> "Inception3/__add___2|OUT"; "13 Inception3/cat_0" -> "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "15 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; @@ -896,11 +893,8 @@ strict digraph { "/nncf_model_input_0|OUT" -> "1 Inception3/__getitem___0"; "/nncf_model_input_0|OUT" -> "5 Inception3/__getitem___1"; "/nncf_model_input_0|OUT" -> "9 Inception3/__getitem___2"; -"Inception3/__mul___0|OUT" -> "4 Inception3/__add___0"; "Inception3/__add___0|OUT" -> "13 Inception3/cat_0"; -"Inception3/__mul___1|OUT" -> "8 Inception3/__add___1"; "Inception3/__add___1|OUT" -> "13 Inception3/cat_0"; -"Inception3/__mul___2|OUT" -> "12 Inception3/__add___2"; "Inception3/__add___2|OUT" -> "13 Inception3/cat_0"; "Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0|WEIGHT" -> "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; "Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0|OUT" -> "17 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; diff --git a/tests/torch/data/reference_graphs/quantized/hw/NPU/inception_v3.dot b/tests/torch/data/reference_graphs/quantized/hw/NPU/inception_v3.dot index d95673c08dd..f1b0199bdea 100644 --- a/tests/torch/data/reference_graphs/quantized/hw/NPU/inception_v3.dot +++ b/tests/torch/data/reference_graphs/quantized/hw/NPU/inception_v3.dot @@ -329,11 +329,8 @@ strict digraph { "327 Inception3/NNCFLinear[fc]/linear_0" [id=327, type=linear]; "328 /nncf_model_output_0" [id=328, type=nncf_model_output]; "/nncf_model_input_0|OUT" [color=purple, id=0, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___0|OUT" [color=purple, id=3, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___0|OUT" [color=purple, id=4, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___1|OUT" [color=purple, id=7, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___1|OUT" [color=purple, id=8, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; -"Inception3/__mul___2|OUT" [color=purple, id=11, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/__add___2|OUT" [color=purple, id=12, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; "Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0|WEIGHT" [color=purple, id=14, label="Quantizer: B:8 M:S SGN:S PC:Y"]; "Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0|OUT" [color=purple, id=16, label="Quantizer: B:8 M:S SGN:ANY PC:N"]; @@ -537,15 +534,15 @@ strict digraph { "0 /nncf_model_input_0" -> "/nncf_model_input_0|OUT"; "1 Inception3/__getitem___0" -> "2 Inception3/unsqueeze_0"; "2 Inception3/unsqueeze_0" -> "3 Inception3/__mul___0"; -"3 Inception3/__mul___0" -> "Inception3/__mul___0|OUT"; +"3 Inception3/__mul___0" -> "4 Inception3/__add___0"; "4 Inception3/__add___0" -> "Inception3/__add___0|OUT"; "5 Inception3/__getitem___1" -> "6 Inception3/unsqueeze_1"; "6 Inception3/unsqueeze_1" -> "7 Inception3/__mul___1"; -"7 Inception3/__mul___1" -> "Inception3/__mul___1|OUT"; +"7 Inception3/__mul___1" -> "8 Inception3/__add___1"; "8 Inception3/__add___1" -> "Inception3/__add___1|OUT"; "9 Inception3/__getitem___2" -> "10 Inception3/unsqueeze_2"; "10 Inception3/unsqueeze_2" -> "11 Inception3/__mul___2"; -"11 Inception3/__mul___2" -> "Inception3/__mul___2|OUT"; +"11 Inception3/__mul___2" -> "12 Inception3/__add___2"; "12 Inception3/__add___2" -> "Inception3/__add___2|OUT"; "13 Inception3/cat_0" -> "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "15 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; @@ -896,11 +893,8 @@ strict digraph { "/nncf_model_input_0|OUT" -> "1 Inception3/__getitem___0"; "/nncf_model_input_0|OUT" -> "5 Inception3/__getitem___1"; "/nncf_model_input_0|OUT" -> "9 Inception3/__getitem___2"; -"Inception3/__mul___0|OUT" -> "4 Inception3/__add___0"; "Inception3/__add___0|OUT" -> "13 Inception3/cat_0"; -"Inception3/__mul___1|OUT" -> "8 Inception3/__add___1"; "Inception3/__add___1|OUT" -> "13 Inception3/cat_0"; -"Inception3/__mul___2|OUT" -> "12 Inception3/__add___2"; "Inception3/__add___2|OUT" -> "13 Inception3/cat_0"; "Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0|WEIGHT" -> "14 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; "Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0|OUT" -> "17 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; diff --git a/tests/torch/data/reference_graphs/quantized/ptq/symmetric/inception_v3.dot b/tests/torch/data/reference_graphs/quantized/ptq/symmetric/inception_v3.dot index b618d750fba..ce50082da57 100644 --- a/tests/torch/data/reference_graphs/quantized/ptq/symmetric/inception_v3.dot +++ b/tests/torch/data/reference_graphs/quantized/ptq/symmetric/inception_v3.dot @@ -4,1673 +4,1667 @@ strict digraph { "2 Inception3/__getitem___0" [id=2, type=__getitem__]; "3 Inception3/unsqueeze_0" [id=3, type=unsqueeze]; "4 Inception3/__mul___0" [id=4, type=__mul__]; -"5 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___0|OUTPUT]/symmetric_quantize_0" [id=5, type=symmetric_quantize]; -"6 Inception3/__add___0" [id=6, type=__add__]; -"7 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" [id=7, type=symmetric_quantize]; -"8 Inception3/__getitem___1" [id=8, type=__getitem__]; -"9 Inception3/unsqueeze_1" [id=9, type=unsqueeze]; -"10 Inception3/__mul___1" [id=10, type=__mul__]; -"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___1|OUTPUT]/symmetric_quantize_0" [id=11, type=symmetric_quantize]; -"12 Inception3/__add___1" [id=12, type=__add__]; -"13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" [id=13, type=symmetric_quantize]; -"14 Inception3/__getitem___2" [id=14, type=__getitem__]; -"15 Inception3/unsqueeze_2" [id=15, type=unsqueeze]; -"16 Inception3/__mul___2" [id=16, type=__mul__]; -"17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0" [id=17, type=symmetric_quantize]; -"18 Inception3/__add___2" [id=18, type=__add__]; -"19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" [id=19, type=symmetric_quantize]; -"20 Inception3/cat_0" [id=20, type=cat]; -"21 Conv2d_1a_3x3.conv.weight" [id=21, type=nncf_model_const]; -"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=22, type=symmetric_quantize]; -"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/Conv2d[conv]/conv2d_0" [id=23, type=conv2d]; -"24 Conv2d_1a_3x3.bn.weight" [id=24, type=nncf_model_const]; -"25 Conv2d_1a_3x3.bn.bias" [id=25, type=nncf_model_const]; -"26 Inception3/BasicConv2d[Conv2d_1a_3x3]/BatchNorm2d[bn]/batch_norm_0" [id=26, type=batch_norm]; -"27 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" [id=27, type=relu_]; -"28 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=28, type=symmetric_quantize]; -"29 Conv2d_2a_3x3.conv.weight" [id=29, type=nncf_model_const]; -"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=30, type=symmetric_quantize]; -"31 Inception3/BasicConv2d[Conv2d_2a_3x3]/Conv2d[conv]/conv2d_0" [id=31, type=conv2d]; -"32 Conv2d_2a_3x3.bn.weight" [id=32, type=nncf_model_const]; -"33 Conv2d_2a_3x3.bn.bias" [id=33, type=nncf_model_const]; -"34 Inception3/BasicConv2d[Conv2d_2a_3x3]/BatchNorm2d[bn]/batch_norm_0" [id=34, type=batch_norm]; -"35 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" [id=35, type=relu_]; -"36 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=36, type=symmetric_quantize]; -"37 Conv2d_2b_3x3.conv.weight" [id=37, type=nncf_model_const]; -"38 Inception3/BasicConv2d[Conv2d_2b_3x3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=38, type=symmetric_quantize]; -"39 Inception3/BasicConv2d[Conv2d_2b_3x3]/Conv2d[conv]/conv2d_0" [id=39, type=conv2d]; -"40 Conv2d_2b_3x3.bn.weight" [id=40, type=nncf_model_const]; -"41 Conv2d_2b_3x3.bn.bias" [id=41, type=nncf_model_const]; -"42 Inception3/BasicConv2d[Conv2d_2b_3x3]/BatchNorm2d[bn]/batch_norm_0" [id=42, type=batch_norm]; -"43 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" [id=43, type=relu_]; -"44 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=44, type=symmetric_quantize]; -"45 Inception3/max_pool2d_0" [id=45, type=max_pool2d]; -"46 Conv2d_3b_1x1.conv.weight" [id=46, type=nncf_model_const]; -"47 Inception3/BasicConv2d[Conv2d_3b_1x1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=47, type=symmetric_quantize]; -"48 Inception3/BasicConv2d[Conv2d_3b_1x1]/Conv2d[conv]/conv2d_0" [id=48, type=conv2d]; -"49 Conv2d_3b_1x1.bn.weight" [id=49, type=nncf_model_const]; -"50 Conv2d_3b_1x1.bn.bias" [id=50, type=nncf_model_const]; -"51 Inception3/BasicConv2d[Conv2d_3b_1x1]/BatchNorm2d[bn]/batch_norm_0" [id=51, type=batch_norm]; -"52 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" [id=52, type=relu_]; -"53 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=53, type=symmetric_quantize]; -"54 Conv2d_4a_3x3.conv.weight" [id=54, type=nncf_model_const]; -"55 Inception3/BasicConv2d[Conv2d_4a_3x3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=55, type=symmetric_quantize]; -"56 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type=nncf_model_const]; -"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0" [id=68, type=batch_norm]; -"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" [id=69, type=relu_]; -"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=70, type=symmetric_quantize]; -"71 Mixed_5b.branch5x5_1.conv.weight" [id=71, type=nncf_model_const]; -"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=72, type=symmetric_quantize]; -"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/Conv2d[conv]/conv2d_0" [id=73, type=conv2d]; -"74 Mixed_5b.branch5x5_1.bn.weight" [id=74, type=nncf_model_const]; -"75 Mixed_5b.branch5x5_1.bn.bias" [id=75, type=nncf_model_const]; -"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/BatchNorm2d[bn]/batch_norm_0" [id=76, type=batch_norm]; -"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" [id=77, type=relu_]; -"78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=78, type=symmetric_quantize]; -"79 Mixed_5b.branch5x5_2.conv.weight" [id=79, type=nncf_model_const]; -"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=80, type=symmetric_quantize]; -"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/Conv2d[conv]/conv2d_0" [id=81, type=conv2d]; -"82 Mixed_5b.branch5x5_2.bn.weight" [id=82, type=nncf_model_const]; -"83 Mixed_5b.branch5x5_2.bn.bias" [id=83, type=nncf_model_const]; -"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/BatchNorm2d[bn]/batch_norm_0" [id=84, type=batch_norm]; -"85 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" [id=85, type=relu_]; -"86 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" 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Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=96, type=symmetric_quantize]; -"97 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0" [id=97, type=conv2d]; -"98 Mixed_5b.branch3x3dbl_2.bn.weight" [id=98, type=nncf_model_const]; -"99 Mixed_5b.branch3x3dbl_2.bn.bias" [id=99, type=nncf_model_const]; -"100 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0" [id=100, type=batch_norm]; -"101 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=101, type=relu_]; -"102 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=102, type=symmetric_quantize]; -"103 Mixed_5b.branch3x3dbl_3.conv.weight" [id=103, type=nncf_model_const]; -"104 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=104, type=symmetric_quantize]; -"105 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/Conv2d[conv]/conv2d_0" [id=105, type=conv2d]; -"106 Mixed_5b.branch3x3dbl_3.bn.weight" [id=106, type=nncf_model_const]; -"107 Mixed_5b.branch3x3dbl_3.bn.bias" [id=107, type=nncf_model_const]; -"108 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/BatchNorm2d[bn]/batch_norm_0" [id=108, type=batch_norm]; -"109 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=109, type=relu_]; -"110 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=110, type=symmetric_quantize]; -"111 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" [id=111, type=avg_pool2d]; -"112 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0" [id=112, type=symmetric_quantize]; -"113 Mixed_5b.branch_pool.conv.weight" [id=113, type=nncf_model_const]; -"114 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" 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Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0" [id=124, type=conv2d]; -"125 Mixed_5c.branch1x1.bn.weight" [id=125, type=nncf_model_const]; -"126 Mixed_5c.branch1x1.bn.bias" [id=126, type=nncf_model_const]; -"127 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0" [id=127, type=batch_norm]; -"128 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" [id=128, type=relu_]; -"129 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=129, type=symmetric_quantize]; -"130 Mixed_5c.branch5x5_1.conv.weight" [id=130, type=nncf_model_const]; -"131 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=131, type=symmetric_quantize]; -"132 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/Conv2d[conv]/conv2d_0" [id=132, type=conv2d]; -"133 Mixed_5c.branch5x5_1.bn.weight" [id=133, type=nncf_model_const]; -"134 Mixed_5c.branch5x5_1.bn.bias" [id=134, type=nncf_model_const]; -"135 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/BatchNorm2d[bn]/batch_norm_0" [id=135, type=batch_norm]; -"136 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" [id=136, type=relu_]; -"137 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=137, type=symmetric_quantize]; -"138 Mixed_5c.branch5x5_2.conv.weight" [id=138, type=nncf_model_const]; -"139 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=139, type=symmetric_quantize]; -"140 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/Conv2d[conv]/conv2d_0" [id=140, type=conv2d]; -"141 Mixed_5c.branch5x5_2.bn.weight" [id=141, type=nncf_model_const]; -"142 Mixed_5c.branch5x5_2.bn.bias" [id=142, type=nncf_model_const]; -"143 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/BatchNorm2d[bn]/batch_norm_0" [id=143, type=batch_norm]; -"144 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" [id=144, type=relu_]; -"145 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=145, type=symmetric_quantize]; -"146 Mixed_5c.branch3x3dbl_1.conv.weight" [id=146, type=nncf_model_const]; -"147 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=147, type=symmetric_quantize]; -"148 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/conv2d_0" [id=148, type=conv2d]; -"149 Mixed_5c.branch3x3dbl_1.bn.weight" [id=149, type=nncf_model_const]; -"150 Mixed_5c.branch3x3dbl_1.bn.bias" [id=150, type=nncf_model_const]; -"151 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0" [id=151, type=batch_norm]; -"152 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=152, type=relu_]; -"153 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=153, type=symmetric_quantize]; -"154 Mixed_5c.branch3x3dbl_2.conv.weight" [id=154, type=nncf_model_const]; -"155 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=155, type=symmetric_quantize]; -"156 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0" [id=156, type=conv2d]; -"157 Mixed_5c.branch3x3dbl_2.bn.weight" [id=157, type=nncf_model_const]; -"158 Mixed_5c.branch3x3dbl_2.bn.bias" [id=158, type=nncf_model_const]; -"159 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0" [id=159, type=batch_norm]; -"160 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=160, type=relu_]; -"161 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=161, type=symmetric_quantize]; -"162 Mixed_5c.branch3x3dbl_3.conv.weight" [id=162, type=nncf_model_const]; -"163 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=163, type=symmetric_quantize]; -"164 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/Conv2d[conv]/conv2d_0" [id=164, type=conv2d]; -"165 Mixed_5c.branch3x3dbl_3.bn.weight" [id=165, type=nncf_model_const]; -"166 Mixed_5c.branch3x3dbl_3.bn.bias" [id=166, type=nncf_model_const]; -"167 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/BatchNorm2d[bn]/batch_norm_0" [id=167, type=batch_norm]; -"168 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=168, type=relu_]; -"169 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=169, type=symmetric_quantize]; -"170 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" [id=170, type=avg_pool2d]; -"171 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Mixed_5d.branch5x5_2.bn.weight" [id=200, type=nncf_model_const]; -"201 Mixed_5d.branch5x5_2.bn.bias" [id=201, type=nncf_model_const]; -"202 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/BatchNorm2d[bn]/batch_norm_0" [id=202, type=batch_norm]; -"203 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" [id=203, type=relu_]; -"204 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=204, type=symmetric_quantize]; -"205 Mixed_5d.branch3x3dbl_1.conv.weight" [id=205, type=nncf_model_const]; -"206 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=206, type=symmetric_quantize]; -"207 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/conv2d_0" [id=207, type=conv2d]; -"208 Mixed_5d.branch3x3dbl_1.bn.weight" [id=208, type=nncf_model_const]; -"209 Mixed_5d.branch3x3dbl_1.bn.bias" [id=209, type=nncf_model_const]; -"210 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Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=265, type=symmetric_quantize]; -"266 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/Conv2d[conv]/conv2d_0" [id=266, type=conv2d]; -"267 Mixed_6a.branch3x3dbl_3.bn.weight" [id=267, type=nncf_model_const]; -"268 Mixed_6a.branch3x3dbl_3.bn.bias" [id=268, type=nncf_model_const]; -"269 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/BatchNorm2d[bn]/batch_norm_0" [id=269, type=batch_norm]; -"270 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=270, type=relu_]; -"271 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=271, type=symmetric_quantize]; -"272 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" [id=272, type=max_pool2d]; -"273 Inception3/InceptionB[Mixed_6a]/cat_0" [id=273, type=cat]; -"274 Mixed_6b.branch1x1.conv.weight" [id=274, type=nncf_model_const]; -"275 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=275, type=symmetric_quantize]; -"276 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0" [id=276, type=conv2d]; -"277 Mixed_6b.branch1x1.bn.weight" [id=277, type=nncf_model_const]; -"278 Mixed_6b.branch1x1.bn.bias" [id=278, type=nncf_model_const]; -"279 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0" [id=279, type=batch_norm]; -"280 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" [id=280, type=relu_]; -"281 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=281, type=symmetric_quantize]; -"282 Mixed_6b.branch7x7_1.conv.weight" [id=282, type=nncf_model_const]; -"283 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=283, type=symmetric_quantize]; -"284 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/Conv2d[conv]/conv2d_0" [id=284, type=conv2d]; -"285 Mixed_6b.branch7x7_1.bn.weight" [id=285, type=nncf_model_const]; -"286 Mixed_6b.branch7x7_1.bn.bias" [id=286, type=nncf_model_const]; -"287 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/BatchNorm2d[bn]/batch_norm_0" [id=287, type=batch_norm]; -"288 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" [id=288, type=relu_]; -"289 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=289, type=symmetric_quantize]; -"290 Mixed_6b.branch7x7_2.conv.weight" [id=290, type=nncf_model_const]; -"291 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=291, type=symmetric_quantize]; -"292 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/Conv2d[conv]/conv2d_0" [id=292, type=conv2d]; -"293 Mixed_6b.branch7x7_2.bn.weight" [id=293, type=nncf_model_const]; 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type=batch_norm]; -"304 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" [id=304, type=relu_]; -"305 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=305, type=symmetric_quantize]; -"306 Mixed_6b.branch7x7dbl_1.conv.weight" [id=306, type=nncf_model_const]; -"307 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=307, type=symmetric_quantize]; -"308 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/Conv2d[conv]/conv2d_0" [id=308, type=conv2d]; -"309 Mixed_6b.branch7x7dbl_1.bn.weight" [id=309, type=nncf_model_const]; -"310 Mixed_6b.branch7x7dbl_1.bn.bias" [id=310, type=nncf_model_const]; -"311 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/BatchNorm2d[bn]/batch_norm_0" [id=311, type=batch_norm]; -"312 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=312, type=relu_]; -"313 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=313, type=symmetric_quantize]; -"314 Mixed_6b.branch7x7dbl_2.conv.weight" [id=314, type=nncf_model_const]; -"315 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=315, type=symmetric_quantize]; -"316 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/Conv2d[conv]/conv2d_0" [id=316, type=conv2d]; -"317 Mixed_6b.branch7x7dbl_2.bn.weight" [id=317, type=nncf_model_const]; -"318 Mixed_6b.branch7x7dbl_2.bn.bias" [id=318, type=nncf_model_const]; -"319 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0" [id=319, type=batch_norm]; -"320 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=320, type=relu_]; -"321 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=321, type=symmetric_quantize]; -"322 Mixed_6b.branch7x7dbl_3.conv.weight" [id=322, type=nncf_model_const]; -"323 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=323, type=symmetric_quantize]; -"324 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/Conv2d[conv]/conv2d_0" [id=324, type=conv2d]; -"325 Mixed_6b.branch7x7dbl_3.bn.weight" [id=325, type=nncf_model_const]; -"326 Mixed_6b.branch7x7dbl_3.bn.bias" [id=326, type=nncf_model_const]; -"327 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/BatchNorm2d[bn]/batch_norm_0" [id=327, type=batch_norm]; -"328 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=328, type=relu_]; -"329 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=329, type=symmetric_quantize]; -"330 Mixed_6b.branch7x7dbl_4.conv.weight" [id=330, type=nncf_model_const]; -"331 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=331, type=symmetric_quantize]; -"332 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/conv2d_0" [id=332, type=conv2d]; -"333 Mixed_6b.branch7x7dbl_4.bn.weight" [id=333, type=nncf_model_const]; -"334 Mixed_6b.branch7x7dbl_4.bn.bias" [id=334, type=nncf_model_const]; -"335 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/BatchNorm2d[bn]/batch_norm_0" [id=335, type=batch_norm]; -"336 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=336, type=relu_]; -"337 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=337, type=symmetric_quantize]; -"338 Mixed_6b.branch7x7dbl_5.conv.weight" [id=338, type=nncf_model_const]; -"339 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=339, type=symmetric_quantize]; 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=349, type=symmetric_quantize]; -"350 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0" [id=350, type=conv2d]; -"351 Mixed_6b.branch_pool.bn.weight" [id=351, type=nncf_model_const]; -"352 Mixed_6b.branch_pool.bn.bias" [id=352, type=nncf_model_const]; -"353 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0" [id=353, type=batch_norm]; -"354 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" [id=354, type=relu_]; -"355 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=355, type=symmetric_quantize]; -"356 Inception3/InceptionC[Mixed_6b]/cat_0" [id=356, type=cat]; -"357 Mixed_6c.branch1x1.conv.weight" [id=357, type=nncf_model_const]; -"358 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" 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Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1"; -"13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" -> "20 Inception3/cat_0"; -"14 Inception3/__getitem___2" -> "15 Inception3/unsqueeze_2"; -"15 Inception3/unsqueeze_2" -> "16 Inception3/__mul___2"; -"16 Inception3/__mul___2" -> "17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0"; -"17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0" -> "18 Inception3/__add___2"; -"18 Inception3/__add___2" -> "19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2"; -"19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" -> "20 Inception3/cat_0"; -"20 Inception3/cat_0" -> "23 Inception3/BasicConv2d[Conv2d_1a_3x3]/Conv2d[conv]/conv2d_0"; -"21 Conv2d_1a_3x3.conv.weight" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; -"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "23 Inception3/BasicConv2d[Conv2d_1a_3x3]/Conv2d[conv]/conv2d_0"; -"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/Conv2d[conv]/conv2d_0" -> "26 Inception3/BasicConv2d[Conv2d_1a_3x3]/BatchNorm2d[bn]/batch_norm_0"; -"24 Conv2d_1a_3x3.bn.weight" -> "26 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "324 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/Conv2d[conv]/conv2d_0"; -"324 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/Conv2d[conv]/conv2d_0" -> "327 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/BatchNorm2d[bn]/batch_norm_0"; -"325 Mixed_6b.branch7x7dbl_3.bn.weight" -> "327 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/BatchNorm2d[bn]/batch_norm_0"; -"326 Mixed_6b.branch7x7dbl_3.bn.bias" -> "327 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/BatchNorm2d[bn]/batch_norm_0"; -"327 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/BatchNorm2d[bn]/batch_norm_0" -> "328 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"328 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "329 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "340 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/Conv2d[conv]/conv2d_0"; -"340 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/Conv2d[conv]/conv2d_0" -> "343 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/BatchNorm2d[bn]/batch_norm_0"; -"341 Mixed_6b.branch7x7dbl_5.bn.weight" -> "343 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/BatchNorm2d[bn]/batch_norm_0"; -"342 Mixed_6b.branch7x7dbl_5.bn.bias" -> "343 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/BatchNorm2d[bn]/batch_norm_0"; -"343 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/BatchNorm2d[bn]/batch_norm_0" -> "344 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"344 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "345 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Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "506 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/Conv2d[conv]/conv2d_0"; -"506 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/Conv2d[conv]/conv2d_0" -> "509 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/BatchNorm2d[bn]/batch_norm_0"; -"507 Mixed_6d.branch7x7dbl_5.bn.weight" -> "509 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/BatchNorm2d[bn]/batch_norm_0"; -"508 Mixed_6d.branch7x7dbl_5.bn.bias" -> "509 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/BatchNorm2d[bn]/batch_norm_0"; -"509 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/BatchNorm2d[bn]/batch_norm_0" -> "510 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"510 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "511 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Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0" -> "519 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; -"517 Mixed_6d.branch_pool.bn.weight" -> "519 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; -"518 Mixed_6d.branch_pool.bn.bias" -> "519 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; -"519 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0" -> "520 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0"; -"520 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" -> "521 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"521 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "522 Inception3/InceptionC[Mixed_6d]/cat_0"; -"522 Inception3/InceptionC[Mixed_6d]/cat_0" -> "525 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Mixed_6e.branch1x1.bn.weight" -> "528 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0"; -"527 Mixed_6e.branch1x1.bn.bias" -> "528 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0"; -"528 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0" -> "529 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0"; -"529 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" -> "530 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"530 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "605 Inception3/InceptionC[Mixed_6e]/cat_0"; -"531 Mixed_6e.branch7x7_1.conv.weight" -> "532 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; -"532 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Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/Conv2d[conv]/conv2d_0" -> "482 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"480 Mixed_6d.branch7x7dbl_2.bn.weight" -> "482 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"481 Mixed_6d.branch7x7dbl_2.bn.bias" -> "482 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"482 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0" -> "483 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"483 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "484 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"484 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "487 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Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/BatchNorm2d[bn]/batch_norm_0" -> "491 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"491 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "492 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"492 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "495 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/conv2d_0"; +"493 Mixed_6d.branch7x7dbl_4.conv.weight" -> "494 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"494 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "495 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/conv2d_0"; +"495 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Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/Conv2d[conv]/conv2d_0" -> "565 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"563 Mixed_6e.branch7x7dbl_2.bn.weight" -> "565 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"564 Mixed_6e.branch7x7dbl_2.bn.bias" -> "565 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"565 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/BatchNorm2d[bn]/batch_norm_0" -> "566 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"566 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "567 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"567 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "570 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Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/BatchNorm2d[bn]/batch_norm_0" -> "574 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"574 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "575 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"575 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "578 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/conv2d_0"; +"576 Mixed_6e.branch7x7dbl_4.conv.weight" -> "577 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"577 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "578 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/Conv2d[conv]/conv2d_0"; +"578 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+"639 Mixed_7a.branch7x7x3_3.bn.bias" -> "640 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/BatchNorm2d[bn]/batch_norm_0"; +"640 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/BatchNorm2d[bn]/batch_norm_0" -> "641 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0"; +"641 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" -> "642 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0"; +"642 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0" -> "645 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/Conv2d[conv]/conv2d_0"; +"643 Mixed_7a.branch7x7x3_4.conv.weight" -> "644 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"644 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "645 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/Conv2d[conv]/conv2d_0"; +"645 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/Conv2d[conv]/conv2d_0" -> "648 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/BatchNorm2d[bn]/batch_norm_0"; +"646 Mixed_7a.branch7x7x3_4.bn.weight" -> "648 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/BatchNorm2d[bn]/batch_norm_0"; +"647 Mixed_7a.branch7x7x3_4.bn.bias" -> "648 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/BatchNorm2d[bn]/batch_norm_0"; +"648 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/BatchNorm2d[bn]/batch_norm_0" -> "649 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0"; +"649 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" -> "650 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0"; +"650 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0" -> "652 Inception3/InceptionD[Mixed_7a]/cat_0"; +"651 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" -> "652 Inception3/InceptionD[Mixed_7a]/cat_0"; +"652 Inception3/InceptionD[Mixed_7a]/cat_0" -> "655 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0"; +"652 Inception3/InceptionD[Mixed_7a]/cat_0" -> "663 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/Conv2d[conv]/conv2d_0"; +"652 Inception3/InceptionD[Mixed_7a]/cat_0" -> "688 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/conv2d_0"; +"652 Inception3/InceptionD[Mixed_7a]/cat_0" -> "719 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0"; +"653 Mixed_7b.branch1x1.conv.weight" -> "654 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"654 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "655 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0"; +"655 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0" -> "658 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0"; +"656 Mixed_7b.branch1x1.bn.weight" -> "658 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0"; +"657 Mixed_7b.branch1x1.bn.bias" -> "658 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0"; +"658 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0" -> "659 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0"; +"659 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" -> "660 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"660 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "729 Inception3/InceptionE[Mixed_7b]/cat_2"; +"661 Mixed_7b.branch3x3_1.conv.weight" -> "662 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"662 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "663 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/Conv2d[conv]/conv2d_0"; +"663 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/Conv2d[conv]/conv2d_0" -> "666 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/BatchNorm2d[bn]/batch_norm_0"; +"664 Mixed_7b.branch3x3_1.bn.weight" -> "666 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/BatchNorm2d[bn]/batch_norm_0"; +"665 Mixed_7b.branch3x3_1.bn.bias" -> "666 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/BatchNorm2d[bn]/batch_norm_0"; +"666 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/BatchNorm2d[bn]/batch_norm_0" -> "667 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0"; +"667 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" -> "668 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"668 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "671 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/conv2d_0"; +"668 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "679 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/Conv2d[conv]/conv2d_0"; +"669 Mixed_7b.branch3x3_2a.conv.weight" -> "670 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"670 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "671 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/conv2d_0"; +"671 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/conv2d_0" -> "674 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/BatchNorm2d[bn]/batch_norm_0"; +"672 Mixed_7b.branch3x3_2a.bn.weight" -> "674 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/BatchNorm2d[bn]/batch_norm_0"; +"673 Mixed_7b.branch3x3_2a.bn.bias" -> "674 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/BatchNorm2d[bn]/batch_norm_0"; +"674 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/BatchNorm2d[bn]/batch_norm_0" -> "675 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0"; +"675 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" -> "676 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Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/BatchNorm2d[bn]/batch_norm_0"; +"682 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/BatchNorm2d[bn]/batch_norm_0" -> "683 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0"; +"683 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" -> "684 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; +"684 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "685 Inception3/InceptionE[Mixed_7b]/cat_0"; +"685 Inception3/InceptionE[Mixed_7b]/cat_0" -> "729 Inception3/InceptionE[Mixed_7b]/cat_2"; +"686 Mixed_7b.branch3x3dbl_1.conv.weight" -> "687 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"687 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "688 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/conv2d_0"; +"688 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/conv2d_0" -> "691 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0"; +"689 Mixed_7b.branch3x3dbl_1.bn.weight" -> "691 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0"; +"690 Mixed_7b.branch3x3dbl_1.bn.bias" -> "691 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0"; +"691 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0" -> "692 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"692 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "693 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"693 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "696 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0"; +"694 Mixed_7b.branch3x3dbl_2.conv.weight" -> "695 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"695 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "696 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0"; +"696 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0" -> "699 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"697 Mixed_7b.branch3x3dbl_2.bn.weight" -> "699 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"698 Mixed_7b.branch3x3dbl_2.bn.bias" -> "699 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"699 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0" -> "700 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"700 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "701 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"701 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "704 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/Conv2d[conv]/conv2d_0"; +"701 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "712 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/Conv2d[conv]/conv2d_0"; +"702 Mixed_7b.branch3x3dbl_3a.conv.weight" -> "703 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Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0" -> "723 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0"; +"721 Mixed_7b.branch_pool.conv.weight" -> "722 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"722 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "723 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0"; +"723 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0" -> "726 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"724 Mixed_7b.branch_pool.bn.weight" -> "726 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"725 Mixed_7b.branch_pool.bn.bias" -> "726 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"726 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0" -> "727 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0"; +"727 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" -> "728 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"728 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "729 Inception3/InceptionE[Mixed_7b]/cat_2"; +"729 Inception3/InceptionE[Mixed_7b]/cat_2" -> "732 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0"; +"729 Inception3/InceptionE[Mixed_7b]/cat_2" -> "740 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/Conv2d[conv]/conv2d_0"; +"729 Inception3/InceptionE[Mixed_7b]/cat_2" -> "765 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/conv2d_0"; +"729 Inception3/InceptionE[Mixed_7b]/cat_2" -> "796 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0"; +"730 Mixed_7c.branch1x1.conv.weight" -> "731 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"731 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "732 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0"; +"732 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0" -> "735 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0"; +"733 Mixed_7c.branch1x1.bn.weight" -> "735 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0"; +"734 Mixed_7c.branch1x1.bn.bias" -> "735 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0"; +"735 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0" -> "736 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0"; +"736 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" -> "737 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"737 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "806 Inception3/InceptionE[Mixed_7c]/cat_2"; +"738 Mixed_7c.branch3x3_1.conv.weight" -> "739 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"739 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "740 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/Conv2d[conv]/conv2d_0"; +"740 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/Conv2d[conv]/conv2d_0" -> "743 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/BatchNorm2d[bn]/batch_norm_0"; +"741 Mixed_7c.branch3x3_1.bn.weight" -> "743 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/BatchNorm2d[bn]/batch_norm_0"; +"742 Mixed_7c.branch3x3_1.bn.bias" -> "743 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/BatchNorm2d[bn]/batch_norm_0"; +"743 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/BatchNorm2d[bn]/batch_norm_0" -> "744 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0"; +"744 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" -> "745 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"745 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "748 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/conv2d_0"; +"745 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "756 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/Conv2d[conv]/conv2d_0"; +"746 Mixed_7c.branch3x3_2a.conv.weight" -> "747 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"747 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "748 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/conv2d_0"; +"748 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/Conv2d[conv]/conv2d_0" -> "751 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/BatchNorm2d[bn]/batch_norm_0"; +"749 Mixed_7c.branch3x3_2a.bn.weight" -> "751 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/BatchNorm2d[bn]/batch_norm_0"; +"750 Mixed_7c.branch3x3_2a.bn.bias" -> "751 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/BatchNorm2d[bn]/batch_norm_0"; +"751 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/BatchNorm2d[bn]/batch_norm_0" -> "752 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0"; +"752 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" -> "753 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; +"753 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "762 Inception3/InceptionE[Mixed_7c]/cat_0"; +"754 Mixed_7c.branch3x3_2b.conv.weight" -> "755 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"755 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "756 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/Conv2d[conv]/conv2d_0"; +"756 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/Conv2d[conv]/conv2d_0" -> "759 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/BatchNorm2d[bn]/batch_norm_0"; +"757 Mixed_7c.branch3x3_2b.bn.weight" -> "759 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/BatchNorm2d[bn]/batch_norm_0"; +"758 Mixed_7c.branch3x3_2b.bn.bias" -> "759 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/BatchNorm2d[bn]/batch_norm_0"; +"759 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/BatchNorm2d[bn]/batch_norm_0" -> "760 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0"; +"760 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" -> "761 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; +"761 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "762 Inception3/InceptionE[Mixed_7c]/cat_0"; +"762 Inception3/InceptionE[Mixed_7c]/cat_0" -> "806 Inception3/InceptionE[Mixed_7c]/cat_2"; +"763 Mixed_7c.branch3x3dbl_1.conv.weight" -> "764 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"764 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "765 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/conv2d_0"; +"765 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/Conv2d[conv]/conv2d_0" -> "768 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0"; +"766 Mixed_7c.branch3x3dbl_1.bn.weight" -> "768 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0"; +"767 Mixed_7c.branch3x3dbl_1.bn.bias" -> "768 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0"; +"768 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0" -> "769 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"769 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "770 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"770 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "773 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0"; +"771 Mixed_7c.branch3x3dbl_2.conv.weight" -> "772 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"772 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "773 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0"; +"773 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0" -> "776 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"774 Mixed_7c.branch3x3dbl_2.bn.weight" -> "776 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"775 Mixed_7c.branch3x3dbl_2.bn.bias" -> "776 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0"; +"776 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0" -> "777 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"777 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "778 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"778 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "781 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/Conv2d[conv]/conv2d_0"; +"778 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "789 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/Conv2d[conv]/conv2d_0"; +"779 Mixed_7c.branch3x3dbl_3a.conv.weight" -> "780 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"780 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "781 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/Conv2d[conv]/conv2d_0"; +"781 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/Conv2d[conv]/conv2d_0" -> "784 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/BatchNorm2d[bn]/batch_norm_0"; +"782 Mixed_7c.branch3x3dbl_3a.bn.weight" -> "784 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/BatchNorm2d[bn]/batch_norm_0"; +"783 Mixed_7c.branch3x3dbl_3a.bn.bias" -> "784 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/BatchNorm2d[bn]/batch_norm_0"; +"784 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/BatchNorm2d[bn]/batch_norm_0" -> "785 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0"; +"785 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "786 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; +"786 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "795 Inception3/InceptionE[Mixed_7c]/cat_1"; +"787 Mixed_7c.branch3x3dbl_3b.conv.weight" -> "788 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"788 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "789 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/Conv2d[conv]/conv2d_0"; +"789 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/Conv2d[conv]/conv2d_0" -> "792 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/BatchNorm2d[bn]/batch_norm_0"; +"790 Mixed_7c.branch3x3dbl_3b.bn.weight" -> "792 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/BatchNorm2d[bn]/batch_norm_0"; +"791 Mixed_7c.branch3x3dbl_3b.bn.bias" -> "792 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/BatchNorm2d[bn]/batch_norm_0"; +"792 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/BatchNorm2d[bn]/batch_norm_0" -> "793 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0"; +"793 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "794 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; +"794 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "795 Inception3/InceptionE[Mixed_7c]/cat_1"; +"795 Inception3/InceptionE[Mixed_7c]/cat_1" -> "806 Inception3/InceptionE[Mixed_7c]/cat_2"; +"796 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" -> "797 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0"; +"797 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" -> "800 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0"; +"798 Mixed_7c.branch_pool.conv.weight" -> "799 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0"; +"799 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/Conv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "800 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0"; +"800 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0" -> "803 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"801 Mixed_7c.branch_pool.bn.weight" -> "803 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"802 Mixed_7c.branch_pool.bn.bias" -> "803 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"803 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0" -> "804 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0"; +"804 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" -> "805 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"805 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "806 Inception3/InceptionE[Mixed_7c]/cat_2"; +"806 Inception3/InceptionE[Mixed_7c]/cat_2" -> "807 Inception3/adaptive_avg_pool2d_0"; +"807 Inception3/adaptive_avg_pool2d_0" -> "808 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0"; +"808 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" -> "809 Inception3/dropout_0"; +"809 Inception3/dropout_0" -> "810 Inception3/view_0"; +"810 Inception3/view_0" -> "814 Inception3/Linear[fc]/linear_0"; +"811 fc.weight" -> "813 Inception3/Linear[fc]/SymmetricQuantizer/symmetric_quantize_0"; +"812 fc.bias" -> "814 Inception3/Linear[fc]/linear_0"; +"813 Inception3/Linear[fc]/SymmetricQuantizer/symmetric_quantize_0" -> "814 Inception3/Linear[fc]/linear_0"; +"814 Inception3/Linear[fc]/linear_0" -> "815 /nncf_model_output_0"; } diff --git a/tests/torch/data/reference_graphs/quantized/ptq/symmetric/rope_model.dot b/tests/torch/data/reference_graphs/quantized/ptq/symmetric/rope_model.dot new file mode 100644 index 00000000000..96b5a48c2e8 --- /dev/null +++ b/tests/torch/data/reference_graphs/quantized/ptq/symmetric/rope_model.dot @@ -0,0 +1,21 @@ +strict digraph { +"0 /nncf_model_input_0" [id=0, type=nncf_model_input]; +"1 RoPEModel/unsqueeze_0" [id=1, type=unsqueeze]; +"2 RoPEModel/reshape_0" [id=2, type=reshape]; +"3 RoPEModel/matmul_0" [id=3, type=matmul]; +"4 RoPEModel/transpose_0" [id=4, type=transpose]; +"5 RoPEModel/cat_0" [id=5, type=cat]; +"6 RoPEModel/sin_0" [id=6, type=sin]; +"7 RoPEModel/cos_0" [id=7, type=cos]; +"8 /nncf_model_output_0" [id=8, type=nncf_model_output]; +"9 /nncf_model_output_1" [id=9, type=nncf_model_output]; +"0 /nncf_model_input_0" -> "1 RoPEModel/unsqueeze_0"; +"1 RoPEModel/unsqueeze_0" -> "3 RoPEModel/matmul_0"; +"2 RoPEModel/reshape_0" -> "3 RoPEModel/matmul_0"; +"3 RoPEModel/matmul_0" -> "4 RoPEModel/transpose_0"; +"4 RoPEModel/transpose_0" -> "5 RoPEModel/cat_0"; +"5 RoPEModel/cat_0" -> "6 RoPEModel/sin_0"; +"5 RoPEModel/cat_0" -> "7 RoPEModel/cos_0"; +"6 RoPEModel/sin_0" -> "8 /nncf_model_output_0"; +"7 RoPEModel/cos_0" -> "9 /nncf_model_output_1"; +} diff --git a/tests/torch/data/reference_graphs/quantized/ptq/symmetric/ssd_vgg.dot b/tests/torch/data/reference_graphs/quantized/ptq/symmetric/ssd_vgg.dot index 1ea8d4da1ad..6b7af37ddc6 100644 --- a/tests/torch/data/reference_graphs/quantized/ptq/symmetric/ssd_vgg.dot +++ b/tests/torch/data/reference_graphs/quantized/ptq/symmetric/ssd_vgg.dot @@ -149,179 +149,178 @@ strict digraph { "147 SSD_VGG/L2Norm[L2Norm]/sum_0" [id=147, type=sum]; "148 SSD_VGG/L2Norm[L2Norm]/SymmetricQuantizer/symmetric_quantize_1" [id=148, type=symmetric_quantize]; "149 SSD_VGG/L2Norm[L2Norm]/sqrt_0" [id=149, type=sqrt]; -"150 SSD_VGG/L2Norm[L2Norm]/SymmetricQuantizer/symmetric_quantize_2" [id=150, type=symmetric_quantize]; -"151 SSD_VGG/L2Norm[L2Norm]/__add___0" [id=151, type=__add__]; -"152 SSD_VGG/L2Norm[L2Norm]/SymmetricQuantizer/symmetric_quantize_3" [id=152, type=symmetric_quantize]; -"153 SSD_VGG/L2Norm[L2Norm]/div_0" [id=153, type=div]; -"154 L2Norm.weight" [id=154, type=nncf_model_const]; -"155 SSD_VGG/L2Norm[L2Norm]/unsqueeze_0" [id=155, type=unsqueeze]; -"156 SSD_VGG/L2Norm[L2Norm]/unsqueeze_1" [id=156, type=unsqueeze]; -"157 SSD_VGG/L2Norm[L2Norm]/unsqueeze_2" [id=157, type=unsqueeze]; -"158 SSD_VGG/L2Norm[L2Norm]/expand_as_0" [id=158, type=expand_as]; +"150 SSD_VGG/L2Norm[L2Norm]/__add___0" [id=150, type=__add__]; +"151 SSD_VGG/L2Norm[L2Norm]/SymmetricQuantizer/symmetric_quantize_2" [id=151, type=symmetric_quantize]; +"152 SSD_VGG/L2Norm[L2Norm]/div_0" [id=152, type=div]; +"153 L2Norm.weight" [id=153, type=nncf_model_const]; +"154 SSD_VGG/L2Norm[L2Norm]/unsqueeze_0" [id=154, type=unsqueeze]; +"155 SSD_VGG/L2Norm[L2Norm]/unsqueeze_1" [id=155, type=unsqueeze]; +"156 SSD_VGG/L2Norm[L2Norm]/unsqueeze_2" [id=156, type=unsqueeze]; +"157 SSD_VGG/L2Norm[L2Norm]/expand_as_0" [id=157, type=expand_as]; +"158 SSD_VGG/L2Norm[L2Norm]/SymmetricQuantizer/symmetric_quantize_3" [id=158, type=symmetric_quantize]; "159 SSD_VGG/L2Norm[L2Norm]/SymmetricQuantizer/symmetric_quantize_4" [id=159, type=symmetric_quantize]; -"160 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SSD_VGG/L2Norm[L2Norm]/div_0"; "98 SSD_VGG/MultiOutputSequential[basenet]/MaxPool2d[33]/max_pool2d_0" -> "102 SSD_VGG/MultiOutputSequential[basenet]/Conv2d[34]/conv2d_0"; "99 basenet.34.weight" -> "101 SSD_VGG/MultiOutputSequential[basenet]/Conv2d[34]/SymmetricQuantizer/symmetric_quantize_0"; "100 basenet.34.bias" -> "102 SSD_VGG/MultiOutputSequential[basenet]/Conv2d[34]/conv2d_0"; @@ -468,192 +467,191 @@ strict digraph { "141 basenet.48.bias" -> "142 SSD_VGG/MultiOutputSequential[basenet]/BatchNorm2d[48]/batch_norm_0"; "142 SSD_VGG/MultiOutputSequential[basenet]/BatchNorm2d[48]/batch_norm_0" -> "143 SSD_VGG/MultiOutputSequential[basenet]/ReLU[49]/relu__0"; "143 SSD_VGG/MultiOutputSequential[basenet]/ReLU[49]/relu__0" -> "144 SSD_VGG/MultiOutputSequential[basenet]/ReLU[49]/SymmetricQuantizer/symmetric_quantize_0"; -"144 SSD_VGG/MultiOutputSequential[basenet]/ReLU[49]/SymmetricQuantizer/symmetric_quantize_0" -> "166 SSD_VGG/MultiOutputSequential[extras]/Conv2d[0]/conv2d_0"; -"144 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SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[3]/contiguous_0" -> "309 SSD_VGG/SSDDetectionOutput[detection_head]/view_3"; +"280 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[3]/permute_1" -> "281 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[3]/contiguous_1"; +"281 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[3]/contiguous_1" -> "316 SSD_VGG/SSDDetectionOutput[detection_head]/view_9"; +"282 detection_head.heads.4.loc.weight" -> "284 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[loc]/SymmetricQuantizer/symmetric_quantize_0"; +"283 detection_head.heads.4.loc.bias" -> "285 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[loc]/conv2d_0"; +"284 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[loc]/SymmetricQuantizer/symmetric_quantize_0" -> "285 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[loc]/conv2d_0"; +"285 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[loc]/conv2d_0" -> "290 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/permute_0"; +"286 detection_head.heads.4.conf.weight" -> "288 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[conf]/SymmetricQuantizer/symmetric_quantize_0"; +"287 detection_head.heads.4.conf.bias" -> "289 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[conf]/conv2d_0"; +"288 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[conf]/SymmetricQuantizer/symmetric_quantize_0" -> "289 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[conf]/conv2d_0"; +"289 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/Conv2d[conf]/conv2d_0" -> "292 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/permute_1"; +"290 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/permute_0" -> "291 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/contiguous_0"; +"291 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/contiguous_0" -> "310 SSD_VGG/SSDDetectionOutput[detection_head]/view_4"; +"292 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/permute_1" -> "293 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/contiguous_1"; +"293 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[4]/contiguous_1" -> "317 SSD_VGG/SSDDetectionOutput[detection_head]/view_10"; +"294 detection_head.heads.5.loc.weight" -> "296 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[loc]/SymmetricQuantizer/symmetric_quantize_0"; +"295 detection_head.heads.5.loc.bias" -> "297 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[loc]/conv2d_0"; +"296 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[loc]/SymmetricQuantizer/symmetric_quantize_0" -> "297 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[loc]/conv2d_0"; +"297 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[loc]/conv2d_0" -> "302 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/permute_0"; +"298 detection_head.heads.5.conf.weight" -> "300 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[conf]/SymmetricQuantizer/symmetric_quantize_0"; +"299 detection_head.heads.5.conf.bias" -> "301 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[conf]/conv2d_0"; +"300 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[conf]/SymmetricQuantizer/symmetric_quantize_0" -> "301 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[conf]/conv2d_0"; +"301 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/Conv2d[conf]/conv2d_0" -> "304 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/permute_1"; +"302 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/permute_0" -> "303 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/contiguous_0"; +"303 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/contiguous_0" -> "311 SSD_VGG/SSDDetectionOutput[detection_head]/view_5"; +"304 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/permute_1" -> "305 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/contiguous_1"; +"305 SSD_VGG/SSDDetectionOutput[detection_head]/ModuleList[heads]/SSDHead[5]/contiguous_1" -> "318 SSD_VGG/SSDDetectionOutput[detection_head]/view_11"; +"306 SSD_VGG/SSDDetectionOutput[detection_head]/view_0" -> "312 SSD_VGG/SSDDetectionOutput[detection_head]/cat_0"; +"307 SSD_VGG/SSDDetectionOutput[detection_head]/view_1" -> "312 SSD_VGG/SSDDetectionOutput[detection_head]/cat_0"; +"308 SSD_VGG/SSDDetectionOutput[detection_head]/view_2" -> "312 SSD_VGG/SSDDetectionOutput[detection_head]/cat_0"; +"309 SSD_VGG/SSDDetectionOutput[detection_head]/view_3" -> "312 SSD_VGG/SSDDetectionOutput[detection_head]/cat_0"; +"310 SSD_VGG/SSDDetectionOutput[detection_head]/view_4" -> "312 SSD_VGG/SSDDetectionOutput[detection_head]/cat_0"; +"311 SSD_VGG/SSDDetectionOutput[detection_head]/view_5" -> "312 SSD_VGG/SSDDetectionOutput[detection_head]/cat_0"; +"313 SSD_VGG/SSDDetectionOutput[detection_head]/view_6" -> "319 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"314 SSD_VGG/SSDDetectionOutput[detection_head]/view_7" -> "319 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"315 SSD_VGG/SSDDetectionOutput[detection_head]/view_8" -> "319 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"316 SSD_VGG/SSDDetectionOutput[detection_head]/view_9" -> "319 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"317 SSD_VGG/SSDDetectionOutput[detection_head]/view_10" -> "319 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"318 SSD_VGG/SSDDetectionOutput[detection_head]/view_11" -> "319 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"319 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1" -> "320 SSD_VGG/SSDDetectionOutput[detection_head]/view_12"; +"320 SSD_VGG/SSDDetectionOutput[detection_head]/view_12" -> "321 SSD_VGG/SSDDetectionOutput[detection_head]/softmax_0"; } diff --git a/tests/torch/data/reference_graphs/quantized/symmetric/inception_v3.dot b/tests/torch/data/reference_graphs/quantized/symmetric/inception_v3.dot index 1082c9c5847..fcf0f2dc959 100644 --- a/tests/torch/data/reference_graphs/quantized/symmetric/inception_v3.dot +++ b/tests/torch/data/reference_graphs/quantized/symmetric/inception_v3.dot @@ -4,1105 +4,1099 @@ strict digraph { "2 Inception3/__getitem___0" [id=2, type=__getitem__]; "3 Inception3/unsqueeze_0" [id=3, type=unsqueeze]; "4 Inception3/__mul___0" [id=4, type=__mul__]; -"5 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___0|OUTPUT]/symmetric_quantize_0" [id=5, type=symmetric_quantize]; -"6 Inception3/__add___0" [id=6, type=__add__]; -"7 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" [id=7, type=symmetric_quantize]; -"8 Inception3/__getitem___1" [id=8, type=__getitem__]; -"9 Inception3/unsqueeze_1" [id=9, type=unsqueeze]; -"10 Inception3/__mul___1" [id=10, type=__mul__]; -"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___1|OUTPUT]/symmetric_quantize_0" [id=11, type=symmetric_quantize]; -"12 Inception3/__add___1" [id=12, type=__add__]; -"13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" [id=13, type=symmetric_quantize]; -"14 Inception3/__getitem___2" [id=14, type=__getitem__]; -"15 Inception3/unsqueeze_2" [id=15, type=unsqueeze]; -"16 Inception3/__mul___2" [id=16, type=__mul__]; -"17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0" [id=17, type=symmetric_quantize]; -"18 Inception3/__add___2" [id=18, type=__add__]; -"19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" [id=19, type=symmetric_quantize]; -"20 Inception3/cat_0" [id=20, type=cat]; -"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=21, type=symmetric_quantize]; -"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=22, type=conv2d]; -"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=23, type=batch_norm]; -"24 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" [id=24, type=relu_]; -"25 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=25, type=symmetric_quantize]; -"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=26, type=symmetric_quantize]; -"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=27, type=conv2d]; -"28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=28, type=batch_norm]; -"29 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" [id=29, type=relu_]; -"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=30, type=symmetric_quantize]; -"31 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=31, type=symmetric_quantize]; -"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" [id=32, type=conv2d]; -"33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=33, type=batch_norm]; -"34 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" [id=34, type=relu_]; -"35 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=35, type=symmetric_quantize]; -"36 Inception3/max_pool2d_0" [id=36, type=max_pool2d]; -"37 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=37, type=symmetric_quantize]; -"38 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" [id=38, type=conv2d]; -"39 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=39, type=batch_norm]; -"40 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" [id=40, type=relu_]; -"41 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=41, type=symmetric_quantize]; -"42 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=42, type=symmetric_quantize]; -"43 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=43, type=conv2d]; -"44 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=44, type=batch_norm]; -"45 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" [id=45, type=relu_]; -"46 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=46, type=symmetric_quantize]; -"47 Inception3/max_pool2d_1" [id=47, type=max_pool2d]; -"48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=48, type=symmetric_quantize]; -"49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=49, type=conv2d]; -"50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=50, type=batch_norm]; -"51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" [id=51, type=relu_]; -"52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=52, type=symmetric_quantize]; -"53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=53, type=symmetric_quantize]; -"54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=54, type=conv2d]; -"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=55, type=batch_norm]; -"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" [id=56, type=relu_]; -"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=57, type=symmetric_quantize]; -"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=58, type=symmetric_quantize]; -"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=59, type=conv2d]; -"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=60, type=batch_norm]; -"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" [id=61, type=relu_]; -"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=62, type=symmetric_quantize]; -"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=63, type=symmetric_quantize]; -"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=64, type=conv2d]; -"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=65, type=batch_norm]; -"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=66, type=relu_]; -"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=67, type=symmetric_quantize]; -"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=68, type=symmetric_quantize]; -"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=69, type=conv2d]; -"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=70, type=batch_norm]; -"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=71, type=relu_]; -"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=72, type=symmetric_quantize]; -"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=73, type=symmetric_quantize]; -"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=74, type=conv2d]; -"75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=75, type=batch_norm]; -"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=76, type=relu_]; -"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=77, type=symmetric_quantize]; -"78 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" [id=78, type=avg_pool2d]; -"79 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0" [id=79, type=symmetric_quantize]; -"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=80, type=symmetric_quantize]; -"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=81, type=conv2d]; -"82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=82, type=batch_norm]; -"83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" [id=83, type=relu_]; -"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=84, type=symmetric_quantize]; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" [id=85, type=cat]; -"86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=86, type=symmetric_quantize]; -"87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=87, type=conv2d]; -"88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=88, type=batch_norm]; -"89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" [id=89, type=relu_]; -"90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=90, type=symmetric_quantize]; -"91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=91, type=symmetric_quantize]; -"92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=92, type=conv2d]; -"93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=93, type=batch_norm]; -"94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" [id=94, type=relu_]; -"95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=95, type=symmetric_quantize]; -"96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=96, type=symmetric_quantize]; -"97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=97, type=conv2d]; -"98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=98, type=batch_norm]; -"99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" [id=99, type=relu_]; -"100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=100, type=symmetric_quantize]; -"101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=101, type=symmetric_quantize]; -"102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=102, type=conv2d]; -"103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=103, type=batch_norm]; -"104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=104, type=relu_]; -"105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=105, type=symmetric_quantize]; -"106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=106, type=symmetric_quantize]; -"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=107, type=conv2d]; -"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=108, type=batch_norm]; -"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=109, type=relu_]; -"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=110, type=symmetric_quantize]; -"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=111, type=symmetric_quantize]; -"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=112, type=conv2d]; -"113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=113, type=batch_norm]; -"114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=114, type=relu_]; -"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=115, type=symmetric_quantize]; -"116 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" [id=116, type=avg_pool2d]; -"117 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0" [id=117, type=symmetric_quantize]; -"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=118, type=symmetric_quantize]; -"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=119, type=conv2d]; -"120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=120, type=batch_norm]; -"121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" [id=121, type=relu_]; -"122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=122, type=symmetric_quantize]; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" [id=123, type=cat]; -"124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=124, type=symmetric_quantize]; -"125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=125, type=conv2d]; -"126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=126, type=batch_norm]; -"127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" [id=127, type=relu_]; -"128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=128, type=symmetric_quantize]; -"129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=129, type=symmetric_quantize]; -"130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=130, type=conv2d]; -"131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=131, type=batch_norm]; -"132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" [id=132, type=relu_]; -"133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=133, type=symmetric_quantize]; -"134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=134, type=symmetric_quantize]; -"135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=135, type=conv2d]; -"136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=136, type=batch_norm]; -"137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" [id=137, type=relu_]; -"138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=138, type=symmetric_quantize]; -"139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=139, type=symmetric_quantize]; -"140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=140, type=conv2d]; -"141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=141, type=batch_norm]; -"142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=142, type=relu_]; -"143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=143, type=symmetric_quantize]; -"144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=144, type=symmetric_quantize]; -"145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=145, type=conv2d]; -"146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=146, type=batch_norm]; -"147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=147, type=relu_]; -"148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=148, type=symmetric_quantize]; -"149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=149, type=symmetric_quantize]; -"150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=150, type=conv2d]; -"151 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=151, type=batch_norm]; -"152 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=152, type=relu_]; -"153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=153, type=symmetric_quantize]; -"154 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" [id=154, type=avg_pool2d]; -"155 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0" [id=155, type=symmetric_quantize]; -"156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=156, type=symmetric_quantize]; -"157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=157, type=conv2d]; -"158 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=158, type=batch_norm]; -"159 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" [id=159, type=relu_]; -"160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=160, type=symmetric_quantize]; -"161 Inception3/InceptionA[Mixed_5d]/cat_0" [id=161, type=cat]; -"162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=162, type=symmetric_quantize]; -"163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" [id=163, type=conv2d]; -"164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=164, type=batch_norm]; -"165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" [id=165, type=relu_]; -"166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=166, type=symmetric_quantize]; -"167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=167, type=symmetric_quantize]; -"168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=168, type=conv2d]; -"169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=169, type=batch_norm]; -"170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=170, type=relu_]; -"171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=171, type=symmetric_quantize]; -"172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=172, type=symmetric_quantize]; -"173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=173, type=conv2d]; -"174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=174, type=batch_norm]; -"175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=175, type=relu_]; -"176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=176, type=symmetric_quantize]; -"177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=177, type=symmetric_quantize]; -"178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=178, type=conv2d]; -"179 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=179, type=batch_norm]; -"180 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=180, type=relu_]; -"181 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=181, type=symmetric_quantize]; -"182 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" [id=182, type=max_pool2d]; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" [id=183, type=cat]; -"184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=184, type=symmetric_quantize]; -"185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=185, type=conv2d]; -"186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=186, type=batch_norm]; -"187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" [id=187, type=relu_]; -"188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=188, type=symmetric_quantize]; -"189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=189, type=symmetric_quantize]; -"190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=190, type=conv2d]; -"191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=191, type=batch_norm]; -"192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" [id=192, type=relu_]; -"193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=193, type=symmetric_quantize]; -"194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=194, type=symmetric_quantize]; -"195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=195, type=conv2d]; -"196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=196, type=batch_norm]; -"197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" [id=197, type=relu_]; -"198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=198, type=symmetric_quantize]; -"199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=199, type=symmetric_quantize]; -"200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=200, type=conv2d]; -"201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=201, type=batch_norm]; -"202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" [id=202, type=relu_]; -"203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=203, type=symmetric_quantize]; -"204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=204, type=symmetric_quantize]; -"205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=205, type=conv2d]; -"206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=206, type=batch_norm]; -"207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=207, type=relu_]; -"208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=208, type=symmetric_quantize]; -"209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=209, type=symmetric_quantize]; -"210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=210, type=conv2d]; -"211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=211, type=batch_norm]; -"212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=212, type=relu_]; -"213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=213, type=symmetric_quantize]; -"214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=214, type=symmetric_quantize]; -"215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=215, type=conv2d]; -"216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=216, type=batch_norm]; -"217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=217, type=relu_]; -"218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=218, type=symmetric_quantize]; -"219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=219, type=symmetric_quantize]; -"220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=220, type=conv2d]; -"221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=221, type=batch_norm]; -"222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=222, type=relu_]; -"223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=223, type=symmetric_quantize]; -"224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=224, type=symmetric_quantize]; -"225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=225, type=conv2d]; -"226 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=226, type=batch_norm]; -"227 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=227, type=relu_]; -"228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=228, type=symmetric_quantize]; -"229 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" [id=229, type=avg_pool2d]; -"230 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0" [id=230, type=symmetric_quantize]; -"231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=231, type=symmetric_quantize]; -"232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=232, type=conv2d]; -"233 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=233, type=batch_norm]; -"234 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" [id=234, type=relu_]; -"235 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=235, type=symmetric_quantize]; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" [id=236, type=cat]; -"237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=237, type=symmetric_quantize]; -"238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=238, type=conv2d]; -"239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=239, type=batch_norm]; -"240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" [id=240, type=relu_]; -"241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=241, type=symmetric_quantize]; -"242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=242, type=symmetric_quantize]; -"243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=243, type=conv2d]; -"244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=244, type=batch_norm]; -"245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" [id=245, type=relu_]; -"246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=246, type=symmetric_quantize]; -"247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=247, type=symmetric_quantize]; -"248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=248, type=conv2d]; -"249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=249, type=batch_norm]; -"250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" [id=250, type=relu_]; -"251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=251, type=symmetric_quantize]; -"252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=252, type=symmetric_quantize]; -"253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=253, type=conv2d]; -"254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=254, type=batch_norm]; -"255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" [id=255, type=relu_]; -"256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=256, type=symmetric_quantize]; -"257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=257, type=symmetric_quantize]; -"258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=258, type=conv2d]; -"259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=259, type=batch_norm]; -"260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=260, type=relu_]; -"261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=261, type=symmetric_quantize]; -"262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=262, type=symmetric_quantize]; -"263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=263, type=conv2d]; -"264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=264, type=batch_norm]; -"265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=265, type=relu_]; -"266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=266, type=symmetric_quantize]; -"267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=267, type=symmetric_quantize]; -"268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=268, type=conv2d]; -"269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=269, type=batch_norm]; -"270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=270, type=relu_]; -"271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=271, type=symmetric_quantize]; -"272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=272, type=symmetric_quantize]; -"273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=273, type=conv2d]; -"274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=274, type=batch_norm]; -"275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=275, type=relu_]; -"276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=276, type=symmetric_quantize]; -"277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=277, type=symmetric_quantize]; -"278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=278, type=conv2d]; -"279 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=279, type=batch_norm]; -"280 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=280, type=relu_]; -"281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=281, type=symmetric_quantize]; -"282 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" [id=282, type=avg_pool2d]; -"283 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0" [id=283, type=symmetric_quantize]; -"284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=284, type=symmetric_quantize]; -"285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=285, type=conv2d]; -"286 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=286, type=batch_norm]; -"287 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" [id=287, type=relu_]; -"288 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=288, type=symmetric_quantize]; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" [id=289, type=cat]; -"290 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=290, type=symmetric_quantize]; -"291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=291, type=conv2d]; -"292 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=292, type=batch_norm]; -"293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" [id=293, type=relu_]; -"294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=294, type=symmetric_quantize]; -"295 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=295, type=symmetric_quantize]; -"296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=296, type=conv2d]; -"297 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=297, type=batch_norm]; -"298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" [id=298, type=relu_]; -"299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=299, type=symmetric_quantize]; -"300 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=300, type=symmetric_quantize]; -"301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=301, type=conv2d]; -"302 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=302, type=batch_norm]; -"303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" [id=303, type=relu_]; -"304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=304, type=symmetric_quantize]; -"305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=305, type=symmetric_quantize]; -"306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=306, type=conv2d]; -"307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=307, type=batch_norm]; -"308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" [id=308, type=relu_]; -"309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=309, type=symmetric_quantize]; -"310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=310, type=symmetric_quantize]; -"311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=311, type=conv2d]; -"312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=312, type=batch_norm]; -"313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=313, type=relu_]; -"314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=314, type=symmetric_quantize]; -"315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=315, type=symmetric_quantize]; -"316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=316, type=conv2d]; -"317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=317, type=batch_norm]; -"318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=318, type=relu_]; -"319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=319, type=symmetric_quantize]; -"320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=320, type=symmetric_quantize]; -"321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=321, type=conv2d]; -"322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=322, type=batch_norm]; -"323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=323, type=relu_]; -"324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=324, type=symmetric_quantize]; -"325 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=325, type=symmetric_quantize]; -"326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=326, type=conv2d]; -"327 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=327, type=batch_norm]; -"328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=328, type=relu_]; -"329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=329, type=symmetric_quantize]; -"330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=330, type=symmetric_quantize]; -"331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=331, type=conv2d]; -"332 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=332, type=batch_norm]; -"333 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=333, type=relu_]; -"334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=334, type=symmetric_quantize]; -"335 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" [id=335, type=avg_pool2d]; -"336 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0" [id=336, type=symmetric_quantize]; -"337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=337, type=symmetric_quantize]; -"338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=338, type=conv2d]; -"339 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=339, type=batch_norm]; -"340 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" [id=340, type=relu_]; -"341 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=341, type=symmetric_quantize]; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" [id=342, type=cat]; -"343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=343, type=symmetric_quantize]; -"344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=344, type=conv2d]; -"345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=345, type=batch_norm]; -"346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" [id=346, type=relu_]; -"347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=347, type=symmetric_quantize]; -"348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=348, type=symmetric_quantize]; -"349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=349, type=conv2d]; -"350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=350, type=batch_norm]; -"351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" [id=351, type=relu_]; -"352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=352, type=symmetric_quantize]; -"353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=353, type=symmetric_quantize]; -"354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=354, type=conv2d]; -"355 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=355, type=batch_norm]; -"356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" [id=356, type=relu_]; -"357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=357, type=symmetric_quantize]; -"358 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=358, type=symmetric_quantize]; -"359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=359, type=conv2d]; -"360 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=360, type=batch_norm]; -"361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" [id=361, type=relu_]; -"362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=362, type=symmetric_quantize]; -"363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=363, type=symmetric_quantize]; -"364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=364, type=conv2d]; -"365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=365, type=batch_norm]; -"366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=366, type=relu_]; -"367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=367, type=symmetric_quantize]; -"368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=368, type=symmetric_quantize]; -"369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=369, type=conv2d]; -"370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=370, type=batch_norm]; -"371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=371, type=relu_]; -"372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=372, type=symmetric_quantize]; -"373 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=373, type=symmetric_quantize]; -"374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=374, type=conv2d]; -"375 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=375, type=batch_norm]; -"376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=376, type=relu_]; -"377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=377, type=symmetric_quantize]; -"378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=378, type=symmetric_quantize]; -"379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=379, type=conv2d]; -"380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=380, type=batch_norm]; -"381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=381, type=relu_]; -"382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=382, type=symmetric_quantize]; -"383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=383, type=symmetric_quantize]; -"384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=384, type=conv2d]; -"385 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=385, type=batch_norm]; -"386 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=386, type=relu_]; -"387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=387, type=symmetric_quantize]; -"388 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" [id=388, type=avg_pool2d]; -"389 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0" [id=389, type=symmetric_quantize]; -"390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=390, type=symmetric_quantize]; -"391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=391, type=conv2d]; -"392 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=392, type=batch_norm]; -"393 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" [id=393, type=relu_]; -"394 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=394, type=symmetric_quantize]; -"395 Inception3/InceptionC[Mixed_6e]/cat_0" [id=395, type=cat]; -"396 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=396, type=symmetric_quantize]; -"397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=397, type=conv2d]; -"398 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=398, type=batch_norm]; -"399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" [id=399, type=relu_]; -"400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" [id=400, type=symmetric_quantize]; -"401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=401, type=symmetric_quantize]; -"402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" [id=402, type=conv2d]; -"403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=403, type=batch_norm]; -"404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" [id=404, type=relu_]; -"405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0" [id=405, type=symmetric_quantize]; -"406 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=406, type=symmetric_quantize]; -"407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" [id=407, type=conv2d]; -"408 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=408, type=batch_norm]; -"409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" [id=409, type=relu_]; -"410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0" [id=410, type=symmetric_quantize]; -"411 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=411, type=symmetric_quantize]; -"412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" [id=412, type=conv2d]; -"413 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=413, type=batch_norm]; -"414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" [id=414, type=relu_]; -"415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0" [id=415, type=symmetric_quantize]; -"416 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=416, type=symmetric_quantize]; -"417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" [id=417, type=conv2d]; -"418 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=418, type=batch_norm]; -"419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" [id=419, type=relu_]; -"420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0" [id=420, type=symmetric_quantize]; -"421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=421, type=symmetric_quantize]; -"422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" [id=422, type=conv2d]; -"423 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=423, type=batch_norm]; -"424 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" [id=424, type=relu_]; -"425 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0" [id=425, type=symmetric_quantize]; -"426 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" [id=426, type=max_pool2d]; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" [id=427, type=cat]; -"428 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=428, type=symmetric_quantize]; -"429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=429, type=conv2d]; -"430 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=430, type=batch_norm]; -"431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" [id=431, type=relu_]; -"432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=432, type=symmetric_quantize]; -"433 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=433, type=symmetric_quantize]; -"434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=434, type=conv2d]; -"435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=435, type=batch_norm]; -"436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" [id=436, type=relu_]; -"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" [id=437, type=symmetric_quantize]; -"438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=438, type=symmetric_quantize]; -"439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" [id=439, type=conv2d]; -"440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=440, type=batch_norm]; -"441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" [id=441, type=relu_]; -"442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" [id=442, type=symmetric_quantize]; -"443 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=443, type=symmetric_quantize]; -"444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" [id=444, type=conv2d]; -"445 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=445, type=batch_norm]; -"446 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" [id=446, type=relu_]; -"447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" [id=447, type=symmetric_quantize]; -"448 Inception3/InceptionE[Mixed_7b]/cat_0" [id=448, type=cat]; -"449 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=449, type=symmetric_quantize]; -"450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=450, type=conv2d]; -"451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=451, type=batch_norm]; -"452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=452, type=relu_]; -"453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=453, type=symmetric_quantize]; -"454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=454, type=symmetric_quantize]; -"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=455, type=conv2d]; -"456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=456, type=batch_norm]; -"457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=457, type=relu_]; -"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=458, type=symmetric_quantize]; -"459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=459, type=symmetric_quantize]; -"460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=460, type=conv2d]; -"461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=461, type=batch_norm]; -"462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=462, type=relu_]; -"463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" [id=463, type=symmetric_quantize]; -"464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=464, type=symmetric_quantize]; -"465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=465, type=conv2d]; -"466 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=466, type=batch_norm]; -"467 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=467, type=relu_]; -"468 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" [id=468, type=symmetric_quantize]; -"469 Inception3/InceptionE[Mixed_7b]/cat_1" [id=469, type=cat]; -"470 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" [id=470, type=avg_pool2d]; -"471 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0" [id=471, type=symmetric_quantize]; -"472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=472, type=symmetric_quantize]; -"473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=473, type=conv2d]; -"474 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=474, type=batch_norm]; -"475 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" [id=475, type=relu_]; -"476 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=476, type=symmetric_quantize]; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" [id=477, type=cat]; -"478 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=478, type=symmetric_quantize]; -"479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=479, type=conv2d]; -"480 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=480, type=batch_norm]; -"481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" [id=481, type=relu_]; -"482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=482, type=symmetric_quantize]; -"483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=483, type=symmetric_quantize]; -"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=484, type=conv2d]; -"485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=485, type=batch_norm]; -"486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" [id=486, type=relu_]; -"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" [id=487, type=symmetric_quantize]; -"488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=488, type=symmetric_quantize]; -"489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" [id=489, type=conv2d]; -"490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=490, type=batch_norm]; -"491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" [id=491, type=relu_]; -"492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" [id=492, type=symmetric_quantize]; -"493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=493, type=symmetric_quantize]; -"494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" [id=494, type=conv2d]; -"495 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=495, type=batch_norm]; -"496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" [id=496, type=relu_]; -"497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" [id=497, type=symmetric_quantize]; -"498 Inception3/InceptionE[Mixed_7c]/cat_0" [id=498, type=cat]; -"499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=499, type=symmetric_quantize]; -"500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=500, type=conv2d]; -"501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=501, type=batch_norm]; -"502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=502, type=relu_]; -"503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=503, type=symmetric_quantize]; -"504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=504, type=symmetric_quantize]; -"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=505, type=conv2d]; -"506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=506, type=batch_norm]; -"507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=507, type=relu_]; -"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=508, type=symmetric_quantize]; -"509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=509, type=symmetric_quantize]; -"510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=510, type=conv2d]; -"511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=511, type=batch_norm]; -"512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=512, type=relu_]; -"513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" [id=513, type=symmetric_quantize]; -"514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=514, type=symmetric_quantize]; -"515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=515, type=conv2d]; -"516 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=516, type=batch_norm]; -"517 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=517, type=relu_]; -"518 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" [id=518, type=symmetric_quantize]; -"519 Inception3/InceptionE[Mixed_7c]/cat_1" [id=519, type=cat]; -"520 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" [id=520, type=avg_pool2d]; -"521 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" [id=521, type=symmetric_quantize]; -"522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=522, type=symmetric_quantize]; -"523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=523, type=conv2d]; -"524 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=524, type=batch_norm]; -"525 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" [id=525, type=relu_]; -"526 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=526, type=symmetric_quantize]; -"527 Inception3/InceptionE[Mixed_7c]/cat_2" [id=527, type=cat]; -"528 Inception3/adaptive_avg_pool2d_0" [id=528, type=adaptive_avg_pool2d]; -"529 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" [id=529, type=symmetric_quantize]; -"530 Inception3/dropout_0" [id=530, type=dropout]; -"531 Inception3/view_0" [id=531, type=view]; -"532 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=532, type=symmetric_quantize]; -"533 Inception3/NNCFLinear[fc]/linear_0" [id=533, type=linear]; -"534 /nncf_model_output_0" [id=534, type=nncf_model_output]; +"5 Inception3/__add___0" [id=5, type=__add__]; +"6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" [id=6, type=symmetric_quantize]; +"7 Inception3/__getitem___1" [id=7, type=__getitem__]; +"8 Inception3/unsqueeze_1" [id=8, type=unsqueeze]; +"9 Inception3/__mul___1" [id=9, type=__mul__]; +"10 Inception3/__add___1" [id=10, type=__add__]; +"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" [id=11, type=symmetric_quantize]; +"12 Inception3/__getitem___2" [id=12, type=__getitem__]; +"13 Inception3/unsqueeze_2" [id=13, type=unsqueeze]; +"14 Inception3/__mul___2" [id=14, type=__mul__]; +"15 Inception3/__add___2" [id=15, type=__add__]; +"16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" [id=16, type=symmetric_quantize]; +"17 Inception3/cat_0" [id=17, type=cat]; +"18 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=18, type=symmetric_quantize]; +"19 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type=symmetric_quantize]; +"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=115, type=symmetric_quantize]; +"116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=116, type=conv2d]; +"117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=117, type=batch_norm]; +"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" [id=118, type=relu_]; +"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=119, type=symmetric_quantize]; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" [id=120, type=cat]; +"121 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=121, type=symmetric_quantize]; +"122 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Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0" [id=152, type=symmetric_quantize]; +"153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=153, type=symmetric_quantize]; +"154 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=154, type=conv2d]; +"155 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=155, type=batch_norm]; +"156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" [id=156, type=relu_]; +"157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=157, type=symmetric_quantize]; +"158 Inception3/InceptionA[Mixed_5d]/cat_0" [id=158, type=cat]; +"159 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" 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Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=167, type=relu_]; +"168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=168, type=symmetric_quantize]; +"169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=169, type=symmetric_quantize]; +"170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=170, type=conv2d]; +"171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=171, type=batch_norm]; +"172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=172, type=relu_]; +"173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=173, type=symmetric_quantize]; +"174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=174, type=symmetric_quantize]; +"175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=175, type=conv2d]; +"176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=176, type=batch_norm]; +"177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=177, type=relu_]; +"178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=178, type=symmetric_quantize]; +"179 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" [id=179, type=max_pool2d]; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" [id=180, type=cat]; +"181 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=181, type=symmetric_quantize]; +"182 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=182, type=conv2d]; +"183 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=183, type=batch_norm]; +"184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" [id=184, type=relu_]; +"185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=185, type=symmetric_quantize]; +"186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=186, type=symmetric_quantize]; +"187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=187, type=conv2d]; +"188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=188, type=batch_norm]; +"189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" 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type=symmetric_quantize]; +"197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=197, type=conv2d]; +"198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=198, type=batch_norm]; +"199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" [id=199, type=relu_]; +"200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=200, type=symmetric_quantize]; +"201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=201, type=symmetric_quantize]; +"202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=202, type=conv2d]; +"203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=203, type=batch_norm]; +"204 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=211, type=symmetric_quantize]; +"212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=212, type=conv2d]; +"213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=213, type=batch_norm]; +"214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=214, type=relu_]; +"215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=215, type=symmetric_quantize]; +"216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=216, type=symmetric_quantize]; +"217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=217, type=conv2d]; +"218 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=225, type=symmetric_quantize]; +"226 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" [id=226, type=avg_pool2d]; +"227 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0" [id=227, type=symmetric_quantize]; +"228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=228, type=symmetric_quantize]; +"229 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=229, type=conv2d]; +"230 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=230, type=batch_norm]; +"231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" [id=231, type=relu_]; +"232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=232, type=symmetric_quantize]; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" [id=233, type=cat]; +"234 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=234, type=symmetric_quantize]; +"235 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=235, type=conv2d]; +"236 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=236, type=batch_norm]; +"237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" [id=237, type=relu_]; +"238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=238, type=symmetric_quantize]; +"239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=239, type=symmetric_quantize]; +"240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=240, type=conv2d]; +"241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=241, type=batch_norm]; +"242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" [id=242, type=relu_]; +"243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=243, type=symmetric_quantize]; +"244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=244, type=symmetric_quantize]; +"245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=245, type=conv2d]; +"246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=246, type=batch_norm]; +"247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" [id=247, type=relu_]; +"248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=248, type=symmetric_quantize]; +"249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=249, type=symmetric_quantize]; +"250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=250, type=conv2d]; +"251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=251, type=batch_norm]; +"252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" [id=252, type=relu_]; +"253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=253, type=symmetric_quantize]; +"254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=254, type=symmetric_quantize]; 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Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=276, type=batch_norm]; +"277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=277, type=relu_]; +"278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=278, type=symmetric_quantize]; +"279 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" [id=279, type=avg_pool2d]; +"280 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0" [id=280, type=symmetric_quantize]; +"281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=281, type=symmetric_quantize]; +"282 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=282, type=conv2d]; +"283 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=283, type=batch_norm]; +"284 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type=symmetric_quantize]; +"307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=307, type=symmetric_quantize]; +"308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=308, type=conv2d]; +"309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=309, type=batch_norm]; +"310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=310, type=relu_]; +"311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=311, type=symmetric_quantize]; +"312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=312, type=symmetric_quantize]; +"313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=313, type=conv2d]; +"314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=314, type=batch_norm]; +"315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=315, type=relu_]; +"316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=316, type=symmetric_quantize]; +"317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=317, type=symmetric_quantize]; +"318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=318, type=conv2d]; +"319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=319, type=batch_norm]; +"320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=320, type=relu_]; +"321 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type=symmetric_quantize]; +"328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=328, type=conv2d]; +"329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=329, type=batch_norm]; +"330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=330, type=relu_]; +"331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=331, type=symmetric_quantize]; +"332 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" [id=332, type=avg_pool2d]; +"333 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0" [id=333, type=symmetric_quantize]; +"334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=334, type=symmetric_quantize]; +"335 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=335, type=conv2d]; +"336 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=336, type=batch_norm]; +"337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" [id=337, type=relu_]; +"338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=338, type=symmetric_quantize]; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" [id=339, type=cat]; +"340 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=340, type=symmetric_quantize]; +"341 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=341, type=conv2d]; +"342 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=342, type=batch_norm]; +"343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" [id=343, type=relu_]; +"344 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type=symmetric_quantize]; +"366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=366, type=conv2d]; +"367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=367, type=batch_norm]; +"368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=368, type=relu_]; +"369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=369, type=symmetric_quantize]; +"370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=370, type=symmetric_quantize]; +"371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=371, type=conv2d]; +"372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=372, type=batch_norm]; +"373 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Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=380, type=symmetric_quantize]; +"381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=381, type=conv2d]; +"382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=382, type=batch_norm]; +"383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=383, type=relu_]; +"384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=384, type=symmetric_quantize]; +"385 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" [id=385, type=avg_pool2d]; +"386 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0" [id=386, type=symmetric_quantize]; +"387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=387, type=symmetric_quantize]; +"388 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=388, type=conv2d]; +"389 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=389, type=batch_norm]; +"390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" [id=390, type=relu_]; +"391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=391, type=symmetric_quantize]; +"392 Inception3/InceptionC[Mixed_6e]/cat_0" [id=392, type=cat]; +"393 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=393, type=symmetric_quantize]; +"394 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Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0" [id=468, type=symmetric_quantize]; +"469 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=469, type=symmetric_quantize]; +"470 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=470, type=conv2d]; +"471 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=471, type=batch_norm]; +"472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" [id=472, type=relu_]; +"473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=473, type=symmetric_quantize]; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" [id=474, type=cat]; +"475 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" 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Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=511, type=symmetric_quantize]; +"512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=512, type=conv2d]; +"513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=513, type=batch_norm]; +"514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=514, type=relu_]; +"515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" [id=515, type=symmetric_quantize]; +"516 Inception3/InceptionE[Mixed_7c]/cat_1" [id=516, type=cat]; +"517 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" [id=517, type=avg_pool2d]; +"518 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" [id=518, type=symmetric_quantize]; +"519 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=519, type=symmetric_quantize]; +"520 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=520, type=conv2d]; +"521 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=521, type=batch_norm]; +"522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" [id=522, type=relu_]; +"523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=523, type=symmetric_quantize]; +"524 Inception3/InceptionE[Mixed_7c]/cat_2" [id=524, type=cat]; +"525 Inception3/adaptive_avg_pool2d_0" [id=525, type=adaptive_avg_pool2d]; +"526 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" [id=526, type=symmetric_quantize]; +"527 Inception3/dropout_0" [id=527, type=dropout]; +"528 Inception3/view_0" [id=528, type=view]; +"529 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=529, type=symmetric_quantize]; +"530 Inception3/NNCFLinear[fc]/linear_0" [id=530, type=linear]; +"531 /nncf_model_output_0" [id=531, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "2 Inception3/__getitem___0"; -"1 SymmetricQuantizer/symmetric_quantize_0" -> "8 Inception3/__getitem___1"; -"1 SymmetricQuantizer/symmetric_quantize_0" -> "14 Inception3/__getitem___2"; +"1 SymmetricQuantizer/symmetric_quantize_0" -> "7 Inception3/__getitem___1"; +"1 SymmetricQuantizer/symmetric_quantize_0" -> "12 Inception3/__getitem___2"; "2 Inception3/__getitem___0" -> "3 Inception3/unsqueeze_0"; "3 Inception3/unsqueeze_0" -> "4 Inception3/__mul___0"; -"4 Inception3/__mul___0" -> "5 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___0|OUTPUT]/symmetric_quantize_0"; -"5 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___0|OUTPUT]/symmetric_quantize_0" -> "6 Inception3/__add___0"; -"6 Inception3/__add___0" -> "7 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0"; -"7 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" -> "20 Inception3/cat_0"; -"8 Inception3/__getitem___1" -> "9 Inception3/unsqueeze_1"; -"9 Inception3/unsqueeze_1" -> "10 Inception3/__mul___1"; -"10 Inception3/__mul___1" -> "11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___1|OUTPUT]/symmetric_quantize_0"; -"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___1|OUTPUT]/symmetric_quantize_0" -> "12 Inception3/__add___1"; -"12 Inception3/__add___1" -> "13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1"; -"13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" -> "20 Inception3/cat_0"; -"14 Inception3/__getitem___2" -> "15 Inception3/unsqueeze_2"; -"15 Inception3/unsqueeze_2" -> "16 Inception3/__mul___2"; -"16 Inception3/__mul___2" -> "17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0"; -"17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0" -> "18 Inception3/__add___2"; -"18 Inception3/__add___2" -> "19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2"; -"19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" -> "20 Inception3/cat_0"; -"20 Inception3/cat_0" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "24 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0"; -"24 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" -> "25 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"25 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "29 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0"; -"29 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" -> "30 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; -"31 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; -"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" -> "33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "34 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0"; -"34 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" -> "35 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"35 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "36 Inception3/max_pool2d_0"; -"36 Inception3/max_pool2d_0" -> "38 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; -"37 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "38 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; -"38 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" -> "39 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"39 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "40 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0"; -"40 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" -> "41 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"41 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "43 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"42 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "43 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"43 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "44 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"44 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "45 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0"; -"45 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" -> "46 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"46 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "47 Inception3/max_pool2d_1"; -"47 Inception3/max_pool2d_1" -> "49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"47 Inception3/max_pool2d_1" -> "54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"47 Inception3/max_pool2d_1" -> "64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"47 Inception3/max_pool2d_1" -> "78 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0"; -"48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0"; -"51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" -> "52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "85 Inception3/InceptionA[Mixed_5b]/cat_0"; -"53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0"; -"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" -> "57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; -"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0"; -"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" -> "62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; -"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "85 Inception3/InceptionA[Mixed_5b]/cat_0"; -"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "85 Inception3/InceptionA[Mixed_5b]/cat_0"; -"78 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" -> "79 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0"; -"79 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0" -> "81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0"; -"83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" -> "84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "85 Inception3/InceptionA[Mixed_5b]/cat_0"; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" -> "87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" -> "92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" -> "102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"85 Inception3/InceptionA[Mixed_5b]/cat_0" -> "116 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0"; -"86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0"; -"89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" -> "90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "123 Inception3/InceptionA[Mixed_5c]/cat_0"; -"91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0"; -"94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" -> "95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; -"95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0"; -"99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" -> "100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; -"100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "123 Inception3/InceptionA[Mixed_5c]/cat_0"; -"101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "123 Inception3/InceptionA[Mixed_5c]/cat_0"; -"116 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" -> "117 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0"; -"117 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0" -> "119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0"; -"121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" -> "122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "123 Inception3/InceptionA[Mixed_5c]/cat_0"; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" -> "125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" -> "130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" -> "140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"123 Inception3/InceptionA[Mixed_5c]/cat_0" -> "154 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0"; -"124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0"; -"127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" -> "128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5d]/cat_0"; -"129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0"; -"132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" -> "133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; -"133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0"; -"137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" -> "138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; -"138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5d]/cat_0"; -"139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "151 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"151 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "152 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"152 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5d]/cat_0"; -"154 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" -> "155 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0"; -"155 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0" -> "157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "158 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"158 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "159 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0"; -"159 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" -> "160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5d]/cat_0"; -"161 Inception3/InceptionA[Mixed_5d]/cat_0" -> "163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; -"161 Inception3/InceptionA[Mixed_5d]/cat_0" -> "168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"161 Inception3/InceptionA[Mixed_5d]/cat_0" -> "182 Inception3/InceptionB[Mixed_6a]/max_pool2d_0"; -"162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; -"163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" -> "164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0"; -"165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" -> "166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "183 Inception3/InceptionB[Mixed_6a]/cat_0"; -"167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "179 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"179 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "180 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"180 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "181 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"181 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "183 Inception3/InceptionB[Mixed_6a]/cat_0"; -"182 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" -> "183 Inception3/InceptionB[Mixed_6a]/cat_0"; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" -> "185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" -> "190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" -> "205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"183 Inception3/InceptionB[Mixed_6a]/cat_0" -> "229 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0"; -"184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0"; -"187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" -> "188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "236 Inception3/InceptionC[Mixed_6b]/cat_0"; -"189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0"; -"192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" -> "193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; -"193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0"; -"197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" -> "198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; -"198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0"; -"202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" -> "203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; -"203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "236 Inception3/InceptionC[Mixed_6b]/cat_0"; -"204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; -"223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "226 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"226 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "227 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"227 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; -"228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "236 Inception3/InceptionC[Mixed_6b]/cat_0"; -"229 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" -> "230 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0"; -"230 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0" -> "232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "233 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"233 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "234 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0"; -"234 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" -> "235 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"235 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "236 Inception3/InceptionC[Mixed_6b]/cat_0"; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" -> "238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" -> "243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" -> "258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"236 Inception3/InceptionC[Mixed_6b]/cat_0" -> "282 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0"; -"237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0"; -"240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" -> "241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6c]/cat_0"; -"242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0"; -"245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" -> "246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; -"246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0"; -"250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" -> "251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; -"251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0"; -"255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" -> "256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; -"256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6c]/cat_0"; -"257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; -"276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "279 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"279 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "280 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"280 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; -"281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6c]/cat_0"; -"282 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" -> "283 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0"; -"283 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0" -> "285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "286 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"286 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "287 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0"; -"287 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" -> "288 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"288 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6c]/cat_0"; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" -> "291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" -> "296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" -> "311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"289 Inception3/InceptionC[Mixed_6c]/cat_0" -> "335 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0"; -"290 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "292 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"292 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0"; -"293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" -> "294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "342 Inception3/InceptionC[Mixed_6d]/cat_0"; -"295 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "297 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"297 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0"; -"298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" -> "299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; -"299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"300 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "302 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"302 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0"; -"303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" -> "304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; -"304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0"; -"308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" -> "309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; -"309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "342 Inception3/InceptionC[Mixed_6d]/cat_0"; -"310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"325 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "327 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"327 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; -"329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "332 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"332 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "333 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"333 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; -"334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "342 Inception3/InceptionC[Mixed_6d]/cat_0"; -"335 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" -> "336 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0"; -"336 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0" -> "338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "339 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"339 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "340 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0"; -"340 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" -> "341 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"341 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "342 Inception3/InceptionC[Mixed_6d]/cat_0"; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" -> "344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" -> "349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" -> "364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"342 Inception3/InceptionC[Mixed_6d]/cat_0" -> "388 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0"; -"343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0"; -"346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" -> "347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "395 Inception3/InceptionC[Mixed_6e]/cat_0"; -"348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0"; -"351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" -> "352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; -"352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "355 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"355 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0"; -"356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" -> "357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; -"357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"358 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "360 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"360 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0"; -"361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" -> "362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; -"362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "395 Inception3/InceptionC[Mixed_6e]/cat_0"; -"363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"373 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "375 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"375 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; -"382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "385 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"385 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "386 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"386 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; -"387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "395 Inception3/InceptionC[Mixed_6e]/cat_0"; -"388 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" -> "389 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0"; -"389 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0" -> "391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "392 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"392 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "393 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0"; -"393 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" -> "394 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"394 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "395 Inception3/InceptionC[Mixed_6e]/cat_0"; -"395 Inception3/InceptionC[Mixed_6e]/cat_0" -> "397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"395 Inception3/InceptionC[Mixed_6e]/cat_0" -> "407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; -"395 Inception3/InceptionC[Mixed_6e]/cat_0" -> "426 Inception3/InceptionD[Mixed_7a]/max_pool2d_0"; -"396 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "398 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"398 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0"; -"399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" -> "400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; -"400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; -"401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; -"402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" -> "403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0"; -"404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" -> "405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0"; -"405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0" -> "427 Inception3/InceptionD[Mixed_7a]/cat_0"; -"406 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; -"407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" -> "408 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"408 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0"; -"409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" -> "410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0"; -"410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; -"411 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; -"412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" -> "413 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"413 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0"; -"414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" -> "415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0"; -"415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0" -> "417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; -"416 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; -"417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" -> "418 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"418 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0"; -"419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" -> "420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0"; -"420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0" -> "422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; -"421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; -"422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" -> "423 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"423 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "424 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0"; -"424 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" -> "425 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0"; -"425 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0" -> "427 Inception3/InceptionD[Mixed_7a]/cat_0"; -"426 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" -> "427 Inception3/InceptionD[Mixed_7a]/cat_0"; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" -> "429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" -> "434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" -> "450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"427 Inception3/InceptionD[Mixed_7a]/cat_0" -> "470 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0"; -"428 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "430 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"430 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0"; -"431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" -> "432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "477 Inception3/InceptionE[Mixed_7b]/cat_2"; -"433 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0"; -"436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" -> "437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; -"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0"; -"441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" -> "442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; -"442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "448 Inception3/InceptionE[Mixed_7b]/cat_0"; -"443 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "445 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"445 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "446 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0"; -"446 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" -> "447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; -"447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "448 Inception3/InceptionE[Mixed_7b]/cat_0"; -"448 Inception3/InceptionE[Mixed_7b]/cat_0" -> "477 Inception3/InceptionE[Mixed_7b]/cat_2"; -"449 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0"; -"462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; -"463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "469 Inception3/InceptionE[Mixed_7b]/cat_1"; -"464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "466 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"466 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "467 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0"; -"467 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "468 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; -"468 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "469 Inception3/InceptionE[Mixed_7b]/cat_1"; -"469 Inception3/InceptionE[Mixed_7b]/cat_1" -> "477 Inception3/InceptionE[Mixed_7b]/cat_2"; -"470 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" -> "471 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0"; -"471 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0" -> "473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "474 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"474 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "475 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0"; -"475 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" -> "476 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"476 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "477 Inception3/InceptionE[Mixed_7b]/cat_2"; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" -> "479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" -> "484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" -> "500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"477 Inception3/InceptionE[Mixed_7b]/cat_2" -> "520 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0"; -"478 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "480 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"480 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0"; -"481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" -> "482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "527 Inception3/InceptionE[Mixed_7c]/cat_2"; -"483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0"; -"486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" -> "487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; -"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0"; -"491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" -> "492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; -"492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "498 Inception3/InceptionE[Mixed_7c]/cat_0"; -"493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "495 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"495 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0"; -"496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" -> "497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; -"497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "498 Inception3/InceptionE[Mixed_7c]/cat_0"; -"498 Inception3/InceptionE[Mixed_7c]/cat_0" -> "527 Inception3/InceptionE[Mixed_7c]/cat_2"; -"499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0"; -"512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; -"513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "519 Inception3/InceptionE[Mixed_7c]/cat_1"; -"514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "516 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"516 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "517 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0"; -"517 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "518 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; -"518 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "519 Inception3/InceptionE[Mixed_7c]/cat_1"; -"519 Inception3/InceptionE[Mixed_7c]/cat_1" -> "527 Inception3/InceptionE[Mixed_7c]/cat_2"; -"520 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" -> "521 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0"; -"521 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" -> "523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "524 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"524 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "525 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0"; -"525 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" -> "526 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"526 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "527 Inception3/InceptionE[Mixed_7c]/cat_2"; -"527 Inception3/InceptionE[Mixed_7c]/cat_2" -> "528 Inception3/adaptive_avg_pool2d_0"; -"528 Inception3/adaptive_avg_pool2d_0" -> "529 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0"; -"529 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" -> "530 Inception3/dropout_0"; -"530 Inception3/dropout_0" -> "531 Inception3/view_0"; -"531 Inception3/view_0" -> "533 Inception3/NNCFLinear[fc]/linear_0"; -"532 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "533 Inception3/NNCFLinear[fc]/linear_0"; -"533 Inception3/NNCFLinear[fc]/linear_0" -> "534 /nncf_model_output_0"; +"4 Inception3/__mul___0" -> "5 Inception3/__add___0"; +"5 Inception3/__add___0" -> "6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0"; +"6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" -> "17 Inception3/cat_0"; +"7 Inception3/__getitem___1" -> "8 Inception3/unsqueeze_1"; +"8 Inception3/unsqueeze_1" -> "9 Inception3/__mul___1"; +"9 Inception3/__mul___1" -> "10 Inception3/__add___1"; +"10 Inception3/__add___1" -> "11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1"; +"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" -> "17 Inception3/cat_0"; +"12 Inception3/__getitem___2" -> "13 Inception3/unsqueeze_2"; +"13 Inception3/unsqueeze_2" -> "14 Inception3/__mul___2"; +"14 Inception3/__mul___2" -> "15 Inception3/__add___2"; +"15 Inception3/__add___2" -> "16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2"; +"16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" -> "17 Inception3/cat_0"; +"17 Inception3/cat_0" -> "19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"18 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "20 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"20 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "21 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0"; +"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "24 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"23 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "24 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"24 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "25 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"25 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "26 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0"; +"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" -> "27 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "29 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; +"28 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "29 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; +"29 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" -> "30 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"30 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "31 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0"; +"31 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" -> "32 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "33 Inception3/max_pool2d_0"; +"33 Inception3/max_pool2d_0" -> "35 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; +"34 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "35 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; +"35 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" -> "36 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"36 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "37 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0"; +"37 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" -> "38 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"38 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "40 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"39 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "40 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"40 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "41 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"41 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "42 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0"; +"42 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" -> "43 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"43 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "44 Inception3/max_pool2d_1"; +"44 Inception3/max_pool2d_1" -> "46 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"44 Inception3/max_pool2d_1" -> "51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"44 Inception3/max_pool2d_1" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"44 Inception3/max_pool2d_1" -> "75 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0"; +"45 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "46 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"46 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "47 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"47 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0"; +"48 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" -> "49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/cat_0"; +"50 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"51 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0"; +"53 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" -> "54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; +"54 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0"; +"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" -> "59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; +"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/cat_0"; +"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/cat_0"; +"75 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" -> "76 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0"; +"76 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0" -> "78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0"; +"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" -> "81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/cat_0"; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" -> "84 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" -> "89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" -> "99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"82 Inception3/InceptionA[Mixed_5b]/cat_0" -> "113 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0"; +"83 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "84 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"84 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "85 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"85 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0"; +"86 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" -> "87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"87 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/cat_0"; +"88 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"89 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"90 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0"; +"91 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" -> "92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; +"92 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"93 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"94 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"95 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0"; +"96 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" -> "97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; +"97 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/cat_0"; +"98 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"99 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"100 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"101 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"102 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"103 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"104 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"105 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"106 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/cat_0"; +"113 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" -> "114 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0"; +"114 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0" -> "116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0"; +"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" -> "119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/cat_0"; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" -> "122 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" -> "127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" -> "137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"120 Inception3/InceptionA[Mixed_5c]/cat_0" -> "151 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0"; +"121 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "122 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"122 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "123 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"123 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0"; +"124 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" -> "125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"125 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5d]/cat_0"; +"126 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"127 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"128 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0"; +"129 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" -> "130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; +"130 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"131 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"132 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"133 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0"; +"134 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" -> "135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; +"135 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5d]/cat_0"; +"136 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"137 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"138 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"139 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"140 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"141 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"142 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"143 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"144 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"145 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"146 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"147 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"148 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"149 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"150 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5d]/cat_0"; +"151 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" -> "152 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0"; +"152 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0" -> "154 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"153 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "154 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"154 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "155 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"155 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0"; +"156 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" -> "157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"157 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5d]/cat_0"; +"158 Inception3/InceptionA[Mixed_5d]/cat_0" -> "160 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; +"158 Inception3/InceptionA[Mixed_5d]/cat_0" -> "165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"158 Inception3/InceptionA[Mixed_5d]/cat_0" -> "179 Inception3/InceptionB[Mixed_6a]/max_pool2d_0"; +"159 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "160 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; +"160 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" -> "161 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"161 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0"; +"162 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" -> "163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"163 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "180 Inception3/InceptionB[Mixed_6a]/cat_0"; +"164 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"165 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"166 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"167 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"168 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"169 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"170 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"171 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"172 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"173 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"174 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"175 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"176 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"177 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"178 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "180 Inception3/InceptionB[Mixed_6a]/cat_0"; +"179 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" -> "180 Inception3/InceptionB[Mixed_6a]/cat_0"; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" -> "182 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" -> "187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" -> "202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"180 Inception3/InceptionB[Mixed_6a]/cat_0" -> "226 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0"; +"181 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "182 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"182 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "183 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"183 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0"; +"184 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" -> "185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"185 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "233 Inception3/InceptionC[Mixed_6b]/cat_0"; +"186 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"187 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"188 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0"; +"189 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" -> "190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; +"190 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"191 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"192 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"193 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0"; +"194 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" -> "195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; +"195 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"196 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"197 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"198 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0"; +"199 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" -> "200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; +"200 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "233 Inception3/InceptionC[Mixed_6b]/cat_0"; +"201 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"202 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"203 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"204 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"205 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"206 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"207 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"208 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"209 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"210 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"211 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"212 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"213 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"214 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"215 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"216 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"217 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"218 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"219 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; +"220 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"221 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"222 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"223 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"224 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; +"225 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "233 Inception3/InceptionC[Mixed_6b]/cat_0"; +"226 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" -> "227 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0"; +"227 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0" -> "229 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"228 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "229 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"229 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "230 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"230 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0"; +"231 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" -> "232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"232 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "233 Inception3/InceptionC[Mixed_6b]/cat_0"; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" -> "235 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" -> "240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" -> "255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"233 Inception3/InceptionC[Mixed_6b]/cat_0" -> "279 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0"; +"234 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "235 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"235 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "236 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"236 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0"; +"237 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" -> "238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"238 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6c]/cat_0"; +"239 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"240 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"241 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0"; +"242 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" -> "243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; +"243 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"244 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"245 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"246 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0"; +"247 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" -> "248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; +"248 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"249 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"250 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"251 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0"; +"252 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" -> "253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; +"253 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6c]/cat_0"; +"254 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"255 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"256 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"257 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"258 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"259 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"260 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"261 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"262 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"263 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"264 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"265 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"266 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"267 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"268 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"269 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"270 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"271 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"272 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; +"273 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"274 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"275 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"276 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"277 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; +"278 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6c]/cat_0"; +"279 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" -> "280 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0"; +"280 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0" -> "282 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"281 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "282 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"282 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "283 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"283 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0"; +"284 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" -> "285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"285 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6c]/cat_0"; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" -> "288 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" -> "293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" -> "308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"286 Inception3/InceptionC[Mixed_6c]/cat_0" -> "332 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0"; +"287 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "288 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"288 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "289 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"289 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "290 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0"; +"290 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" -> "291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"291 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "339 Inception3/InceptionC[Mixed_6d]/cat_0"; +"292 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"293 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"294 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "295 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0"; +"295 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" -> "296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; +"296 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"297 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"298 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"299 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "300 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0"; +"300 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" -> "301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; +"301 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"302 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"303 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"304 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0"; +"305 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" -> "306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; +"306 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "339 Inception3/InceptionC[Mixed_6d]/cat_0"; +"307 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"308 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"309 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"310 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"311 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"312 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"313 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"314 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"315 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"316 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"317 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"318 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"319 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"320 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"321 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"322 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"323 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"324 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "325 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"325 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; +"326 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"327 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"328 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"329 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"330 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; +"331 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "339 Inception3/InceptionC[Mixed_6d]/cat_0"; +"332 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" -> "333 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0"; +"333 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0" -> "335 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"334 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "335 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"335 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "336 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"336 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0"; +"337 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" -> "338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"338 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "339 Inception3/InceptionC[Mixed_6d]/cat_0"; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" -> "341 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" -> "346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" -> "361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"339 Inception3/InceptionC[Mixed_6d]/cat_0" -> "385 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0"; +"340 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "341 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"341 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "342 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"342 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0"; +"343 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" -> "344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"344 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "392 Inception3/InceptionC[Mixed_6e]/cat_0"; +"345 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"346 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"347 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0"; +"348 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" -> "349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; +"349 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"350 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"351 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"352 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0"; +"353 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" -> "354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; +"354 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"355 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"356 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"357 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "358 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0"; +"358 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" -> "359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; +"359 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "392 Inception3/InceptionC[Mixed_6e]/cat_0"; +"360 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"361 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"362 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"363 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"364 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"365 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"366 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"367 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"368 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"369 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"370 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"371 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"372 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "373 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"373 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"374 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"375 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"376 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"377 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"378 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; +"379 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"380 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"381 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"382 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"383 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; +"384 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "392 Inception3/InceptionC[Mixed_6e]/cat_0"; +"385 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" -> "386 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0"; +"386 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0" -> "388 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"387 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "388 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"388 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "389 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"389 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0"; +"390 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" -> "391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"391 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "392 Inception3/InceptionC[Mixed_6e]/cat_0"; +"392 Inception3/InceptionC[Mixed_6e]/cat_0" -> "394 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"392 Inception3/InceptionC[Mixed_6e]/cat_0" -> "404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; +"392 Inception3/InceptionC[Mixed_6e]/cat_0" -> "423 Inception3/InceptionD[Mixed_7a]/max_pool2d_0"; +"393 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "394 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"394 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "395 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"395 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "396 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0"; +"396 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" -> "397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"397 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; +"398 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; +"399 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" -> "400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"400 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0"; +"401 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" -> "402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0"; +"402 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0" -> "424 Inception3/InceptionD[Mixed_7a]/cat_0"; +"403 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; +"404 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" -> "405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"405 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "406 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0"; +"406 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" -> "407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"407 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; +"408 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; +"409 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" -> "410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"410 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "411 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0"; +"411 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" -> "412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0"; +"412 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0" -> "414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; +"413 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; +"414 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" -> "415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"415 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "416 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0"; +"416 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" -> "417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0"; +"417 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0" -> "419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; +"418 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; +"419 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" -> "420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"420 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0"; +"421 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" -> "422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0"; +"422 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0" -> "424 Inception3/InceptionD[Mixed_7a]/cat_0"; +"423 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" -> "424 Inception3/InceptionD[Mixed_7a]/cat_0"; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" -> "426 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" -> "431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" -> "447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"424 Inception3/InceptionD[Mixed_7a]/cat_0" -> "467 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0"; +"425 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "426 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"426 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "427 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"427 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "428 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0"; +"428 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" -> "429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"429 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "474 Inception3/InceptionE[Mixed_7b]/cat_2"; +"430 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"431 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"432 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "433 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0"; +"433 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" -> "434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"434 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"435 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"436 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"437 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0"; +"438 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" -> "439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; +"439 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "445 Inception3/InceptionE[Mixed_7b]/cat_0"; +"440 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"441 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"442 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "443 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0"; +"443 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" -> "444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; +"444 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "445 Inception3/InceptionE[Mixed_7b]/cat_0"; +"445 Inception3/InceptionE[Mixed_7b]/cat_0" -> "474 Inception3/InceptionE[Mixed_7b]/cat_2"; +"446 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"447 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "448 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"448 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "449 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"449 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"450 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"451 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"452 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"453 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"454 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"455 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"456 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"457 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"458 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0"; +"459 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; +"460 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "466 Inception3/InceptionE[Mixed_7b]/cat_1"; +"461 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"462 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"463 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0"; +"464 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; +"465 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "466 Inception3/InceptionE[Mixed_7b]/cat_1"; +"466 Inception3/InceptionE[Mixed_7b]/cat_1" -> "474 Inception3/InceptionE[Mixed_7b]/cat_2"; +"467 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" -> "468 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0"; +"468 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0" -> "470 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"469 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "470 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"470 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "471 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"471 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0"; +"472 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" -> "473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"473 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "474 Inception3/InceptionE[Mixed_7b]/cat_2"; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" -> "476 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" -> "481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" -> "497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"474 Inception3/InceptionE[Mixed_7b]/cat_2" -> "517 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0"; +"475 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "476 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"476 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "477 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"477 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "478 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0"; +"478 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" -> "479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"479 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "524 Inception3/InceptionE[Mixed_7c]/cat_2"; +"480 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"481 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"482 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0"; +"483 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" -> "484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"484 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"485 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"486 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"487 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0"; +"488 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" -> "489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; +"489 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "495 Inception3/InceptionE[Mixed_7c]/cat_0"; +"490 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"491 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"492 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0"; +"493 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" -> "494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; +"494 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "495 Inception3/InceptionE[Mixed_7c]/cat_0"; +"495 Inception3/InceptionE[Mixed_7c]/cat_0" -> "524 Inception3/InceptionE[Mixed_7c]/cat_2"; +"496 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"497 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "498 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"498 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"499 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"500 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"501 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"502 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"503 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"504 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"505 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"506 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"507 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"508 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0"; +"509 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; +"510 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "516 Inception3/InceptionE[Mixed_7c]/cat_1"; +"511 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"512 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"513 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0"; +"514 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; +"515 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "516 Inception3/InceptionE[Mixed_7c]/cat_1"; +"516 Inception3/InceptionE[Mixed_7c]/cat_1" -> "524 Inception3/InceptionE[Mixed_7c]/cat_2"; +"517 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" -> "518 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0"; +"518 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" -> "520 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"519 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "520 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"520 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "521 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"521 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0"; +"522 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" -> "523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"523 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "524 Inception3/InceptionE[Mixed_7c]/cat_2"; +"524 Inception3/InceptionE[Mixed_7c]/cat_2" -> "525 Inception3/adaptive_avg_pool2d_0"; +"525 Inception3/adaptive_avg_pool2d_0" -> "526 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0"; +"526 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" -> "527 Inception3/dropout_0"; +"527 Inception3/dropout_0" -> "528 Inception3/view_0"; +"528 Inception3/view_0" -> "530 Inception3/NNCFLinear[fc]/linear_0"; +"529 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "530 Inception3/NNCFLinear[fc]/linear_0"; +"530 Inception3/NNCFLinear[fc]/linear_0" -> "531 /nncf_model_output_0"; } diff --git a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_bi_seq.dot b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_bi_seq.dot index e4d86695b80..21608b8d568 100644 --- a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_bi_seq.dot +++ b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_bi_seq.dot @@ -12,133 +12,129 @@ strict digraph { "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=11, type=symmetric_quantize]; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=13, type=symmetric_quantize]; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=15, type=symmetric_quantize]; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=17, type=symmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=19, type=symmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=21, type=symmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=24, type=symmetric_quantize]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=26, type=linear]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=27, type=symmetric_quantize]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=28, type=cat]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=29, type=view]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=30, type=symmetric_quantize]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=31, type=linear]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=32, type=symmetric_quantize]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=33, type=symmetric_quantize]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=34, type=linear]; -"35 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NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "65 /nncf_model_output_2"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "63 /nncf_model_output_0"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "64 /nncf_model_output_1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "63 /nncf_model_output_2"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "61 /nncf_model_output_0"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "62 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_bi_stacked.dot b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_bi_stacked.dot index e628b5f05c5..babaaca0fcb 100644 --- a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_bi_stacked.dot +++ b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_bi_stacked.dot @@ -12,262 +12,254 @@ strict digraph { "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=11, type=symmetric_quantize]; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=13, type=symmetric_quantize]; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=15, type=symmetric_quantize]; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=17, type=symmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=19, type=symmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=21, type=symmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=24, type=symmetric_quantize]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=26, type=linear]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=27, type=symmetric_quantize]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=28, type=cat]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=29, type=view]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=30, type=symmetric_quantize]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=31, type=linear]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=32, type=symmetric_quantize]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=33, type=symmetric_quantize]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=34, type=linear]; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=35, type=symmetric_quantize]; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=36, type=__add__]; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=37, type=chunk]; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=38, type=sigmoid]; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=39, type=symmetric_quantize]; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=40, type=sigmoid]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=13, type=tanh]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=14, type=symmetric_quantize]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=15, type=sigmoid]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=16, type=symmetric_quantize]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=17, type=__mul__]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=18, type=symmetric_quantize]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=19, type=__mul__]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=20, type=symmetric_quantize]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=21, type=__add__]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=22, type=tanh]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=23, type=symmetric_quantize]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=24, type=__mul__]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=25, type=linear]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=26, type=symmetric_quantize]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=27, type=cat]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=28, type=view]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=29, type=symmetric_quantize]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=30, type=linear]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=31, type=symmetric_quantize]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=32, type=symmetric_quantize]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=33, type=linear]; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=34, type=symmetric_quantize]; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=35, type=__add__]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=36, type=chunk]; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=37, type=sigmoid]; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=38, type=symmetric_quantize]; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=39, type=sigmoid]; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=40, type=tanh]; "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=41, type=symmetric_quantize]; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=42, type=tanh]; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=42, type=sigmoid]; "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=43, type=symmetric_quantize]; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=44, type=sigmoid]; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" [id=44, type=__mul__]; "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=45, type=symmetric_quantize]; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" [id=46, type=__mul__]; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" [id=46, type=__mul__]; "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=47, type=symmetric_quantize]; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" [id=48, type=__mul__]; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=49, type=symmetric_quantize]; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" [id=50, type=__add__]; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" [id=51, type=tanh]; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=52, type=symmetric_quantize]; 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NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "119 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "117 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; -"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; -"70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0"; -"72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0" -> "73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; -"74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; -"76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; -"78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1" -> "79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; -"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1"; -"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "119 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; -"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "117 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "85 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" -> "87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0"; -"87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" -> "116 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; -"91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; -"94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0" -> "95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; -"98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0"; -"100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0" -> "101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; -"102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; -"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" -> "105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; -"106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; -"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1"; -"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "119 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" -> "110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "117 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0"; -"115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" -> "116 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"116 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "123 /nncf_model_output_2"; -"117 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "118 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"118 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "121 /nncf_model_output_0"; -"119 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "120 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"120 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "122 /nncf_model_output_1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; +"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; +"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0"; +"69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; +"71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; +"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0" -> "74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; +"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1" -> "76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" -> "79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0"; +"84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"85 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; +"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; +"91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0" -> "92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; +"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0"; +"96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0" -> "97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; +"98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; +"100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" -> "101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; +"102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" -> "103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; +"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1"; +"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" -> "106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; +"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" -> "111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0"; +"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"112 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "119 /nncf_model_output_2"; +"113 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "114 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"114 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "117 /nncf_model_output_0"; +"115 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "116 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"116 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "118 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_cell.dot b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_cell.dot index 3d2f2097475..35f2c733ec6 100644 --- a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_cell.dot +++ b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_cell.dot @@ -12,21 +12,20 @@ strict digraph { "10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=11, type=symmetric_quantize]; "12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=13, type=symmetric_quantize]; -"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=15, type=symmetric_quantize]; -"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=17, type=symmetric_quantize]; -"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=19, type=symmetric_quantize]; -"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=21, type=symmetric_quantize]; -"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=24, type=symmetric_quantize]; -"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 /nncf_model_output_0" [id=26, type=nncf_model_output]; -"27 /nncf_model_output_1" [id=27, type=nncf_model_output]; +"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" [id=13, type=tanh]; +"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=14, type=symmetric_quantize]; +"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=15, type=sigmoid]; +"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=16, type=symmetric_quantize]; +"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" [id=17, type=__mul__]; +"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=18, type=symmetric_quantize]; +"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" [id=19, type=__mul__]; +"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=20, type=symmetric_quantize]; +"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" [id=21, type=__add__]; +"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" [id=22, type=tanh]; +"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=23, type=symmetric_quantize]; +"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" [id=24, type=__mul__]; +"25 /nncf_model_output_0" [id=25, type=nncf_model_output]; +"26 /nncf_model_output_1" [id=26, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "3 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "3 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; @@ -38,23 +37,22 @@ strict digraph { "8 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___0" -> "9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0"; "9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0"; "9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0"; -"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0"; +"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2"; "10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; -"12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0"; -"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" -> "15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; -"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" -> "19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; -"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" -> "21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; -"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1"; -"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "27 /nncf_model_output_1"; -"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; -"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" -> "26 /nncf_model_output_0"; +"11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; +"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; +"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "26 /nncf_model_output_1"; +"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 /nncf_model_output_0"; } diff --git a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_uni_seq.dot b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_uni_seq.dot index 0fcb464e8a3..9d38196ebfb 100644 --- a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_uni_seq.dot +++ b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_uni_seq.dot @@ -12,70 +12,68 @@ strict digraph { "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=11, type=symmetric_quantize]; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=13, type=symmetric_quantize]; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=15, type=symmetric_quantize]; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=17, type=symmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=19, type=symmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=21, type=symmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; 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NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=32, type=view]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=33, type=cat]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=34, type=view]; -"35 /nncf_model_output_0" [id=35, type=nncf_model_output]; -"36 /nncf_model_output_1" [id=36, type=nncf_model_output]; -"37 /nncf_model_output_2" [id=37, type=nncf_model_output]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=13, type=tanh]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=14, type=symmetric_quantize]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=15, type=sigmoid]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=16, type=symmetric_quantize]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=17, type=__mul__]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=18, type=symmetric_quantize]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=19, type=__mul__]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=20, type=symmetric_quantize]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=21, type=__add__]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=22, type=tanh]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=23, type=symmetric_quantize]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=24, type=__mul__]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=25, type=linear]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=26, type=symmetric_quantize]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=27, type=cat]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=28, type=view]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=29, type=cat]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=30, type=cat]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=31, type=view]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=32, type=cat]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=33, type=view]; +"34 /nncf_model_output_0" [id=34, type=nncf_model_output]; +"35 /nncf_model_output_1" [id=35, type=nncf_model_output]; +"36 /nncf_model_output_2" [id=36, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "4 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; "4 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; -"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "37 /nncf_model_output_2"; -"31 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"32 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "35 /nncf_model_output_0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"34 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "36 /nncf_model_output_1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "36 /nncf_model_output_2"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "34 /nncf_model_output_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "35 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_uni_stacked.dot b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_uni_stacked.dot index cdc8e0e6995..3adfb2df79b 100644 --- a/tests/torch/data/reference_graphs/quantized/symmetric/lstm_uni_stacked.dot +++ b/tests/torch/data/reference_graphs/quantized/symmetric/lstm_uni_stacked.dot @@ -12,136 +12,132 @@ strict digraph { "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=10, type=sigmoid]; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=11, type=symmetric_quantize]; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=12, type=sigmoid]; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=13, type=symmetric_quantize]; -"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=14, type=tanh]; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=15, type=symmetric_quantize]; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=17, type=symmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=18, type=__mul__]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=19, type=symmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=20, type=__mul__]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=21, type=symmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=22, type=__add__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=23, type=tanh]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=24, type=symmetric_quantize]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=25, type=__mul__]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=26, type=linear]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=27, type=symmetric_quantize]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=28, type=cat]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=29, type=view]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=30, type=cat]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" [id=31, type=symmetric_quantize]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=32, type=symmetric_quantize]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=33, type=linear]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=34, type=symmetric_quantize]; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=35, type=symmetric_quantize]; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=36, type=linear]; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=37, type=symmetric_quantize]; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=38, type=__add__]; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=39, type=chunk]; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=40, type=sigmoid]; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=41, type=symmetric_quantize]; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=42, type=sigmoid]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=13, type=tanh]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=14, type=symmetric_quantize]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=15, type=sigmoid]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=16, type=symmetric_quantize]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=17, type=__mul__]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=18, type=symmetric_quantize]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=19, type=__mul__]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=20, type=symmetric_quantize]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=21, type=__add__]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=22, type=tanh]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=23, type=symmetric_quantize]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=24, type=__mul__]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=25, type=linear]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=26, type=symmetric_quantize]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=27, type=cat]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=28, type=view]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=29, type=cat]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" [id=30, type=symmetric_quantize]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=31, type=symmetric_quantize]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=32, type=linear]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=33, type=symmetric_quantize]; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=34, 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NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "67 /nncf_model_output_2"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "65 /nncf_model_output_0"; -"63 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"64 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "66 /nncf_model_output_1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "65 /nncf_model_output_2"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "63 /nncf_model_output_0"; +"61 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"62 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "64 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized/synthetic_model/ConvolutionWithMinModel.dot b/tests/torch/data/reference_graphs/quantized/synthetic_model/ConvolutionWithMinModel.dot new file mode 100644 index 00000000000..9bcc79d93ec --- /dev/null +++ b/tests/torch/data/reference_graphs/quantized/synthetic_model/ConvolutionWithMinModel.dot @@ -0,0 +1,11 @@ +strict digraph { +"0 /nncf_model_input_0" [id=0, type=nncf_model_input]; +"1 SymmetricQuantizer/symmetric_quantize_0" [id=1, type=symmetric_quantize]; +"2 ConvolutionWithMinModel/conv2d_0" [id=2, type=conv2d]; +"3 ConvolutionWithMinModel/minimum_0" [id=3, type=minimum]; +"4 /nncf_model_output_0" [id=4, type=nncf_model_output]; +"0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; +"1 SymmetricQuantizer/symmetric_quantize_0" -> "2 ConvolutionWithMinModel/conv2d_0"; +"2 ConvolutionWithMinModel/conv2d_0" -> "3 ConvolutionWithMinModel/minimum_0"; +"3 ConvolutionWithMinModel/minimum_0" -> "4 /nncf_model_output_0"; +} diff --git a/tests/torch/data/reference_graphs/quantized/synthetic_model/min.dot b/tests/torch/data/reference_graphs/quantized/synthetic_model/min.dot deleted file mode 100644 index b39a51cfca9..00000000000 --- a/tests/torch/data/reference_graphs/quantized/synthetic_model/min.dot +++ /dev/null @@ -1,9 +0,0 @@ -strict digraph { -"0 /nncf_model_input_0" [id=0, type=nncf_model_input]; -"1 SymmetricQuantizer/symmetric_quantize_0" [id=1, type=symmetric_quantize]; -"2 TestModel/min_0" [id=2, type=min]; -"3 /nncf_model_output_0" [id=3, type=nncf_model_output]; -"0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; -"1 SymmetricQuantizer/symmetric_quantize_0" -> "2 TestModel/min_0"; -"2 TestModel/min_0" -> "3 /nncf_model_output_0"; -} diff --git a/tests/torch/data/reference_graphs/quantized_rb_sparsity/inception_v3.dot b/tests/torch/data/reference_graphs/quantized_rb_sparsity/inception_v3.dot index 7dc1f3469ba..c6de45da7ca 100644 --- a/tests/torch/data/reference_graphs/quantized_rb_sparsity/inception_v3.dot +++ b/tests/torch/data/reference_graphs/quantized_rb_sparsity/inception_v3.dot @@ -4,1485 +4,1479 @@ strict digraph { "2 Inception3/__getitem___0" [id=2, type=__getitem__]; "3 Inception3/unsqueeze_0" [id=3, type=unsqueeze]; "4 Inception3/__mul___0" [id=4, type=__mul__]; -"5 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___0|OUTPUT]/symmetric_quantize_0" [id=5, type=symmetric_quantize]; -"6 Inception3/__add___0" [id=6, type=__add__]; -"7 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" [id=7, type=symmetric_quantize]; -"8 Inception3/__getitem___1" [id=8, type=__getitem__]; -"9 Inception3/unsqueeze_1" [id=9, type=unsqueeze]; -"10 Inception3/__mul___1" [id=10, type=__mul__]; -"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___1|OUTPUT]/symmetric_quantize_0" [id=11, type=symmetric_quantize]; -"12 Inception3/__add___1" [id=12, type=__add__]; -"13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" [id=13, type=symmetric_quantize]; -"14 Inception3/__getitem___2" [id=14, type=__getitem__]; -"15 Inception3/unsqueeze_2" [id=15, type=unsqueeze]; -"16 Inception3/__mul___2" [id=16, type=__mul__]; -"17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0" [id=17, type=symmetric_quantize]; -"18 Inception3/__add___2" [id=18, type=__add__]; -"19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" [id=19, type=symmetric_quantize]; -"20 Inception3/cat_0" [id=20, type=cat]; -"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=21, type=calc_rb_binary_mask]; -"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=22, type=apply_binary_mask]; -"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=23, type=symmetric_quantize]; -"24 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=24, type=conv2d]; -"25 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=25, type=batch_norm]; -"26 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" [id=26, type=relu_]; -"27 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=27, type=symmetric_quantize]; -"28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=28, type=calc_rb_binary_mask]; -"29 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=29, type=apply_binary_mask]; -"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=30, type=symmetric_quantize]; -"31 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=31, type=conv2d]; -"32 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=32, type=batch_norm]; -"33 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" [id=33, type=relu_]; -"34 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=34, type=symmetric_quantize]; -"35 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=35, type=calc_rb_binary_mask]; -"36 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=36, type=apply_binary_mask]; -"37 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=37, type=symmetric_quantize]; -"38 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" [id=38, type=conv2d]; -"39 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=39, type=batch_norm]; -"40 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" [id=40, type=relu_]; -"41 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=41, type=symmetric_quantize]; -"42 Inception3/max_pool2d_0" [id=42, type=max_pool2d]; -"43 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=43, type=calc_rb_binary_mask]; -"44 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=44, type=apply_binary_mask]; -"45 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=45, type=symmetric_quantize]; -"46 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" [id=46, type=conv2d]; -"47 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=47, type=batch_norm]; -"48 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" [id=48, type=relu_]; -"49 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=49, type=symmetric_quantize]; -"50 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=50, type=calc_rb_binary_mask]; -"51 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=51, type=apply_binary_mask]; -"52 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=52, type=symmetric_quantize]; -"53 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=53, type=conv2d]; -"54 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=54, type=batch_norm]; -"55 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" [id=55, type=relu_]; -"56 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=56, type=symmetric_quantize]; -"57 Inception3/max_pool2d_1" [id=57, type=max_pool2d]; -"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=58, type=calc_rb_binary_mask]; -"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=59, type=apply_binary_mask]; -"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=60, type=symmetric_quantize]; -"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=61, type=conv2d]; -"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=62, type=batch_norm]; -"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" [id=63, type=relu_]; -"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=64, type=symmetric_quantize]; -"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=65, type=calc_rb_binary_mask]; -"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=66, type=apply_binary_mask]; -"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=67, type=symmetric_quantize]; -"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=68, type=conv2d]; -"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=69, type=batch_norm]; -"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" [id=70, type=relu_]; -"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=71, type=symmetric_quantize]; -"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=72, type=calc_rb_binary_mask]; -"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=73, type=apply_binary_mask]; -"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=74, type=symmetric_quantize]; -"75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=75, type=conv2d]; -"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=76, type=batch_norm]; -"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" [id=77, type=relu_]; -"78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=78, type=symmetric_quantize]; -"79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=79, type=calc_rb_binary_mask]; -"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=80, type=apply_binary_mask]; -"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=81, type=symmetric_quantize]; -"82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=82, type=conv2d]; -"83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=83, type=batch_norm]; -"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=84, type=relu_]; -"85 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=85, type=symmetric_quantize]; -"86 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=86, type=calc_rb_binary_mask]; -"87 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=87, type=apply_binary_mask]; -"88 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=88, type=symmetric_quantize]; -"89 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=89, type=conv2d]; -"90 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=90, type=batch_norm]; -"91 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=91, type=relu_]; -"92 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=92, type=symmetric_quantize]; -"93 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=93, type=calc_rb_binary_mask]; -"94 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=94, type=apply_binary_mask]; -"95 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=95, type=symmetric_quantize]; -"96 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=96, type=conv2d]; -"97 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=97, type=batch_norm]; -"98 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=98, type=relu_]; -"99 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=99, type=symmetric_quantize]; -"100 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" [id=100, type=avg_pool2d]; -"101 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0" [id=101, type=symmetric_quantize]; -"102 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=102, type=calc_rb_binary_mask]; -"103 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=103, type=apply_binary_mask]; -"104 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=104, type=symmetric_quantize]; -"105 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=105, type=conv2d]; -"106 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=106, type=batch_norm]; -"107 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" [id=107, type=relu_]; -"108 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=108, type=symmetric_quantize]; -"109 Inception3/InceptionA[Mixed_5b]/cat_0" [id=109, type=cat]; -"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=110, type=calc_rb_binary_mask]; -"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=111, type=apply_binary_mask]; -"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=112, type=symmetric_quantize]; -"113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=113, type=conv2d]; -"114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=114, type=batch_norm]; -"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" [id=115, type=relu_]; -"116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=116, type=symmetric_quantize]; -"117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=117, type=calc_rb_binary_mask]; -"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=118, type=apply_binary_mask]; -"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=119, type=symmetric_quantize]; -"120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=120, type=conv2d]; -"121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=121, type=batch_norm]; -"122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" [id=122, type=relu_]; -"123 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=123, type=symmetric_quantize]; -"124 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=124, type=calc_rb_binary_mask]; -"125 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=125, type=apply_binary_mask]; -"126 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=126, type=symmetric_quantize]; -"127 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=127, type=conv2d]; -"128 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=128, type=batch_norm]; -"129 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" [id=129, type=relu_]; -"130 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=130, type=symmetric_quantize]; -"131 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=131, type=calc_rb_binary_mask]; -"132 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=132, type=apply_binary_mask]; -"133 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=133, type=symmetric_quantize]; -"134 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=134, type=conv2d]; -"135 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=135, type=batch_norm]; -"136 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=136, type=relu_]; -"137 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=137, type=symmetric_quantize]; -"138 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=138, type=calc_rb_binary_mask]; -"139 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=139, type=apply_binary_mask]; -"140 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=140, type=symmetric_quantize]; -"141 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=141, type=conv2d]; -"142 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=142, type=batch_norm]; -"143 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=143, type=relu_]; -"144 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=144, type=symmetric_quantize]; -"145 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=145, type=calc_rb_binary_mask]; -"146 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=146, type=apply_binary_mask]; -"147 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=147, type=symmetric_quantize]; -"148 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=148, type=conv2d]; -"149 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=149, type=batch_norm]; -"150 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=150, type=relu_]; -"151 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=151, type=symmetric_quantize]; -"152 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" [id=152, type=avg_pool2d]; -"153 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0" [id=153, type=symmetric_quantize]; -"154 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=154, type=calc_rb_binary_mask]; -"155 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=155, type=apply_binary_mask]; -"156 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=156, type=symmetric_quantize]; -"157 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=157, type=conv2d]; -"158 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=158, type=batch_norm]; -"159 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" [id=159, type=relu_]; -"160 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=160, type=symmetric_quantize]; -"161 Inception3/InceptionA[Mixed_5c]/cat_0" [id=161, type=cat]; -"162 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=162, type=calc_rb_binary_mask]; -"163 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=163, type=apply_binary_mask]; -"164 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=164, type=symmetric_quantize]; -"165 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=165, type=conv2d]; -"166 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=166, type=batch_norm]; -"167 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" [id=167, type=relu_]; -"168 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=168, type=symmetric_quantize]; -"169 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=169, type=calc_rb_binary_mask]; -"170 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=170, type=apply_binary_mask]; -"171 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=171, type=symmetric_quantize]; -"172 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=172, type=conv2d]; -"173 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=173, type=batch_norm]; -"174 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" [id=174, type=relu_]; -"175 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=175, type=symmetric_quantize]; -"176 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=176, type=calc_rb_binary_mask]; -"177 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=177, type=apply_binary_mask]; -"178 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=178, type=symmetric_quantize]; -"179 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=179, type=conv2d]; -"180 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=180, type=batch_norm]; -"181 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" [id=181, type=relu_]; -"182 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=182, type=symmetric_quantize]; -"183 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=183, type=calc_rb_binary_mask]; -"184 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=184, type=apply_binary_mask]; -"185 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=185, type=symmetric_quantize]; -"186 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=186, type=conv2d]; -"187 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=187, type=batch_norm]; -"188 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=188, type=relu_]; -"189 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=189, type=symmetric_quantize]; -"190 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=190, type=calc_rb_binary_mask]; -"191 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=191, type=apply_binary_mask]; -"192 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=192, type=symmetric_quantize]; -"193 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=193, type=conv2d]; -"194 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=194, type=batch_norm]; -"195 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=195, type=relu_]; -"196 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=196, type=symmetric_quantize]; -"197 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=197, type=calc_rb_binary_mask]; -"198 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=198, type=apply_binary_mask]; -"199 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=199, type=symmetric_quantize]; -"200 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=200, type=conv2d]; -"201 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=201, type=batch_norm]; -"202 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=202, type=relu_]; -"203 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=203, type=symmetric_quantize]; -"204 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" [id=204, type=avg_pool2d]; -"205 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0" [id=205, type=symmetric_quantize]; -"206 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=206, type=calc_rb_binary_mask]; -"207 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=207, type=apply_binary_mask]; -"208 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=208, type=symmetric_quantize]; -"209 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=209, type=conv2d]; -"210 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=210, type=batch_norm]; -"211 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" [id=211, type=relu_]; -"212 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=212, type=symmetric_quantize]; -"213 Inception3/InceptionA[Mixed_5d]/cat_0" [id=213, type=cat]; -"214 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=214, type=calc_rb_binary_mask]; -"215 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=215, type=apply_binary_mask]; -"216 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=216, type=symmetric_quantize]; -"217 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" [id=217, type=conv2d]; -"218 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=218, type=batch_norm]; -"219 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" [id=219, type=relu_]; -"220 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=220, type=symmetric_quantize]; -"221 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=221, type=calc_rb_binary_mask]; -"222 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=222, type=apply_binary_mask]; -"223 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=223, type=symmetric_quantize]; -"224 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=224, type=conv2d]; -"225 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=225, type=batch_norm]; -"226 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=226, type=relu_]; -"227 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=227, type=symmetric_quantize]; -"228 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=228, type=calc_rb_binary_mask]; -"229 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=229, type=apply_binary_mask]; -"230 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=230, type=symmetric_quantize]; -"231 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=231, type=conv2d]; -"232 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=232, type=batch_norm]; -"233 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=233, type=relu_]; -"234 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=234, type=symmetric_quantize]; -"235 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=235, type=calc_rb_binary_mask]; -"236 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=236, type=apply_binary_mask]; -"237 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=237, type=symmetric_quantize]; -"238 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=238, type=conv2d]; -"239 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=239, type=batch_norm]; -"240 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=240, type=relu_]; -"241 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=241, type=symmetric_quantize]; -"242 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" [id=242, type=max_pool2d]; -"243 Inception3/InceptionB[Mixed_6a]/cat_0" [id=243, type=cat]; -"244 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=244, type=calc_rb_binary_mask]; -"245 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=245, type=apply_binary_mask]; -"246 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=246, type=symmetric_quantize]; -"247 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=247, type=conv2d]; -"248 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=248, type=batch_norm]; -"249 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" [id=249, type=relu_]; -"250 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=250, type=symmetric_quantize]; -"251 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=251, type=calc_rb_binary_mask]; -"252 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=252, type=apply_binary_mask]; -"253 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=253, type=symmetric_quantize]; -"254 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=254, type=conv2d]; -"255 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=255, type=batch_norm]; -"256 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" [id=256, type=relu_]; -"257 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=257, type=symmetric_quantize]; -"258 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=258, type=calc_rb_binary_mask]; -"259 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=259, type=apply_binary_mask]; -"260 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=260, type=symmetric_quantize]; -"261 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=261, type=conv2d]; -"262 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=262, type=batch_norm]; -"263 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" [id=263, type=relu_]; -"264 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=264, type=symmetric_quantize]; -"265 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=265, type=calc_rb_binary_mask]; -"266 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=266, type=apply_binary_mask]; -"267 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=267, type=symmetric_quantize]; -"268 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=268, type=conv2d]; -"269 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=269, type=batch_norm]; -"270 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" [id=270, type=relu_]; -"271 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=271, type=symmetric_quantize]; -"272 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=272, type=calc_rb_binary_mask]; -"273 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=273, type=apply_binary_mask]; -"274 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=274, type=symmetric_quantize]; -"275 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=275, type=conv2d]; -"276 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=276, type=batch_norm]; -"277 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=277, type=relu_]; -"278 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=278, type=symmetric_quantize]; -"279 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=279, type=calc_rb_binary_mask]; -"280 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=280, type=apply_binary_mask]; -"281 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=281, type=symmetric_quantize]; -"282 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=282, type=conv2d]; -"283 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=283, type=batch_norm]; -"284 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=284, type=relu_]; -"285 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=285, type=symmetric_quantize]; -"286 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=286, type=calc_rb_binary_mask]; -"287 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=287, type=apply_binary_mask]; -"288 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=288, type=symmetric_quantize]; -"289 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=289, type=conv2d]; -"290 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=290, type=batch_norm]; -"291 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=291, type=relu_]; -"292 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=292, type=symmetric_quantize]; -"293 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=293, type=calc_rb_binary_mask]; -"294 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=294, type=apply_binary_mask]; -"295 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=295, type=symmetric_quantize]; -"296 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=296, type=conv2d]; -"297 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=297, type=batch_norm]; -"298 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=298, type=relu_]; -"299 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=299, type=symmetric_quantize]; -"300 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=300, type=calc_rb_binary_mask]; -"301 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=301, type=apply_binary_mask]; -"302 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=302, type=symmetric_quantize]; -"303 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=303, type=conv2d]; -"304 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=304, type=batch_norm]; -"305 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=305, type=relu_]; -"306 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=306, type=symmetric_quantize]; -"307 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" [id=307, type=avg_pool2d]; -"308 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0" [id=308, type=symmetric_quantize]; -"309 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=309, type=calc_rb_binary_mask]; -"310 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=310, type=apply_binary_mask]; -"311 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=311, type=symmetric_quantize]; -"312 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=312, type=conv2d]; -"313 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=313, type=batch_norm]; -"314 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" [id=314, type=relu_]; -"315 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=315, type=symmetric_quantize]; -"316 Inception3/InceptionC[Mixed_6b]/cat_0" [id=316, type=cat]; -"317 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=317, type=calc_rb_binary_mask]; -"318 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=318, type=apply_binary_mask]; -"319 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=319, type=symmetric_quantize]; -"320 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=320, type=conv2d]; -"321 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=321, type=batch_norm]; -"322 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" [id=322, type=relu_]; -"323 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=323, type=symmetric_quantize]; -"324 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=324, type=calc_rb_binary_mask]; -"325 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=325, type=apply_binary_mask]; -"326 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=326, type=symmetric_quantize]; -"327 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=327, type=conv2d]; -"328 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=328, type=batch_norm]; -"329 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" [id=329, type=relu_]; -"330 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=330, type=symmetric_quantize]; -"331 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=331, type=calc_rb_binary_mask]; -"332 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=332, type=apply_binary_mask]; -"333 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=333, type=symmetric_quantize]; -"334 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=334, type=conv2d]; -"335 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=335, type=batch_norm]; -"336 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" [id=336, type=relu_]; -"337 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=337, type=symmetric_quantize]; -"338 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=338, type=calc_rb_binary_mask]; -"339 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=339, type=apply_binary_mask]; -"340 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=340, type=symmetric_quantize]; -"341 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=341, type=conv2d]; -"342 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=342, type=batch_norm]; -"343 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" [id=343, type=relu_]; -"344 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=344, type=symmetric_quantize]; -"345 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=345, type=calc_rb_binary_mask]; -"346 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=346, type=apply_binary_mask]; -"347 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=347, type=symmetric_quantize]; -"348 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=348, type=conv2d]; -"349 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=349, type=batch_norm]; -"350 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=350, type=relu_]; -"351 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=351, type=symmetric_quantize]; -"352 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=352, type=calc_rb_binary_mask]; -"353 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=353, type=apply_binary_mask]; -"354 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=354, type=symmetric_quantize]; -"355 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=355, type=conv2d]; -"356 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=356, type=batch_norm]; -"357 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=357, type=relu_]; -"358 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=358, type=symmetric_quantize]; -"359 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=359, type=calc_rb_binary_mask]; -"360 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=360, type=apply_binary_mask]; -"361 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=361, type=symmetric_quantize]; -"362 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=362, type=conv2d]; -"363 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=363, type=batch_norm]; -"364 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=364, type=relu_]; -"365 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=365, type=symmetric_quantize]; -"366 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=366, type=calc_rb_binary_mask]; -"367 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=367, type=apply_binary_mask]; -"368 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=368, type=symmetric_quantize]; -"369 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=369, type=conv2d]; -"370 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=370, type=batch_norm]; -"371 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=371, type=relu_]; -"372 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=372, type=symmetric_quantize]; -"373 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=373, type=calc_rb_binary_mask]; -"374 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=374, type=apply_binary_mask]; -"375 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=375, type=symmetric_quantize]; -"376 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=376, type=conv2d]; -"377 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=377, type=batch_norm]; -"378 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=378, type=relu_]; -"379 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=379, type=symmetric_quantize]; -"380 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" [id=380, type=avg_pool2d]; -"381 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0" [id=381, type=symmetric_quantize]; -"382 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=382, type=calc_rb_binary_mask]; -"383 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=383, type=apply_binary_mask]; -"384 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=384, type=symmetric_quantize]; -"385 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=385, type=conv2d]; -"386 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=386, type=batch_norm]; -"387 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" [id=387, type=relu_]; -"388 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=388, type=symmetric_quantize]; -"389 Inception3/InceptionC[Mixed_6c]/cat_0" [id=389, type=cat]; -"390 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=390, type=calc_rb_binary_mask]; -"391 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=391, type=apply_binary_mask]; -"392 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=392, type=symmetric_quantize]; -"393 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=393, type=conv2d]; -"394 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=394, type=batch_norm]; -"395 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" [id=395, type=relu_]; -"396 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=396, type=symmetric_quantize]; -"397 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=397, type=calc_rb_binary_mask]; -"398 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=398, type=apply_binary_mask]; -"399 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=399, type=symmetric_quantize]; -"400 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=400, type=conv2d]; -"401 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=401, type=batch_norm]; -"402 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" [id=402, type=relu_]; -"403 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=403, type=symmetric_quantize]; -"404 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=404, type=calc_rb_binary_mask]; -"405 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=405, type=apply_binary_mask]; -"406 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=406, type=symmetric_quantize]; -"407 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=407, type=conv2d]; -"408 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=408, type=batch_norm]; -"409 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" [id=409, type=relu_]; -"410 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=410, type=symmetric_quantize]; -"411 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=411, type=calc_rb_binary_mask]; -"412 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=412, type=apply_binary_mask]; -"413 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=413, type=symmetric_quantize]; -"414 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=414, type=conv2d]; -"415 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=415, type=batch_norm]; -"416 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" [id=416, type=relu_]; -"417 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=417, type=symmetric_quantize]; -"418 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=418, type=calc_rb_binary_mask]; -"419 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=419, type=apply_binary_mask]; -"420 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=420, type=symmetric_quantize]; -"421 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=421, type=conv2d]; -"422 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=422, type=batch_norm]; -"423 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=423, type=relu_]; -"424 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=424, type=symmetric_quantize]; -"425 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=425, type=calc_rb_binary_mask]; -"426 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=426, type=apply_binary_mask]; -"427 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=427, type=symmetric_quantize]; -"428 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=428, type=conv2d]; -"429 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=429, type=batch_norm]; -"430 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=430, type=relu_]; -"431 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=431, type=symmetric_quantize]; -"432 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=432, type=calc_rb_binary_mask]; -"433 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=433, type=apply_binary_mask]; -"434 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=434, type=symmetric_quantize]; -"435 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=435, type=conv2d]; -"436 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=436, type=batch_norm]; -"437 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=437, type=relu_]; -"438 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=438, type=symmetric_quantize]; -"439 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=439, type=calc_rb_binary_mask]; -"440 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=440, type=apply_binary_mask]; -"441 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=441, type=symmetric_quantize]; -"442 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=442, type=conv2d]; -"443 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=443, type=batch_norm]; -"444 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=444, type=relu_]; -"445 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=445, type=symmetric_quantize]; -"446 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=446, type=calc_rb_binary_mask]; -"447 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=447, type=apply_binary_mask]; -"448 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=448, type=symmetric_quantize]; -"449 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=449, type=conv2d]; -"450 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=450, type=batch_norm]; -"451 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=451, type=relu_]; -"452 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=452, type=symmetric_quantize]; -"453 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" [id=453, type=avg_pool2d]; -"454 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0" [id=454, type=symmetric_quantize]; -"455 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=455, type=calc_rb_binary_mask]; -"456 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=456, type=apply_binary_mask]; -"457 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=457, type=symmetric_quantize]; -"458 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=458, type=conv2d]; -"459 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=459, type=batch_norm]; -"460 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" [id=460, type=relu_]; -"461 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=461, type=symmetric_quantize]; -"462 Inception3/InceptionC[Mixed_6d]/cat_0" [id=462, type=cat]; -"463 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=463, type=calc_rb_binary_mask]; -"464 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=464, type=apply_binary_mask]; -"465 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=465, type=symmetric_quantize]; -"466 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=466, type=conv2d]; -"467 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=467, type=batch_norm]; -"468 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" [id=468, type=relu_]; -"469 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=469, type=symmetric_quantize]; -"470 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=470, type=calc_rb_binary_mask]; -"471 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=471, type=apply_binary_mask]; -"472 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=472, type=symmetric_quantize]; -"473 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=473, type=conv2d]; -"474 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=474, type=batch_norm]; -"475 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" [id=475, type=relu_]; -"476 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=476, type=symmetric_quantize]; -"477 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=477, type=calc_rb_binary_mask]; -"478 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=478, type=apply_binary_mask]; -"479 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=479, type=symmetric_quantize]; -"480 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=480, type=conv2d]; -"481 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=481, type=batch_norm]; -"482 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" [id=482, type=relu_]; -"483 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=483, type=symmetric_quantize]; -"484 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=484, type=calc_rb_binary_mask]; -"485 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=485, type=apply_binary_mask]; -"486 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=486, type=symmetric_quantize]; -"487 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=487, type=conv2d]; -"488 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=488, type=batch_norm]; -"489 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" [id=489, type=relu_]; -"490 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=490, type=symmetric_quantize]; -"491 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=491, type=calc_rb_binary_mask]; -"492 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=492, type=apply_binary_mask]; -"493 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=493, type=symmetric_quantize]; -"494 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=494, type=conv2d]; -"495 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=495, type=batch_norm]; -"496 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" [id=496, type=relu_]; -"497 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=497, type=symmetric_quantize]; -"498 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=498, type=calc_rb_binary_mask]; -"499 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=499, type=apply_binary_mask]; -"500 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=500, type=symmetric_quantize]; -"501 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=501, type=conv2d]; -"502 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=502, type=batch_norm]; -"503 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" [id=503, type=relu_]; -"504 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=504, type=symmetric_quantize]; -"505 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=505, type=calc_rb_binary_mask]; -"506 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=506, type=apply_binary_mask]; -"507 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=507, type=symmetric_quantize]; -"508 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=508, type=conv2d]; -"509 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=509, type=batch_norm]; -"510 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=510, type=relu_]; -"511 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=511, type=symmetric_quantize]; -"512 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=512, type=calc_rb_binary_mask]; -"513 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=513, type=apply_binary_mask]; -"514 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=514, type=symmetric_quantize]; -"515 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" [id=515, type=conv2d]; -"516 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=516, type=batch_norm]; -"517 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" [id=517, type=relu_]; -"518 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" [id=518, type=symmetric_quantize]; -"519 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=519, type=calc_rb_binary_mask]; -"520 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=520, type=apply_binary_mask]; -"521 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=521, type=symmetric_quantize]; -"522 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=522, type=conv2d]; -"523 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=523, type=batch_norm]; -"524 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=524, type=relu_]; -"525 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=525, type=symmetric_quantize]; -"526 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" [id=526, type=avg_pool2d]; -"527 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0" [id=527, type=symmetric_quantize]; -"528 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=528, type=calc_rb_binary_mask]; -"529 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=529, type=apply_binary_mask]; -"530 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=530, type=symmetric_quantize]; -"531 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=531, type=conv2d]; -"532 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=532, type=batch_norm]; -"533 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" [id=533, type=relu_]; -"534 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=534, type=symmetric_quantize]; -"535 Inception3/InceptionC[Mixed_6e]/cat_0" [id=535, type=cat]; -"536 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=536, type=calc_rb_binary_mask]; -"537 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=537, type=apply_binary_mask]; -"538 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=538, type=symmetric_quantize]; -"539 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=539, type=conv2d]; -"540 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=540, type=batch_norm]; -"541 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" [id=541, type=relu_]; -"542 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" [id=542, type=symmetric_quantize]; -"543 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=543, type=calc_rb_binary_mask]; -"544 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=544, type=apply_binary_mask]; -"545 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=545, type=symmetric_quantize]; -"546 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" [id=546, type=conv2d]; -"547 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=547, type=batch_norm]; -"548 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" [id=548, type=relu_]; -"549 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0" [id=549, type=symmetric_quantize]; -"550 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=550, type=calc_rb_binary_mask]; -"551 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=551, type=apply_binary_mask]; -"552 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=552, type=symmetric_quantize]; -"553 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" [id=553, type=conv2d]; -"554 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=554, type=batch_norm]; -"555 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" [id=555, type=relu_]; -"556 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0" [id=556, type=symmetric_quantize]; -"557 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=557, type=calc_rb_binary_mask]; -"558 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=558, type=apply_binary_mask]; -"559 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=559, type=symmetric_quantize]; -"560 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" [id=560, type=conv2d]; -"561 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=561, type=batch_norm]; -"562 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" [id=562, type=relu_]; -"563 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0" [id=563, type=symmetric_quantize]; -"564 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=564, type=calc_rb_binary_mask]; -"565 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=565, type=apply_binary_mask]; -"566 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=566, type=symmetric_quantize]; -"567 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" [id=567, type=conv2d]; -"568 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=568, type=batch_norm]; -"569 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" [id=569, type=relu_]; -"570 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0" [id=570, type=symmetric_quantize]; -"571 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=571, type=calc_rb_binary_mask]; -"572 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=572, type=apply_binary_mask]; -"573 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=573, type=symmetric_quantize]; -"574 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" [id=574, type=conv2d]; -"575 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=575, type=batch_norm]; -"576 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" [id=576, type=relu_]; -"577 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0" [id=577, type=symmetric_quantize]; -"578 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" [id=578, type=max_pool2d]; -"579 Inception3/InceptionD[Mixed_7a]/cat_0" [id=579, type=cat]; -"580 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=580, type=calc_rb_binary_mask]; -"581 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=581, type=apply_binary_mask]; -"582 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=582, type=symmetric_quantize]; -"583 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=583, type=conv2d]; -"584 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=584, type=batch_norm]; -"585 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" [id=585, type=relu_]; -"586 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=586, type=symmetric_quantize]; -"587 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=587, type=calc_rb_binary_mask]; -"588 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=588, type=apply_binary_mask]; -"589 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=589, type=symmetric_quantize]; -"590 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=590, type=conv2d]; -"591 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=591, type=batch_norm]; -"592 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" [id=592, type=relu_]; -"593 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" [id=593, type=symmetric_quantize]; -"594 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=594, type=calc_rb_binary_mask]; -"595 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=595, type=apply_binary_mask]; -"596 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=596, type=symmetric_quantize]; -"597 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" [id=597, type=conv2d]; -"598 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=598, type=batch_norm]; -"599 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" [id=599, type=relu_]; -"600 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" [id=600, type=symmetric_quantize]; -"601 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=601, type=calc_rb_binary_mask]; -"602 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=602, type=apply_binary_mask]; -"603 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=603, type=symmetric_quantize]; -"604 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" [id=604, type=conv2d]; -"605 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=605, type=batch_norm]; -"606 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" [id=606, type=relu_]; -"607 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" [id=607, type=symmetric_quantize]; -"608 Inception3/InceptionE[Mixed_7b]/cat_0" [id=608, type=cat]; -"609 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=609, type=calc_rb_binary_mask]; -"610 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=610, type=apply_binary_mask]; -"611 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=611, type=symmetric_quantize]; -"612 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=612, type=conv2d]; -"613 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=613, type=batch_norm]; -"614 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=614, type=relu_]; -"615 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=615, type=symmetric_quantize]; -"616 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=616, type=calc_rb_binary_mask]; -"617 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=617, type=apply_binary_mask]; -"618 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=618, type=symmetric_quantize]; -"619 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=619, type=conv2d]; -"620 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=620, type=batch_norm]; -"621 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=621, type=relu_]; -"622 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=622, type=symmetric_quantize]; -"623 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=623, type=calc_rb_binary_mask]; -"624 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=624, type=apply_binary_mask]; -"625 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=625, type=symmetric_quantize]; -"626 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=626, type=conv2d]; -"627 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=627, type=batch_norm]; -"628 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=628, type=relu_]; -"629 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" [id=629, type=symmetric_quantize]; -"630 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=630, type=calc_rb_binary_mask]; -"631 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=631, type=apply_binary_mask]; -"632 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=632, type=symmetric_quantize]; -"633 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=633, type=conv2d]; -"634 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=634, type=batch_norm]; -"635 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=635, type=relu_]; -"636 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" [id=636, type=symmetric_quantize]; -"637 Inception3/InceptionE[Mixed_7b]/cat_1" [id=637, type=cat]; -"638 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" [id=638, type=avg_pool2d]; -"639 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0" [id=639, type=symmetric_quantize]; -"640 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=640, type=calc_rb_binary_mask]; -"641 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=641, type=apply_binary_mask]; -"642 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=642, type=symmetric_quantize]; -"643 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=643, type=conv2d]; -"644 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=644, type=batch_norm]; -"645 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" [id=645, type=relu_]; -"646 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=646, type=symmetric_quantize]; -"647 Inception3/InceptionE[Mixed_7b]/cat_2" [id=647, type=cat]; -"648 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=648, type=calc_rb_binary_mask]; -"649 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=649, type=apply_binary_mask]; -"650 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=650, type=symmetric_quantize]; -"651 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=651, type=conv2d]; -"652 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=652, type=batch_norm]; -"653 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" [id=653, type=relu_]; -"654 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=654, type=symmetric_quantize]; -"655 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=655, type=calc_rb_binary_mask]; -"656 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=656, type=apply_binary_mask]; -"657 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=657, type=symmetric_quantize]; -"658 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" [id=658, type=conv2d]; -"659 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=659, type=batch_norm]; -"660 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" [id=660, type=relu_]; -"661 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" [id=661, type=symmetric_quantize]; -"662 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=662, type=calc_rb_binary_mask]; -"663 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=663, type=apply_binary_mask]; -"664 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=664, type=symmetric_quantize]; -"665 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" [id=665, type=conv2d]; -"666 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=666, type=batch_norm]; -"667 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" [id=667, type=relu_]; -"668 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" [id=668, type=symmetric_quantize]; -"669 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=669, type=calc_rb_binary_mask]; -"670 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=670, type=apply_binary_mask]; -"671 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=671, type=symmetric_quantize]; -"672 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" [id=672, type=conv2d]; -"673 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=673, type=batch_norm]; -"674 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" [id=674, type=relu_]; -"675 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" [id=675, type=symmetric_quantize]; -"676 Inception3/InceptionE[Mixed_7c]/cat_0" [id=676, type=cat]; -"677 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=677, type=calc_rb_binary_mask]; -"678 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=678, type=apply_binary_mask]; -"679 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=679, type=symmetric_quantize]; -"680 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=680, type=conv2d]; -"681 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=681, type=batch_norm]; -"682 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=682, type=relu_]; -"683 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=683, type=symmetric_quantize]; -"684 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=684, type=calc_rb_binary_mask]; -"685 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=685, type=apply_binary_mask]; -"686 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=686, type=symmetric_quantize]; -"687 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=687, type=conv2d]; -"688 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=688, type=batch_norm]; -"689 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=689, type=relu_]; -"690 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=690, type=symmetric_quantize]; -"691 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=691, type=calc_rb_binary_mask]; -"692 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=692, type=apply_binary_mask]; -"693 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=693, type=symmetric_quantize]; -"694 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=694, type=conv2d]; -"695 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=695, type=batch_norm]; -"696 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=696, type=relu_]; -"697 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" [id=697, type=symmetric_quantize]; -"698 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=698, type=calc_rb_binary_mask]; -"699 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=699, type=apply_binary_mask]; -"700 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=700, type=symmetric_quantize]; -"701 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=701, type=conv2d]; -"702 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=702, type=batch_norm]; -"703 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=703, type=relu_]; -"704 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" [id=704, type=symmetric_quantize]; -"705 Inception3/InceptionE[Mixed_7c]/cat_1" [id=705, type=cat]; -"706 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" [id=706, type=avg_pool2d]; -"707 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" [id=707, type=symmetric_quantize]; -"708 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=708, type=calc_rb_binary_mask]; -"709 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=709, type=apply_binary_mask]; -"710 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=710, type=symmetric_quantize]; -"711 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=711, type=conv2d]; -"712 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=712, type=batch_norm]; -"713 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" [id=713, type=relu_]; -"714 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=714, type=symmetric_quantize]; -"715 Inception3/InceptionE[Mixed_7c]/cat_2" [id=715, type=cat]; -"716 Inception3/adaptive_avg_pool2d_0" [id=716, type=adaptive_avg_pool2d]; -"717 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" [id=717, type=symmetric_quantize]; -"718 Inception3/dropout_0" [id=718, type=dropout]; -"719 Inception3/view_0" [id=719, type=view]; -"720 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=720, type=calc_rb_binary_mask]; -"721 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=721, type=apply_binary_mask]; -"722 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=722, type=symmetric_quantize]; -"723 Inception3/NNCFLinear[fc]/linear_0" [id=723, type=linear]; -"724 /nncf_model_output_0" [id=724, type=nncf_model_output]; +"5 Inception3/__add___0" [id=5, type=__add__]; +"6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" [id=6, type=symmetric_quantize]; +"7 Inception3/__getitem___1" [id=7, type=__getitem__]; +"8 Inception3/unsqueeze_1" [id=8, type=unsqueeze]; +"9 Inception3/__mul___1" [id=9, type=__mul__]; +"10 Inception3/__add___1" [id=10, type=__add__]; +"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" [id=11, type=symmetric_quantize]; +"12 Inception3/__getitem___2" [id=12, type=__getitem__]; +"13 Inception3/unsqueeze_2" [id=13, type=unsqueeze]; +"14 Inception3/__mul___2" [id=14, type=__mul__]; +"15 Inception3/__add___2" [id=15, type=__add__]; +"16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" [id=16, type=symmetric_quantize]; +"17 Inception3/cat_0" [id=17, type=cat]; +"18 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=18, type=calc_rb_binary_mask]; +"19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=19, type=apply_binary_mask]; +"20 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=20, type=symmetric_quantize]; +"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=21, type=conv2d]; +"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=22, type=batch_norm]; +"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" [id=23, type=relu_]; +"24 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=24, type=symmetric_quantize]; +"25 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=25, type=calc_rb_binary_mask]; +"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=26, type=apply_binary_mask]; +"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=27, type=symmetric_quantize]; +"28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=28, type=conv2d]; +"29 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=29, type=batch_norm]; +"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" [id=30, type=relu_]; +"31 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=31, type=symmetric_quantize]; +"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=32, type=calc_rb_binary_mask]; +"33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=33, type=apply_binary_mask]; +"34 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=34, type=symmetric_quantize]; +"35 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" [id=35, type=conv2d]; +"36 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=36, type=batch_norm]; +"37 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" [id=37, type=relu_]; +"38 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=38, type=symmetric_quantize]; +"39 Inception3/max_pool2d_0" [id=39, type=max_pool2d]; +"40 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=40, type=calc_rb_binary_mask]; +"41 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=41, type=apply_binary_mask]; +"42 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=42, type=symmetric_quantize]; +"43 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" [id=43, type=conv2d]; +"44 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=44, type=batch_norm]; +"45 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" [id=45, type=relu_]; +"46 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=46, type=symmetric_quantize]; +"47 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=47, type=calc_rb_binary_mask]; +"48 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=48, type=apply_binary_mask]; +"49 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=49, type=symmetric_quantize]; +"50 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" [id=50, type=conv2d]; +"51 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=51, type=batch_norm]; +"52 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" [id=52, type=relu_]; +"53 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=53, type=symmetric_quantize]; +"54 Inception3/max_pool2d_1" [id=54, type=max_pool2d]; +"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=55, type=calc_rb_binary_mask]; +"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=56, type=apply_binary_mask]; +"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=57, type=symmetric_quantize]; +"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=58, type=conv2d]; +"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=59, type=batch_norm]; +"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" [id=60, type=relu_]; +"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=61, type=symmetric_quantize]; +"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=62, type=calc_rb_binary_mask]; +"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=63, type=apply_binary_mask]; +"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=64, type=symmetric_quantize]; +"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=65, type=conv2d]; +"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=66, type=batch_norm]; +"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" [id=67, type=relu_]; +"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=68, type=symmetric_quantize]; +"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=69, type=calc_rb_binary_mask]; +"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=70, type=apply_binary_mask]; +"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=71, type=symmetric_quantize]; +"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=72, type=conv2d]; +"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=73, type=batch_norm]; +"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" [id=74, type=relu_]; +"75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=75, type=symmetric_quantize]; +"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=76, type=calc_rb_binary_mask]; +"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=77, type=apply_binary_mask]; +"78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=78, type=symmetric_quantize]; +"79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=79, type=conv2d]; +"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=80, type=batch_norm]; +"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=81, type=relu_]; +"82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=82, type=symmetric_quantize]; +"83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=83, type=calc_rb_binary_mask]; +"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=84, type=apply_binary_mask]; +"85 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=85, type=symmetric_quantize]; +"86 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=86, type=conv2d]; +"87 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=87, type=batch_norm]; +"88 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=88, type=relu_]; +"89 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=89, type=symmetric_quantize]; +"90 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=90, type=calc_rb_binary_mask]; +"91 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=91, type=apply_binary_mask]; +"92 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=92, type=symmetric_quantize]; +"93 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=93, type=conv2d]; +"94 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=94, type=batch_norm]; +"95 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=95, type=relu_]; +"96 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=96, type=symmetric_quantize]; +"97 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" [id=97, type=avg_pool2d]; +"98 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0" [id=98, type=symmetric_quantize]; +"99 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=99, type=calc_rb_binary_mask]; +"100 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=100, type=apply_binary_mask]; +"101 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=101, type=symmetric_quantize]; +"102 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=102, type=conv2d]; +"103 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=103, type=batch_norm]; +"104 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" [id=104, type=relu_]; +"105 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=105, type=symmetric_quantize]; +"106 Inception3/InceptionA[Mixed_5b]/cat_0" [id=106, type=cat]; +"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=107, type=calc_rb_binary_mask]; +"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=108, type=apply_binary_mask]; +"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=109, type=symmetric_quantize]; +"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=110, type=conv2d]; +"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=111, type=batch_norm]; +"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" [id=112, type=relu_]; +"113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=113, type=symmetric_quantize]; +"114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=114, type=calc_rb_binary_mask]; +"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=115, type=apply_binary_mask]; +"116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=116, type=symmetric_quantize]; +"117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=117, type=conv2d]; +"118 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Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=130, type=symmetric_quantize]; +"131 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=131, type=conv2d]; +"132 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=132, type=batch_norm]; +"133 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=133, type=relu_]; +"134 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=134, type=symmetric_quantize]; +"135 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=135, type=calc_rb_binary_mask]; +"136 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Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=142, type=calc_rb_binary_mask]; +"143 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=143, type=apply_binary_mask]; +"144 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=144, type=symmetric_quantize]; +"145 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=145, type=conv2d]; +"146 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=146, type=batch_norm]; +"147 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=147, type=relu_]; +"148 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=148, type=symmetric_quantize]; +"149 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" [id=149, type=avg_pool2d]; +"150 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0" [id=150, type=symmetric_quantize]; +"151 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=151, type=calc_rb_binary_mask]; +"152 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=152, type=apply_binary_mask]; +"153 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=153, type=symmetric_quantize]; +"154 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=154, type=conv2d]; +"155 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=155, type=batch_norm]; +"156 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" [id=156, type=relu_]; +"157 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=157, type=symmetric_quantize]; +"158 Inception3/InceptionA[Mixed_5c]/cat_0" [id=158, type=cat]; +"159 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=159, type=calc_rb_binary_mask]; +"160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=160, type=apply_binary_mask]; +"161 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=161, type=symmetric_quantize]; +"162 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=162, type=conv2d]; +"163 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=163, type=batch_norm]; +"164 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" [id=164, type=relu_]; +"165 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=165, type=symmetric_quantize]; +"166 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=166, type=calc_rb_binary_mask]; +"167 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=167, type=apply_binary_mask]; +"168 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=168, type=symmetric_quantize]; +"169 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" [id=169, type=conv2d]; +"170 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=170, type=batch_norm]; +"171 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" [id=171, type=relu_]; +"172 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" [id=172, type=symmetric_quantize]; +"173 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=173, type=calc_rb_binary_mask]; +"174 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=174, type=apply_binary_mask]; +"175 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=175, type=symmetric_quantize]; +"176 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" [id=176, type=conv2d]; +"177 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=177, type=batch_norm]; +"178 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" [id=178, type=relu_]; +"179 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" [id=179, type=symmetric_quantize]; +"180 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=180, type=calc_rb_binary_mask]; +"181 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=181, type=apply_binary_mask]; +"182 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=182, type=symmetric_quantize]; +"183 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=183, type=conv2d]; +"184 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=184, type=batch_norm]; +"185 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=185, type=relu_]; +"186 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=186, type=symmetric_quantize]; +"187 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=187, type=calc_rb_binary_mask]; +"188 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=188, type=apply_binary_mask]; +"189 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=189, type=symmetric_quantize]; +"190 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=190, type=conv2d]; +"191 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=191, type=batch_norm]; +"192 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=192, type=relu_]; +"193 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=193, type=symmetric_quantize]; +"194 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=194, type=calc_rb_binary_mask]; +"195 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=195, type=apply_binary_mask]; +"196 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=196, type=symmetric_quantize]; +"197 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=197, type=conv2d]; +"198 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=198, type=batch_norm]; +"199 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=199, type=relu_]; +"200 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=200, type=symmetric_quantize]; +"201 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" [id=201, type=avg_pool2d]; +"202 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0" [id=202, type=symmetric_quantize]; +"203 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=203, type=calc_rb_binary_mask]; +"204 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=204, type=apply_binary_mask]; +"205 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=205, type=symmetric_quantize]; +"206 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=206, type=conv2d]; +"207 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=207, type=batch_norm]; +"208 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" [id=208, type=relu_]; +"209 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=209, type=symmetric_quantize]; +"210 Inception3/InceptionA[Mixed_5d]/cat_0" [id=210, type=cat]; +"211 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=211, type=calc_rb_binary_mask]; +"212 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=212, type=apply_binary_mask]; +"213 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=213, type=symmetric_quantize]; +"214 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" [id=214, type=conv2d]; +"215 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=215, type=batch_norm]; +"216 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" [id=216, type=relu_]; +"217 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0" [id=217, type=symmetric_quantize]; +"218 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=218, type=calc_rb_binary_mask]; +"219 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=219, type=apply_binary_mask]; +"220 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=220, type=symmetric_quantize]; +"221 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=221, type=conv2d]; +"222 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=222, type=batch_norm]; +"223 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=223, type=relu_]; +"224 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=224, type=symmetric_quantize]; +"225 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=225, type=calc_rb_binary_mask]; +"226 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=226, type=apply_binary_mask]; +"227 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=227, type=symmetric_quantize]; +"228 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=228, type=conv2d]; +"229 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=229, type=batch_norm]; +"230 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=230, type=relu_]; +"231 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=231, type=symmetric_quantize]; +"232 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=232, type=calc_rb_binary_mask]; +"233 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=233, type=apply_binary_mask]; +"234 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=234, type=symmetric_quantize]; +"235 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=235, type=conv2d]; +"236 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=236, type=batch_norm]; +"237 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" [id=237, type=relu_]; +"238 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=238, type=symmetric_quantize]; +"239 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" [id=239, type=max_pool2d]; +"240 Inception3/InceptionB[Mixed_6a]/cat_0" [id=240, type=cat]; +"241 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=241, type=calc_rb_binary_mask]; +"242 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=242, type=apply_binary_mask]; +"243 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=243, type=symmetric_quantize]; +"244 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=244, type=conv2d]; +"245 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=245, type=batch_norm]; +"246 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" [id=246, type=relu_]; +"247 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=247, type=symmetric_quantize]; +"248 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=248, type=calc_rb_binary_mask]; +"249 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=255, type=calc_rb_binary_mask]; +"256 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=256, type=apply_binary_mask]; +"257 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=257, type=symmetric_quantize]; +"258 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" [id=258, type=conv2d]; +"259 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=259, type=batch_norm]; +"260 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" [id=260, type=relu_]; +"261 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" [id=261, type=symmetric_quantize]; +"262 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=262, type=calc_rb_binary_mask]; +"263 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=263, type=apply_binary_mask]; +"264 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=264, type=symmetric_quantize]; +"265 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" [id=265, type=conv2d]; +"266 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=266, type=batch_norm]; +"267 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" [id=267, type=relu_]; +"268 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" [id=268, type=symmetric_quantize]; +"269 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=269, type=calc_rb_binary_mask]; +"270 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=270, type=apply_binary_mask]; +"271 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=271, type=symmetric_quantize]; +"272 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=272, type=conv2d]; +"273 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=285, type=symmetric_quantize]; +"286 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" [id=286, type=conv2d]; +"287 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=287, type=batch_norm]; +"288 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" [id=288, type=relu_]; +"289 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" [id=289, type=symmetric_quantize]; +"290 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=290, type=calc_rb_binary_mask]; +"291 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Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=297, type=calc_rb_binary_mask]; +"298 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=298, type=apply_binary_mask]; +"299 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=299, type=symmetric_quantize]; +"300 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" [id=300, type=conv2d]; +"301 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=301, type=batch_norm]; +"302 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" [id=302, type=relu_]; +"303 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" [id=303, type=symmetric_quantize]; +"304 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" [id=304, type=avg_pool2d]; +"305 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0" [id=305, type=symmetric_quantize]; +"306 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=306, type=calc_rb_binary_mask]; +"307 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=307, type=apply_binary_mask]; +"308 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=308, type=symmetric_quantize]; +"309 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=309, type=conv2d]; +"310 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=310, type=batch_norm]; +"311 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" [id=311, type=relu_]; +"312 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=312, type=symmetric_quantize]; +"313 Inception3/InceptionC[Mixed_6b]/cat_0" [id=313, type=cat]; +"314 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=314, type=calc_rb_binary_mask]; +"315 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=315, type=apply_binary_mask]; +"316 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=316, type=symmetric_quantize]; +"317 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=317, type=conv2d]; +"318 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=318, type=batch_norm]; +"319 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" [id=319, type=relu_]; +"320 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=320, type=symmetric_quantize]; +"321 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=321, type=calc_rb_binary_mask]; +"322 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=322, type=apply_binary_mask]; +"323 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=323, type=symmetric_quantize]; +"324 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" [id=324, type=conv2d]; +"325 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=325, type=batch_norm]; +"326 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" [id=326, type=relu_]; +"327 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" [id=327, type=symmetric_quantize]; +"328 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type=apply_binary_mask]; +"579 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=579, type=symmetric_quantize]; +"580 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" [id=580, type=conv2d]; +"581 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=581, type=batch_norm]; +"582 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" [id=582, type=relu_]; +"583 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" [id=583, type=symmetric_quantize]; +"584 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=584, type=calc_rb_binary_mask]; +"585 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type=conv2d]; +"610 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=610, type=batch_norm]; +"611 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=611, type=relu_]; +"612 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=612, type=symmetric_quantize]; +"613 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=613, type=calc_rb_binary_mask]; +"614 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=614, type=apply_binary_mask]; +"615 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=615, type=symmetric_quantize]; +"616 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Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=674, type=calc_rb_binary_mask]; +"675 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=675, type=apply_binary_mask]; +"676 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=676, type=symmetric_quantize]; +"677 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" [id=677, type=conv2d]; +"678 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=678, type=batch_norm]; +"679 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=679, type=relu_]; +"680 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" [id=680, type=symmetric_quantize]; +"681 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=681, type=calc_rb_binary_mask]; +"682 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=682, type=apply_binary_mask]; +"683 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=683, type=symmetric_quantize]; +"684 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" [id=684, type=conv2d]; +"685 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=685, type=batch_norm]; +"686 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=686, type=relu_]; +"687 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" [id=687, type=symmetric_quantize]; +"688 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=688, type=calc_rb_binary_mask]; +"689 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=689, type=apply_binary_mask]; +"690 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=690, type=symmetric_quantize]; +"691 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" [id=691, type=conv2d]; +"692 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=692, type=batch_norm]; +"693 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=693, type=relu_]; +"694 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" [id=694, type=symmetric_quantize]; +"695 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=695, type=calc_rb_binary_mask]; +"696 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=696, type=apply_binary_mask]; +"697 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=697, type=symmetric_quantize]; +"698 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" [id=698, type=conv2d]; +"699 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=699, type=batch_norm]; +"700 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" [id=700, type=relu_]; +"701 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" [id=701, type=symmetric_quantize]; +"702 Inception3/InceptionE[Mixed_7c]/cat_1" [id=702, type=cat]; +"703 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" [id=703, type=avg_pool2d]; +"704 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" [id=704, type=symmetric_quantize]; +"705 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=705, type=calc_rb_binary_mask]; +"706 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=706, type=apply_binary_mask]; +"707 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=707, type=symmetric_quantize]; +"708 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" [id=708, type=conv2d]; +"709 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" [id=709, type=batch_norm]; +"710 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" [id=710, type=relu_]; +"711 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" [id=711, type=symmetric_quantize]; +"712 Inception3/InceptionE[Mixed_7c]/cat_2" [id=712, type=cat]; +"713 Inception3/adaptive_avg_pool2d_0" [id=713, type=adaptive_avg_pool2d]; +"714 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" [id=714, type=symmetric_quantize]; +"715 Inception3/dropout_0" [id=715, type=dropout]; +"716 Inception3/view_0" [id=716, type=view]; +"717 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=717, type=calc_rb_binary_mask]; +"718 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=718, type=apply_binary_mask]; +"719 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=719, type=symmetric_quantize]; +"720 Inception3/NNCFLinear[fc]/linear_0" [id=720, type=linear]; +"721 /nncf_model_output_0" [id=721, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "2 Inception3/__getitem___0"; -"1 SymmetricQuantizer/symmetric_quantize_0" -> "8 Inception3/__getitem___1"; -"1 SymmetricQuantizer/symmetric_quantize_0" -> "14 Inception3/__getitem___2"; +"1 SymmetricQuantizer/symmetric_quantize_0" -> "7 Inception3/__getitem___1"; +"1 SymmetricQuantizer/symmetric_quantize_0" -> "12 Inception3/__getitem___2"; "2 Inception3/__getitem___0" -> "3 Inception3/unsqueeze_0"; "3 Inception3/unsqueeze_0" -> "4 Inception3/__mul___0"; -"4 Inception3/__mul___0" -> "5 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___0|OUTPUT]/symmetric_quantize_0"; -"5 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___0|OUTPUT]/symmetric_quantize_0" -> "6 Inception3/__add___0"; -"6 Inception3/__add___0" -> "7 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0"; -"7 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" -> "20 Inception3/cat_0"; -"8 Inception3/__getitem___1" -> "9 Inception3/unsqueeze_1"; -"9 Inception3/unsqueeze_1" -> "10 Inception3/__mul___1"; -"10 Inception3/__mul___1" -> "11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___1|OUTPUT]/symmetric_quantize_0"; -"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___1|OUTPUT]/symmetric_quantize_0" -> "12 Inception3/__add___1"; -"12 Inception3/__add___1" -> "13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1"; -"13 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" -> "20 Inception3/cat_0"; -"14 Inception3/__getitem___2" -> "15 Inception3/unsqueeze_2"; -"15 Inception3/unsqueeze_2" -> "16 Inception3/__mul___2"; -"16 Inception3/__mul___2" -> "17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0"; -"17 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__mul___2|OUTPUT]/symmetric_quantize_0" -> "18 Inception3/__add___2"; -"18 Inception3/__add___2" -> "19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2"; -"19 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" -> "20 Inception3/cat_0"; -"20 Inception3/cat_0" -> "24 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "24 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"24 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "25 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"25 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "26 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0"; -"26 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" -> "27 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"27 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "31 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "29 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"29 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "30 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "31 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"31 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "32 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"32 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "33 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0"; -"33 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" -> "34 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"34 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "38 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; -"35 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "36 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"36 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "37 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"37 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "38 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; -"38 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" -> "39 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"39 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "40 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0"; -"40 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" -> "41 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"41 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "42 Inception3/max_pool2d_0"; -"42 Inception3/max_pool2d_0" -> "46 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; -"43 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "44 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"44 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "45 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"45 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "46 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; -"46 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" -> "47 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"47 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "48 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0"; -"48 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" -> "49 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"49 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "53 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"50 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "51 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"51 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "52 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"52 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "53 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; -"53 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "54 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"54 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "55 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0"; -"55 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" -> "56 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"56 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "57 Inception3/max_pool2d_1"; -"57 Inception3/max_pool2d_1" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"57 Inception3/max_pool2d_1" -> "68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"57 Inception3/max_pool2d_1" -> "82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"57 Inception3/max_pool2d_1" -> "100 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0"; -"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0"; -"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" -> "64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "109 Inception3/InceptionA[Mixed_5b]/cat_0"; -"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0"; -"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" -> "71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; -"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0"; -"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" -> "78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; -"78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "109 Inception3/InceptionA[Mixed_5b]/cat_0"; -"79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "85 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"85 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "89 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"86 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "87 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"87 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "88 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"88 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "89 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"89 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "90 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"90 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "91 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"91 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "92 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"92 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "96 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"93 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "94 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"94 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "95 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"95 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "96 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"96 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "97 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"97 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "98 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"98 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "99 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"99 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "109 Inception3/InceptionA[Mixed_5b]/cat_0"; -"100 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" -> "101 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0"; -"101 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0" -> "105 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"102 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "103 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"103 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "104 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"104 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "105 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"105 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "106 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"106 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "107 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0"; -"107 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" -> "108 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"108 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "109 Inception3/InceptionA[Mixed_5b]/cat_0"; -"109 Inception3/InceptionA[Mixed_5b]/cat_0" -> "113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"109 Inception3/InceptionA[Mixed_5b]/cat_0" -> "120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"109 Inception3/InceptionA[Mixed_5b]/cat_0" -> "134 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"109 Inception3/InceptionA[Mixed_5b]/cat_0" -> "152 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0"; -"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0"; -"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" -> "116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5c]/cat_0"; -"117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0"; -"122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" -> "123 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; -"123 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "127 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"124 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "125 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"125 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "126 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"126 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "127 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"127 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "128 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"128 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "129 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0"; -"129 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" -> "130 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; -"130 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5c]/cat_0"; -"131 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "132 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"132 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "133 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"133 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "134 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"134 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "135 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"135 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "136 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"136 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "137 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"137 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "141 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"138 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "139 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"139 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "140 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"140 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "141 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"141 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "142 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"142 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "143 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"143 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "144 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"144 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "148 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"145 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "146 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"146 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "147 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"147 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "148 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"148 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "149 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"149 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "150 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"150 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "151 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"151 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5c]/cat_0"; -"152 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" -> "153 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0"; -"153 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0" -> "157 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"154 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "155 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"155 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "156 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"156 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "157 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"157 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "158 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"158 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "159 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0"; -"159 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" -> "160 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"160 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "161 Inception3/InceptionA[Mixed_5c]/cat_0"; -"161 Inception3/InceptionA[Mixed_5c]/cat_0" -> "165 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"161 Inception3/InceptionA[Mixed_5c]/cat_0" -> "172 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"161 Inception3/InceptionA[Mixed_5c]/cat_0" -> "186 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"161 Inception3/InceptionA[Mixed_5c]/cat_0" -> "204 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0"; -"162 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "163 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"163 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "164 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"164 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "165 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"165 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "166 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"166 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "167 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0"; -"167 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" -> "168 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"168 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "213 Inception3/InceptionA[Mixed_5d]/cat_0"; -"169 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "170 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"170 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "171 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"171 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "172 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; -"172 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "173 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"173 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "174 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0"; -"174 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" -> "175 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; -"175 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "179 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"176 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "177 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"177 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "178 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"178 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "179 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; -"179 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "180 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"180 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "181 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0"; -"181 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" -> "182 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; -"182 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "213 Inception3/InceptionA[Mixed_5d]/cat_0"; -"183 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "184 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"184 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "185 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"185 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "186 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"186 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "187 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"187 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "188 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"188 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "189 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"189 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "193 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"190 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "191 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"191 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "192 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"192 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "193 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"193 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "194 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"194 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "195 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"195 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "196 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"196 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "200 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"197 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "198 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"198 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "199 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"199 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "200 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"200 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "201 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"201 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "202 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"202 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "203 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"203 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "213 Inception3/InceptionA[Mixed_5d]/cat_0"; -"204 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" -> "205 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0"; -"205 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0" -> "209 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"206 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "207 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"207 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "208 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"208 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "209 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"209 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "210 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"210 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "211 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0"; -"211 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" -> "212 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"212 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "213 Inception3/InceptionA[Mixed_5d]/cat_0"; -"213 Inception3/InceptionA[Mixed_5d]/cat_0" -> "217 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; -"213 Inception3/InceptionA[Mixed_5d]/cat_0" -> "224 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"213 Inception3/InceptionA[Mixed_5d]/cat_0" -> "242 Inception3/InceptionB[Mixed_6a]/max_pool2d_0"; -"214 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "215 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"215 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "216 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"216 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "217 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; -"217 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" -> "218 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"218 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "219 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0"; -"219 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" -> "220 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0"; -"220 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "243 Inception3/InceptionB[Mixed_6a]/cat_0"; -"221 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "222 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"222 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "223 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"223 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "224 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"224 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "225 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"225 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "226 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"226 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "227 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"227 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "231 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"228 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "229 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"229 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "230 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"230 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "231 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"231 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "232 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"232 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "233 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"233 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "234 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"234 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "238 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"235 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "236 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"236 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "237 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"237 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "238 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"238 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "239 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"239 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "240 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0"; -"240 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "241 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"241 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "243 Inception3/InceptionB[Mixed_6a]/cat_0"; -"242 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" -> "243 Inception3/InceptionB[Mixed_6a]/cat_0"; -"243 Inception3/InceptionB[Mixed_6a]/cat_0" -> "247 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"243 Inception3/InceptionB[Mixed_6a]/cat_0" -> "254 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"243 Inception3/InceptionB[Mixed_6a]/cat_0" -> "275 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"243 Inception3/InceptionB[Mixed_6a]/cat_0" -> "307 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0"; -"244 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "245 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"245 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "246 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"246 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "247 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"247 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "248 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"248 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "249 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0"; -"249 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" -> "250 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"250 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "316 Inception3/InceptionC[Mixed_6b]/cat_0"; -"251 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "252 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"252 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "253 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"253 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "254 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"254 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "255 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"255 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "256 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0"; -"256 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" -> "257 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; -"257 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "261 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"258 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "259 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"259 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "260 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"260 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "261 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"261 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "262 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"262 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "263 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0"; -"263 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" -> "264 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; -"264 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "268 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"265 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "266 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"266 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "267 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"267 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "268 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"268 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "269 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"269 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "270 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0"; -"270 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" -> "271 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; -"271 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "316 Inception3/InceptionC[Mixed_6b]/cat_0"; -"272 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "273 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"273 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "274 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"274 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "275 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"275 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "276 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"276 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "277 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"277 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "278 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"278 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "282 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"279 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "280 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"280 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "281 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"281 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "282 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"282 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "283 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"283 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "284 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"284 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "285 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"285 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"286 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "287 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"287 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "288 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"288 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "289 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"289 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "290 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"290 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "291 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"291 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "292 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"292 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "296 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"293 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "294 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"294 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "295 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"295 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "296 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"296 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "297 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"297 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "298 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"298 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "299 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; -"299 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "303 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"300 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "301 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"301 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "302 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"302 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "303 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"303 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "304 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"304 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "305 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"305 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "306 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; -"306 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "316 Inception3/InceptionC[Mixed_6b]/cat_0"; -"307 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" -> "308 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0"; -"308 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0" -> "312 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"309 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "310 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"310 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "311 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"311 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "312 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"312 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "313 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"313 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "314 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0"; -"314 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" -> "315 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"315 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "316 Inception3/InceptionC[Mixed_6b]/cat_0"; -"316 Inception3/InceptionC[Mixed_6b]/cat_0" -> "320 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"316 Inception3/InceptionC[Mixed_6b]/cat_0" -> "327 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"316 Inception3/InceptionC[Mixed_6b]/cat_0" -> "348 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"316 Inception3/InceptionC[Mixed_6b]/cat_0" -> "380 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0"; -"317 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "318 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"318 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "319 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"319 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "320 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"320 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "321 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"321 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "322 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0"; -"322 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" -> "323 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"323 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "389 Inception3/InceptionC[Mixed_6c]/cat_0"; -"324 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "325 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"325 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "326 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"326 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "327 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"327 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "328 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"328 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "329 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0"; -"329 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" -> "330 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; -"330 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "334 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"331 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "332 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"332 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "333 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"333 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "334 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"334 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "335 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"335 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "336 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0"; -"336 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" -> "337 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; -"337 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "341 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"338 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "339 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"339 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "340 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"340 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "341 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"341 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "342 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"342 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "343 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0"; -"343 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" -> "344 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; -"344 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "389 Inception3/InceptionC[Mixed_6c]/cat_0"; -"345 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "346 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"346 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "347 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"347 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "348 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"348 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "349 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"349 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "350 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"350 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "351 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"351 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "355 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"352 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "353 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"353 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "354 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"354 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "355 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"355 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "356 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"356 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "357 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"357 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "358 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"358 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "362 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"359 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "360 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"360 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "361 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"361 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "362 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"362 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "363 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"363 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "364 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"364 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "365 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"365 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "369 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"366 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "367 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"367 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "368 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"368 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "369 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"369 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "370 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"370 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "371 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"371 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "372 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; -"372 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "376 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"373 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "374 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"374 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "375 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"375 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "376 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"376 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "377 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"377 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "378 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"378 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "379 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; -"379 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "389 Inception3/InceptionC[Mixed_6c]/cat_0"; -"380 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" -> "381 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0"; -"381 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0" -> "385 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"382 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "383 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"383 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "384 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"384 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "385 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"385 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "386 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"386 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "387 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0"; -"387 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" -> "388 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"388 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "389 Inception3/InceptionC[Mixed_6c]/cat_0"; -"389 Inception3/InceptionC[Mixed_6c]/cat_0" -> "393 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"389 Inception3/InceptionC[Mixed_6c]/cat_0" -> "400 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"389 Inception3/InceptionC[Mixed_6c]/cat_0" -> "421 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"389 Inception3/InceptionC[Mixed_6c]/cat_0" -> "453 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0"; -"390 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "391 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"391 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "392 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"392 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "393 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"393 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "394 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"394 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "395 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0"; -"395 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" -> "396 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"396 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "462 Inception3/InceptionC[Mixed_6d]/cat_0"; -"397 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "398 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"398 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "399 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"399 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "400 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"400 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "401 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"401 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "402 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0"; -"402 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" -> "403 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; -"403 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "407 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"404 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "405 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"405 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "406 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"406 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "407 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"407 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "408 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"408 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "409 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0"; -"409 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" -> "410 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; -"410 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "414 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"411 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "412 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"412 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "413 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"413 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "414 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"414 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "415 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"415 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "416 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0"; -"416 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" -> "417 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; -"417 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "462 Inception3/InceptionC[Mixed_6d]/cat_0"; -"418 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "419 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"419 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "420 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"420 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "421 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"421 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "422 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"422 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "423 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"423 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "424 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"424 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "428 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"425 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "426 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"426 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "427 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"427 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "428 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"428 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "429 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"429 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "430 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"430 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "431 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"431 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "435 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"432 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "433 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"433 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "434 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"434 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "435 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"435 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "436 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"436 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "437 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"437 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "438 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"438 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "442 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"439 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "440 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"440 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "441 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"441 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "442 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"442 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "443 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"443 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "444 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"444 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "445 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; -"445 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "449 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"446 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "447 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"447 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "448 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"448 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "449 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"449 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "450 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"450 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "451 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"451 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "452 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; -"452 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "462 Inception3/InceptionC[Mixed_6d]/cat_0"; -"453 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" -> "454 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0"; -"454 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0" -> "458 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"455 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "456 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"456 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "457 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"457 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "458 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"458 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "459 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"459 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "460 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0"; -"460 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" -> "461 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"461 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "462 Inception3/InceptionC[Mixed_6d]/cat_0"; -"462 Inception3/InceptionC[Mixed_6d]/cat_0" -> "466 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"462 Inception3/InceptionC[Mixed_6d]/cat_0" -> "473 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"462 Inception3/InceptionC[Mixed_6d]/cat_0" -> "494 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"462 Inception3/InceptionC[Mixed_6d]/cat_0" -> "526 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0"; -"463 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "464 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"464 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "465 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"465 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "466 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"466 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "467 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"467 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "468 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0"; -"468 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" -> "469 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"469 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "535 Inception3/InceptionC[Mixed_6e]/cat_0"; -"470 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "471 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"471 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "472 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"472 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "473 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; -"473 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "474 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"474 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "475 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0"; -"475 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" -> "476 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; -"476 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "480 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"477 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "478 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"478 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "479 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"479 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "480 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; -"480 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "481 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"481 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "482 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0"; -"482 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" -> "483 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; -"483 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "487 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"484 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "485 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"485 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "486 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"486 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "487 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; -"487 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "488 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"488 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "489 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0"; -"489 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" -> "490 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; -"490 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "535 Inception3/InceptionC[Mixed_6e]/cat_0"; -"491 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "492 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"492 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "493 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"493 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "494 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"494 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "495 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"495 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "496 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0"; -"496 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "497 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"497 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "501 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"498 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "499 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"499 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "500 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"500 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "501 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"501 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "502 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"502 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "503 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0"; -"503 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "504 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"504 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "508 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"505 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "506 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"506 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "507 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"507 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "508 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; -"508 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "509 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"509 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "510 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0"; -"510 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "511 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; -"511 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "515 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"512 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "513 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"513 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "514 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"514 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "515 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; -"515 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "516 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"516 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "517 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0"; -"517 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "518 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; -"518 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "522 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"519 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "520 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"520 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "521 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"521 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "522 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; -"522 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "523 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"523 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "524 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0"; -"524 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "525 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; -"525 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "535 Inception3/InceptionC[Mixed_6e]/cat_0"; -"526 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" -> "527 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0"; -"527 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0" -> "531 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"528 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "529 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"529 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "530 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"530 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "531 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"531 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "532 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"532 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "533 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0"; -"533 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" -> "534 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"534 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "535 Inception3/InceptionC[Mixed_6e]/cat_0"; -"535 Inception3/InceptionC[Mixed_6e]/cat_0" -> "539 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"535 Inception3/InceptionC[Mixed_6e]/cat_0" -> "553 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; -"535 Inception3/InceptionC[Mixed_6e]/cat_0" -> "578 Inception3/InceptionD[Mixed_7a]/max_pool2d_0"; -"536 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "537 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"537 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "538 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"538 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "539 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"539 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "540 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"540 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "541 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0"; -"541 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" -> "542 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; -"542 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "546 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; -"543 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "544 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"544 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "545 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"545 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "546 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; -"546 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" -> "547 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"547 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "548 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0"; -"548 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" -> "549 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0"; -"549 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0" -> "579 Inception3/InceptionD[Mixed_7a]/cat_0"; -"550 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "551 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"551 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "552 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"552 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "553 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; -"553 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" -> "554 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"554 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "555 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0"; -"555 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" -> "556 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0"; -"556 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "560 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; -"557 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "558 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"558 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "559 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"559 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "560 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; -"560 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" -> "561 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"561 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "562 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0"; -"562 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" -> "563 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0"; -"563 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0" -> "567 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; -"564 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "565 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"565 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "566 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"566 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "567 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; -"567 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" -> "568 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"568 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "569 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0"; -"569 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" -> "570 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0"; -"570 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0" -> "574 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; -"571 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "572 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"572 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "573 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"573 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "574 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; -"574 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" -> "575 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"575 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "576 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0"; -"576 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" -> "577 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0"; -"577 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0" -> "579 Inception3/InceptionD[Mixed_7a]/cat_0"; -"578 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" -> "579 Inception3/InceptionD[Mixed_7a]/cat_0"; -"579 Inception3/InceptionD[Mixed_7a]/cat_0" -> "583 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"579 Inception3/InceptionD[Mixed_7a]/cat_0" -> "590 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"579 Inception3/InceptionD[Mixed_7a]/cat_0" -> "612 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"579 Inception3/InceptionD[Mixed_7a]/cat_0" -> "638 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0"; -"580 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "581 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"581 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "582 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"582 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "583 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"583 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "584 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"584 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "585 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0"; -"585 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" -> "586 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"586 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "647 Inception3/InceptionE[Mixed_7b]/cat_2"; -"587 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "588 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"588 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "589 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"589 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "590 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"590 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "591 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"591 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "592 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0"; -"592 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" -> "593 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; -"593 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "597 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"593 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "604 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"594 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "595 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"595 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "596 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"596 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "597 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"597 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "598 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"598 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "599 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0"; -"599 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" -> "600 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; -"600 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "608 Inception3/InceptionE[Mixed_7b]/cat_0"; -"601 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "602 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"602 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "603 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"603 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "604 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"604 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "605 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"605 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "606 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0"; -"606 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" -> "607 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; -"607 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "608 Inception3/InceptionE[Mixed_7b]/cat_0"; -"608 Inception3/InceptionE[Mixed_7b]/cat_0" -> "647 Inception3/InceptionE[Mixed_7b]/cat_2"; -"609 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "610 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"610 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "611 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"611 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "612 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"612 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "613 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"613 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "614 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"614 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "615 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"615 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "619 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"616 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "617 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"617 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "618 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"618 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "619 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"619 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "620 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"620 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "621 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"621 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "622 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"622 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "626 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"622 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "633 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"623 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "624 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"624 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "625 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"625 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "626 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"626 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "627 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"627 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "628 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0"; -"628 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "629 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; -"629 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "637 Inception3/InceptionE[Mixed_7b]/cat_1"; -"630 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "631 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"631 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "632 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"632 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "633 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"633 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "634 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"634 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "635 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0"; -"635 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "636 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; -"636 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "637 Inception3/InceptionE[Mixed_7b]/cat_1"; -"637 Inception3/InceptionE[Mixed_7b]/cat_1" -> "647 Inception3/InceptionE[Mixed_7b]/cat_2"; -"638 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" -> "639 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0"; -"639 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0" -> "643 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"640 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "641 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"641 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "642 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"642 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "643 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"643 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "644 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"644 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "645 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0"; -"645 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" -> "646 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"646 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "647 Inception3/InceptionE[Mixed_7b]/cat_2"; -"647 Inception3/InceptionE[Mixed_7b]/cat_2" -> "651 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"647 Inception3/InceptionE[Mixed_7b]/cat_2" -> "658 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"647 Inception3/InceptionE[Mixed_7b]/cat_2" -> "680 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"647 Inception3/InceptionE[Mixed_7b]/cat_2" -> "706 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0"; -"648 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "649 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"649 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "650 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"650 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "651 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; -"651 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "652 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"652 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "653 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0"; -"653 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" -> "654 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; -"654 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "715 Inception3/InceptionE[Mixed_7c]/cat_2"; -"655 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "656 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"656 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "657 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"657 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "658 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; -"658 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "659 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"659 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "660 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0"; -"660 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" -> "661 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; -"661 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "665 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"661 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "672 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"662 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "663 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"663 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "664 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"664 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "665 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; -"665 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "666 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"666 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "667 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0"; -"667 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" -> "668 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; -"668 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "676 Inception3/InceptionE[Mixed_7c]/cat_0"; -"669 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "670 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"670 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "671 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"671 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "672 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; -"672 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "673 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"673 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "674 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0"; -"674 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" -> "675 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; -"675 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "676 Inception3/InceptionE[Mixed_7c]/cat_0"; -"676 Inception3/InceptionE[Mixed_7c]/cat_0" -> "715 Inception3/InceptionE[Mixed_7c]/cat_2"; -"677 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "678 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"678 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "679 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"679 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "680 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; -"680 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "681 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"681 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "682 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0"; -"682 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "683 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; -"683 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "687 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"684 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "685 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"685 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "686 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"686 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "687 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; -"687 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "688 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"688 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "689 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0"; -"689 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "690 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; -"690 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "694 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"690 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "701 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"691 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "692 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"692 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "693 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"693 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "694 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; -"694 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "695 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"695 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "696 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0"; -"696 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "697 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; -"697 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "705 Inception3/InceptionE[Mixed_7c]/cat_1"; -"698 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "699 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"699 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "700 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"700 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "701 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; -"701 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "702 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"702 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "703 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0"; -"703 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "704 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; -"704 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "705 Inception3/InceptionE[Mixed_7c]/cat_1"; -"705 Inception3/InceptionE[Mixed_7c]/cat_1" -> "715 Inception3/InceptionE[Mixed_7c]/cat_2"; -"706 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" -> "707 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0"; -"707 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" -> "711 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"708 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "709 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"709 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "710 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"710 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "711 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; -"711 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "712 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; -"712 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "713 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0"; -"713 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" -> "714 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; -"714 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "715 Inception3/InceptionE[Mixed_7c]/cat_2"; -"715 Inception3/InceptionE[Mixed_7c]/cat_2" -> "716 Inception3/adaptive_avg_pool2d_0"; -"716 Inception3/adaptive_avg_pool2d_0" -> "717 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0"; -"717 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" -> "718 Inception3/dropout_0"; -"718 Inception3/dropout_0" -> "719 Inception3/view_0"; -"719 Inception3/view_0" -> "723 Inception3/NNCFLinear[fc]/linear_0"; -"720 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "721 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"721 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "722 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"722 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "723 Inception3/NNCFLinear[fc]/linear_0"; -"723 Inception3/NNCFLinear[fc]/linear_0" -> "724 /nncf_model_output_0"; +"4 Inception3/__mul___0" -> "5 Inception3/__add___0"; +"5 Inception3/__add___0" -> "6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0"; +"6 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_0" -> "17 Inception3/cat_0"; +"7 Inception3/__getitem___1" -> "8 Inception3/unsqueeze_1"; +"8 Inception3/unsqueeze_1" -> "9 Inception3/__mul___1"; +"9 Inception3/__mul___1" -> "10 Inception3/__add___1"; +"10 Inception3/__add___1" -> "11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1"; +"11 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_1" -> "17 Inception3/cat_0"; +"12 Inception3/__getitem___2" -> "13 Inception3/unsqueeze_2"; +"13 Inception3/unsqueeze_2" -> "14 Inception3/__mul___2"; +"14 Inception3/__mul___2" -> "15 Inception3/__add___2"; +"15 Inception3/__add___2" -> "16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2"; +"16 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/__add___0|OUTPUT;Inception3/__add___1|OUTPUT;Inception3/__add___2|OUTPUT]/symmetric_quantize_2" -> "17 Inception3/cat_0"; +"17 Inception3/cat_0" -> "21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"18 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"19 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "20 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"20 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"21 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"22 Inception3/BasicConv2d[Conv2d_1a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "23 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0"; +"23 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" -> "24 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"24 Inception3/BasicConv2d[Conv2d_1a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"25 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "26 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"26 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"27 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"28 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "29 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"29 Inception3/BasicConv2d[Conv2d_2a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "30 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0"; +"30 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" -> "31 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"31 Inception3/BasicConv2d[Conv2d_2a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "35 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; +"32 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"33 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "34 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"34 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "35 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0"; +"35 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFConv2d[conv]/conv2d_0" -> "36 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"36 Inception3/BasicConv2d[Conv2d_2b_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "37 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0"; +"37 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" -> "38 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"38 Inception3/BasicConv2d[Conv2d_2b_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "39 Inception3/max_pool2d_0"; +"39 Inception3/max_pool2d_0" -> "43 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; +"40 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "41 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"41 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "42 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"42 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "43 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0"; +"43 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFConv2d[conv]/conv2d_0" -> "44 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"44 Inception3/BasicConv2d[Conv2d_3b_1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "45 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0"; +"45 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" -> "46 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"46 Inception3/BasicConv2d[Conv2d_3b_1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "50 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"47 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "48 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"48 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "49 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"49 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "50 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0"; +"50 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFConv2d[conv]/conv2d_0" -> "51 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"51 Inception3/BasicConv2d[Conv2d_4a_3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "52 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0"; +"52 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" -> "53 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"53 Inception3/BasicConv2d[Conv2d_4a_3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "54 Inception3/max_pool2d_1"; +"54 Inception3/max_pool2d_1" -> "58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"54 Inception3/max_pool2d_1" -> "65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"54 Inception3/max_pool2d_1" -> "79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"54 Inception3/max_pool2d_1" -> "97 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0"; +"55 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"56 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"57 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"58 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"59 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0"; +"60 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" -> "61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"61 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "106 Inception3/InceptionA[Mixed_5b]/cat_0"; +"62 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"63 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"64 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"65 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"66 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0"; +"67 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/relu__0" -> "68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; +"68 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"69 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"70 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"71 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"72 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"73 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0"; +"74 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/relu__0" -> "75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; +"75 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "106 Inception3/InceptionA[Mixed_5b]/cat_0"; +"76 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"77 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"78 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"79 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"80 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"81 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"82 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "86 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"83 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"84 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "85 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"85 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "86 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"86 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "87 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"87 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "88 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"88 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "89 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"89 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "93 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"90 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "91 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"91 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "92 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"92 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "93 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"93 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "94 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"94 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "95 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"95 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "96 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"96 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "106 Inception3/InceptionA[Mixed_5b]/cat_0"; +"97 Inception3/InceptionA[Mixed_5b]/avg_pool2d_0" -> "98 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0"; +"98 Inception3/InceptionA[Mixed_5b]/SymmetricQuantizer/symmetric_quantize_0" -> "102 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"99 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "100 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"100 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "101 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"101 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "102 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"102 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "103 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"103 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "104 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0"; +"104 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/relu__0" -> "105 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"105 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "106 Inception3/InceptionA[Mixed_5b]/cat_0"; +"106 Inception3/InceptionA[Mixed_5b]/cat_0" -> "110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"106 Inception3/InceptionA[Mixed_5b]/cat_0" -> "117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"106 Inception3/InceptionA[Mixed_5b]/cat_0" -> "131 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"106 Inception3/InceptionA[Mixed_5b]/cat_0" -> "149 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0"; +"107 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"108 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"109 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"110 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"111 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0"; +"112 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/relu__0" -> "113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"113 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5c]/cat_0"; +"114 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"115 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"116 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"117 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"118 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0"; +"119 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/relu__0" -> "120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; +"120 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "124 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"121 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"122 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "123 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"123 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "124 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"124 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "125 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"125 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "126 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0"; +"126 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/relu__0" -> "127 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; +"127 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5c]/cat_0"; +"128 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "129 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"129 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "130 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"130 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "131 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"131 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "132 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"132 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "133 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"133 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "134 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"134 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "138 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"135 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "136 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"136 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "137 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"137 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "138 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"138 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "139 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"139 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "140 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"140 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "141 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"141 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "145 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"142 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "143 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"143 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "144 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"144 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "145 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"145 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "146 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"146 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "147 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"147 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "148 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"148 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5c]/cat_0"; +"149 Inception3/InceptionA[Mixed_5c]/avg_pool2d_0" -> "150 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0"; +"150 Inception3/InceptionA[Mixed_5c]/SymmetricQuantizer/symmetric_quantize_0" -> "154 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"151 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "152 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"152 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "153 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"153 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "154 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"154 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "155 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"155 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "156 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0"; +"156 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/relu__0" -> "157 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"157 Inception3/InceptionA[Mixed_5c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "158 Inception3/InceptionA[Mixed_5c]/cat_0"; +"158 Inception3/InceptionA[Mixed_5c]/cat_0" -> "162 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"158 Inception3/InceptionA[Mixed_5c]/cat_0" -> "169 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"158 Inception3/InceptionA[Mixed_5c]/cat_0" -> "183 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"158 Inception3/InceptionA[Mixed_5c]/cat_0" -> "201 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0"; +"159 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"160 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "161 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"161 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "162 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"162 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "163 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"163 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "164 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0"; +"164 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/relu__0" -> "165 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"165 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "210 Inception3/InceptionA[Mixed_5d]/cat_0"; +"166 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "167 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"167 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "168 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"168 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "169 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0"; +"169 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFConv2d[conv]/conv2d_0" -> "170 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"170 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "171 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0"; +"171 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/relu__0" -> "172 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0"; +"172 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_1]/SymmetricQuantizer/symmetric_quantize_0" -> "176 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"173 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "174 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"174 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "175 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"175 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "176 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0"; +"176 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFConv2d[conv]/conv2d_0" -> "177 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"177 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "178 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0"; +"178 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/relu__0" -> "179 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0"; +"179 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch5x5_2]/SymmetricQuantizer/symmetric_quantize_0" -> "210 Inception3/InceptionA[Mixed_5d]/cat_0"; +"180 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "181 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"181 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "182 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"182 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "183 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"183 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "184 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"184 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "185 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"185 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "186 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"186 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "190 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"187 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "188 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"188 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "189 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"189 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "190 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"190 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "191 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"191 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "192 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"192 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "193 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"193 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "197 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"194 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "195 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"195 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "196 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"196 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "197 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"197 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "198 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"198 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "199 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"199 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "200 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"200 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "210 Inception3/InceptionA[Mixed_5d]/cat_0"; +"201 Inception3/InceptionA[Mixed_5d]/avg_pool2d_0" -> "202 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0"; +"202 Inception3/InceptionA[Mixed_5d]/SymmetricQuantizer/symmetric_quantize_0" -> "206 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"203 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "204 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"204 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "205 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"205 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "206 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"206 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "207 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"207 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "208 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0"; +"208 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/relu__0" -> "209 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"209 Inception3/InceptionA[Mixed_5d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "210 Inception3/InceptionA[Mixed_5d]/cat_0"; +"210 Inception3/InceptionA[Mixed_5d]/cat_0" -> "214 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; +"210 Inception3/InceptionA[Mixed_5d]/cat_0" -> "221 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"210 Inception3/InceptionA[Mixed_5d]/cat_0" -> "239 Inception3/InceptionB[Mixed_6a]/max_pool2d_0"; +"211 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "212 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"212 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "213 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"213 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "214 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0"; +"214 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFConv2d[conv]/conv2d_0" -> "215 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"215 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "216 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0"; +"216 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/relu__0" -> "217 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0"; +"217 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3]/SymmetricQuantizer/symmetric_quantize_0" -> "240 Inception3/InceptionB[Mixed_6a]/cat_0"; +"218 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "219 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"219 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "220 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"220 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "221 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"221 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "222 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"222 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "223 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"223 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "224 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"224 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "228 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"225 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "226 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"226 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "227 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"227 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "228 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"228 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "229 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"229 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "230 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"230 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "231 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"231 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "235 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"232 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "233 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"233 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "234 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"234 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "235 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"235 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "236 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"236 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "237 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0"; +"237 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/relu__0" -> "238 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"238 Inception3/InceptionB[Mixed_6a]/BasicConv2d[branch3x3dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "240 Inception3/InceptionB[Mixed_6a]/cat_0"; +"239 Inception3/InceptionB[Mixed_6a]/max_pool2d_0" -> "240 Inception3/InceptionB[Mixed_6a]/cat_0"; +"240 Inception3/InceptionB[Mixed_6a]/cat_0" -> "244 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"240 Inception3/InceptionB[Mixed_6a]/cat_0" -> "251 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"240 Inception3/InceptionB[Mixed_6a]/cat_0" -> "272 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"240 Inception3/InceptionB[Mixed_6a]/cat_0" -> "304 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0"; +"241 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "242 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"242 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "243 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"243 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "244 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"244 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "245 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"245 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "246 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0"; +"246 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/relu__0" -> "247 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"247 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "313 Inception3/InceptionC[Mixed_6b]/cat_0"; +"248 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "249 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"249 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "250 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"250 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "251 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"251 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "252 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"252 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "253 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0"; +"253 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/relu__0" -> "254 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; +"254 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "258 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"255 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "256 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"256 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "257 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"257 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "258 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"258 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "259 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"259 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "260 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0"; +"260 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/relu__0" -> "261 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; +"261 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "265 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"262 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "263 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"263 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "264 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"264 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "265 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"265 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "266 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"266 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "267 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0"; +"267 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/relu__0" -> "268 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; +"268 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "313 Inception3/InceptionC[Mixed_6b]/cat_0"; +"269 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "270 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"270 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "271 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"271 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "272 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"272 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "273 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"273 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "274 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"274 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "275 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"275 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "279 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"276 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "277 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"277 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "278 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"278 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "279 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"279 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "280 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"280 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "281 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"281 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "282 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"282 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"283 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "284 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"284 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "285 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"285 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "286 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"286 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "287 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"287 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "288 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"288 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "289 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"289 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "293 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"290 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "291 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"291 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "292 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"292 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "293 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"293 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "294 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"294 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "295 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"295 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "296 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; +"296 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "300 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"297 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "298 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"298 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "299 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"299 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "300 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"300 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "301 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"301 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "302 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"302 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "303 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; +"303 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "313 Inception3/InceptionC[Mixed_6b]/cat_0"; +"304 Inception3/InceptionC[Mixed_6b]/avg_pool2d_0" -> "305 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0"; +"305 Inception3/InceptionC[Mixed_6b]/SymmetricQuantizer/symmetric_quantize_0" -> "309 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"306 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "307 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"307 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "308 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"308 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "309 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"309 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "310 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"310 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "311 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0"; +"311 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/relu__0" -> "312 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"312 Inception3/InceptionC[Mixed_6b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "313 Inception3/InceptionC[Mixed_6b]/cat_0"; +"313 Inception3/InceptionC[Mixed_6b]/cat_0" -> "317 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"313 Inception3/InceptionC[Mixed_6b]/cat_0" -> "324 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"313 Inception3/InceptionC[Mixed_6b]/cat_0" -> "345 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"313 Inception3/InceptionC[Mixed_6b]/cat_0" -> "377 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0"; +"314 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "315 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"315 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "316 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"316 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "317 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"317 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "318 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"318 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "319 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0"; +"319 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/relu__0" -> "320 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"320 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "386 Inception3/InceptionC[Mixed_6c]/cat_0"; +"321 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "322 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"322 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "323 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"323 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "324 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"324 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "325 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"325 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "326 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0"; +"326 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/relu__0" -> "327 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; +"327 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "331 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"328 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "329 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"329 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "330 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"330 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "331 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"331 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "332 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"332 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "333 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0"; +"333 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/relu__0" -> "334 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; +"334 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "338 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"335 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "336 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"336 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "337 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"337 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "338 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"338 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "339 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"339 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "340 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0"; +"340 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/relu__0" -> "341 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; +"341 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "386 Inception3/InceptionC[Mixed_6c]/cat_0"; +"342 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "343 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"343 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "344 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"344 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "345 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"345 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "346 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"346 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "347 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"347 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "348 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"348 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "352 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"349 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "350 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"350 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "351 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"351 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "352 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"352 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "353 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"353 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "354 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"354 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "355 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"355 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "359 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"356 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "357 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"357 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "358 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"358 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "359 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"359 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "360 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"360 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "361 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"361 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "362 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"362 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "366 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"363 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "364 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"364 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "365 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"365 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "366 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"366 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "367 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"367 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "368 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"368 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "369 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; +"369 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "373 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"370 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "371 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"371 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "372 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"372 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "373 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"373 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "374 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"374 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "375 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"375 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "376 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; +"376 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "386 Inception3/InceptionC[Mixed_6c]/cat_0"; +"377 Inception3/InceptionC[Mixed_6c]/avg_pool2d_0" -> "378 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0"; +"378 Inception3/InceptionC[Mixed_6c]/SymmetricQuantizer/symmetric_quantize_0" -> "382 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"379 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "380 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"380 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "381 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"381 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "382 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"382 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "383 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"383 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "384 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0"; +"384 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/relu__0" -> "385 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"385 Inception3/InceptionC[Mixed_6c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "386 Inception3/InceptionC[Mixed_6c]/cat_0"; +"386 Inception3/InceptionC[Mixed_6c]/cat_0" -> "390 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"386 Inception3/InceptionC[Mixed_6c]/cat_0" -> "397 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"386 Inception3/InceptionC[Mixed_6c]/cat_0" -> "418 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"386 Inception3/InceptionC[Mixed_6c]/cat_0" -> "450 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0"; +"387 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "388 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"388 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "389 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"389 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "390 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"390 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "391 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"391 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "392 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0"; +"392 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/relu__0" -> "393 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"393 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "459 Inception3/InceptionC[Mixed_6d]/cat_0"; +"394 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "395 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"395 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "396 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"396 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "397 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"397 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "398 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"398 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "399 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0"; +"399 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/relu__0" -> "400 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; +"400 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "404 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"401 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "402 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"402 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "403 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"403 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "404 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"404 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "405 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"405 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "406 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0"; +"406 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/relu__0" -> "407 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; +"407 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "411 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"408 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "409 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"409 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "410 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"410 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "411 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"411 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "412 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"412 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "413 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0"; +"413 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/relu__0" -> "414 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; +"414 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "459 Inception3/InceptionC[Mixed_6d]/cat_0"; +"415 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "416 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"416 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "417 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"417 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "418 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"418 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "419 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"419 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "420 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"420 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "421 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"421 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "425 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"422 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "423 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"423 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "424 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"424 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "425 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"425 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "426 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"426 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "427 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"427 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "428 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"428 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "432 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"429 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "430 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"430 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "431 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"431 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "432 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"432 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "433 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"433 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "434 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"434 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "435 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"435 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "439 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"436 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "437 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"437 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "438 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"438 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "439 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"439 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "440 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"440 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "441 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"441 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "442 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; +"442 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "446 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"443 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "444 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"444 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "445 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"445 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "446 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"446 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "447 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"447 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "448 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"448 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "449 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; +"449 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "459 Inception3/InceptionC[Mixed_6d]/cat_0"; +"450 Inception3/InceptionC[Mixed_6d]/avg_pool2d_0" -> "451 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0"; +"451 Inception3/InceptionC[Mixed_6d]/SymmetricQuantizer/symmetric_quantize_0" -> "455 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"452 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "453 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"453 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "454 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"454 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "455 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"455 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "456 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"456 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "457 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0"; +"457 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/relu__0" -> "458 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"458 Inception3/InceptionC[Mixed_6d]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "459 Inception3/InceptionC[Mixed_6d]/cat_0"; +"459 Inception3/InceptionC[Mixed_6d]/cat_0" -> "463 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"459 Inception3/InceptionC[Mixed_6d]/cat_0" -> "470 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"459 Inception3/InceptionC[Mixed_6d]/cat_0" -> "491 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"459 Inception3/InceptionC[Mixed_6d]/cat_0" -> "523 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0"; +"460 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "461 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"461 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "462 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"462 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "463 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"463 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "464 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"464 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "465 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0"; +"465 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/relu__0" -> "466 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"466 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "532 Inception3/InceptionC[Mixed_6e]/cat_0"; +"467 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "468 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"468 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "469 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"469 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "470 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0"; +"470 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFConv2d[conv]/conv2d_0" -> "471 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"471 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "472 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0"; +"472 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/relu__0" -> "473 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0"; +"473 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_1]/SymmetricQuantizer/symmetric_quantize_0" -> "477 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"474 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "475 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"475 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "476 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"476 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "477 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0"; +"477 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFConv2d[conv]/conv2d_0" -> "478 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"478 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "479 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0"; +"479 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/relu__0" -> "480 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0"; +"480 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_2]/SymmetricQuantizer/symmetric_quantize_0" -> "484 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"481 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "482 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"482 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "483 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"483 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "484 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0"; +"484 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFConv2d[conv]/conv2d_0" -> "485 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"485 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "486 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0"; +"486 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/relu__0" -> "487 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0"; +"487 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7_3]/SymmetricQuantizer/symmetric_quantize_0" -> "532 Inception3/InceptionC[Mixed_6e]/cat_0"; +"488 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "489 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"489 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "490 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"490 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "491 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"491 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "492 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"492 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "493 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0"; +"493 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/relu__0" -> "494 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"494 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "498 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"495 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "496 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"496 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "497 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"497 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "498 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"498 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "499 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"499 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "500 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0"; +"500 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/relu__0" -> "501 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"501 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "505 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"502 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "503 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"503 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "504 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"504 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "505 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0"; +"505 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFConv2d[conv]/conv2d_0" -> "506 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"506 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "507 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0"; +"507 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/relu__0" -> "508 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0"; +"508 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_3]/SymmetricQuantizer/symmetric_quantize_0" -> "512 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"509 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "510 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"510 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "511 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"511 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "512 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0"; +"512 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFConv2d[conv]/conv2d_0" -> "513 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"513 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "514 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0"; +"514 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/relu__0" -> "515 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0"; +"515 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_4]/SymmetricQuantizer/symmetric_quantize_0" -> "519 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"516 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "517 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"517 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "518 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"518 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "519 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0"; +"519 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFConv2d[conv]/conv2d_0" -> "520 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"520 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "521 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0"; +"521 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/relu__0" -> "522 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0"; +"522 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch7x7dbl_5]/SymmetricQuantizer/symmetric_quantize_0" -> "532 Inception3/InceptionC[Mixed_6e]/cat_0"; +"523 Inception3/InceptionC[Mixed_6e]/avg_pool2d_0" -> "524 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0"; +"524 Inception3/InceptionC[Mixed_6e]/SymmetricQuantizer/symmetric_quantize_0" -> "528 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"525 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "526 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"526 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "527 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"527 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "528 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"528 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "529 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"529 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "530 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0"; +"530 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/relu__0" -> "531 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"531 Inception3/InceptionC[Mixed_6e]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "532 Inception3/InceptionC[Mixed_6e]/cat_0"; +"532 Inception3/InceptionC[Mixed_6e]/cat_0" -> "536 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"532 Inception3/InceptionC[Mixed_6e]/cat_0" -> "550 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; +"532 Inception3/InceptionC[Mixed_6e]/cat_0" -> "575 Inception3/InceptionD[Mixed_7a]/max_pool2d_0"; +"533 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "534 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"534 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "535 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"535 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "536 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"536 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "537 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"537 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "538 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0"; +"538 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/relu__0" -> "539 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"539 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "543 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; +"540 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "541 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"541 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "542 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"542 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "543 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0"; +"543 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFConv2d[conv]/conv2d_0" -> "544 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"544 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "545 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0"; +"545 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/relu__0" -> "546 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0"; +"546 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch3x3_2]/SymmetricQuantizer/symmetric_quantize_0" -> "576 Inception3/InceptionD[Mixed_7a]/cat_0"; +"547 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "548 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"548 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "549 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"549 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "550 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0"; +"550 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFConv2d[conv]/conv2d_0" -> "551 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"551 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "552 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0"; +"552 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/relu__0" -> "553 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"553 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "557 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; +"554 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "555 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"555 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "556 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"556 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "557 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0"; +"557 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFConv2d[conv]/conv2d_0" -> "558 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"558 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "559 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0"; +"559 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/relu__0" -> "560 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0"; +"560 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_2]/SymmetricQuantizer/symmetric_quantize_0" -> "564 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; +"561 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "562 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"562 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "563 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"563 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "564 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0"; +"564 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFConv2d[conv]/conv2d_0" -> "565 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"565 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "566 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0"; +"566 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/relu__0" -> "567 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0"; +"567 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_3]/SymmetricQuantizer/symmetric_quantize_0" -> "571 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; +"568 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "569 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"569 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "570 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"570 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "571 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0"; +"571 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFConv2d[conv]/conv2d_0" -> "572 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"572 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "573 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0"; +"573 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/relu__0" -> "574 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0"; +"574 Inception3/InceptionD[Mixed_7a]/BasicConv2d[branch7x7x3_4]/SymmetricQuantizer/symmetric_quantize_0" -> "576 Inception3/InceptionD[Mixed_7a]/cat_0"; +"575 Inception3/InceptionD[Mixed_7a]/max_pool2d_0" -> "576 Inception3/InceptionD[Mixed_7a]/cat_0"; +"576 Inception3/InceptionD[Mixed_7a]/cat_0" -> "580 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"576 Inception3/InceptionD[Mixed_7a]/cat_0" -> "587 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"576 Inception3/InceptionD[Mixed_7a]/cat_0" -> "609 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"576 Inception3/InceptionD[Mixed_7a]/cat_0" -> "635 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0"; +"577 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "578 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"578 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "579 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"579 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "580 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"580 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "581 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"581 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "582 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0"; +"582 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/relu__0" -> "583 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"583 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "644 Inception3/InceptionE[Mixed_7b]/cat_2"; +"584 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "585 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"585 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "586 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"586 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "587 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"587 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "588 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"588 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "589 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0"; +"589 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/relu__0" -> "590 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"590 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "594 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"590 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "601 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"591 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "592 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"592 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "593 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"593 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "594 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"594 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "595 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"595 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "596 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0"; +"596 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/relu__0" -> "597 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; +"597 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "605 Inception3/InceptionE[Mixed_7b]/cat_0"; +"598 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "599 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"599 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "600 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"600 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "601 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"601 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "602 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"602 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "603 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0"; +"603 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/relu__0" -> "604 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; +"604 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "605 Inception3/InceptionE[Mixed_7b]/cat_0"; +"605 Inception3/InceptionE[Mixed_7b]/cat_0" -> "644 Inception3/InceptionE[Mixed_7b]/cat_2"; +"606 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "607 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"607 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "608 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"608 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "609 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"609 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "610 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"610 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "611 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"611 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "612 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"612 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "616 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"613 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "614 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"614 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "615 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"615 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "616 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"616 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "617 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"617 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "618 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"618 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "619 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"619 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "623 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"619 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "630 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"620 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "621 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"621 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "622 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"622 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "623 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"623 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "624 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"624 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "625 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0"; +"625 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "626 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; +"626 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "634 Inception3/InceptionE[Mixed_7b]/cat_1"; +"627 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "628 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"628 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "629 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"629 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "630 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"630 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "631 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"631 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "632 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0"; +"632 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "633 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; +"633 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "634 Inception3/InceptionE[Mixed_7b]/cat_1"; +"634 Inception3/InceptionE[Mixed_7b]/cat_1" -> "644 Inception3/InceptionE[Mixed_7b]/cat_2"; +"635 Inception3/InceptionE[Mixed_7b]/avg_pool2d_0" -> "636 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0"; +"636 Inception3/InceptionE[Mixed_7b]/SymmetricQuantizer/symmetric_quantize_0" -> "640 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"637 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "638 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"638 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "639 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"639 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "640 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"640 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "641 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"641 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "642 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0"; +"642 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/relu__0" -> "643 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"643 Inception3/InceptionE[Mixed_7b]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "644 Inception3/InceptionE[Mixed_7b]/cat_2"; +"644 Inception3/InceptionE[Mixed_7b]/cat_2" -> "648 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"644 Inception3/InceptionE[Mixed_7b]/cat_2" -> "655 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"644 Inception3/InceptionE[Mixed_7b]/cat_2" -> "677 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"644 Inception3/InceptionE[Mixed_7b]/cat_2" -> "703 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0"; +"645 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "646 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"646 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "647 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"647 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "648 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0"; +"648 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFConv2d[conv]/conv2d_0" -> "649 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"649 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "650 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0"; +"650 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/relu__0" -> "651 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0"; +"651 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch1x1]/SymmetricQuantizer/symmetric_quantize_0" -> "712 Inception3/InceptionE[Mixed_7c]/cat_2"; +"652 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "653 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"653 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "654 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"654 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "655 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0"; +"655 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFConv2d[conv]/conv2d_0" -> "656 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"656 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "657 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0"; +"657 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/relu__0" -> "658 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0"; +"658 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "662 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"658 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_1]/SymmetricQuantizer/symmetric_quantize_0" -> "669 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"659 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "660 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"660 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "661 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"661 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "662 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0"; +"662 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFConv2d[conv]/conv2d_0" -> "663 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"663 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "664 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0"; +"664 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/relu__0" -> "665 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0"; +"665 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2a]/SymmetricQuantizer/symmetric_quantize_0" -> "673 Inception3/InceptionE[Mixed_7c]/cat_0"; +"666 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "667 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"667 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "668 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"668 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "669 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0"; +"669 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFConv2d[conv]/conv2d_0" -> "670 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"670 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "671 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0"; +"671 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/relu__0" -> "672 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0"; +"672 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3_2b]/SymmetricQuantizer/symmetric_quantize_0" -> "673 Inception3/InceptionE[Mixed_7c]/cat_0"; +"673 Inception3/InceptionE[Mixed_7c]/cat_0" -> "712 Inception3/InceptionE[Mixed_7c]/cat_2"; +"674 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "675 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"675 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "676 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"676 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "677 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0"; +"677 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFConv2d[conv]/conv2d_0" -> "678 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"678 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "679 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0"; +"679 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" -> "680 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0"; +"680 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/SymmetricQuantizer/symmetric_quantize_0" -> "684 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"681 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "682 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"682 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "683 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"683 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "684 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0"; +"684 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFConv2d[conv]/conv2d_0" -> "685 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"685 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "686 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0"; +"686 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" -> "687 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0"; +"687 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "691 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"687 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/SymmetricQuantizer/symmetric_quantize_0" -> "698 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"688 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "689 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"689 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "690 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"690 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "691 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0"; +"691 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFConv2d[conv]/conv2d_0" -> "692 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"692 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "693 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0"; +"693 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" -> "694 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0"; +"694 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/SymmetricQuantizer/symmetric_quantize_0" -> "702 Inception3/InceptionE[Mixed_7c]/cat_1"; +"695 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "696 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"696 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "697 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"697 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "698 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0"; +"698 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFConv2d[conv]/conv2d_0" -> "699 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"699 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "700 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0"; +"700 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "701 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0"; +"701 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/SymmetricQuantizer/symmetric_quantize_0" -> "702 Inception3/InceptionE[Mixed_7c]/cat_1"; +"702 Inception3/InceptionE[Mixed_7c]/cat_1" -> "712 Inception3/InceptionE[Mixed_7c]/cat_2"; +"703 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" -> "704 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0"; +"704 Inception3/InceptionE[Mixed_7c]/SymmetricQuantizer/symmetric_quantize_0" -> "708 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"705 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "706 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"706 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "707 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"707 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "708 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0"; +"708 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFConv2d[conv]/conv2d_0" -> "709 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0"; +"709 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/NNCFBatchNorm2d[bn]/batch_norm_0" -> "710 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0"; +"710 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" -> "711 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0"; +"711 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/SymmetricQuantizer/symmetric_quantize_0" -> "712 Inception3/InceptionE[Mixed_7c]/cat_2"; +"712 Inception3/InceptionE[Mixed_7c]/cat_2" -> "713 Inception3/adaptive_avg_pool2d_0"; +"713 Inception3/adaptive_avg_pool2d_0" -> "714 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0"; +"714 Inception3/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[Inception3/adaptive_avg_pool2d_0|OUTPUT]/symmetric_quantize_0" -> "715 Inception3/dropout_0"; +"715 Inception3/dropout_0" -> "716 Inception3/view_0"; +"716 Inception3/view_0" -> "720 Inception3/NNCFLinear[fc]/linear_0"; +"717 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "718 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"718 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "719 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"719 Inception3/NNCFLinear[fc]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "720 Inception3/NNCFLinear[fc]/linear_0"; +"720 Inception3/NNCFLinear[fc]/linear_0" -> "721 /nncf_model_output_0"; } diff --git a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_bi_seq.dot b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_bi_seq.dot index 5ccb993eb77..0d819f535c7 100644 --- a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_bi_seq.dot +++ b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_bi_seq.dot @@ -16,66 +16,64 @@ strict digraph { "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=14, type=sigmoid]; "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=15, type=symmetric_quantize]; "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=17, type=symmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=18, type=tanh]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=19, type=symmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=20, type=sigmoid]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=21, type=symmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=22, type=__mul__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=23, type=symmetric_quantize]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=24, type=__mul__]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=25, type=symmetric_quantize]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=26, type=__add__]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=27, type=tanh]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=28, type=symmetric_quantize]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=29, type=__mul__]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=30, type=linear]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=31, type=symmetric_quantize]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=32, type=cat]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=33, type=view]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=34, type=calc_rb_binary_mask]; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=35, type=apply_binary_mask]; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=36, type=symmetric_quantize]; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=37, type=linear]; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=38, type=symmetric_quantize]; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=39, type=calc_rb_binary_mask]; -"40 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type=__add__]; -"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" [id=59, type=tanh]; -"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=60, type=symmetric_quantize]; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" [id=61, type=__mul__]; -"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=62, type=linear]; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=63, type=symmetric_quantize]; -"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [id=64, type=cat]; -"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" [id=65, type=view]; -"66 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/nncf_model_output_0" [id=69, type=nncf_model_output]; +"70 /nncf_model_output_1" [id=70, type=nncf_model_output]; +"71 /nncf_model_output_2" [id=71, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"1 SymmetricQuantizer/symmetric_quantize_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"1 SymmetricQuantizer/symmetric_quantize_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "4 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; "4 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; @@ -84,77 +82,75 @@ strict digraph { "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "73 /nncf_model_output_2"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"68 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "71 /nncf_model_output_0"; -"69 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"70 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "72 /nncf_model_output_1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"64 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "71 /nncf_model_output_2"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"66 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "69 /nncf_model_output_0"; +"67 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "70 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_bi_stacked.dot b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_bi_stacked.dot index b6eec9c9f7e..e5750bfa4eb 100644 --- a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_bi_stacked.dot +++ b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_bi_stacked.dot @@ -16,132 +16,128 @@ strict digraph { "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=14, type=sigmoid]; "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=15, type=symmetric_quantize]; "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=17, type=symmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=18, type=tanh]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=19, type=symmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=20, type=sigmoid]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=21, type=symmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=22, type=__mul__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=23, type=symmetric_quantize]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=24, type=__mul__]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=25, type=symmetric_quantize]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=26, type=__add__]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=27, type=tanh]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=28, type=symmetric_quantize]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=29, type=__mul__]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=30, type=linear]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=31, type=symmetric_quantize]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=32, type=cat]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=33, type=view]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=34, type=calc_rb_binary_mask]; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=35, type=apply_binary_mask]; -"36 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[id=87, type=__mul__]; +"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=88, type=symmetric_quantize]; +"89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" [id=89, type=__add__]; +"90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" [id=90, type=tanh]; "91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=91, type=symmetric_quantize]; -"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" [id=92, type=__add__]; -"93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" [id=93, type=tanh]; -"94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=94, type=symmetric_quantize]; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" [id=95, type=__mul__]; -"96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=96, type=linear]; -"97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=97, type=symmetric_quantize]; -"98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [id=98, type=cat]; -"99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" [id=99, type=view]; -"100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=100, type=calc_rb_binary_mask]; -"101 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NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=105, type=calc_rb_binary_mask]; -"106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=106, type=apply_binary_mask]; -"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=107, type=symmetric_quantize]; -"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=108, type=linear]; -"109 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type=__mul__]; -"128 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=128, type=linear]; -"129 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=129, type=symmetric_quantize]; -"130 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [id=130, type=cat]; -"131 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" [id=131, type=view]; -"132 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" [id=132, type=cat]; -"133 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=133, type=cat]; -"134 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=134, type=view]; -"135 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=135, type=cat]; -"136 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=136, type=view]; -"137 /nncf_model_output_0" [id=137, type=nncf_model_output]; -"138 /nncf_model_output_1" [id=138, type=nncf_model_output]; -"139 /nncf_model_output_2" [id=139, type=nncf_model_output]; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" [id=92, type=__mul__]; +"93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=93, type=linear]; +"94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=94, type=symmetric_quantize]; +"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [id=95, type=cat]; +"96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" [id=96, type=view]; +"97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=97, type=calc_rb_binary_mask]; +"98 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NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=113, type=symmetric_quantize]; +"114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=114, type=sigmoid]; +"115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=115, type=symmetric_quantize]; +"116 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" [id=116, type=__mul__]; +"117 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=117, type=symmetric_quantize]; +"118 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" [id=118, type=__mul__]; +"119 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=119, type=symmetric_quantize]; +"120 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" [id=120, type=__add__]; +"121 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" [id=121, type=tanh]; +"122 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=122, type=symmetric_quantize]; +"123 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" [id=123, type=__mul__]; +"124 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=124, type=linear]; +"125 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SymmetricQuantizer/symmetric_quantize_0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"1 SymmetricQuantizer/symmetric_quantize_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"1 SymmetricQuantizer/symmetric_quantize_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "4 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; "4 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; @@ -150,156 +146,152 @@ strict digraph { "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "135 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "133 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "135 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "133 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; -"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; -"78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0" -> "79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0"; -"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0"; -"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; -"82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0"; -"84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0" -> "85 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"85 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; -"86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; -"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; -"90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1" -> "91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; -"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1"; -"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "135 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" -> "94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "133 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" -> "99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0"; -"99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" -> "132 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; -"105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "128 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; -"110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0" -> "111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0"; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "116 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0"; -"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "118 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"113 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "122 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; -"114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "120 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0"; -"116 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0" -> "117 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"117 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "122 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; -"118 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "119 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"119 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "127 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; -"120 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" -> "121 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"121 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "124 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; -"122 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" -> "123 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"123 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "124 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; -"124 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "125 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1"; -"124 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "135 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"125 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" -> "126 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"126 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "127 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; -"127 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "128 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"127 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "130 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"127 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "133 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"128 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "129 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"130 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" -> "131 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0"; -"131 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" -> "132 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"132 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "139 /nncf_model_output_2"; -"133 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "134 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"134 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "137 /nncf_model_output_0"; -"135 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "136 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"136 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "138 /nncf_model_output_1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "131 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "129 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "131 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "129 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"64 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; +"71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; +"76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "85 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0"; +"81 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"82 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; +"83 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"84 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; +"85 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0" -> "86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"86 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; +"87 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1" -> "88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"88 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; +"89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1"; +"89 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "131 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"90 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" -> "91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"91 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"92 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "129 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"93 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "94 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"95 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" -> "96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0"; +"96 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" -> "128 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"97 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"98 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"99 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"100 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"101 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; +"102 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"103 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"104 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "124 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"105 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"106 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; +"107 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0" -> "108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0"; +"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0"; +"108 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"109 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"110 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "118 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; +"111 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "116 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0"; +"112 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0" -> "113 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"113 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "118 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; +"114 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"115 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "123 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; +"116 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" -> "117 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"117 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "120 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; +"118 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" -> "119 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"119 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "120 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; +"120 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "121 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1"; +"120 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "131 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"121 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" -> "122 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"122 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "123 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; +"123 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "124 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"123 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "126 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"123 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "129 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"124 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "125 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"126 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" -> "127 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0"; +"127 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" -> "128 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"128 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "135 /nncf_model_output_2"; +"129 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "130 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"130 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "133 /nncf_model_output_0"; +"131 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "132 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"132 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "134 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_cell.dot b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_cell.dot index 52d13590220..8702b647bf3 100644 --- a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_cell.dot +++ b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_cell.dot @@ -16,21 +16,20 @@ strict digraph { "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=14, type=sigmoid]; "15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=15, type=symmetric_quantize]; "16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=16, type=sigmoid]; -"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=17, type=symmetric_quantize]; -"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" [id=18, type=tanh]; -"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=19, type=symmetric_quantize]; -"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=20, type=sigmoid]; -"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=21, type=symmetric_quantize]; -"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" [id=22, type=__mul__]; -"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=23, type=symmetric_quantize]; -"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" [id=24, type=__mul__]; -"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=25, type=symmetric_quantize]; -"26 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" [id=26, type=__add__]; -"27 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" [id=27, type=tanh]; -"28 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=28, type=symmetric_quantize]; -"29 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" [id=29, type=__mul__]; -"30 /nncf_model_output_0" [id=30, type=nncf_model_output]; -"31 /nncf_model_output_1" [id=31, type=nncf_model_output]; +"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" [id=17, type=tanh]; +"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=18, type=symmetric_quantize]; +"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=19, type=sigmoid]; +"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=20, type=symmetric_quantize]; +"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" [id=21, type=__mul__]; +"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=22, type=symmetric_quantize]; +"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" [id=23, type=__mul__]; +"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=24, type=symmetric_quantize]; +"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" [id=25, type=__add__]; +"26 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" [id=26, type=tanh]; +"27 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=27, type=symmetric_quantize]; +"28 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" [id=28, type=__mul__]; +"29 /nncf_model_output_0" [id=29, type=nncf_model_output]; +"30 /nncf_model_output_1" [id=30, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "5 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "3 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; @@ -46,23 +45,22 @@ strict digraph { "12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___0" -> "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0"; "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0"; "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0"; -"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0"; +"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2"; "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0"; -"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" -> "19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; -"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "29 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; -"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" -> "23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "26 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; -"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" -> "25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "26 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; -"26 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "27 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1"; -"26 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "31 /nncf_model_output_1"; -"27 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" -> "28 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"28 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "29 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; -"29 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" -> "30 /nncf_model_output_0"; +"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; +"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0"; +"17 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" -> "18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"18 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; +"19 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"20 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "28 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; +"21 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" -> "22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"22 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; +"23 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" -> "24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"24 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; +"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "26 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1"; +"25 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "30 /nncf_model_output_1"; +"26 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" -> "27 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"27 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "28 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; +"28 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" -> "29 /nncf_model_output_0"; } diff --git a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_uni_seq.dot b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_uni_seq.dot index 00d7b7d191a..e58e64141a9 100644 --- a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_uni_seq.dot +++ b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_uni_seq.dot @@ -16,31 +16,30 @@ strict digraph { "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=14, type=sigmoid]; "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=15, type=symmetric_quantize]; "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=17, type=symmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=18, type=tanh]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=19, type=symmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=20, type=sigmoid]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=21, type=symmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=22, type=__mul__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=23, type=symmetric_quantize]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=24, type=__mul__]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=25, type=symmetric_quantize]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=26, type=__add__]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=27, type=tanh]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=28, type=symmetric_quantize]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=29, type=__mul__]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=30, type=linear]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=31, type=symmetric_quantize]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=32, type=cat]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=33, type=view]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=34, type=cat]; -"35 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=35, type=cat]; -"36 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=36, type=view]; -"37 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=37, type=cat]; -"38 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=38, type=view]; -"39 /nncf_model_output_0" [id=39, type=nncf_model_output]; -"40 /nncf_model_output_1" [id=40, type=nncf_model_output]; -"41 /nncf_model_output_2" [id=41, type=nncf_model_output]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=17, type=tanh]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=18, type=symmetric_quantize]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=19, type=sigmoid]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=20, type=symmetric_quantize]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=21, type=__mul__]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=22, type=symmetric_quantize]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=23, type=__mul__]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=24, type=symmetric_quantize]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=25, type=__add__]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=26, type=tanh]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=27, type=symmetric_quantize]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=28, type=__mul__]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=29, type=linear]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=30, type=symmetric_quantize]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=31, type=cat]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=32, type=view]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=33, type=cat]; +"34 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=34, type=cat]; +"35 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=35, type=view]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=36, type=cat]; +"37 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=37, type=view]; +"38 /nncf_model_output_0" [id=38, type=nncf_model_output]; +"39 /nncf_model_output_1" [id=39, type=nncf_model_output]; +"40 /nncf_model_output_2" [id=40, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; @@ -51,39 +50,38 @@ strict digraph { "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "41 /nncf_model_output_2"; -"35 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"36 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "39 /nncf_model_output_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"38 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "40 /nncf_model_output_1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "40 /nncf_model_output_2"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "38 /nncf_model_output_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "39 /nncf_model_output_1"; } diff --git a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_uni_stacked.dot b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_uni_stacked.dot index a6f2888cf58..5ce71fea2fe 100644 --- a/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_uni_stacked.dot +++ b/tests/torch/data/reference_graphs/quantized_rb_sparsity/lstm_uni_stacked.dot @@ -16,65 +16,63 @@ strict digraph { "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=14, type=sigmoid]; "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=15, type=symmetric_quantize]; "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=16, type=sigmoid]; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=17, type=symmetric_quantize]; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=18, type=tanh]; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=19, type=symmetric_quantize]; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=20, type=sigmoid]; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=21, type=symmetric_quantize]; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=22, type=__mul__]; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=23, type=symmetric_quantize]; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=24, type=__mul__]; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=25, type=symmetric_quantize]; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=26, type=__add__]; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=27, type=tanh]; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=28, type=symmetric_quantize]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=29, type=__mul__]; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=30, type=linear]; -"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=31, type=symmetric_quantize]; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=32, type=cat]; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=33, type=view]; -"34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=34, type=cat]; -"35 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" [id=35, type=symmetric_quantize]; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=36, type=calc_rb_binary_mask]; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=37, type=apply_binary_mask]; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=38, type=symmetric_quantize]; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=39, type=linear]; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=40, type=symmetric_quantize]; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=41, type=calc_rb_binary_mask]; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=42, type=apply_binary_mask]; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=43, type=symmetric_quantize]; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=44, type=linear]; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=45, type=symmetric_quantize]; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=46, type=__add__]; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=47, type=chunk]; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=48, type=sigmoid]; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=49, type=symmetric_quantize]; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=50, type=sigmoid]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=17, type=tanh]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=18, type=symmetric_quantize]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=19, type=sigmoid]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=20, type=symmetric_quantize]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=21, type=__mul__]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=22, type=symmetric_quantize]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=23, type=__mul__]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=24, type=symmetric_quantize]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=25, type=__add__]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=26, type=tanh]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=27, type=symmetric_quantize]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=28, type=__mul__]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=29, type=linear]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=30, type=symmetric_quantize]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=31, type=cat]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=32, type=view]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=33, type=cat]; +"34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" [id=34, type=symmetric_quantize]; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=35, type=calc_rb_binary_mask]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=36, type=apply_binary_mask]; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=37, type=symmetric_quantize]; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" [id=38, type=linear]; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=39, type=symmetric_quantize]; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" [id=40, type=calc_rb_binary_mask]; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" [id=41, type=apply_binary_mask]; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=42, type=symmetric_quantize]; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=43, type=linear]; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=44, type=symmetric_quantize]; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=45, type=__add__]; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=46, type=chunk]; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=47, type=sigmoid]; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" [id=48, type=symmetric_quantize]; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=49, type=sigmoid]; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=50, type=tanh]; "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" [id=51, type=symmetric_quantize]; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=52, type=tanh]; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=52, type=sigmoid]; "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" [id=53, type=symmetric_quantize]; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=54, type=sigmoid]; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" [id=54, type=__mul__]; "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" [id=55, type=symmetric_quantize]; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" [id=56, type=__mul__]; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" [id=56, type=__mul__]; "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" [id=57, type=symmetric_quantize]; -"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" [id=58, type=__mul__]; -"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=59, type=symmetric_quantize]; -"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" [id=60, type=__add__]; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" [id=61, type=tanh]; -"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" [id=62, type=symmetric_quantize]; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" [id=63, type=__mul__]; -"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=64, type=linear]; -"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=65, type=symmetric_quantize]; -"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [id=66, type=cat]; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" [id=67, type=view]; -"68 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" [id=68, type=cat]; -"69 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=69, type=cat]; -"70 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=70, type=view]; -"71 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=71, type=cat]; -"72 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=72, type=view]; -"73 /nncf_model_output_0" [id=73, type=nncf_model_output]; -"74 /nncf_model_output_1" [id=74, type=nncf_model_output]; -"75 /nncf_model_output_2" [id=75, type=nncf_model_output]; +"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" [id=58, type=__add__]; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" [id=59, type=tanh]; +"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" [id=60, type=symmetric_quantize]; +"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" [id=61, type=__mul__]; +"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" [id=62, type=linear]; +"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" [id=63, type=symmetric_quantize]; +"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [id=64, type=cat]; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" [id=65, type=view]; +"66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" [id=66, type=cat]; +"67 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=67, type=cat]; +"68 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=68, type=view]; +"69 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=69, type=cat]; +"70 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=70, type=view]; +"71 /nncf_model_output_0" [id=71, type=nncf_model_output]; +"72 /nncf_model_output_1" [id=72, type=nncf_model_output]; +"73 /nncf_model_output_2" [id=73, type=nncf_model_output]; "0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0"; "1 SymmetricQuantizer/symmetric_quantize_0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "3 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; @@ -85,79 +83,77 @@ strict digraph { "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; -"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; -"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; -"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; -"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; -"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; -"34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0"; -"35 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; -"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; -"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; -"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; -"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; -"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; -"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; -"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; -"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; -"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; -"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; -"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; -"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; -"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; -"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; -"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6"; -"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_6" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; -"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; -"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; -"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; -"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; -"68 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "75 /nncf_model_output_2"; -"69 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; -"70 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "73 /nncf_model_output_0"; -"71 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "72 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; -"72 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "74 /nncf_model_output_1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"24 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/SymmetricQuantizer/symmetric_quantize_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/linear_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[input_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/calc_rb_binary_mask_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[0]/RBSparsifyingWeight[op]/apply_binary_mask_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/ModuleDict[pre_ops]/UpdateWeight[1]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"45 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_0" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_1" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2"; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_2" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_3" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_4" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/SymmetricQuantizer/symmetric_quantize_5" -> "61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0"; +"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"61 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"62 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/linear_0" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/NNCFLinear[hidden_linear]/SymmetricQuantizer/symmetric_quantize_0"; +"64 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"65 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"66 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" -> "73 /nncf_model_output_2"; +"67 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/view_0" -> "71 /nncf_model_output_0"; +"69 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +"70 NNCF_RNN/StackedRNN[rnn_impl]/view_1" -> "72 /nncf_model_output_1"; } diff --git a/tests/torch/experimental/search_building_blocks/test_search_building_blocks.py b/tests/torch/experimental/search_building_blocks/test_search_building_blocks.py index 49886324452..72ef59d5338 100644 --- a/tests/torch/experimental/search_building_blocks/test_search_building_blocks.py +++ b/tests/torch/experimental/search_building_blocks/test_search_building_blocks.py @@ -22,7 +22,7 @@ from nncf.experimental.torch.search_building_blocks.search_blocks import BuildingBlocks from nncf.experimental.torch.search_building_blocks.search_blocks import GroupedBlockIDs from nncf.experimental.torch.search_building_blocks.search_blocks import get_building_blocks -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import create_compressed_model_and_algo_for_test from tests.torch.helpers import get_empty_config from tests.torch.nas.helpers import move_model_to_cuda_if_available diff --git a/tests/torch/experimental/search_building_blocks/test_transformer_blocks.py b/tests/torch/experimental/search_building_blocks/test_transformer_blocks.py index db38cec3717..c5ddcbc4dc4 100644 --- a/tests/torch/experimental/search_building_blocks/test_transformer_blocks.py +++ b/tests/torch/experimental/search_building_blocks/test_transformer_blocks.py @@ -33,7 +33,7 @@ from nncf.experimental.torch.search_building_blocks.search_blocks import get_building_blocks from nncf.experimental.torch.search_building_blocks.search_blocks import get_indexes_of_overlapping_blocks_min from nncf.experimental.torch.search_building_blocks.search_blocks import get_indexes_of_overlapping_blocks_seq -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import create_compressed_model_and_algo_for_test from tests.torch.helpers import get_empty_config from tests.torch.nas.helpers import move_model_to_cuda_if_available diff --git a/tests/torch/experimental/sparsify_activations/test_algo.py b/tests/torch/experimental/sparsify_activations/test_algo.py index b7214aaa5fa..2059d7ef947 100644 --- a/tests/torch/experimental/sparsify_activations/test_algo.py +++ b/tests/torch/experimental/sparsify_activations/test_algo.py @@ -28,8 +28,8 @@ from nncf.scopes import IgnoredScope from nncf.torch.model_creation import wrap_model from nncf.torch.nncf_network import NNCFNetwork -from tests.shared.nx_graph import compare_nx_graph_with_reference -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.experimental.sparsify_activations.helpers import ThreeLinearModel from tests.torch.experimental.sparsify_activations.helpers import count_sparsifier_patterns_in_ov from tests.torch.experimental.sparsify_activations.helpers import dummy_llama_model diff --git a/tests/torch/fx/graph.dot b/tests/torch/fx/graph.dot new file mode 100644 index 00000000000..5c3da0685bb --- /dev/null +++ b/tests/torch/fx/graph.dot @@ -0,0 +1,413 @@ +strict digraph { +"0 arg0_1"; +"1 quantize_per_tensor_default"; +"2 dequantize_per_tensor_default"; +"3 conv2d_scale_0_updated_constant0"; +"4 compressed_weight_updated_constant0"; +"5 conv1_weight_bias_0_0"; +"6 mul_tensor"; +"7 conv2d"; +"8 relu_"; +"9 quantize_per_tensor_default_1"; +"10 dequantize_per_tensor_default_1"; +"11 max_pool2d"; +"12 conv2d_1_scale_0_updated_constant0"; +"13 compressed_weight_updated_constant1"; +"14 layer1_0_conv1_weight_bias_0_0"; +"15 mul_tensor_1"; +"16 conv2d_1"; +"17 relu__1"; +"18 quantize_per_tensor_default_2"; +"19 dequantize_per_tensor_default_2"; +"20 conv2d_2_scale_0_updated_constant0"; +"21 compressed_weight_updated_constant2"; +"22 layer1_0_conv2_weight_bias_0_0"; +"23 mul_tensor_2"; +"24 conv2d_2"; +"25 quantize_per_tensor_default_3"; +"26 dequantize_per_tensor_default_3"; +"27 add_"; +"28 relu__2"; +"29 quantize_per_tensor_default_4"; +"30 dequantize_per_tensor_default_5"; +"31 dequantize_per_tensor_default_4"; +"32 conv2d_3_scale_0_updated_constant0"; +"33 compressed_weight_updated_constant3"; +"34 layer1_1_conv1_weight_bias_0_0"; +"35 mul_tensor_3"; +"36 conv2d_3"; +"37 relu__3"; +"38 quantize_per_tensor_default_5"; +"39 dequantize_per_tensor_default_6"; +"40 conv2d_4_scale_0_updated_constant0"; +"41 compressed_weight_updated_constant4"; +"42 layer1_1_conv2_weight_bias_0_0"; +"43 mul_tensor_4"; +"44 conv2d_4"; +"45 quantize_per_tensor_default_6"; +"46 dequantize_per_tensor_default_7"; +"47 add__1"; +"48 relu__4"; +"49 quantize_per_tensor_default_7"; +"50 dequantize_per_tensor_default_9"; +"51 dequantize_per_tensor_default_8"; +"52 conv2d_5_scale_0_updated_constant0"; +"53 compressed_weight_updated_constant5"; +"54 layer2_0_conv1_weight_bias_0_0"; +"55 mul_tensor_5"; +"56 conv2d_5"; +"57 relu__5"; +"58 quantize_per_tensor_default_8"; +"59 dequantize_per_tensor_default_10"; +"60 conv2d_6_scale_0_updated_constant0"; +"61 compressed_weight_updated_constant6"; +"62 layer2_0_conv2_weight_bias_0_0"; +"63 mul_tensor_6"; +"64 conv2d_6"; +"65 quantize_per_tensor_default_9"; +"66 dequantize_per_tensor_default_11"; +"67 conv2d_7_scale_0_updated_constant0"; +"68 compressed_weight_updated_constant7"; +"69 layer2_0_downsample_0_weight_bias_0_0"; +"70 mul_tensor_7"; +"71 conv2d_7"; +"72 quantize_per_tensor_default_10"; +"73 dequantize_per_tensor_default_12"; +"74 add__2"; +"75 relu__6"; +"76 quantize_per_tensor_default_11"; +"77 dequantize_per_tensor_default_14"; +"78 dequantize_per_tensor_default_13"; +"79 conv2d_8_scale_0_updated_constant0"; +"80 compressed_weight_updated_constant8"; +"81 layer2_1_conv1_weight_bias_0_0"; +"82 mul_tensor_8"; +"83 conv2d_8"; +"84 relu__7"; +"85 quantize_per_tensor_default_12"; +"86 dequantize_per_tensor_default_15"; +"87 conv2d_9_scale_0_updated_constant0"; +"88 compressed_weight_updated_constant9"; +"89 layer2_1_conv2_weight_bias_0_0"; +"90 mul_tensor_9"; +"91 conv2d_9"; +"92 quantize_per_tensor_default_13"; +"93 dequantize_per_tensor_default_16"; +"94 add__3"; +"95 relu__8"; +"96 quantize_per_tensor_default_14"; +"97 dequantize_per_tensor_default_18"; +"98 dequantize_per_tensor_default_17"; +"99 conv2d_10_scale_0_updated_constant0"; +"100 compressed_weight_updated_constant10"; +"101 layer3_0_conv1_weight_bias_0_0"; +"102 mul_tensor_10"; +"103 conv2d_10"; +"104 relu__9"; +"105 quantize_per_tensor_default_15"; +"106 dequantize_per_tensor_default_19"; +"107 conv2d_11_scale_0_updated_constant0"; +"108 compressed_weight_updated_constant11"; +"109 layer3_0_conv2_weight_bias_0_0"; +"110 mul_tensor_11"; +"111 conv2d_11"; +"112 quantize_per_tensor_default_16"; +"113 dequantize_per_tensor_default_20"; +"114 conv2d_12_scale_0_updated_constant0"; +"115 compressed_weight_updated_constant12"; +"116 layer3_0_downsample_0_weight_bias_0_0"; +"117 mul_tensor_12"; +"118 conv2d_12"; +"119 quantize_per_tensor_default_17"; +"120 dequantize_per_tensor_default_21"; +"121 add__4"; +"122 relu__10"; +"123 quantize_per_tensor_default_18"; +"124 dequantize_per_tensor_default_23"; +"125 dequantize_per_tensor_default_22"; +"126 conv2d_13_scale_0_updated_constant0"; +"127 compressed_weight_updated_constant13"; +"128 layer3_1_conv1_weight_bias_0_0"; +"129 mul_tensor_13"; +"130 conv2d_13"; +"131 relu__11"; +"132 quantize_per_tensor_default_19"; +"133 dequantize_per_tensor_default_24"; +"134 conv2d_14_scale_0_updated_constant0"; +"135 compressed_weight_updated_constant14"; +"136 layer3_1_conv2_weight_bias_0_0"; +"137 mul_tensor_14"; +"138 conv2d_14"; +"139 quantize_per_tensor_default_20"; +"140 dequantize_per_tensor_default_25"; +"141 add__5"; +"142 relu__12"; +"143 quantize_per_tensor_default_21"; +"144 dequantize_per_tensor_default_27"; +"145 dequantize_per_tensor_default_26"; +"146 conv2d_15_scale_0_updated_constant0"; +"147 compressed_weight_updated_constant15"; +"148 layer4_0_conv1_weight_bias_0_0"; +"149 mul_tensor_15"; +"150 conv2d_15"; +"151 relu__13"; +"152 quantize_per_tensor_default_22"; +"153 dequantize_per_tensor_default_28"; +"154 conv2d_16_scale_0_updated_constant0"; +"155 compressed_weight_updated_constant16"; +"156 layer4_0_conv2_weight_bias_0_0"; +"157 mul_tensor_16"; +"158 conv2d_16"; +"159 quantize_per_tensor_default_23"; +"160 dequantize_per_tensor_default_29"; +"161 conv2d_17_scale_0_updated_constant0"; +"162 compressed_weight_updated_constant17"; +"163 layer4_0_downsample_0_weight_bias_0_0"; +"164 mul_tensor_17"; +"165 conv2d_17"; +"166 quantize_per_tensor_default_24"; +"167 dequantize_per_tensor_default_30"; +"168 add__6"; +"169 relu__14"; +"170 quantize_per_tensor_default_25"; +"171 dequantize_per_tensor_default_32"; +"172 dequantize_per_tensor_default_31"; +"173 conv2d_18_scale_0_updated_constant0"; +"174 compressed_weight_updated_constant18"; +"175 layer4_1_conv1_weight_bias_0_0"; +"176 mul_tensor_18"; +"177 conv2d_18"; +"178 relu__15"; +"179 quantize_per_tensor_default_26"; +"180 dequantize_per_tensor_default_33"; +"181 conv2d_19_scale_0_updated_constant0"; +"182 compressed_weight_updated_constant19"; +"183 layer4_1_conv2_weight_bias_0_0"; +"184 mul_tensor_19"; +"185 conv2d_19"; +"186 quantize_per_tensor_default_27"; +"187 dequantize_per_tensor_default_34"; +"188 add__7"; +"189 relu__16"; +"190 quantize_per_tensor_default_28"; +"191 dequantize_per_tensor_default_35"; +"192 adaptive_avg_pool2d"; +"193 quantize_per_tensor_default_29"; +"194 dequantize_per_tensor_default_36"; +"195 flatten"; +"196 linear_scale_0_updated_constant0"; +"197 compressed_weight_updated_constant20"; +"198 _param_constant61_0_0"; +"199 mul_tensor_20"; +"200 linear"; +"201 output"; +"0 arg0_1" -> "1 quantize_per_tensor_default" [label="(1, 3, 224, 224) \n0 -> 0", style=solid]; +"1 quantize_per_tensor_default" -> "2 dequantize_per_tensor_default" [label="(1, 3, 224, 224) \n0 -> 0", style=solid]; +"2 dequantize_per_tensor_default" -> "7 conv2d" [label="(1, 3, 224, 224) \n0 -> 0", style=solid]; +"3 conv2d_scale_0_updated_constant0" -> "6 mul_tensor" [label="(64, 1, 1, 1) \n0 -> 1", style=solid]; +"4 compressed_weight_updated_constant0" -> "6 mul_tensor" [label="(64, 3, 7, 7) \n0 -> 0", style=solid]; +"5 conv1_weight_bias_0_0" -> "7 conv2d" [label="(64,) \n0 -> 2", style=solid]; +"6 mul_tensor" -> "7 conv2d" [label="(64, 3, 7, 7) \n0 -> 1", style=solid]; +"7 conv2d" -> "8 relu_" [label="(1, 64, 112, 112) \n0 -> 0", style=solid]; +"8 relu_" -> "9 quantize_per_tensor_default_1" [label="(1, 64, 112, 112) \n0 -> 0", style=solid]; +"9 quantize_per_tensor_default_1" -> "10 dequantize_per_tensor_default_1" [label="(1, 64, 112, 112) \n0 -> 0", style=solid]; +"10 dequantize_per_tensor_default_1" -> "11 max_pool2d" [label="(1, 64, 112, 112) \n0 -> 0", style=solid]; +"11 max_pool2d" -> "27 add_" [label="(1, 64, 56, 56) \n0 -> 1", style=solid]; +"11 max_pool2d" -> "16 conv2d_1" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"12 conv2d_1_scale_0_updated_constant0" -> "15 mul_tensor_1" [label="(64, 1, 1, 1) \n0 -> 1", style=solid]; +"13 compressed_weight_updated_constant1" -> "15 mul_tensor_1" [label="(64, 64, 3, 3) \n0 -> 0", style=solid]; +"14 layer1_0_conv1_weight_bias_0_0" -> "16 conv2d_1" [label="(64,) \n0 -> 2", style=solid]; +"15 mul_tensor_1" -> "16 conv2d_1" [label="(64, 64, 3, 3) \n0 -> 1", style=solid]; +"16 conv2d_1" -> "17 relu__1" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"17 relu__1" -> "18 quantize_per_tensor_default_2" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"18 quantize_per_tensor_default_2" -> "19 dequantize_per_tensor_default_2" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"19 dequantize_per_tensor_default_2" -> "24 conv2d_2" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"20 conv2d_2_scale_0_updated_constant0" -> "23 mul_tensor_2" [label="(64, 1, 1, 1) \n0 -> 1", style=solid]; +"21 compressed_weight_updated_constant2" -> "23 mul_tensor_2" [label="(64, 64, 3, 3) \n0 -> 0", style=solid]; +"22 layer1_0_conv2_weight_bias_0_0" -> "24 conv2d_2" [label="(64,) \n0 -> 2", style=solid]; +"23 mul_tensor_2" -> "24 conv2d_2" [label="(64, 64, 3, 3) \n0 -> 1", style=solid]; +"24 conv2d_2" -> "25 quantize_per_tensor_default_3" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"25 quantize_per_tensor_default_3" -> "26 dequantize_per_tensor_default_3" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"26 dequantize_per_tensor_default_3" -> "27 add_" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"27 add_" -> "28 relu__2" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"28 relu__2" -> "29 quantize_per_tensor_default_4" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"29 quantize_per_tensor_default_4" -> "31 dequantize_per_tensor_default_4" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"29 quantize_per_tensor_default_4" -> "30 dequantize_per_tensor_default_5" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"30 dequantize_per_tensor_default_5" -> "47 add__1" [label="(1, 64, 56, 56) \n0 -> 1", style=solid]; +"31 dequantize_per_tensor_default_4" -> "36 conv2d_3" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"32 conv2d_3_scale_0_updated_constant0" -> "35 mul_tensor_3" [label="(64, 1, 1, 1) \n0 -> 1", style=solid]; +"33 compressed_weight_updated_constant3" -> "35 mul_tensor_3" [label="(64, 64, 3, 3) \n0 -> 0", style=solid]; +"34 layer1_1_conv1_weight_bias_0_0" -> "36 conv2d_3" [label="(64,) \n0 -> 2", style=solid]; +"35 mul_tensor_3" -> "36 conv2d_3" [label="(64, 64, 3, 3) \n0 -> 1", style=solid]; +"36 conv2d_3" -> "37 relu__3" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"37 relu__3" -> "38 quantize_per_tensor_default_5" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"38 quantize_per_tensor_default_5" -> "39 dequantize_per_tensor_default_6" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"39 dequantize_per_tensor_default_6" -> "44 conv2d_4" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"40 conv2d_4_scale_0_updated_constant0" -> "43 mul_tensor_4" [label="(64, 1, 1, 1) \n0 -> 1", style=solid]; +"41 compressed_weight_updated_constant4" -> "43 mul_tensor_4" [label="(64, 64, 3, 3) \n0 -> 0", style=solid]; +"42 layer1_1_conv2_weight_bias_0_0" -> "44 conv2d_4" [label="(64,) \n0 -> 2", style=solid]; +"43 mul_tensor_4" -> "44 conv2d_4" [label="(64, 64, 3, 3) \n0 -> 1", style=solid]; +"44 conv2d_4" -> "45 quantize_per_tensor_default_6" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"45 quantize_per_tensor_default_6" -> "46 dequantize_per_tensor_default_7" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"46 dequantize_per_tensor_default_7" -> "47 add__1" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"47 add__1" -> "48 relu__4" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"48 relu__4" -> "49 quantize_per_tensor_default_7" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"49 quantize_per_tensor_default_7" -> "51 dequantize_per_tensor_default_8" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"49 quantize_per_tensor_default_7" -> "50 dequantize_per_tensor_default_9" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"50 dequantize_per_tensor_default_9" -> "71 conv2d_7" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"51 dequantize_per_tensor_default_8" -> "56 conv2d_5" [label="(1, 64, 56, 56) \n0 -> 0", style=solid]; +"52 conv2d_5_scale_0_updated_constant0" -> "55 mul_tensor_5" [label="(128, 1, 1, 1) \n0 -> 1", style=solid]; +"53 compressed_weight_updated_constant5" -> "55 mul_tensor_5" [label="(128, 64, 3, 3) \n0 -> 0", style=solid]; +"54 layer2_0_conv1_weight_bias_0_0" -> "56 conv2d_5" [label="(128,) \n0 -> 2", style=solid]; +"55 mul_tensor_5" -> "56 conv2d_5" [label="(128, 64, 3, 3) \n0 -> 1", style=solid]; +"56 conv2d_5" -> "57 relu__5" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"57 relu__5" -> "58 quantize_per_tensor_default_8" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"58 quantize_per_tensor_default_8" -> "59 dequantize_per_tensor_default_10" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"59 dequantize_per_tensor_default_10" -> "64 conv2d_6" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"60 conv2d_6_scale_0_updated_constant0" -> "63 mul_tensor_6" [label="(128, 1, 1, 1) \n0 -> 1", style=solid]; +"61 compressed_weight_updated_constant6" -> "63 mul_tensor_6" [label="(128, 128, 3, 3) \n0 -> 0", style=solid]; +"62 layer2_0_conv2_weight_bias_0_0" -> "64 conv2d_6" [label="(128,) \n0 -> 2", style=solid]; +"63 mul_tensor_6" -> "64 conv2d_6" [label="(128, 128, 3, 3) \n0 -> 1", style=solid]; +"64 conv2d_6" -> "65 quantize_per_tensor_default_9" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"65 quantize_per_tensor_default_9" -> "66 dequantize_per_tensor_default_11" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"66 dequantize_per_tensor_default_11" -> "74 add__2" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"67 conv2d_7_scale_0_updated_constant0" -> "70 mul_tensor_7" [label="(128, 1, 1, 1) \n0 -> 1", style=solid]; +"68 compressed_weight_updated_constant7" -> "70 mul_tensor_7" [label="(128, 64, 1, 1) \n0 -> 0", style=solid]; +"69 layer2_0_downsample_0_weight_bias_0_0" -> "71 conv2d_7" [label="(128,) \n0 -> 2", style=solid]; +"70 mul_tensor_7" -> "71 conv2d_7" [label="(128, 64, 1, 1) \n0 -> 1", style=solid]; +"71 conv2d_7" -> "72 quantize_per_tensor_default_10" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"72 quantize_per_tensor_default_10" -> "73 dequantize_per_tensor_default_12" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"73 dequantize_per_tensor_default_12" -> "74 add__2" [label="(1, 128, 28, 28) \n0 -> 1", style=solid]; +"74 add__2" -> "75 relu__6" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"75 relu__6" -> "76 quantize_per_tensor_default_11" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"76 quantize_per_tensor_default_11" -> "78 dequantize_per_tensor_default_13" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"76 quantize_per_tensor_default_11" -> "77 dequantize_per_tensor_default_14" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"77 dequantize_per_tensor_default_14" -> "94 add__3" [label="(1, 128, 28, 28) \n0 -> 1", style=solid]; +"78 dequantize_per_tensor_default_13" -> "83 conv2d_8" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"79 conv2d_8_scale_0_updated_constant0" -> "82 mul_tensor_8" [label="(128, 1, 1, 1) \n0 -> 1", style=solid]; +"80 compressed_weight_updated_constant8" -> "82 mul_tensor_8" [label="(128, 128, 3, 3) \n0 -> 0", style=solid]; +"81 layer2_1_conv1_weight_bias_0_0" -> "83 conv2d_8" [label="(128,) \n0 -> 2", style=solid]; +"82 mul_tensor_8" -> "83 conv2d_8" [label="(128, 128, 3, 3) \n0 -> 1", style=solid]; +"83 conv2d_8" -> "84 relu__7" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"84 relu__7" -> "85 quantize_per_tensor_default_12" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"85 quantize_per_tensor_default_12" -> "86 dequantize_per_tensor_default_15" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"86 dequantize_per_tensor_default_15" -> "91 conv2d_9" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"87 conv2d_9_scale_0_updated_constant0" -> "90 mul_tensor_9" [label="(128, 1, 1, 1) \n0 -> 1", style=solid]; +"88 compressed_weight_updated_constant9" -> "90 mul_tensor_9" [label="(128, 128, 3, 3) \n0 -> 0", style=solid]; +"89 layer2_1_conv2_weight_bias_0_0" -> "91 conv2d_9" [label="(128,) \n0 -> 2", style=solid]; +"90 mul_tensor_9" -> "91 conv2d_9" [label="(128, 128, 3, 3) \n0 -> 1", style=solid]; +"91 conv2d_9" -> "92 quantize_per_tensor_default_13" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"92 quantize_per_tensor_default_13" -> "93 dequantize_per_tensor_default_16" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"93 dequantize_per_tensor_default_16" -> "94 add__3" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"94 add__3" -> "95 relu__8" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"95 relu__8" -> "96 quantize_per_tensor_default_14" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"96 quantize_per_tensor_default_14" -> "98 dequantize_per_tensor_default_17" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"96 quantize_per_tensor_default_14" -> "97 dequantize_per_tensor_default_18" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"97 dequantize_per_tensor_default_18" -> "118 conv2d_12" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"98 dequantize_per_tensor_default_17" -> "103 conv2d_10" [label="(1, 128, 28, 28) \n0 -> 0", style=solid]; +"99 conv2d_10_scale_0_updated_constant0" -> "102 mul_tensor_10" [label="(256, 1, 1, 1) \n0 -> 1", style=solid]; +"100 compressed_weight_updated_constant10" -> "102 mul_tensor_10" [label="(256, 128, 3, 3) \n0 -> 0", style=solid]; +"101 layer3_0_conv1_weight_bias_0_0" -> "103 conv2d_10" [label="(256,) \n0 -> 2", style=solid]; +"102 mul_tensor_10" -> "103 conv2d_10" [label="(256, 128, 3, 3) \n0 -> 1", style=solid]; +"103 conv2d_10" -> "104 relu__9" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"104 relu__9" -> "105 quantize_per_tensor_default_15" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"105 quantize_per_tensor_default_15" -> "106 dequantize_per_tensor_default_19" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"106 dequantize_per_tensor_default_19" -> "111 conv2d_11" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"107 conv2d_11_scale_0_updated_constant0" -> "110 mul_tensor_11" [label="(256, 1, 1, 1) \n0 -> 1", style=solid]; +"108 compressed_weight_updated_constant11" -> "110 mul_tensor_11" [label="(256, 256, 3, 3) \n0 -> 0", style=solid]; +"109 layer3_0_conv2_weight_bias_0_0" -> "111 conv2d_11" [label="(256,) \n0 -> 2", style=solid]; +"110 mul_tensor_11" -> "111 conv2d_11" [label="(256, 256, 3, 3) \n0 -> 1", style=solid]; +"111 conv2d_11" -> "112 quantize_per_tensor_default_16" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"112 quantize_per_tensor_default_16" -> "113 dequantize_per_tensor_default_20" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"113 dequantize_per_tensor_default_20" -> "121 add__4" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"114 conv2d_12_scale_0_updated_constant0" -> "117 mul_tensor_12" [label="(256, 1, 1, 1) \n0 -> 1", style=solid]; +"115 compressed_weight_updated_constant12" -> "117 mul_tensor_12" [label="(256, 128, 1, 1) \n0 -> 0", style=solid]; +"116 layer3_0_downsample_0_weight_bias_0_0" -> "118 conv2d_12" [label="(256,) \n0 -> 2", style=solid]; +"117 mul_tensor_12" -> "118 conv2d_12" [label="(256, 128, 1, 1) \n0 -> 1", style=solid]; +"118 conv2d_12" -> "119 quantize_per_tensor_default_17" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"119 quantize_per_tensor_default_17" -> "120 dequantize_per_tensor_default_21" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"120 dequantize_per_tensor_default_21" -> "121 add__4" [label="(1, 256, 14, 14) \n0 -> 1", style=solid]; +"121 add__4" -> "122 relu__10" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"122 relu__10" -> "123 quantize_per_tensor_default_18" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"123 quantize_per_tensor_default_18" -> "125 dequantize_per_tensor_default_22" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"123 quantize_per_tensor_default_18" -> "124 dequantize_per_tensor_default_23" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"124 dequantize_per_tensor_default_23" -> "141 add__5" [label="(1, 256, 14, 14) \n0 -> 1", style=solid]; +"125 dequantize_per_tensor_default_22" -> "130 conv2d_13" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"126 conv2d_13_scale_0_updated_constant0" -> "129 mul_tensor_13" [label="(256, 1, 1, 1) \n0 -> 1", style=solid]; +"127 compressed_weight_updated_constant13" -> "129 mul_tensor_13" [label="(256, 256, 3, 3) \n0 -> 0", style=solid]; +"128 layer3_1_conv1_weight_bias_0_0" -> "130 conv2d_13" [label="(256,) \n0 -> 2", style=solid]; +"129 mul_tensor_13" -> "130 conv2d_13" [label="(256, 256, 3, 3) \n0 -> 1", style=solid]; +"130 conv2d_13" -> "131 relu__11" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"131 relu__11" -> "132 quantize_per_tensor_default_19" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"132 quantize_per_tensor_default_19" -> "133 dequantize_per_tensor_default_24" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"133 dequantize_per_tensor_default_24" -> "138 conv2d_14" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"134 conv2d_14_scale_0_updated_constant0" -> "137 mul_tensor_14" [label="(256, 1, 1, 1) \n0 -> 1", style=solid]; +"135 compressed_weight_updated_constant14" -> "137 mul_tensor_14" [label="(256, 256, 3, 3) \n0 -> 0", style=solid]; +"136 layer3_1_conv2_weight_bias_0_0" -> "138 conv2d_14" [label="(256,) \n0 -> 2", style=solid]; +"137 mul_tensor_14" -> "138 conv2d_14" [label="(256, 256, 3, 3) \n0 -> 1", style=solid]; +"138 conv2d_14" -> "139 quantize_per_tensor_default_20" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"139 quantize_per_tensor_default_20" -> "140 dequantize_per_tensor_default_25" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"140 dequantize_per_tensor_default_25" -> "141 add__5" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"141 add__5" -> "142 relu__12" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"142 relu__12" -> "143 quantize_per_tensor_default_21" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"143 quantize_per_tensor_default_21" -> "145 dequantize_per_tensor_default_26" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"143 quantize_per_tensor_default_21" -> "144 dequantize_per_tensor_default_27" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"144 dequantize_per_tensor_default_27" -> "165 conv2d_17" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"145 dequantize_per_tensor_default_26" -> "150 conv2d_15" [label="(1, 256, 14, 14) \n0 -> 0", style=solid]; +"146 conv2d_15_scale_0_updated_constant0" -> "149 mul_tensor_15" [label="(512, 1, 1, 1) \n0 -> 1", style=solid]; +"147 compressed_weight_updated_constant15" -> "149 mul_tensor_15" [label="(512, 256, 3, 3) \n0 -> 0", style=solid]; +"148 layer4_0_conv1_weight_bias_0_0" -> "150 conv2d_15" [label="(512,) \n0 -> 2", style=solid]; +"149 mul_tensor_15" -> "150 conv2d_15" [label="(512, 256, 3, 3) \n0 -> 1", style=solid]; +"150 conv2d_15" -> "151 relu__13" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"151 relu__13" -> "152 quantize_per_tensor_default_22" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"152 quantize_per_tensor_default_22" -> "153 dequantize_per_tensor_default_28" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"153 dequantize_per_tensor_default_28" -> "158 conv2d_16" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"154 conv2d_16_scale_0_updated_constant0" -> "157 mul_tensor_16" [label="(512, 1, 1, 1) \n0 -> 1", style=solid]; +"155 compressed_weight_updated_constant16" -> "157 mul_tensor_16" [label="(512, 512, 3, 3) \n0 -> 0", style=solid]; +"156 layer4_0_conv2_weight_bias_0_0" -> "158 conv2d_16" [label="(512,) \n0 -> 2", style=solid]; +"157 mul_tensor_16" -> "158 conv2d_16" [label="(512, 512, 3, 3) \n0 -> 1", style=solid]; +"158 conv2d_16" -> "159 quantize_per_tensor_default_23" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"159 quantize_per_tensor_default_23" -> "160 dequantize_per_tensor_default_29" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"160 dequantize_per_tensor_default_29" -> "168 add__6" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"161 conv2d_17_scale_0_updated_constant0" -> "164 mul_tensor_17" [label="(512, 1, 1, 1) \n0 -> 1", style=solid]; +"162 compressed_weight_updated_constant17" -> "164 mul_tensor_17" [label="(512, 256, 1, 1) \n0 -> 0", style=solid]; +"163 layer4_0_downsample_0_weight_bias_0_0" -> "165 conv2d_17" [label="(512,) \n0 -> 2", style=solid]; +"164 mul_tensor_17" -> "165 conv2d_17" [label="(512, 256, 1, 1) \n0 -> 1", style=solid]; +"165 conv2d_17" -> "166 quantize_per_tensor_default_24" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"166 quantize_per_tensor_default_24" -> "167 dequantize_per_tensor_default_30" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"167 dequantize_per_tensor_default_30" -> "168 add__6" [label="(1, 512, 7, 7) \n0 -> 1", style=solid]; +"168 add__6" -> "169 relu__14" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"169 relu__14" -> "170 quantize_per_tensor_default_25" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"170 quantize_per_tensor_default_25" -> "172 dequantize_per_tensor_default_31" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"170 quantize_per_tensor_default_25" -> "171 dequantize_per_tensor_default_32" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"171 dequantize_per_tensor_default_32" -> "188 add__7" [label="(1, 512, 7, 7) \n0 -> 1", style=solid]; +"172 dequantize_per_tensor_default_31" -> "177 conv2d_18" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"173 conv2d_18_scale_0_updated_constant0" -> "176 mul_tensor_18" [label="(512, 1, 1, 1) \n0 -> 1", style=solid]; +"174 compressed_weight_updated_constant18" -> "176 mul_tensor_18" [label="(512, 512, 3, 3) \n0 -> 0", style=solid]; +"175 layer4_1_conv1_weight_bias_0_0" -> "177 conv2d_18" [label="(512,) \n0 -> 2", style=solid]; +"176 mul_tensor_18" -> "177 conv2d_18" [label="(512, 512, 3, 3) \n0 -> 1", style=solid]; +"177 conv2d_18" -> "178 relu__15" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"178 relu__15" -> "179 quantize_per_tensor_default_26" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"179 quantize_per_tensor_default_26" -> "180 dequantize_per_tensor_default_33" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"180 dequantize_per_tensor_default_33" -> "185 conv2d_19" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"181 conv2d_19_scale_0_updated_constant0" -> "184 mul_tensor_19" [label="(512, 1, 1, 1) \n0 -> 1", style=solid]; +"182 compressed_weight_updated_constant19" -> "184 mul_tensor_19" [label="(512, 512, 3, 3) \n0 -> 0", style=solid]; +"183 layer4_1_conv2_weight_bias_0_0" -> "185 conv2d_19" [label="(512,) \n0 -> 2", style=solid]; +"184 mul_tensor_19" -> "185 conv2d_19" [label="(512, 512, 3, 3) \n0 -> 1", style=solid]; +"185 conv2d_19" -> "186 quantize_per_tensor_default_27" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"186 quantize_per_tensor_default_27" -> "187 dequantize_per_tensor_default_34" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"187 dequantize_per_tensor_default_34" -> "188 add__7" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"188 add__7" -> "189 relu__16" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"189 relu__16" -> "190 quantize_per_tensor_default_28" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"190 quantize_per_tensor_default_28" -> "191 dequantize_per_tensor_default_35" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"191 dequantize_per_tensor_default_35" -> "192 adaptive_avg_pool2d" [label="(1, 512, 7, 7) \n0 -> 0", style=solid]; +"192 adaptive_avg_pool2d" -> "193 quantize_per_tensor_default_29" [label="(1, 512, 1, 1) \n0 -> 0", style=solid]; +"193 quantize_per_tensor_default_29" -> "194 dequantize_per_tensor_default_36" [label="(1, 512, 1, 1) \n0 -> 0", style=solid]; +"194 dequantize_per_tensor_default_36" -> "195 flatten" [label="(1, 512, 1, 1) \n0 -> 0", style=solid]; +"195 flatten" -> "200 linear" [label="(1, 512) \n0 -> 0", style=solid]; +"196 linear_scale_0_updated_constant0" -> "199 mul_tensor_20" [label="(1000, 1) \n0 -> 1", style=solid]; +"197 compressed_weight_updated_constant20" -> "199 mul_tensor_20" [label="(1000, 512) \n0 -> 0", style=solid]; +"198 _param_constant61_0_0" -> "200 linear" [label="(1000,) \n0 -> 2", style=solid]; +"199 mul_tensor_20" -> "200 linear" [label="(1000, 512) \n0 -> 1", style=solid]; +"200 linear" -> "201 output" [label="(1, 1000) \n0 -> 0", style=solid]; +} diff --git a/tests/torch/fx/helpers.py b/tests/torch/fx/helpers.py index fc0f48274da..a63f8f76076 100644 --- a/tests/torch/fx/helpers.py +++ b/tests/torch/fx/helpers.py @@ -21,6 +21,17 @@ from fastdownload import FastDownload from torch.fx.passes.graph_drawer import FxGraphDrawer +from nncf.experimental.torch.fx.transformations import apply_quantization_transformations +from nncf.torch.dynamic_graph.patch_pytorch import disable_patching +from nncf.torch.graph.graph import PTNNCFGraph +from nncf.torch.graph.operator_metatypes import PTConstNoopMetatype +from nncf.torch.graph.operator_metatypes import PTModuleConv2dMetatype +from nncf.torch.graph.operator_metatypes import PTModuleDepthwiseConv2dSubtype +from nncf.torch.graph.operator_metatypes import PTSumMetatype +from tests.cross_fw.test_templates.models import NNCFGraphToTest +from tests.cross_fw.test_templates.models import NNCFGraphToTestDepthwiseConv +from tests.cross_fw.test_templates.models import NNCFGraphToTestSumAggregation + class TinyImagenetDatasetManager: DATASET_URL = "http://cs231n.stanford.edu/tiny-imagenet-200.zip" @@ -109,3 +120,63 @@ def create_data_loaders(self): def visualize_fx_model(model: torch.fx.GraphModule, output_svg_path: str): g = FxGraphDrawer(model, output_svg_path) g.get_dot_graph().write_svg(output_svg_path) + + +def get_torch_fx_model(model: torch.nn.Module, ex_input: torch.Tensor) -> torch.fx.GraphModule: + """ + Converts given module to GraphModule. + + :param model: Given torch Module. + :return: Exported GraphModule. + """ + try: + named_param = next(model.named_parameters()) + except StopIteration: + named_param = None + if named_param is None: + device = torch.device("cpu") + else: + device = named_param[1].device + + ex_input = ex_input.to(device) + model.eval() + with torch.no_grad(): + with disable_patching(): + return torch.export.export_for_training(model, args=(ex_input,)).module() + + +def get_torch_fx_model_q_transformed(model: torch.nn.Module, ex_input: torch.Tensor) -> torch.fx.GraphModule: + """ + Converts given module to GraphModule and applies required quantization transformations to it. + + :param model: Given torch Module. + :return: Exported GraphModule. + """ + fx_model = get_torch_fx_model(model, ex_input) + apply_quantization_transformations(fx_model) + return fx_model + + +def get_single_conv_nncf_graph() -> NNCFGraphToTest: + return NNCFGraphToTest( + conv_metatype=PTModuleConv2dMetatype, + nncf_graph_cls=PTNNCFGraph, + const_metatype=PTConstNoopMetatype, + ) + + +def get_depthwise_conv_nncf_graph() -> NNCFGraphToTestDepthwiseConv: + return NNCFGraphToTestDepthwiseConv( + depthwise_conv_metatype=PTModuleDepthwiseConv2dSubtype, + nncf_graph_cls=PTNNCFGraph, + const_metatype=PTConstNoopMetatype, + ) + + +def get_sum_aggregation_nncf_graph() -> NNCFGraphToTestSumAggregation: + return NNCFGraphToTestSumAggregation( + conv_metatype=PTModuleConv2dMetatype, + sum_metatype=PTSumMetatype, + nncf_graph_cls=PTNNCFGraph, + const_metatype=PTConstNoopMetatype, + ) diff --git a/tests/torch/fx/test_bias_correction.py b/tests/torch/fx/test_bias_correction.py new file mode 100644 index 00000000000..06db212fe79 --- /dev/null +++ b/tests/torch/fx/test_bias_correction.py @@ -0,0 +1,220 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +from typing import Any, Dict, List + +import numpy as np +import openvino as ov +import pytest +import torch.fx + +from nncf.common.factory import NNCFGraphFactory +from nncf.experimental.torch.fx.model_utils import remove_fq_from_inputs +from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter +from nncf.experimental.torch.fx.node_utils import get_bias_value +from nncf.quantization.algorithms.bias_correction.torch_fx_backend import FXBiasCorrectionAlgoBackend +from tests.cross_fw.test_templates.helpers import ConvTestModel +from tests.cross_fw.test_templates.helpers import DepthwiseConvTestModel +from tests.cross_fw.test_templates.helpers import MultipleConvTestModel +from tests.cross_fw.test_templates.helpers import SplittedModel +from tests.cross_fw.test_templates.helpers import TransposeConvTestModel +from tests.cross_fw.test_templates.test_bias_correction import TemplateTestBCAlgorithm +from tests.torch.fx.helpers import get_torch_fx_model_q_transformed + + +class TestFXBCAlgorithm(TemplateTestBCAlgorithm): + + @staticmethod + def list_to_backend_type(data: List) -> torch.Tensor: + return torch.tensor(data) + + @staticmethod + def get_backend() -> FXBiasCorrectionAlgoBackend: + return FXBiasCorrectionAlgoBackend + + @staticmethod + def backend_specific_model(model: torch.nn.Module, tmp_dir: str) -> torch.fx.GraphModule: + return get_torch_fx_model_q_transformed(model, torch.ones(model.INPUT_SIZE)) + + @staticmethod + def fn_to_type(tensor) -> np.ndarray: + return np.array(tensor) + + @staticmethod + def get_transform_fn() -> callable: + def transform_fn(data_item): + return torch.tensor(data_item[0]) + + return transform_fn + + @staticmethod + def map_references(ref_biases: Dict, model_cls: Any) -> Dict[str, List]: + if model_cls is ConvTestModel or model_cls is DepthwiseConvTestModel: + return {"conv2d": ref_biases["/conv/Conv"]} + if model_cls is TransposeConvTestModel: + return {"conv_transpose2d": ref_biases["/conv/ConvTranspose"]} + mapping = dict() + for name, value in ref_biases.items(): + conv_idx = int(name[re.search(r"\d", name).start()]) + conv_idx -= 1 + conv_idx = "_" + str(conv_idx) if conv_idx else "" + fx_name = "conv2d" + conv_idx + mapping[fx_name] = value + return mapping + + @staticmethod + def remove_fq_from_inputs(model: torch.fx.GraphModule) -> torch.fx.GraphModule: + graph = GraphConverter.create_nncf_graph(model) + return remove_fq_from_inputs(model, graph) + + @staticmethod + def check_bias(model: ov.Model, ref_biases: Dict) -> None: + nncf_graph = NNCFGraphFactory.create(model) + for ref_name, ref_value in ref_biases.items(): + node = nncf_graph.get_node_by_name(ref_name) + ref_value = torch.tensor(ref_value) + curr_value = get_bias_value(node, nncf_graph, model).data + curr_value = curr_value.reshape(ref_value.shape) + assert all(torch.isclose(curr_value, ref_value, atol=0.01)), f"{curr_value} != {ref_value}" + + @pytest.mark.parametrize( + "layer_name, ref_data", + ( + ( + "conv2d", + { + "collected_inputs": { + ("concat", 1): ("arg0_1", 0), + ("conv2d", 0): ("concat", 0), + }, + "subgraph_data": { + "subgraph_input_ids": {("conv2d", 0)}, + "subgraph_output_ids": {("getitem", 0), ("max_pool2d", 0), ("getitem_1", 0)}, + }, + }, + ), + ( + "conv2d_1", + { + "collected_inputs": { + ("conv2d", 0): ("concat", 0), + ("conv2d_1", 0): ("max_pool2d", 0), + ("conv2d_3", 0): ("getitem", 0), + ("conv2d_5", 0): ("getitem_1", 0), + }, + "subgraph_data": { + "subgraph_input_ids": {("conv2d_1", 0)}, + "subgraph_output_ids": {("relu_1", 0)}, + }, + }, + ), + ( + "conv2d_2", + { + "collected_inputs": { + ("conv2d", 0): ("concat", 0), + ("conv2d_1", 0): ("max_pool2d", 0), + ("conv2d_2", 0): ("relu_1", 0), + ("conv2d_3", 0): ("getitem", 0), + ("conv2d_5", 0): ("getitem_1", 0), + }, + "subgraph_data": { + "subgraph_input_ids": {("conv2d", 0), ("conv2d_2", 0)}, + "subgraph_output_ids": {("getitem", 0), ("getitem_1", 0)}, + }, + }, + ), + ( + "conv2d_3", + { + "collected_inputs": { + ("conv2d_3", 0): ("getitem", 0), + ("conv2d_5", 0): ("getitem_1", 0), + }, + "subgraph_data": { + "subgraph_input_ids": {("conv2d_3", 0)}, + "subgraph_output_ids": {("relu_2", 0)}, + }, + }, + ), + ( + "conv2d_5", + { + "collected_inputs": { + ("conv2d_4", 0): ("relu_2", 0), + ("conv2d_5", 0): ("getitem_1", 0), + }, + "subgraph_data": { + "subgraph_input_ids": {("conv2d_4", 0), ("conv2d_5", 0)}, + "subgraph_output_ids": {("add_3", 0), ("concat_1", 0)}, + }, + }, + ), + ( + "conv2d_9", + { + "collected_inputs": { + ("conv2d_7", 0): ("conv_6", 0), + ("conv2d_8", 0): ("add_2", 0), + ("conv2d_9", 0): ("concat", 0), + }, + "subgraph_data": { + "subgraph_input_ids": { + ("conv2d_7", 0), + ("conv2d_8", 0), + ("conv2d_9", 0), + }, + "subgraph_output_ids": {("concat_2", 0)}, + }, + }, + ), + ( + "matmul", + { + "collected_inputs": { + ("matmul", 0): ("reshape", 0), + }, + "subgraph_data": { + "subgraph_input_ids": {("matmul", 0)}, + "subgraph_output_ids": {("reshape_1", 0), ("add_4", 0)}, + }, + }, + ), + ), + ) + def test__get_subgraph_data_for_node(self, quantized_test_model, layer_name, ref_data): + return super().test__get_subgraph_data_for_node(quantized_test_model, layer_name, ref_data) + + @pytest.mark.parametrize( + "model_cls, ref_stat_inputs_map", + ( + ( + SplittedModel, + { + ("conv2d", 0): ("concat", 0), + ("concat", 1): ("x", 0), + }, + ), + ( + MultipleConvTestModel, + { + ("conv2d", 0): ("x", 0), + ("conv2d_2", 0): ("x", 0), + }, + ), + (ConvTestModel, {("conv2d", 0): ("x", 0)}), + (DepthwiseConvTestModel, {("conv2d", 0): ("x", 0)}), + (TransposeConvTestModel, {("conv_transpose2d", 0): ("x", 0)}), + ), + ) + def test_verify_collected_stat_inputs_map(self, model_cls, ref_stat_inputs_map, tmpdir): + return super().test_verify_collected_stat_inputs_map(model_cls, ref_stat_inputs_map, tmpdir) diff --git a/tests/torch/fx/test_compress_weights.py b/tests/torch/fx/test_compress_weights.py new file mode 100644 index 00000000000..874a6c64d16 --- /dev/null +++ b/tests/torch/fx/test_compress_weights.py @@ -0,0 +1,300 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict + +import pytest +import torch + +import nncf +from nncf import BackupMode +from nncf import CompressWeightsMode +from nncf.common.factory import NNCFGraphFactory +from nncf.data.dataset import Dataset +from nncf.experimental.torch.fx.node_utils import get_tensor_constant_from_node +from nncf.quantization import compress_weights +from nncf.quantization.advanced_parameters import AdvancedCompressionParameters +from nncf.torch.dynamic_graph.patch_pytorch import disable_patching +from tests.torch.fx.helpers import get_torch_fx_model +from tests.torch.ptq.test_weights_compression import ALL_SENSITIVITY_METRICS +from tests.torch.ptq.test_weights_compression import DATA_BASED_SENSITIVITY_METRICS +from tests.torch.ptq.test_weights_compression import INT4_MODES +from tests.torch.ptq.test_weights_compression import INT8_MODES +from tests.torch.ptq.test_weights_compression import SUPPORTED_MODES +from tests.torch.ptq.test_weights_compression import UNSUPPORTED_MODES +from tests.torch.ptq.test_weights_compression import ConvolutionModel +from tests.torch.ptq.test_weights_compression import DTypeModel +from tests.torch.ptq.test_weights_compression import EmptyModel +from tests.torch.ptq.test_weights_compression import FunctionalModel +from tests.torch.ptq.test_weights_compression import MatMulModel +from tests.torch.test_models.synthetic import ShortTransformer + + +def get_model_size(model): + param_size = 0 + for param in model.parameters(): + param_size += param.nelement() * param.element_size() + buffer_size = 0 + for buffer in model.buffers(): + buffer_size += buffer.nelement() * buffer.element_size() + + model_size_mb = (param_size + buffer_size) / 1024**2 + + return model_size_mb + + +def get_compressed_modules_weights( + compressed_model: torch.fx.GraphModule, dtype: torch.dtype, compressed_node_weight_port: Dict[str, int] +): + n_target_modules = 0 + n_compressed_weights = 0 + + for node in compressed_model.graph.nodes: + if node.op == "call_function" and hasattr(node.target, "overloadpacket"): + node_type = str(node.target.overloadpacket).split(".")[1] + if node_type in compressed_node_weight_port: + n_target_modules += 1 + weight_port_id = compressed_node_weight_port[node_type] + weight_decompressor_node = node.all_input_nodes[weight_port_id] + if weight_decompressor_node.all_input_nodes: + compressed_weight_node = weight_decompressor_node.all_input_nodes[0] + weight = get_tensor_constant_from_node(compressed_weight_node, compressed_model).data + if weight.dtype == dtype: + n_compressed_weights += 1 + + return n_target_modules, n_compressed_weights + + +@pytest.mark.parametrize("mode", SUPPORTED_MODES) +def test_compress_weights(mode): + model = ShortTransformer(8, 16) + input_ids = torch.randint(0, 10, (8,)) + exported_model = get_torch_fx_model(model, input_ids) + kwargs = {} + if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]: + kwargs["group_size"] = 4 + compressed_model = compress_weights(exported_model, mode=mode, **kwargs) + dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8 + n_compressed_weights = 0 + n_target_modules = 0 + compressed_node_weight_port = {"linear": 1, "embedding": 0} + + n_target_modules, n_compressed_weights = get_compressed_modules_weights( + compressed_model, dtype, compressed_node_weight_port + ) + assert n_target_modules == n_compressed_weights + + +@pytest.mark.parametrize("mode", INT8_MODES) +def test_compress_weights_graph_edge(mode): + model = ShortTransformer(5, 10) + input_ids = torch.randint(0, 10, (5,)) + exported_model = get_torch_fx_model(model, input_ids) + compressed_model = compress_weights(exported_model, mode=mode) + nncf_graph = NNCFGraphFactory.create(compressed_model) + for node in nncf_graph.get_all_nodes(): + if "weights_decompressor" in node.node_name and node.node_type == "call_module": + decompressor_node_edge = nncf_graph.get_input_edges(node)[0] + decompressor_constant_edge = nncf_graph.get_edge(node, nncf_graph.get_next_nodes(node)[0]) + assert decompressor_node_edge.tensor_shape == decompressor_constant_edge.tensor_shape + + +@pytest.mark.parametrize("mode", SUPPORTED_MODES) +def test_compress_weights_shared_weights(mocker, mode): + with disable_patching(): + model = ShortTransformer(8, 16, share_weights=True) + input_ids = torch.randint(0, 10, (8,)) + exported_model = get_torch_fx_model(model, input_ids) + kwargs = {} + if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]: + kwargs["group_size"] = 4 + compressed_model = compress_weights(exported_model, mode=mode, **kwargs) + dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8 + n_compressed_weights = 0 + n_target_modules = 0 + compressed_node_weight_port = {"linear": 1, "embedding": 0} + + n_target_modules, n_compressed_weights = get_compressed_modules_weights( + compressed_model, dtype, compressed_node_weight_port + ) + assert n_target_modules == n_compressed_weights + + num_decompression_nodes = 0 + spies = [] + for node in compressed_model.graph.nodes: + if node.op == "call_module" and "decompress" in node.name: + num_decompression_nodes += 1 + decompressor_module = getattr(compressed_model, node.target) + spy = mocker.spy(decompressor_module, "forward") + spies.append(spy) + assert num_decompression_nodes == 2 + + compressed_model(input_ids) + + for spy in spies: + assert spy.call_count == 1 + + +@pytest.mark.parametrize("mode", SUPPORTED_MODES) +def test_compressed_model_inference(mode): + torch.manual_seed(42) + model = ShortTransformer(8, 16, share_weights=True) + input_ids = torch.randint(0, 10, (8,)) + exported_model = get_torch_fx_model(model, input_ids) + exported_model_output = exported_model(input_ids) + kwargs = {} + if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]: + kwargs["group_size"] = 4 + compressed_model = compress_weights(exported_model, mode=mode, **kwargs) + compressed_model_outputs = compressed_model(input_ids) + + assert ( + exported_model_output.shape == compressed_model_outputs.shape + ), "Compressed model output shape is not equal to the model output shape" + assert torch.all(torch.isclose(exported_model_output, compressed_model_outputs, atol=1)).item() + + +@pytest.mark.parametrize("mode", SUPPORTED_MODES) +def test_compress_weights_model_size_conv(mode): + dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8 + model = ConvolutionModel() + + input_ids = torch.randint(0, 10, [1, 3, 256, 256]) + exported_model = get_torch_fx_model(model, input_ids) + model_size = get_model_size(exported_model) + compressed_model = compress_weights(exported_model, mode=mode) + compressed_model_size = get_model_size(compressed_model) + + n_compressed_weights = 0 + n_target_modules = 0 + compressed_node_weight_port = {"linear": 1, "conv2d": 1, "conv_transpose2d": 1} + + n_target_modules, n_compressed_weights = get_compressed_modules_weights( + compressed_model, dtype, compressed_node_weight_port + ) + + assert n_compressed_weights == n_target_modules + assert compressed_model_size < model_size + + +@pytest.mark.parametrize("mode", SUPPORTED_MODES) +def test_compress_weights_functional_model(mode): + model = FunctionalModel() + decompressor_type = ( + "symmetric" if mode in (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT4_SYM) else "asymmetric" + ) + + input_ids = torch.randint(0, 10, [1, 3, 256, 256]) + exported_model = get_torch_fx_model(model, input_ids) + compressed_model = compress_weights(exported_model, mode=mode) + + n_compressed_weights = 0 + + for node in compressed_model.graph.nodes: + if decompressor_type in node.name: + n_compressed_weights += 1 + assert n_compressed_weights == 4 + + +@pytest.mark.parametrize("mode", INT8_MODES) +@pytest.mark.parametrize( + "params", + ( + {"ratio": 0.5}, + {"group_size": 64}, + {"all_layers": True}, + {"all_layers": False}, + *({"sensitivity_metric": metric} for metric in ALL_SENSITIVITY_METRICS), + {"gptq": True}, + {"awq": True}, + {"scale_estimation": True}, + {"lora_correction": True}, + {"dataset": Dataset([1])}, + {"backup_mode": BackupMode.NONE}, + {"backup_mode": BackupMode.INT8_ASYM}, + {"backup_mode": BackupMode.INT8_SYM}, + {"advanced_parameters": AdvancedCompressionParameters(statistics_path="anything")}, + ), +) +def test_raise_error_with_unsupported_params_for_int8(mode, params): + dummy_torch_model = EmptyModel() + dummy_input = torch.Tensor() + exported_model = get_torch_fx_model(dummy_torch_model, dummy_input) + with pytest.raises(nncf.ParameterNotSupportedError): + compress_weights(exported_model, mode=mode, **params) + + +@pytest.mark.parametrize("mode", INT4_MODES) +@pytest.mark.parametrize( + "params", + ( + *({"sensitivity_metric": metric} for metric in DATA_BASED_SENSITIVITY_METRICS), + {"gptq": True}, + {"awq": True}, + {"scale_estimation": True}, + {"lora_correction": True}, + {"dataset": Dataset([1])}, + ), +) +def test_raise_error_with_unsupported_params_for_int4(mode, params): + dummy_torch_model = EmptyModel() + dummy_input = torch.Tensor() + exported_model = get_torch_fx_model(dummy_torch_model, dummy_input) + with pytest.raises(nncf.ParameterNotSupportedError): + compress_weights(exported_model, mode=mode, **params) + + +@pytest.mark.parametrize("mode", UNSUPPORTED_MODES) +def test_raise_error_with_not_int8(mode): + dummy_torch_model = EmptyModel() + dummy_input = torch.Tensor() + exported_model = get_torch_fx_model(dummy_torch_model, dummy_input) + with pytest.raises(nncf.ParameterNotSupportedError): + compress_weights(exported_model, mode=mode) + + +def test_raise_error_for_statistics_caching(): + dummy_torch_model = EmptyModel() + dummy_input = torch.Tensor() + exported_model = get_torch_fx_model(dummy_torch_model, dummy_input) + with pytest.raises(nncf.ParameterNotSupportedError): + compress_weights(exported_model, advanced_parameters=AdvancedCompressionParameters(statistics_path="anything")) + + +def test_get_dtype_attribute_of_parameter(): + model = DTypeModel() + dummy_input = torch.randint(0, 10, [3, 3]) + exported_model = get_torch_fx_model(model, dummy_input) + compressed_model = compress_weights(exported_model) + assert compressed_model.matmul_updated_constant0.dtype == torch.uint8 + compressed_model(dummy_input) + assert compressed_model.matmul_updated_constant0.dtype == torch.uint8 + + +@pytest.mark.parametrize("dtype", ("float16", "float32")) +def test_model_devices_and_precisions(use_cuda, dtype): + if use_cuda and not torch.cuda.is_available(): + pytest.skip("Skipping for CPU-only setups") + device = torch.device("cuda" if use_cuda else "cpu") + dtype = torch.float16 if dtype == "float16" else torch.float32 + + model = MatMulModel().to(device) + if dtype == torch.float16: + model.half() + dummy_input = torch.rand((1, 256), dtype=dtype, device=device) + exported_model = get_torch_fx_model(model, dummy_input) + compressed_model = compress_weights(exported_model) + result = compressed_model(dummy_input) + + # Scale should always be in float16 + assert compressed_model.state_dict()["asymmetric_weights_decompressor_matmul._scale"].dtype == torch.float16 + # Result should be in the precision of the model + assert result.dtype == dtype diff --git a/tests/torch/fx/test_fast_bias_correction.py b/tests/torch/fx/test_fast_bias_correction.py index dcc5b1e5e8d..8c94a32cafd 100644 --- a/tests/torch/fx/test_fast_bias_correction.py +++ b/tests/torch/fx/test_fast_bias_correction.py @@ -14,14 +14,12 @@ import pytest import torch import torch.fx -from torch._export import capture_pre_autograd_graph from nncf.common.factory import NNCFGraphFactory -from nncf.experimental.torch.fx.transformations import apply_quantization_transformations from nncf.quantization.algorithms.fast_bias_correction.torch_fx_backend import FXFastBiasCorrectionAlgoBackend -from nncf.torch.dynamic_graph.patch_pytorch import disable_patching from nncf.torch.model_graph_manager import OPERATORS_WITH_BIAS_METATYPES from tests.cross_fw.test_templates.test_fast_bias_correction import TemplateTestFBCAlgorithm +from tests.torch.fx.helpers import get_torch_fx_model_q_transformed class TestTorchFXFBCAlgorithm(TemplateTestFBCAlgorithm): @@ -33,21 +31,9 @@ def list_to_backend_type(data: List) -> torch.Tensor: def get_backend() -> FXFastBiasCorrectionAlgoBackend: return FXFastBiasCorrectionAlgoBackend - def _get_fx_model(model: torch.nn.Module) -> torch.fx.GraphModule: - device = next(model.named_parameters())[1].device - input_shape = model.INPUT_SIZE - if input_shape is None: - input_shape = [1, 3, 32, 32] - ex_input = torch.ones(input_shape).to(device) - model.eval() - with disable_patching(): - fx_model = capture_pre_autograd_graph(model, args=(ex_input,)) - apply_quantization_transformations(fx_model) - return fx_model - @staticmethod def backend_specific_model(model: torch.nn.Module, tmp_dir: str) -> torch.fx.GraphModule: - fx_model = TestTorchFXFBCAlgorithm._get_fx_model(model) + fx_model = get_torch_fx_model_q_transformed(model, torch.ones(model.INPUT_SIZE)) return fx_model @staticmethod @@ -86,8 +72,7 @@ def list_to_backend_type(data: List) -> torch.Tensor: @staticmethod def backend_specific_model(model: bool, tmp_dir: str): - fx_cuda_model = TestTorchFXFBCAlgorithm._get_fx_model(model.cuda()) - return fx_cuda_model + return get_torch_fx_model_q_transformed(model.cuda(), torch.ones(model.INPUT_SIZE)) @staticmethod def fn_to_type(tensor): diff --git a/tests/torch/fx/test_min_max.py b/tests/torch/fx/test_min_max.py new file mode 100644 index 00000000000..5efce2ec019 --- /dev/null +++ b/tests/torch/fx/test_min_max.py @@ -0,0 +1,85 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Tuple + +import pytest + +from nncf.common.graph.graph import NNCFGraph +from nncf.common.graph.layer_attributes import BaseLayerAttributes +from nncf.common.graph.transformations.commands import TargetType +from nncf.quantization.algorithms.min_max.backend import MinMaxAlgoBackend +from nncf.quantization.algorithms.min_max.torch_fx_backend import FXMinMaxAlgoBackend +from nncf.torch.graph.graph import PTNNCFGraph +from nncf.torch.graph.operator_metatypes import PTConstNoopMetatype +from nncf.torch.graph.operator_metatypes import PTConv2dMetatype +from nncf.torch.graph.operator_metatypes import PTDepthwiseConv2dSubtype +from nncf.torch.graph.operator_metatypes import PTLinearMetatype +from nncf.torch.graph.transformations.commands import PTTargetPoint +from tests.cross_fw.test_templates.models import NNCFGraphToTest +from tests.cross_fw.test_templates.test_min_max import TemplateTestGetChannelAxes +from tests.cross_fw.test_templates.test_min_max import TemplateTestGetTargetPointShape +from tests.cross_fw.test_templates.test_min_max import TemplateTestMinMaxAlgorithm + + +class TestTorchFXMinMaxAlgorithm(TemplateTestMinMaxAlgorithm): + @property + def backend(self) -> MinMaxAlgoBackend: + return FXMinMaxAlgoBackend + + @property + def conv_metatype(self): + return PTConv2dMetatype + + def create_target_point(self, target_point_type: TargetType, name: str, port_id: int) -> PTTargetPoint: + if target_point_type == TargetType.POST_LAYER_OPERATION: + port_id = None + return PTTargetPoint(target_point_type, name, input_port_id=port_id) + + +class TestTorchFXGetTargetPointShape(TemplateTestGetTargetPointShape, TestTorchFXMinMaxAlgorithm): + def get_nncf_graph(self, weight_port_id: int, weight_shape: Tuple[int]) -> NNCFGraph: + return NNCFGraphToTest( + conv_metatype=PTConv2dMetatype, nncf_graph_cls=PTNNCFGraph, const_metatype=PTConstNoopMetatype + ).nncf_graph + + +class TestTorchFXGetChannelAxes(TemplateTestGetChannelAxes, TestTorchFXMinMaxAlgorithm): + @property + def depthwiseconv_metatype(self): + return PTDepthwiseConv2dSubtype + + @property + def matmul_metatype(self): + return PTLinearMetatype + + @staticmethod + def get_conv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> BaseLayerAttributes: + # This method isn't needed for Torch FX backend + return None + + @staticmethod + def get_depthwiseconv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> BaseLayerAttributes: + # This method isn't needed for Torch FX backend + return None + + @staticmethod + def get_matmul_node_attrs( + weight_port_id: int, transpose_weight: Tuple[int], weight_shape: Tuple[int] + ) -> BaseLayerAttributes: + # This method isn't needed for Torch FX backend + return None + + def test_get_channel_axes_matmul_node_ov_onnx(self): + pytest.skip("Test is not applied for Torch FX backend.") + + def test_get_channel_axes_deptwiseconv_node_ov(self): + pytest.skip("Test is not applied for Torch FX backend.") diff --git a/tests/torch/fx/test_model_transformer.py b/tests/torch/fx/test_model_transformer.py index 7edb8fa482d..d42d52f4d8d 100644 --- a/tests/torch/fx/test_model_transformer.py +++ b/tests/torch/fx/test_model_transformer.py @@ -9,22 +9,51 @@ # See the License for the specific language governing permissions and # limitations under the License. +from copy import deepcopy from dataclasses import dataclass from pathlib import Path from typing import Any, Tuple import pytest import torch -from torch._export import capture_pre_autograd_graph +import torch.ao.quantization +import torch.fx +from torch.ao.quantization.fx.utils import create_getattr_from_value +from torch.ao.quantization.observer import MinMaxObserver +from torch.ao.quantization.observer import PerChannelMinMaxObserver +from torch.quantization.fake_quantize import FakeQuantize +import nncf +import nncf.common +import nncf.common.factory +from nncf.common.factory import NNCFGraphFactory +from nncf.common.graph.transformations.commands import TargetType from nncf.common.graph.transformations.layout import TransformationLayout +from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode +from nncf.experimental.torch.fx.constant_folding import constant_fold from nncf.experimental.torch.fx.model_transformer import FXModelTransformer from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter -from nncf.torch import disable_patching +from nncf.experimental.torch.fx.node_utils import get_graph_node_by_name +from nncf.experimental.torch.fx.node_utils import get_tensor_constant_from_node +from nncf.experimental.torch.fx.transformations import _get_connected_nodes +from nncf.experimental.torch.fx.transformations import _set_new_node_meta +from nncf.experimental.torch.fx.transformations import bias_update_transformation_builder +from nncf.experimental.torch.fx.transformations import compress_post_quantize_transformation +from nncf.experimental.torch.fx.transformations import constant_update_transformation_builder +from nncf.experimental.torch.fx.transformations import fold_constant_except_qdq +from nncf.experimental.torch.fx.transformations import leaf_module_insertion_transformation_builder +from nncf.experimental.torch.fx.transformations import module_insertion_transformation_builder +from nncf.experimental.torch.fx.transformations import node_removal_transformation_builder +from nncf.experimental.torch.fx.transformations import output_insertion_transformation_builder +from nncf.experimental.torch.fx.transformations import qdq_insertion_transformation_builder from nncf.torch.graph.transformations.commands import PTModelExtractionCommand +from nncf.torch.graph.transformations.commands import PTTargetPoint +from tests.torch.fx.helpers import get_torch_fx_model from tests.torch.test_compressed_graph import check_graph +from tests.torch.test_models.synthetic import ConstantFoldingTestModel from tests.torch.test_models.synthetic import ConvolutionWithAllConstantInputsModel from tests.torch.test_models.synthetic import ConvolutionWithNotTensorBiasModel +from tests.torch.test_models.synthetic import MultiBranchesConnectedModel @dataclass @@ -32,10 +61,10 @@ class ModelExtractionTestCase: model: torch.nn.Module input_shape: Tuple[int, ...] command: PTModelExtractionCommand - ref: None = None -EXTRACTED_GRAPHS_DIR_NAME = Path("fx") / "extracted" +EXTRACTED_GRAPHS_DIR_NAME = str(Path("fx") / "extracted") +TRANSFORMED_GRAPH_DIR_NAME = str(Path("fx") / "transformed") MODEL_EXTRACTION_CASES = ( ModelExtractionTestCase( @@ -44,22 +73,425 @@ class ModelExtractionTestCase: ModelExtractionTestCase( ConvolutionWithAllConstantInputsModel, (1, 1, 3, 3), PTModelExtractionCommand(["conv2d"], ["conv2d"]) ), + ModelExtractionTestCase( + MultiBranchesConnectedModel, (1, 3, 3, 3), PTModelExtractionCommand(["conv2d_1"], ["add__1"]) + ), + ModelExtractionTestCase( + MultiBranchesConnectedModel, (1, 3, 3, 3), PTModelExtractionCommand(["conv2d"], ["add_", "add"]) + ), + ModelExtractionTestCase( + MultiBranchesConnectedModel, (1, 3, 3, 3), PTModelExtractionCommand(["conv2d", "conv2d_1"], ["add_", "add__1"]) + ), + ModelExtractionTestCase( + MultiBranchesConnectedModel, (1, 3, 3, 3), PTModelExtractionCommand(["conv2d"], ["conv2d_2"]) + ), + ModelExtractionTestCase( + MultiBranchesConnectedModel, (1, 3, 3, 3), PTModelExtractionCommand(["conv2d"], ["add__1"]) + ), ) +def get_test_id(test_case: ModelExtractionTestCase): + return test_case.model.__name__ + "_".join(test_case.command.input_node_names + test_case.command.output_node_names) + + def idfn(value: Any): if isinstance(value, ModelExtractionTestCase): - return value.model.__name__ + return get_test_id(value) return None +def _target_point_to_str(target_point: PTTargetPoint) -> str: + return "_".join( + map(str, (target_point.target_node_name, target_point.target_type.value, target_point.input_port_id)) + ) + + @pytest.mark.parametrize("test_case", MODEL_EXTRACTION_CASES, ids=idfn) def test_model_extraction(test_case: ModelExtractionTestCase): - with torch.no_grad(): - with disable_patching(): - captured_model = capture_pre_autograd_graph(test_case.model(), (torch.ones(test_case.input_shape),)) + captured_model = get_torch_fx_model(test_case.model(), torch.ones(test_case.input_shape)) + layout = TransformationLayout() layout.register(test_case.command) extracted_model = FXModelTransformer(captured_model).transform(layout) nncf_graph = GraphConverter.create_nncf_graph(extracted_model) - check_graph(nncf_graph, f"{test_case.model.__name__}.dot", str(EXTRACTED_GRAPHS_DIR_NAME)) + check_graph(nncf_graph, f"{get_test_id(test_case)}.dot", EXTRACTED_GRAPHS_DIR_NAME, extended=True) + + +MultiBranchesConnectedModel_TARGET_POINTS = ( + PTTargetPoint(TargetType.OPERATOR_PRE_HOOK, "conv2d", input_port_id=0), + PTTargetPoint(TargetType.OPERATOR_PRE_HOOK, "conv2d", input_port_id=1), + PTTargetPoint(TargetType.OPERATION_WITH_WEIGHTS, "conv2d_1", input_port_id=1), + PTTargetPoint(TargetType.OPERATOR_POST_HOOK, "conv2d"), +) + + +@pytest.mark.parametrize("leaf", [False, True], ids=["no_leaf", "leaf"]) +def test_model_insertion_transformation(leaf: bool): + class TestInsertModule(torch.nn.Module): + def forward(self, x): + return x + 1 + + target_points = list(MultiBranchesConnectedModel_TARGET_POINTS) + target_node_name = "TEST_MODULE" + test_module_instance = TestInsertModule() + builder = leaf_module_insertion_transformation_builder if leaf else module_insertion_transformation_builder + transformation = builder(test_module_instance, target_points, target_node_name) + + model = MultiBranchesConnectedModel() + captured_model = get_torch_fx_model(model, torch.ones((1, 3, 3, 3))) + transformation(captured_model) + + nncf_graph = GraphConverter.create_nncf_graph(captured_model) + assert getattr(captured_model, target_node_name) is test_module_instance + check_graph(nncf_graph, f"model_insertion{'_leaf' if leaf else ''}.dot", TRANSFORMED_GRAPH_DIR_NAME, extended=True) + + +@pytest.mark.parametrize("bias", [True, False], ids=["bias", "constant"]) +def test_constant_update_transformation(bias: bool): + model = MultiBranchesConnectedModel() + captured_model = get_torch_fx_model(model, torch.ones((1, 3, 3, 3))) + nncf_graph = GraphConverter.create_nncf_graph(captured_model) + target_node = nncf_graph.get_node_by_name("conv2d" if bias else "add_") + + builder = bias_update_transformation_builder if bias else constant_update_transformation_builder + new_value = torch.tensor((42.0,)) + transformation = builder(target_node, value=new_value, input_port_id=1) + transformation(captured_model) + + add_node = get_graph_node_by_name(captured_model.graph, "add_") + assert get_tensor_constant_from_node(add_node.args[1], captured_model) == new_value + + transformed_nncf_graph = GraphConverter.create_nncf_graph(captured_model) + check_graph(transformed_nncf_graph, "constant_update.dot", TRANSFORMED_GRAPH_DIR_NAME, extended=True) + + +@pytest.mark.parametrize("bias", [True, False], ids=["bias", "constant"]) +def test_constant_update_transformation_no_constant(bias: bool): + model = MultiBranchesConnectedModel() + captured_model = get_torch_fx_model(model, torch.ones((1, 3, 3, 3))) + nncf_graph = GraphConverter.create_nncf_graph(captured_model) + target_node = nncf_graph.get_node_by_name("add") + + builder = bias_update_transformation_builder if bias else constant_update_transformation_builder + new_value = torch.tensor((42.0,)) + transformation = builder(target_node, value=new_value, input_port_id=1) + with pytest.raises(nncf.InternalError): + transformation(captured_model) + + +@pytest.mark.parametrize( + "q_min,q_max,dtype", + [(-128, 127, torch.qint8), (0, 255, torch.quint8)], + ids=["int8", "uint8"], +) +class TestQDQInsertion: + REF_SCALE = torch.tensor([1.0]) + REF_ZERO_POINT = torch.tensor([0.0]) + + def _get_quantizer( + self, per_channel: bool, symmetric: bool, q_min: torch.Tensor, q_max: torch.Tensor, dtype: torch.dtype + ) -> FakeQuantize: + if symmetric: + qscheme = torch.per_channel_symmetric if per_channel else torch.per_tensor_symmetric + else: + qscheme = torch.per_channel_affine if per_channel else torch.per_tensor_affine + observer = PerChannelMinMaxObserver if per_channel else MinMaxObserver + + quantizer = FakeQuantize( + observer=observer, + quant_min=q_min, + quant_max=q_max, + dtype=dtype, + qscheme=qscheme, + eps=1e-5, + ) + quantizer.scale = self.REF_SCALE + quantizer.zero_point = self.REF_ZERO_POINT + + return quantizer + + def _check_qdq_params( + self, captured_model: torch.fx.GraphModule, target_point: PTTargetPoint, dtype: torch.dtype, per_channel: bool + ): + target_node = get_graph_node_by_name(captured_model.graph, target_point.target_node_name) + if target_point.target_type in [TargetType.OPERATION_WITH_WEIGHTS, TargetType.OPERATOR_PRE_HOOK]: + dq_node = target_node.args[target_point.input_port_id] + q_node = dq_node.args[0] + else: + q_node = list(target_node.users)[0] + + ref_dtype = torch.int8 if dtype == torch.qint8 else torch.uint8 + if per_channel: + + def get_value(node: torch.fx.Node): + return get_tensor_constant_from_node(node, captured_model) + + else: + + def get_value(node: torch.fx.Node): + return node + + assert q_node.args[-1] == ref_dtype + assert get_value(q_node.args[1]) == self.REF_SCALE + assert get_value(q_node.args[2]) == self.REF_ZERO_POINT + for dq_node in q_node.users: + assert get_value(dq_node.args[1]) == self.REF_SCALE + assert get_value(dq_node.args[2]) == self.REF_ZERO_POINT + assert dq_node.args[-1] == ref_dtype + + @pytest.mark.parametrize("target_point", MultiBranchesConnectedModel_TARGET_POINTS) + def test_one_target_point( + self, + is_per_channel: bool, + quantization_mode: QuantizationMode, + q_min: int, + q_max: int, + dtype: torch.dtype, + target_point: PTTargetPoint, + ): + symmetric = quantization_mode == QuantizationMode.SYMMETRIC + quantizer = self._get_quantizer(is_per_channel, symmetric, q_min, q_max, dtype) + transformation = qdq_insertion_transformation_builder(quantizer, [target_point]) + + model = MultiBranchesConnectedModel() + captured_model = get_torch_fx_model(model, torch.ones((1, 3, 3, 3))) + transformation(captured_model) + + self._check_qdq_params(captured_model, target_point, dtype, is_per_channel) + + nncf_graph = GraphConverter.create_nncf_graph(captured_model) + ref_name = ( + f"qdq_insert_{_target_point_to_str(target_point)}" + f"_{'per_channel' if is_per_channel else 'per_tensor'}.dot" + ) + check_graph( + nncf_graph, + ref_name, + TRANSFORMED_GRAPH_DIR_NAME, + extended=True, + ) + + @pytest.mark.parametrize( + "target_points,weights", + [ + ( + [ + PTTargetPoint(TargetType.OPERATOR_PRE_HOOK, "conv2d", input_port_id=0), + PTTargetPoint(TargetType.OPERATOR_PRE_HOOK, "conv2d", input_port_id=1), + PTTargetPoint(TargetType.OPERATOR_POST_HOOK, "conv2d"), + ], + False, + ), + ( + [ + PTTargetPoint(TargetType.OPERATION_WITH_WEIGHTS, "conv2d", input_port_id=1), + PTTargetPoint(TargetType.OPERATION_WITH_WEIGHTS, "conv2d_1", input_port_id=1), + ], + True, + ), + ], + ) + def test_shared_target_point( + self, + is_per_channel: bool, + quantization_mode: QuantizationMode, + q_min: int, + q_max: int, + dtype: torch.dtype, + target_points: PTTargetPoint, + weights: bool, + ): + symmetric = quantization_mode == QuantizationMode.SYMMETRIC + quantizer = self._get_quantizer(is_per_channel, symmetric, q_min, q_max, dtype) + transformation = qdq_insertion_transformation_builder(quantizer, target_points) + + model = MultiBranchesConnectedModel() + captured_model = get_torch_fx_model(model, torch.ones((1, 3, 3, 3))) + if not weights: + with pytest.raises(nncf.InternalError): + transformation(captured_model) + return + + transformation(captured_model) + + for target_point in target_points: + self._check_qdq_params(captured_model, target_point, dtype, is_per_channel) + + nncf_graph = GraphConverter.create_nncf_graph(captured_model) + ref_name = ( + f"qdq_shared_insert_{'weights' if weights else 'activations'}" + f"_{'per_channel' if is_per_channel else 'per_tensor'}.dot" + ) + check_graph( + nncf_graph, + ref_name, + TRANSFORMED_GRAPH_DIR_NAME, + extended=True, + ) + + +def test_node_removal_transformation(): + model = MultiBranchesConnectedModel() + captured_model = get_torch_fx_model(model, torch.ones((1, 3, 3, 3))) + nncf_graph = GraphConverter.create_nncf_graph(captured_model) + node = nncf_graph.get_node_by_name("conv2d") + transformation = node_removal_transformation_builder(node, input_port_id=0) + transformation(captured_model) + transformed_nncf_graph = GraphConverter.create_nncf_graph(captured_model) + check_graph(transformed_nncf_graph, "node_removal_ref.dot", TRANSFORMED_GRAPH_DIR_NAME, extended=True) + + +@pytest.mark.parametrize("tuple_output", [False, True], ids=["node_out", "tuple_out"]) +@pytest.mark.parametrize("target_point", MultiBranchesConnectedModel_TARGET_POINTS) +def test_output_insertion_transformation(tuple_output: bool, target_point: PTTargetPoint): + model = MultiBranchesConnectedModel() + captured_model = get_torch_fx_model(model, torch.ones((1, 3, 3, 3))) + + if not tuple_output: + output_node = [node for node in captured_model.graph.nodes if node.op == "output"][0] + output_node.args = (output_node.args[0][0],) + captured_model.recompile() + + transformation = output_insertion_transformation_builder(target_point) + transformation(captured_model) + + nncf_graph = GraphConverter.create_nncf_graph(captured_model) + ref_name = f"output_insertion_{_target_point_to_str(target_point)}.dot" + check_graph( + nncf_graph, + ref_name, + TRANSFORMED_GRAPH_DIR_NAME, + extended=True, + ) + + +def count_constants(model: torch.fx.GraphModule) -> int: + num_constant_nodes = 0 + for node in model.graph.nodes: + if node.op == "get_attr": + num_constant_nodes += 1 + return num_constant_nodes + + +def insert_qdq_nodes( + model: torch.fx.GraphModule, + correct_pattern: bool, + per_channel: bool, + node_name: str = "conv2d", + w_const_node_name: str = "conv_a_weight", +): + const_node = get_graph_node_by_name(model.graph, w_const_node_name) + if per_channel: + quantize_op = torch.ops.quantized_decomposed.quantize_per_channel.default + dequantize_op = torch.ops.quantized_decomposed.dequantize_per_channel.default + else: + quantize_op = torch.ops.quantized_decomposed.quantize_per_tensor.default + dequantize_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default + + conv_node = get_graph_node_by_name(model.graph, node_name) + if per_channel: + with model.graph.inserting_before(conv_node): + scale_node = create_getattr_from_value( + model, + model.graph, + "scale_node", + torch.ones([3]), + ) + zp_node = create_getattr_from_value( + model, + model.graph, + "weight_node", + torch.ones([3]), + ) + qdq_args = (scale_node, zp_node, 0, -128, 127, torch.int8) + else: + qdq_args = (1.0, 1, -128, 127, torch.int8) + with model.graph.inserting_before(conv_node): + q_node = model.graph.create_node("call_function", quantize_op, (const_node,) + qdq_args, {}) + if not correct_pattern: + add_op = torch.ops.aten.add.Tensor + add_node = model.graph.create_node("call_function", add_op, (q_node, 0), {}) + dq_node = model.graph.create_node("call_function", dequantize_op, (add_node,) + qdq_args, {}) + _set_new_node_meta(q_node, (const_node,) + qdq_args, quantize_op, model) + _set_new_node_meta(add_node, (q_node, 0), add_op, model) + _set_new_node_meta(dq_node, (add_node,) + qdq_args, dequantize_op, model) + else: + dq_node = model.graph.create_node("call_function", dequantize_op, (q_node,) + qdq_args, {}) + _set_new_node_meta(q_node, (const_node,) + qdq_args, quantize_op, model) + _set_new_node_meta(dq_node, (q_node,) + qdq_args, dequantize_op, model) + conv_node.replace_input_with(const_node, dq_node) + model.graph.eliminate_dead_code() + model.recompile() + + +def test_compress_post_quantize_transformation(is_per_channel: bool): + model = MultiBranchesConnectedModel() + ex_input = torch.ones(1, 3, 224, 224) + + model_with_correct_pattern = get_torch_fx_model(model, ex_input) + insert_qdq_nodes(model_with_correct_pattern, correct_pattern=True, per_channel=is_per_channel) + compress_post_quantize_transformation(model_with_correct_pattern) + graph_name = f"compress_post_quantize_{'per_channel' if is_per_channel else 'per_tensor'}_valid.dot" + check_graph( + NNCFGraphFactory.create(model_with_correct_pattern), + graph_name, + TRANSFORMED_GRAPH_DIR_NAME, + extended=True, + ) + + model_with_incorrect_pattern = get_torch_fx_model(model, ex_input) + insert_qdq_nodes(model_with_incorrect_pattern, correct_pattern=False, per_channel=is_per_channel) + compress_post_quantize_transformation(model_with_incorrect_pattern) + graph_name = f"compress_post_quantize_{'per_channel' if is_per_channel else 'per_tensor'}_invalid.dot" + check_graph( + NNCFGraphFactory.create(model_with_incorrect_pattern), + graph_name, + TRANSFORMED_GRAPH_DIR_NAME, + extended=True, + ) + + +def test_get_connected_nodes(): + model = MultiBranchesConnectedModel() + ex_inputs = torch.ones((1, 3, 3, 3)) + captured_model = get_torch_fx_model(model, ex_inputs) + connected_nodes_list = _get_connected_nodes(captured_model.graph) + assert len(connected_nodes_list) == 16 + + add_node = get_graph_node_by_name(captured_model.graph, "add__1") + conv_1_node = get_graph_node_by_name(captured_model.graph, "conv2d") + conv_2_node = get_graph_node_by_name(captured_model.graph, "conv2d_1") + add_node.replace_input_with(conv_2_node, conv_1_node) + connected_nodes_list = _get_connected_nodes(captured_model.graph) + assert len(connected_nodes_list) == 13 + + +def test_constant_folding(): + model = ConstantFoldingTestModel() + captured_model = get_torch_fx_model(model, torch.ones(model.INPUT_SIZE)) + folded_model = deepcopy(captured_model) + constant_fold(folded_model) + ex_input = torch.ones(model.INPUT_SIZE) + assert torch.allclose(captured_model(ex_input), folded_model(ex_input)) + + nncf_graph = GraphConverter.create_nncf_graph(folded_model) + check_graph(nncf_graph, "folded_model.dot", TRANSFORMED_GRAPH_DIR_NAME, extended=True) + + +def test_constant_folding_with_constraints(is_per_channel): + model = ConstantFoldingTestModel() + model_with_correct_pattern = get_torch_fx_model(model, torch.ones(model.INPUT_SIZE)) + + insert_qdq_nodes( + model_with_correct_pattern, + correct_pattern=True, + per_channel=is_per_channel, + node_name="linear_1", + w_const_node_name="linear_act_weight", + ) + fold_constant_except_qdq(model_with_correct_pattern) + + nncf_graph = GraphConverter.create_nncf_graph(model_with_correct_pattern) + dot_file_name = f"folded_model_with_constraints_{'per_channel' if is_per_channel else 'per_tensor'}.dot" + check_graph(nncf_graph, dot_file_name, TRANSFORMED_GRAPH_DIR_NAME, extended=True) diff --git a/tests/torch/fx/test_models.py b/tests/torch/fx/test_models.py index 1d86ec4fd63..a43c4fafb56 100644 --- a/tests/torch/fx/test_models.py +++ b/tests/torch/fx/test_models.py @@ -14,7 +14,7 @@ from dataclasses import dataclass from functools import partial from pathlib import Path -from typing import Callable, Dict, Tuple, Type +from typing import Callable, Dict, Tuple, Type, Union import openvino.torch # noqa import pytest @@ -25,21 +25,28 @@ import torch.utils.data import torch.utils.data.distributed import torchvision.models as models -from torch._export import capture_pre_autograd_graph import nncf from nncf.common.graph.graph import NNCFNodeName from nncf.common.graph.operator_metatypes import OperatorMetatype from nncf.common.utils.os import safe_open from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter +from nncf.experimental.torch.fx.node_utils import get_tensor_constant_from_node +from nncf.experimental.torch.fx.quantization.backend_parameters import FXBackendParameters +from nncf.experimental.torch.fx.transformations import DEQUANTIZE_NODE_TARGETS +from nncf.experimental.torch.fx.transformations import _get_node_inputs from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters -from nncf.torch.dynamic_graph.patch_pytorch import disable_patching -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch import test_models +from tests.torch.fx.helpers import get_torch_fx_model +from tests.torch.fx.test_sanity import count_q_dq from tests.torch.test_compressed_graph import check_graph +from tests.torch.test_models.synthetic import MultiBranchesConnectedModel +from tests.torch.test_models.synthetic import ShortTransformer FX_DIR_NAME = Path("fx") FX_QUANTIZED_DIR_NAME = Path("fx") / "quantized" +FX_QUANTIZED_COMPRESSED_DIR_NAME = Path("fx") / "post_quantization_compressed" @dataclass @@ -60,6 +67,7 @@ def torchvision_model_case(model_id: str, input_shape: Tuple[int,]): torchvision_model_case("vit_b_16", (1, 3, 224, 224)), torchvision_model_case("swin_v2_s", (1, 3, 224, 224)), ModelCase(test_models.UNet, "unet", [1, 3, 224, 224]), + ModelCase(partial(ShortTransformer, 5, 10), "synthetic_transformer", [5]), ) @@ -71,18 +79,19 @@ def get_json_filename(model_name): return model_name + ".json" -def get_full_path_to_json(model_json_name: str) -> str: - path_to_dir = TEST_ROOT / "torch" / "data" / "reference_graphs" / "fx" / "reference_metatypes" +def get_full_path_to_json(model_json_name: str, attributes: bool = False) -> str: + property_to_check = "reference_metatypes" if not attributes else "reference_attributes" + path_to_dir = TEST_ROOT / "torch" / "data" / "reference_graphs" / "fx" / property_to_check path_to_json = path_to_dir / model_json_name return path_to_json -def get_ref_metatypes_from_json( - model_name: str, model_metatypes: Dict[NNCFNodeName, Type[OperatorMetatype]] -) -> Dict[NNCFNodeName, Type[OperatorMetatype]]: +def get_ref_from_json( + model_name: str, model_metatypes: Dict[NNCFNodeName, Union[Type[OperatorMetatype], bool]], attributes=False +) -> Dict[NNCFNodeName, Union[Type[OperatorMetatype], bool]]: model_json_name = get_json_filename(model_name) - complete_path = get_full_path_to_json(model_json_name) + complete_path = get_full_path_to_json(model_json_name, attributes) json_parent_dir = Path(complete_path).parent @@ -98,61 +107,133 @@ def get_ref_metatypes_from_json( @pytest.mark.parametrize("test_case", TEST_MODELS, ids=[m.model_id for m in TEST_MODELS]) def test_model(test_case: ModelCase): - with disable_patching(): - device = torch.device("cpu") - model_name = test_case.model_id - model = test_case.model_builder() - model.to(device) + device = torch.device("cpu") + model_name = test_case.model_id + model = test_case.model_builder() + model.to(device) - with torch.no_grad(): - ex_input = torch.ones(test_case.input_shape) - model.eval() - exported_model = capture_pre_autograd_graph(model, args=(ex_input,)) - nncf_graph = GraphConverter.create_nncf_graph(exported_model) + dtype = torch.int32 if test_case.model_id == "synthetic_transformer" else torch.float32 + ex_input = torch.ones(test_case.input_shape, dtype=dtype) + exported_model = get_torch_fx_model(model, ex_input) + nncf_graph = GraphConverter.create_nncf_graph(exported_model) - # Check NNCFGrpah - dot_filename = get_dot_filename(model_name) - check_graph(nncf_graph, dot_filename, FX_DIR_NAME) + # Check NNCFGrpah + dot_filename = get_dot_filename(model_name) + check_graph(nncf_graph, dot_filename, FX_DIR_NAME, extended=True) - # Check metatypes - model_metatypes = {n.node_name: n.metatype.__name__ for n in nncf_graph.get_all_nodes()} - ref_metatypes = get_ref_metatypes_from_json(model_name, model_metatypes) - assert model_metatypes == ref_metatypes + # Check metatypes + model_metatypes = {n.node_name: n.metatype.__name__ for n in nncf_graph.get_all_nodes()} + ref_metatypes = get_ref_from_json(model_name, model_metatypes) + assert model_metatypes == ref_metatypes TEST_MODELS_QUANIZED = ( - (ModelCase(test_models.UNet, "unet", [1, 3, 224, 224]), {}), - (torchvision_model_case("resnet18", (1, 3, 224, 224)), {}), - (torchvision_model_case("mobilenet_v3_small", (1, 3, 224, 224)), {}), - (torchvision_model_case("vit_b_16", (1, 3, 224, 224)), {"model_type": nncf.ModelType.TRANSFORMER}), - (torchvision_model_case("swin_v2_s", (1, 3, 224, 224)), {"model_type": nncf.ModelType.TRANSFORMER}), + ( + ModelCase(test_models.UNet, "unet", [1, 3, 224, 224]), + {}, + [(46, 50), (23, 27)], + ), + ( + torchvision_model_case("resnet18", (1, 3, 224, 224)), + {}, + [(51, 58), (30, 37)], + ), + ( + torchvision_model_case("mobilenet_v3_small", (1, 3, 224, 224)), + {}, + [(97, 112), (61, 76)], + ), + ( + torchvision_model_case("vit_b_16", (1, 3, 224, 224)), + {"model_type": nncf.ModelType.TRANSFORMER}, + [(124, 124), (74, 74)], + ), + ( + torchvision_model_case("swin_v2_s", (1, 3, 224, 224)), + {"model_type": nncf.ModelType.TRANSFORMER}, + [ + (250, 250), + (149, 149), + ], + ), + ( + ModelCase(partial(ShortTransformer, 5, 10), "synthetic_transformer", [5]), + {"model_type": nncf.ModelType.TRANSFORMER}, + [(4, 4), (2, 2)], + ), ) +@pytest.mark.parametrize("compress_weights", [True, False]) @pytest.mark.parametrize( - ("model_case", "quantization_parameters"), TEST_MODELS_QUANIZED, ids=[m[0].model_id for m in TEST_MODELS_QUANIZED] + ("model_case", "quantization_parameters", "compress_n_qdq"), + TEST_MODELS_QUANIZED, + ids=[m[0].model_id for m in TEST_MODELS_QUANIZED], ) -def test_quantized_model(model_case: ModelCase, quantization_parameters): - with disable_patching(): - model = model_case.model_builder() - example_input = torch.ones(model_case.input_shape) - - with torch.no_grad(): - model.eval() - fx_model = capture_pre_autograd_graph(model, args=(example_input,)) - - def transform_fn(data_item): - return data_item.to("cpu") - - calibration_dataset = nncf.Dataset([example_input], transform_fn) - - quantization_parameters["advanced_parameters"] = AdvancedQuantizationParameters(disable_bias_correction=True) - quantization_parameters["subset_size"] = 1 - - quantized_model = nncf.quantize(fx_model, calibration_dataset, **quantization_parameters) - # Uncomment to visualize torch fx graph - # from tests.torch.fx.helpers import visualize_fx_model - # visualize_fx_model(quantized_model, f"{model_case.model_id}_int8.svg") - - nncf_graph = GraphConverter.create_nncf_graph(quantized_model) - check_graph(nncf_graph, get_dot_filename(model_case.model_id), FX_QUANTIZED_DIR_NAME) +def test_quantized_model(model_case: ModelCase, quantization_parameters, compress_weights: bool, compress_n_qdq: int): + model = model_case.model_builder() + dtype = torch.int32 if model_case.model_id == "synthetic_transformer" else torch.float32 + example_input = torch.ones(model_case.input_shape, dtype=dtype) + + fx_model = get_torch_fx_model(model, example_input) + + def transform_fn(data_item): + return data_item.to("cpu") + + calibration_dataset = nncf.Dataset([example_input], transform_fn) + + quantization_parameters["advanced_parameters"] = AdvancedQuantizationParameters( + disable_bias_correction=True, backend_params={FXBackendParameters.COMPRESS_WEIGHTS: compress_weights} + ) + quantization_parameters["subset_size"] = 1 + + quantized_model = nncf.quantize(fx_model, calibration_dataset, **quantization_parameters) + # Uncomment to visualize torch fx graph + # from tests.torch.fx.helpers import visualize_fx_model + # visualize_fx_model(quantized_model, f"{model_case.model_id}_int8.svg") + save_dir = FX_QUANTIZED_COMPRESSED_DIR_NAME if compress_weights else FX_QUANTIZED_DIR_NAME + nncf_graph = GraphConverter.create_nncf_graph(quantized_model) + check_graph(nncf_graph, get_dot_filename(model_case.model_id), save_dir, extended=True) + q_nodes, dq_nodes = count_q_dq(quantized_model) + assert q_nodes == compress_n_qdq[compress_weights][0] + assert dq_nodes == compress_n_qdq[compress_weights][1] + check_fq_values(quantized_model) + check_compressed_post_quantized(quantized_model) + + +def check_fq_values(quantized_model): + for node in quantized_model.graph.nodes: + if node.target not in DEQUANTIZE_NODE_TARGETS: + continue + args = [] + quantize_node = node.args[0] + args = _get_node_inputs(quantize_node, quantized_model) + if not args: + continue + result = node.target(quantize_node.target(*args), *args[1:]) + fp_value = get_tensor_constant_from_node(quantize_node.args[0], quantized_model) + assert torch.all(result == fp_value) + + +def check_compressed_post_quantized(quantized_model): + for node in quantized_model.graph.nodes: + if node.name[:3] != "mul": + continue + args = [] + args = _get_node_inputs(node, quantized_model) + if not args: + continue + assert args[0].dtype == torch.int8 + result = node.target(*args) + assert result.dtype == torch.float32 + + +def test_is_shared_attribute_default(): + model = MultiBranchesConnectedModel() + ex_inputs = torch.ones((1, 3, 3, 3)) + fx_model = get_torch_fx_model(model, ex_inputs) + nncf_graph = GraphConverter.create_nncf_graph(fx_model) + + shared_attributes = {n.node_name: n.is_shared() for n in nncf_graph.get_all_nodes()} + ref_attributes = get_ref_from_json("default_shared_attribute_test_model", shared_attributes, attributes=True) + assert shared_attributes == ref_attributes diff --git a/tests/torch/fx/test_ptq_params.py b/tests/torch/fx/test_ptq_params.py new file mode 100644 index 00000000000..04a852a8a71 --- /dev/null +++ b/tests/torch/fx/test_ptq_params.py @@ -0,0 +1,133 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +import torch + +from nncf.common.graph.patterns import GraphPattern +from nncf.common.graph.patterns.manager import PatternsManager +from nncf.common.graph.transformations.commands import TargetType +from nncf.common.graph.transformations.commands import TransformationType +from nncf.common.utils.backend import BackendType +from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand +from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter +from nncf.parameters import TargetDevice +from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization +from nncf.quantization.algorithms.min_max.torch_fx_backend import FXMinMaxAlgoBackend +from nncf.scopes import IgnoredScope +from nncf.torch.graph.graph import PTNNCFGraph +from nncf.torch.graph.graph import PTTargetPoint +from nncf.torch.graph.operator_metatypes import PTCatMetatype +from nncf.torch.graph.operator_metatypes import PTConv2dMetatype +from nncf.torch.graph.operator_metatypes import PTLinearMetatype +from nncf.torch.graph.operator_metatypes import PTSoftmaxMetatype +from tests.common.quantization.metatypes import CatTestMetatype +from tests.common.quantization.metatypes import Conv2dTestMetatype +from tests.common.quantization.metatypes import LinearTestMetatype +from tests.common.quantization.metatypes import SoftmaxTestMetatype +from tests.cross_fw.test_templates.test_ptq_params import TemplateTestPTQParams +from tests.torch.fx.helpers import get_single_conv_nncf_graph +from tests.torch.fx.helpers import get_torch_fx_model_q_transformed +from tests.torch.ptq.helpers import get_single_no_weight_matmul_nncf_graph +from tests.torch.test_models.synthetic import LinearPTQParamsTestModel + + +def get_hw_patterns(device: TargetDevice = TargetDevice.ANY) -> GraphPattern: + return PatternsManager.get_full_hw_pattern_graph(backend=BackendType.TORCH_FX, device=device) + + +def get_ignored_patterns(device: TargetDevice = TargetDevice.ANY) -> GraphPattern: + return PatternsManager.get_full_ignored_pattern_graph(backend=BackendType.TORCH_FX, device=device) + + +@pytest.mark.parametrize("target_device", TargetDevice) +def test_target_device(target_device): + min_max_algo = MinMaxQuantization(target_device=target_device) + min_max_algo._backend_entity = FXMinMaxAlgoBackend() + assert min_max_algo._target_device == target_device + + +class TestPTQParams(TemplateTestPTQParams): + def get_algo_backend(self): + return FXMinMaxAlgoBackend() + + def check_quantize_outputs_fq_num(self, quantize_outputs, act_num_q, weight_num_q): + if quantize_outputs: + assert act_num_q == 2 + else: + assert act_num_q == 1 + assert weight_num_q == 1 + + def check_unified_scale_layout(self, layout, unified_scale_group): + assert len(layout.transformations) == len(unified_scale_group) + for t, ref_tp in zip(layout.transformations, unified_scale_group): + assert isinstance(t, FXApplyTransformationCommand) + assert t.transformation_fn.__closure__[1].cell_contents[0] == ref_tp + assert t.type == TransformationType.INSERT + assert t.transformation_fn.__closure__[0].cell_contents.zero_point == 0 + assert torch.allclose( + t.transformation_fn.__closure__[0].cell_contents.scale, torch.tensor(0.031496062874794006) + ) + + def target_point(self, target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint: + return PTTargetPoint(target_type, target_node_name, input_port_id=port_id) + + def get_backend_tensor(self, value): + return torch.tensor(value) + + @property + def metatypes_mapping(self): + return { + Conv2dTestMetatype: PTConv2dMetatype, + LinearTestMetatype: PTLinearMetatype, + SoftmaxTestMetatype: PTSoftmaxMetatype, + CatTestMetatype: PTCatMetatype, + } + + @property + def nncf_graph_cls(self): + return PTNNCFGraph + + @pytest.fixture(scope="session") + def test_params(self): + linear_model = LinearPTQParamsTestModel() + linear_model = get_torch_fx_model_q_transformed(linear_model, torch.ones((1, 3, 32, 32))) + + return { + "test_range_estimator_per_tensor": { + "model": linear_model, + "nncf_graph": GraphConverter.create_nncf_graph(linear_model), + "stat_points_num": 5, + }, + "test_quantize_outputs": { + "nncf_graph": get_single_conv_nncf_graph().nncf_graph, + "hw_patterns": get_hw_patterns(), + "ignored_patterns": get_ignored_patterns(), + }, + "test_ignored_scopes": { + "nncf_graph": get_single_conv_nncf_graph().nncf_graph, + "hw_patterns": get_hw_patterns(), + "ignored_patterns": get_ignored_patterns(), + }, + "test_model_type_pass": { + "nncf_graph": get_single_no_weight_matmul_nncf_graph().nncf_graph, + "hw_patterns": get_hw_patterns(), + "ignored_patterns": get_ignored_patterns(), + }, + "test_validate_scope": { + "nncf_graph": get_single_conv_nncf_graph().nncf_graph, + "ignored_patterns": get_ignored_patterns(), + }, + } + + @pytest.fixture(params=[(IgnoredScope([]), 1, 1), (IgnoredScope(["/Conv_1_0"]), 0, 0)]) + def ignored_scopes_data(self, request): + return request.param diff --git a/tests/torch/fx/test_quantizer_config.py b/tests/torch/fx/test_quantizer_config.py new file mode 100644 index 00000000000..b5927bc2caa --- /dev/null +++ b/tests/torch/fx/test_quantizer_config.py @@ -0,0 +1,54 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest + +import nncf.torch.graph.operator_metatypes as om +from nncf.common.utils.backend import BackendType +from nncf.quantization.algorithms.min_max.torch_fx_backend import FXMinMaxAlgoBackend +from tests.cross_fw.test_templates.models import NNCFGraphToTest +from tests.cross_fw.test_templates.models import NNCFGraphToTestDepthwiseConv +from tests.cross_fw.test_templates.models import NNCFGraphToTestSumAggregation +from tests.cross_fw.test_templates.models import NNCFGraphTransformer +from tests.cross_fw.test_templates.test_quantizer_config import TemplateTestQuantizerConfig +from tests.torch.fx.helpers import get_depthwise_conv_nncf_graph +from tests.torch.fx.helpers import get_single_conv_nncf_graph +from tests.torch.fx.helpers import get_sum_aggregation_nncf_graph + + +class TestQuantizerConfig(TemplateTestQuantizerConfig): + def get_algo_backend(self): + return FXMinMaxAlgoBackend() + + def get_backend_type(self): + return BackendType.TORCH_FX + + @pytest.fixture + def single_conv_nncf_graph(self) -> NNCFGraphToTest: + return get_single_conv_nncf_graph() + + @pytest.fixture + def depthwise_conv_nncf_graph(self) -> NNCFGraphToTestDepthwiseConv: + return get_depthwise_conv_nncf_graph() + + @pytest.fixture + def conv_sum_aggregation_nncf_graph(self) -> NNCFGraphToTestSumAggregation: + return get_sum_aggregation_nncf_graph() + + @pytest.fixture + def transformer_nncf_graph(self) -> NNCFGraphToTest: + return NNCFGraphTransformer( + matmul_metatype=om.PTMatMulMetatype, + softmax_metatype=om.PTSoftmaxMetatype, + mul_metatype=om.PTMulMetatype, + const_metatype=om.PTConstNoopMetatype, + transpose_metatype=om.PTTransposeMetatype, + ) diff --git a/tests/torch/fx/test_sanity.py b/tests/torch/fx/test_sanity.py index a3f0d828260..eaa5b6c0530 100644 --- a/tests/torch/fx/test_sanity.py +++ b/tests/torch/fx/test_sanity.py @@ -23,7 +23,6 @@ import torch.utils.data import torch.utils.data.distributed import torchvision.models as models -from torch._export import capture_pre_autograd_graph import nncf from nncf.common.logging.track_progress import track @@ -52,9 +51,9 @@ class SanitySampleCase: SanitySampleCase( "resnet18", "https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/302_resnet18_fp32_v1.pth", - 55.35, - 51, - 58, + 55.30, + 30, + 37, ), ) @@ -133,7 +132,7 @@ def transform_fn(data_item): with torch.no_grad(): ex_input = next(iter(calibration_dataset.get_inference_data())) model.eval() - exported_model = capture_pre_autograd_graph(model, args=(ex_input,)) + exported_model = torch.export.export_for_training(model, args=(ex_input,)).module() quantized_model = nncf.quantize(exported_model, calibration_dataset) quantized_model = torch.compile(quantized_model, backend="openvino") diff --git a/tests/torch/fx/test_smooth_quant.py b/tests/torch/fx/test_smooth_quant.py index a6b468fc58e..9caf968d1fd 100644 --- a/tests/torch/fx/test_smooth_quant.py +++ b/tests/torch/fx/test_smooth_quant.py @@ -15,10 +15,8 @@ import openvino.runtime as ov import pytest import torch -from torch._export import capture_pre_autograd_graph from nncf import IgnoredScope -from nncf.experimental.torch.fx.transformations import apply_quantization_transformations from nncf.parameters import ModelType from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters @@ -26,13 +24,13 @@ from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization from nncf.quantization.algorithms.smooth_quant.torch_fx_backend import FXSmoothQuantAlgoBackend from nncf.quantization.algorithms.smooth_quant.torch_fx_backend import FXSQMultiply -from nncf.torch import disable_patching from nncf.torch.graph.operator_metatypes import PTConv2dMetatype from nncf.torch.graph.operator_metatypes import PTLinearMetatype from tests.cross_fw.test_templates.helpers import ConvTestModel from tests.cross_fw.test_templates.helpers import LinearMultiShapeModel from tests.cross_fw.test_templates.helpers import ShareWeghtsConvAndShareLinearModel from tests.cross_fw.test_templates.test_smooth_quant import TemplateTestSQAlgorithm +from tests.torch.fx.helpers import get_torch_fx_model_q_transformed PT_LINEAR_MODEL_MM_MAP = {"Linear1": "linear_3", "Linear2": "linear_2", "Linear3": "linear", "Linear4": "linear_1"} @@ -97,10 +95,7 @@ def get_backend() -> Type[FXSmoothQuantAlgoBackend]: @staticmethod def backend_specific_model(model: torch.nn.Module, tmp_dir: str) -> ov.Model: - with disable_patching(): - captured_model = capture_pre_autograd_graph(model.eval(), (torch.rand(model.INPUT_SIZE),)) - apply_quantization_transformations(captured_model) - return captured_model + return get_torch_fx_model_q_transformed(model, torch.ones(model.INPUT_SIZE)) @staticmethod def check_scales(model: torch.nn.Module, reference_values: Dict[str, np.ndarray], model_cls) -> None: diff --git a/tests/torch/fx/test_statistics_aggregator.py b/tests/torch/fx/test_statistics_aggregator.py index d9e16b509e3..26e19d05608 100644 --- a/tests/torch/fx/test_statistics_aggregator.py +++ b/tests/torch/fx/test_statistics_aggregator.py @@ -15,7 +15,6 @@ import pytest import torch from torch import nn -from torch._export import capture_pre_autograd_graph from nncf import Dataset from nncf.common.graph.transformations.commands import TargetType @@ -23,9 +22,9 @@ from nncf.experimental.torch.fx.statistics.aggregator import FXStatisticsAggregator from nncf.quantization.algorithms.fast_bias_correction.torch_fx_backend import FXFastBiasCorrectionAlgoBackend from nncf.quantization.algorithms.min_max.torch_fx_backend import FXMinMaxAlgoBackend -from nncf.torch.dynamic_graph.patch_pytorch import disable_patching from nncf.torch.graph.graph import PTTargetPoint from tests.common.test_statistics_aggregator import TemplateTestStatisticsAggregator +from tests.torch.fx.helpers import get_torch_fx_model IDENTITY_NODE_NAME = "add" CONV_NODE_NAME = "conv2d" @@ -60,9 +59,7 @@ def get_fast_bias_correction_algo_backend_cls(self) -> Type[FXFastBiasCorrection def get_backend_model(self, dataset_samples): sample = dataset_samples[0].reshape(INPUT_SHAPE[1:]) conv_w = self.dataset_samples_to_conv_w(np.array(sample)) - with disable_patching(): - model = capture_pre_autograd_graph(IdentityConv(conv_w), args=(torch.ones(INPUT_SHAPE),)) - return model + return get_torch_fx_model(IdentityConv(conv_w), torch.ones(INPUT_SHAPE)) def get_statistics_aggregator(self, dataset): return FXStatisticsAggregator(dataset) diff --git a/tests/torch/fx/test_statistics_caching.py b/tests/torch/fx/test_statistics_caching.py new file mode 100644 index 00000000000..1533769c69a --- /dev/null +++ b/tests/torch/fx/test_statistics_caching.py @@ -0,0 +1,22 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nncf.common.graph.transformations.commands import TargetType +from nncf.experimental.torch.fx.statistics.aggregator import FXStatisticsAggregator +from nncf.torch.graph.transformations.commands import PTTargetPoint +from tests.cross_fw.test_templates.test_statistics_caching import TemplateTestStatisticsCaching + + +class TestStatisticsCaching(TemplateTestStatisticsCaching): + def create_target_point(self, target_point_type: TargetType, name: str, port_id: int) -> PTTargetPoint: + return PTTargetPoint(target_type=target_point_type, target_node_name=name, input_port_id=port_id) + + def get_statistics_aggregator(self): + return FXStatisticsAggregator(None) diff --git a/tests/torch/helpers.py b/tests/torch/helpers.py index 227dfa6cf60..1e3d102f2ed 100644 --- a/tests/torch/helpers.py +++ b/tests/torch/helpers.py @@ -50,8 +50,8 @@ from nncf.torch.nncf_module_replacement import get_original_module_scope_from_nncf_module_scope from nncf.torch.nncf_network import NNCFNetwork from nncf.torch.utils import get_all_modules_by_type -from tests.shared.command import Command as BaseCommand -from tests.shared.helpers import BaseTensorListComparator +from tests.cross_fw.shared.command import Command as BaseCommand +from tests.cross_fw.shared.helpers import BaseTensorListComparator TensorType = Union[torch.Tensor, np.ndarray, numbers.Number] diff --git a/tests/torch/modules/test_rnn.py b/tests/torch/modules/test_rnn.py index 441ac156242..2dd817d7f12 100644 --- a/tests/torch/modules/test_rnn.py +++ b/tests/torch/modules/test_rnn.py @@ -236,7 +236,7 @@ def test_export_lstm_cell(tmp_path): for node in model.graph.node: if node.op_type == "FakeQuantize": onnx_num += 1 - assert onnx_num == 12 + assert onnx_num == 11 @pytest.mark.parametrize( @@ -454,7 +454,7 @@ def test_export_stacked_bi_lstm(tmp_path): for node in model.graph.node: if node.op_type == "FakeQuantize": onnx_num += 1 - assert onnx_num == 46 + assert onnx_num == 42 class TestNumberOfNodes: @@ -513,8 +513,8 @@ def hook(model, input_, counter): _ = model(test_data.x, test_hidden) # NB: below may always fail in debug due to superfluous 'cat' nodes - assert model.nncf.get_graph().get_nodes_count() == 124 - assert len(counters) + 2 == 46 # 8 WQ + 36 AQ + 1 input AQ + 1 reset point AQ + assert model.nncf.get_graph().get_nodes_count() == 120 + assert len(counters) + 2 == 42 # 8 WQ + 32 AQ + 1 input AQ + 1 reset point AQ for counter in counters.values(): assert counter.count == p.seq_length assert counter_for_input_quantizer.count == 1 diff --git a/tests/torch/nas/test_all_elasticity.py b/tests/torch/nas/test_all_elasticity.py index 0e7b869df3c..0abccad39e5 100644 --- a/tests/torch/nas/test_all_elasticity.py +++ b/tests/torch/nas/test_all_elasticity.py @@ -20,7 +20,7 @@ from nncf.experimental.torch.nas.bootstrapNAS.training.model_creator_helpers import resume_compression_from_state from nncf.torch.exporter import PTExporter from nncf.torch.model_creation import create_nncf_network -from tests.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference from tests.torch.helpers import register_bn_adaptation_init_args from tests.torch.nas.creators import create_bnas_model_and_ctrl_by_test_desc from tests.torch.nas.creators import create_bootstrap_nas_training_algo diff --git a/tests/torch/nas/test_elastic_width.py b/tests/torch/nas/test_elastic_width.py index 68c83fe7b30..84aa66eb668 100644 --- a/tests/torch/nas/test_elastic_width.py +++ b/tests/torch/nas/test_elastic_width.py @@ -16,7 +16,7 @@ from nncf.experimental.torch.nas.bootstrapNAS.elasticity.visualization import SubnetGraph from nncf.torch.utils import get_model_device -from tests.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference from tests.torch.helpers import create_conv from tests.torch.helpers import get_empty_config from tests.torch.nas.creators import create_bootstrap_nas_training_algo diff --git a/tests/torch/nas/test_sanity_sample.py b/tests/torch/nas/test_sanity_sample.py index aad3e9f9de7..7fd0b0cc27e 100644 --- a/tests/torch/nas/test_sanity_sample.py +++ b/tests/torch/nas/test_sanity_sample.py @@ -26,8 +26,8 @@ ProgressiveShrinkingController, ) from nncf.experimental.torch.nas.bootstrapNAS.training.scheduler import BootstrapNASScheduler -from tests.shared.command import arg_list_from_arg_dict -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.command import arg_list_from_arg_dict +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.sample_test_validator import SampleType from tests.torch.sample_test_validator import SanitySampleValidator from tests.torch.sample_test_validator import SanityTestCaseDescriptor diff --git a/tests/torch/pruning/experimental/test_nodes_grouping.py b/tests/torch/pruning/experimental/test_nodes_grouping.py index d906e1e8eda..d8ce8037360 100644 --- a/tests/torch/pruning/experimental/test_nodes_grouping.py +++ b/tests/torch/pruning/experimental/test_nodes_grouping.py @@ -19,7 +19,7 @@ from nncf import NNCFConfig from nncf.torch.model_creation import create_nncf_network -from tests.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference from tests.torch.test_compressed_graph import GeneralModelDesc from tests.torch.test_compressed_graph import IModelDesc from tests.torch.test_compressed_graph import get_full_path_to_the_graph diff --git a/tests/torch/ptq/test_fq_params_calculation.py b/tests/torch/ptq/test_fq_params_calculation.py index 0a3b537df1d..af5d061262b 100644 --- a/tests/torch/ptq/test_fq_params_calculation.py +++ b/tests/torch/ptq/test_fq_params_calculation.py @@ -25,9 +25,9 @@ from nncf.torch.nncf_network import NNCFNetwork from nncf.torch.quantization.layers import QUANTIZATION_MODULES from nncf.torch.utils import get_all_modules_by_type -from tests.shared.helpers import compare_stats -from tests.shared.helpers import load_json -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.helpers import compare_stats +from tests.cross_fw.shared.helpers import load_json +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import TwoConvTestModel from tests.torch.helpers import create_random_mock_dataloader diff --git a/tests/torch/ptq/test_graphs.py b/tests/torch/ptq/test_graphs.py index 2dd649712f7..eba35163c7c 100644 --- a/tests/torch/ptq/test_graphs.py +++ b/tests/torch/ptq/test_graphs.py @@ -16,6 +16,7 @@ import torch from nncf import Dataset +from nncf.parameters import ModelType from nncf.parameters import TargetDevice from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization @@ -23,6 +24,7 @@ from nncf.torch.layers import NNCF_RNN from nncf.torch.layers import LSTMCellNNCF from tests.cross_fw.test_templates.helpers import EmbeddingModel +from tests.cross_fw.test_templates.helpers import RoPEModel from tests.cross_fw.test_templates.helpers import ScaledDotProductAttentionModel from tests.torch import test_models from tests.torch.quantization.test_algo_quantization import SharedLayersModel @@ -50,6 +52,7 @@ def get_model_name(description): TEST_MODELS_DESC = [ (ModelDesc("embedding_model", EmbeddingModel, [1, 10]), {}), + (ModelDesc("rope_model", RoPEModel, [1, 10]), {"model_type": ModelType.TRANSFORMER}), ( ModelDesc( "scaled_dot_product_attention_model", diff --git a/tests/torch/ptq/test_min_max.py b/tests/torch/ptq/test_min_max.py index 5c005592d4e..d099fd587c1 100644 --- a/tests/torch/ptq/test_min_max.py +++ b/tests/torch/ptq/test_min_max.py @@ -13,9 +13,7 @@ import pytest from nncf.common.graph.graph import NNCFGraph -from nncf.common.graph.layer_attributes import ConstantLayerAttributes -from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes -from nncf.common.graph.layer_attributes import LinearLayerAttributes +from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.graph.transformations.commands import TargetType from nncf.quantization.algorithms.min_max.backend import MinMaxAlgoBackend from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend @@ -48,23 +46,8 @@ def create_target_point(self, target_point_type: TargetType, name: str, port_id: class TestTorchGetTargetPointShape(TemplateTestGetTargetPointShape, TestTorchMinMaxAlgorithm): def get_nncf_graph(self, weight_port_id: int, weight_shape: Tuple[int]) -> NNCFGraph: - conv_layer_attrs = ConvolutionLayerAttributes( - weight_requires_grad=True, - in_channels=weight_shape[1], - out_channels=weight_shape[0], - kernel_size=weight_shape[2:], - stride=1, - dilations=1, - groups=1, - transpose=False, - padding_values=[], - ) return NNCFGraphToTest( - PTConv2dMetatype, - conv_layer_attrs, - PTNNCFGraph, - const_metatype=PTConstNoopMetatype, - const_layer_attrs=ConstantLayerAttributes("w", shape=weight_shape), + conv_metatype=PTConv2dMetatype, nncf_graph_cls=PTNNCFGraph, const_metatype=PTConstNoopMetatype ).nncf_graph @@ -78,36 +61,21 @@ def matmul_metatype(self): return PTLinearMetatype @staticmethod - def get_conv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> ConvolutionLayerAttributes: - return ConvolutionLayerAttributes( - weight_requires_grad=False, - in_channels=weight_shape[0], - out_channels=weight_shape[1], - kernel_size=weight_shape[2:], - stride=1, - dilations=1, - groups=1, - transpose=False, - padding_values=[], - ) + def get_conv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> BaseLayerAttributes: + # This method isn't needed for Torch backend + return None @staticmethod - def get_depthwiseconv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> ConvolutionLayerAttributes: - return ConvolutionLayerAttributes( - weight_requires_grad=False, - in_channels=weight_shape[1], - out_channels=weight_shape[2], - kernel_size=weight_shape[3:], - stride=1, - dilations=1, - groups=weight_shape[0], - transpose=False, - padding_values=[], - ) + def get_depthwiseconv_node_attrs(weight_port_id: int, weight_shape: Tuple[int]) -> BaseLayerAttributes: + # This method isn't needed for Torch backend + return None @staticmethod - def get_matmul_node_attrs(weight_port_id: int, transpose_weight: Tuple[int], weight_shape: Tuple[int]): - return LinearLayerAttributes(False, in_features=weight_shape[0], out_features=weight_shape[1]) + def get_matmul_node_attrs( + weight_port_id: int, transpose_weight: Tuple[int], weight_shape: Tuple[int] + ) -> BaseLayerAttributes: + # This method isn't needed for Torch backend + return None def test_get_channel_axes_matmul_node_ov_onnx(self): pytest.skip("Test is not applied for Torch backend.") diff --git a/tests/torch/ptq/test_ptq_params.py b/tests/torch/ptq/test_ptq_params.py index 026d1da3086..4d4eff8276d 100644 --- a/tests/torch/ptq/test_ptq_params.py +++ b/tests/torch/ptq/test_ptq_params.py @@ -34,12 +34,11 @@ from tests.common.quantization.metatypes import LinearTestMetatype from tests.common.quantization.metatypes import SoftmaxTestMetatype from tests.cross_fw.test_templates.test_ptq_params import TemplateTestPTQParams -from tests.torch.helpers import create_bn -from tests.torch.helpers import create_conv from tests.torch.helpers import create_depthwise_conv from tests.torch.ptq.helpers import get_nncf_network from tests.torch.ptq.helpers import get_single_conv_nncf_graph from tests.torch.ptq.helpers import get_single_no_weight_matmul_nncf_graph +from tests.torch.test_models.synthetic import LinearPTQParamsTestModel def get_hw_patterns(device: TargetDevice = TargetDevice.ANY) -> GraphPattern: @@ -55,24 +54,8 @@ def get_nncf_network(self): return get_nncf_network(self) -class LinearTestModel(nn.Module, ToNNCFNetworkInterface): - def __init__(self): - super().__init__() - self.conv1 = create_conv(3, 3, 1) - self.bn1 = create_bn(3) - self.relu = nn.ReLU() - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.conv2 = create_conv(3, 1, 1) - self.bn2 = create_bn(1) - - def forward(self, x): - # input_shape = [1, 3, 32, 32] - x = self.relu(self.conv1(x)) - x = self.bn1(x) - x = self.avg_pool(x) - x = self.relu(self.conv2(x)) - x = self.bn2(x) - return x +class PTLinearTestModel(LinearPTQParamsTestModel, ToNNCFNetworkInterface): + pass class OneDepthwiseConvModel(nn.Module, ToNNCFNetworkInterface): @@ -133,7 +116,7 @@ def nncf_graph_cls(self): @pytest.fixture(scope="session") def test_params(self): - linear_model = LinearTestModel().get_nncf_network() + linear_model = PTLinearTestModel().get_nncf_network() depthwise_model = OneDepthwiseConvModel().get_nncf_network() return { diff --git a/tests/torch/ptq/test_quantizer_config.py b/tests/torch/ptq/test_quantizer_config.py index acc8cb4002d..1b64b6cbd87 100644 --- a/tests/torch/ptq/test_quantizer_config.py +++ b/tests/torch/ptq/test_quantizer_config.py @@ -11,10 +11,14 @@ import pytest +import nncf.torch.graph.operator_metatypes as om +from nncf.common.utils.backend import BackendType from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend +from nncf.torch.graph.graph import PTNNCFGraph from tests.cross_fw.test_templates.models import NNCFGraphToTest from tests.cross_fw.test_templates.models import NNCFGraphToTestDepthwiseConv from tests.cross_fw.test_templates.models import NNCFGraphToTestSumAggregation +from tests.cross_fw.test_templates.models import NNCFGraphTransformer from tests.cross_fw.test_templates.test_quantizer_config import TemplateTestQuantizerConfig from tests.torch.ptq.helpers import get_depthwise_conv_nncf_graph from tests.torch.ptq.helpers import get_single_conv_nncf_graph @@ -25,6 +29,9 @@ class TestQuantizerConfig(TemplateTestQuantizerConfig): def get_algo_backend(self): return PTMinMaxAlgoBackend() + def get_backend_type(self): + return BackendType.TORCH + @pytest.fixture def single_conv_nncf_graph(self) -> NNCFGraphToTest: return get_single_conv_nncf_graph() @@ -36,3 +43,14 @@ def depthwise_conv_nncf_graph(self) -> NNCFGraphToTestDepthwiseConv: @pytest.fixture def conv_sum_aggregation_nncf_graph(self) -> NNCFGraphToTestSumAggregation: return get_sum_aggregation_nncf_graph() + + @pytest.fixture + def transformer_nncf_graph(self) -> NNCFGraphToTest: + return NNCFGraphTransformer( + matmul_metatype=om.PTMatMulMetatype, + softmax_metatype=om.PTSoftmaxMetatype, + mul_metatype=om.PTMulMetatype, + const_metatype=om.PTConstNoopMetatype, + transpose_metatype=om.PTTransposeMetatype, + nncf_graph_cls=PTNNCFGraph, + ) diff --git a/tests/torch/ptq/test_reducers_and_aggregators.py b/tests/torch/ptq/test_reducers_and_aggregators.py index c657b222802..6427804c4e0 100644 --- a/tests/torch/ptq/test_reducers_and_aggregators.py +++ b/tests/torch/ptq/test_reducers_and_aggregators.py @@ -18,18 +18,18 @@ import nncf from nncf.common.graph.layer_attributes import Dtype +from nncf.experimental.common.tensor_statistics.collectors import AbsMaxReducer +from nncf.experimental.common.tensor_statistics.collectors import AbsQuantileReducer +from nncf.experimental.common.tensor_statistics.collectors import BatchMeanReducer +from nncf.experimental.common.tensor_statistics.collectors import MaxReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanPerChReducer +from nncf.experimental.common.tensor_statistics.collectors import MeanReducer +from nncf.experimental.common.tensor_statistics.collectors import MinReducer +from nncf.experimental.common.tensor_statistics.collectors import QuantileReducer from nncf.experimental.common.tensor_statistics.collectors import TensorCollector from nncf.tensor import Tensor from nncf.tensor import functions as fns from nncf.torch.tensor_statistics.algo import create_register_input_hook -from nncf.torch.tensor_statistics.collectors import PTAbsMaxReducer -from nncf.torch.tensor_statistics.collectors import PTAbsQuantileReducer -from nncf.torch.tensor_statistics.collectors import PTBatchMeanReducer -from nncf.torch.tensor_statistics.collectors import PTMaxReducer -from nncf.torch.tensor_statistics.collectors import PTMeanPerChanelReducer -from nncf.torch.tensor_statistics.collectors import PTMeanReducer -from nncf.torch.tensor_statistics.collectors import PTMinReducer -from nncf.torch.tensor_statistics.collectors import PTQuantileReducer from tests.common.experimental.test_reducers_and_aggregators import TemplateTestReducersAggregators @@ -46,14 +46,14 @@ def _get_torch_tensor(self, x: np.ndarray, dtype: Optional[Dtype] = None): @pytest.fixture(scope="module") def reducers(self): return { - "min": PTMinReducer, - "max": PTMaxReducer, - "abs_max": PTAbsMaxReducer, - "mean": PTMeanReducer, - "quantile": PTQuantileReducer, - "abs_quantile": PTAbsQuantileReducer, - "batch_mean": PTBatchMeanReducer, - "mean_per_ch": PTMeanPerChanelReducer, + "min": MinReducer, + "max": MaxReducer, + "abs_max": AbsMaxReducer, + "mean": MeanReducer, + "quantile": QuantileReducer, + "abs_quantile": AbsQuantileReducer, + "batch_mean": BatchMeanReducer, + "mean_per_ch": MeanPerChReducer, } def all_close(self, val, ref) -> bool: diff --git a/tests/torch/ptq/test_statistics_caching.py b/tests/torch/ptq/test_statistics_caching.py new file mode 100644 index 00000000000..6fd527ce15c --- /dev/null +++ b/tests/torch/ptq/test_statistics_caching.py @@ -0,0 +1,22 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nncf.common.graph.transformations.commands import TargetType +from nncf.torch.graph.transformations.commands import PTTargetPoint +from nncf.torch.statistics.aggregator import PTStatisticsAggregator +from tests.cross_fw.test_templates.test_statistics_caching import TemplateTestStatisticsCaching + + +class TestStatisticsCaching(TemplateTestStatisticsCaching): + def create_target_point(self, target_point_type: TargetType, name: str, port_id: int) -> PTTargetPoint: + return PTTargetPoint(target_type=target_point_type, target_node_name=name, input_port_id=port_id) + + def get_statistics_aggregator(self): + return PTStatisticsAggregator(None) diff --git a/tests/torch/ptq/test_weights_compression.py b/tests/torch/ptq/test_weights_compression.py index 5e4ca75e128..95c03013333 100644 --- a/tests/torch/ptq/test_weights_compression.py +++ b/tests/torch/ptq/test_weights_compression.py @@ -13,12 +13,22 @@ import torch import torch.nn.functional as F +import nncf +from nncf import BackupMode from nncf import CompressWeightsMode from nncf import SensitivityMetric from nncf.quantization import compress_weights +from nncf.quantization.advanced_parameters import AdvancedCompressionParameters from nncf.torch import wrap_model -from nncf.torch.quantization.layers import AsymmetricWeightsDecompressor -from nncf.torch.quantization.layers import SymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT4AsymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT4SymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor +from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor +from nncf.torch.quantization.quantize_functions import pack_int4 +from nncf.torch.quantization.quantize_functions import pack_uint4 +from nncf.torch.quantization.quantize_functions import unpack_int4 +from nncf.torch.quantization.quantize_functions import unpack_uint4 +from tests.torch.test_models.synthetic import ShortTransformer DATA_BASED_SENSITIVITY_METRICS = ( SensitivityMetric.HESSIAN_INPUT_ACTIVATION, @@ -29,35 +39,16 @@ ALL_SENSITIVITY_METRICS = DATA_BASED_SENSITIVITY_METRICS + (SensitivityMetric.WEIGHT_QUANTIZATION_ERROR,) -SUPPORTED_MODES = (CompressWeightsMode.INT8, CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM) -UNSUPPORTED_MODES = ( - CompressWeightsMode.INT4_SYM, - CompressWeightsMode.INT4_ASYM, - CompressWeightsMode.NF4, -) - - -class ShortTransformer(torch.nn.Module): - def __init__(self, in_features, num_embeddings, share_weights=False): - super().__init__() - self.wte = torch.nn.Embedding(num_embeddings, in_features) - self.linear = torch.nn.Linear(in_features, in_features) - self.lm_head = torch.nn.Linear(in_features, num_embeddings) - - if share_weights: - self.lm_head.weight = self.wte.weight - - def forward(self, input_ids): - x = self.wte(input_ids) - x = self.linear(x) - res = self.lm_head(x) - return res +INT8_MODES = (CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM) +INT4_MODES = (CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM) +SUPPORTED_MODES = INT8_MODES + INT4_MODES +UNSUPPORTED_MODES = (CompressWeightsMode.NF4, CompressWeightsMode.E2M1) class MatMulModel(torch.nn.Module): def __init__(self): super().__init__() - self.w = torch.nn.Parameter(torch.ones(size=(300, 300), dtype=torch.float32)) + self.w = torch.nn.Parameter(torch.ones(size=(256, 256), dtype=torch.float32)) def forward(self, input): return input @ self.w @@ -67,7 +58,7 @@ class FunctionalModel(torch.nn.Module): def __init__(self): super().__init__() self.conv_w = torch.nn.Parameter(torch.ones(size=(5, 3, 3, 3), dtype=torch.float32)) - self.matmul_w = torch.nn.Parameter(torch.ones(size=(1, 3, 300, 300), dtype=torch.float32)) + self.matmul_w = torch.nn.Parameter(torch.ones(size=(1, 3, 256, 256), dtype=torch.float32)) self.conv_tr_w = torch.nn.Parameter(torch.rand(size=(5, 4, 3, 3))) self.nested_matmul = MatMulModel() @@ -107,14 +98,18 @@ def forward(self, input_): return x -@pytest.mark.parametrize("mode", (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM)) +@pytest.mark.parametrize("mode", SUPPORTED_MODES) def test_compress_weights(mode): - model = ShortTransformer(5, 10) + model = ShortTransformer(8, 16) dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8 - input_ids = torch.randint(0, 10, (5,)) + input_ids = torch.randint(0, 10, (8,)) wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True) - compressed_model = compress_weights(wrapped_model, mode=mode) + + kwargs = {} + if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]: + kwargs["group_size"] = 4 + compressed_model = compress_weights(wrapped_model, mode=mode, **kwargs) n_compressed_weights = 0 n_target_modules = 0 @@ -128,14 +123,19 @@ def test_compress_weights(mode): assert n_compressed_weights == n_target_modules -@pytest.mark.parametrize("mode", (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM)) +@pytest.mark.parametrize("mode", SUPPORTED_MODES) def test_compress_weights_functional_model(mode): model = FunctionalModel() - decompressor_type = ( - SymmetricWeightsDecompressor if mode == CompressWeightsMode.INT8_SYM else AsymmetricWeightsDecompressor - ) + decompressor_map = { + CompressWeightsMode.INT8_SYM: (INT8SymmetricWeightsDecompressor,), + CompressWeightsMode.INT8_ASYM: (INT8AsymmetricWeightsDecompressor,), + CompressWeightsMode.INT4_SYM: (INT4SymmetricWeightsDecompressor, INT8AsymmetricWeightsDecompressor), + CompressWeightsMode.INT4_ASYM: (INT4AsymmetricWeightsDecompressor, INT8AsymmetricWeightsDecompressor), + } - input_ids = torch.randint(0, 10, [1, 3, 300, 300]) + decompressor_type = decompressor_map[mode] + + input_ids = torch.randint(0, 10, [1, 3, 256, 256]) wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True) compressed_model = compress_weights(wrapped_model, mode=mode) @@ -165,14 +165,18 @@ def test_compress_weights_conv(): assert n_compressed_weights == n_target_modules -@pytest.mark.parametrize("mode", (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM)) +@pytest.mark.parametrize("mode", SUPPORTED_MODES) def test_compress_shared_weights(mocker, mode): - model = ShortTransformer(5, 10, share_weights=True) + model = ShortTransformer(8, 16, share_weights=True) dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8 - input_ids = torch.randint(0, 10, (5,)) + input_ids = torch.randint(0, 10, (8,)) wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True) - compressed_model = compress_weights(wrapped_model, mode=mode) + + kwargs = {} + if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]: + kwargs["group_size"] = 4 + compressed_model = compress_weights(wrapped_model, mode=mode, **kwargs) n_compressed_weights = 0 n_target_modules = 0 @@ -201,7 +205,7 @@ def forward(self, input): return input -@pytest.mark.parametrize("mode", SUPPORTED_MODES) +@pytest.mark.parametrize("mode", INT8_MODES) @pytest.mark.parametrize( "params", ( @@ -214,13 +218,36 @@ def forward(self, input): {"awq": True}, {"scale_estimation": True}, {"lora_correction": True}, + {"backup_mode": BackupMode.NONE}, + {"backup_mode": BackupMode.INT8_ASYM}, + {"backup_mode": BackupMode.INT8_SYM}, + {"advanced_parameters": AdvancedCompressionParameters(statistics_path="anything")}, ), ) def test_raise_error_with_unsupported_params_for_int8(mode, params): dummy_torch_model = EmptyModel() dummy_input = torch.Tensor() wrapped_model = wrap_model(dummy_torch_model, example_input=dummy_input, trace_parameters=True) - with pytest.raises(AttributeError): + with pytest.raises(nncf.ParameterNotSupportedError): + compress_weights(wrapped_model, mode=mode, **params) + + +@pytest.mark.parametrize("mode", INT4_MODES) +@pytest.mark.parametrize( + "params", + ( + *({"sensitivity_metric": metric} for metric in DATA_BASED_SENSITIVITY_METRICS), + {"gptq": True}, + {"awq": True}, + {"scale_estimation": True}, + {"lora_correction": True}, + ), +) +def test_raise_error_with_unsupported_params_for_int4(mode, params): + dummy_torch_model = EmptyModel() + dummy_input = torch.Tensor() + wrapped_model = wrap_model(dummy_torch_model, example_input=dummy_input, trace_parameters=True) + with pytest.raises(nncf.ParameterNotSupportedError): compress_weights(wrapped_model, mode=mode, **params) @@ -229,10 +256,18 @@ def test_raise_error_with_not_int8(mode): dummy_torch_model = EmptyModel() dummy_input = torch.Tensor() wrapped_model = wrap_model(dummy_torch_model, example_input=dummy_input, trace_parameters=True) - with pytest.raises(AttributeError): + with pytest.raises(nncf.ParameterNotSupportedError): compress_weights(wrapped_model, mode=mode) +def test_raise_error_for_statistics_caching(): + dummy_torch_model = EmptyModel() + dummy_input = torch.Tensor() + wrapped_model = wrap_model(dummy_torch_model, example_input=dummy_input, trace_parameters=True) + with pytest.raises(nncf.ParameterNotSupportedError): + compress_weights(wrapped_model, advanced_parameters=AdvancedCompressionParameters(statistics_path="anything")) + + class DTypeModel(torch.nn.Module): def __init__(self): super().__init__() @@ -265,7 +300,7 @@ def test_model_devices_and_precisions(use_cuda, dtype): if dtype == torch.float16: model.half() - dummy_input = torch.rand((1, 300), dtype=dtype, device=device) + dummy_input = torch.rand((1, 256), dtype=dtype, device=device) wrapped_model = wrap_model(model, example_input=dummy_input, trace_parameters=True) compressed_model = compress_weights(wrapped_model) result = compressed_model(dummy_input) @@ -274,3 +309,21 @@ def test_model_devices_and_precisions(use_cuda, dtype): assert compressed_model.state_dict()["_nncf.external_op.weights_decompressor_w._scale"].dtype == torch.float16 # Result should be in the precision of the model assert result.dtype == dtype + + +def test_pack_uint4(): + w_uint8 = torch.randint(0, 15, (4, 4), dtype=torch.uint8) + packed_w = pack_uint4(w_uint8) + assert packed_w.dtype == torch.uint8 + assert packed_w.numel() * 2 == w_uint8.numel() + unpacked_w = unpack_uint4(packed_w).reshape(w_uint8.shape) + assert torch.all(unpacked_w == w_uint8) + + +def test_pack_int4(): + w_int8 = torch.randint(-8, 7, (4, 4), dtype=torch.int8) + packed_w = pack_int4(w_int8) + assert packed_w.dtype == torch.uint8 + assert packed_w.numel() * 2 == w_int8.numel() + unpacked_w = unpack_int4(packed_w).reshape(w_int8.shape) + assert torch.all(unpacked_w == w_int8) diff --git a/tests/torch/pytorch_patch_isolated.py b/tests/torch/pytorch_patch_isolated.py index c3f0044dd8b..3447061bbd4 100644 --- a/tests/torch/pytorch_patch_isolated.py +++ b/tests/torch/pytorch_patch_isolated.py @@ -17,7 +17,7 @@ import pytest import torch -from tests.shared.isolation_runner import ISOLATION_RUN_ENV_VAR +from tests.cross_fw.shared.isolation_runner import ISOLATION_RUN_ENV_VAR def clean_source_code(code_source): @@ -79,7 +79,7 @@ def test_jit_script_exception_preserves_patching_isolated(): assert "nncf" in torch.nn.Module.__call__.__code__.co_filename -def compile_and_run_test_model() -> torch.Tensor: +def compile_and_run_test_model(compile_forward: bool) -> torch.Tensor: class TestModel(torch.nn.Module): def __init__(self): super().__init__() @@ -96,14 +96,20 @@ def forward(self, x): state_dict[k] = torch.rand(v.shape) model.load_state_dict(state_dict) - compiled_model = torch.compile(model) + if compile_forward: + compiled_model = model + compiled_model.forward = torch.compile(model.forward) + else: + compiled_model = torch.compile(model) + assert "_torchdynamo_orig_callable" in compiled_model.forward.__dict__ return compiled_model(torch.rand([1, 3, 5, 5])) @pytest.mark.skipif(ISOLATION_RUN_ENV_VAR not in os.environ, reason="Should be run via isolation proxy") def test_compile(): - before_nncf = compile_and_run_test_model() + compile_forward = os.environ.get("COMPILE_FORWARD", None) == "1" + before_nncf = compile_and_run_test_model(compile_forward) import nncf.torch # noqa: F401 - after_nncf = compile_and_run_test_model() + after_nncf = compile_and_run_test_model(compile_forward) assert torch.allclose(before_nncf, after_nncf) diff --git a/tests/torch/qat/test_qat_classification.py b/tests/torch/qat/test_qat_classification.py index 394ebfa0071..1ecda84fb24 100644 --- a/tests/torch/qat/test_qat_classification.py +++ b/tests/torch/qat/test_qat_classification.py @@ -48,7 +48,7 @@ from nncf.common.compression import BaseCompressionAlgorithmController from nncf.torch.initialization import default_criterion_fn from nncf.torch.utils import is_main_process -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT CONFIGS = list((PROJECT_ROOT / Path("examples/torch/classification/configs/quantization")).glob("*")) diff --git a/tests/torch/qat/test_qat_object_detection.py b/tests/torch/qat/test_qat_object_detection.py index 16a8ecd9611..e8a4c3df885 100644 --- a/tests/torch/qat/test_qat_object_detection.py +++ b/tests/torch/qat/test_qat_object_detection.py @@ -46,7 +46,7 @@ from examples.torch.object_detection.main import train_epoch from nncf import NNCFConfig from nncf.common.compression import BaseCompressionAlgorithmController -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT CONFIGS = list((PROJECT_ROOT / Path("examples/torch/object_detection/configs")).glob("*")) diff --git a/tests/torch/qat/test_qat_segmentation.py b/tests/torch/qat/test_qat_segmentation.py index f3dff7ef899..005795736bb 100644 --- a/tests/torch/qat/test_qat_segmentation.py +++ b/tests/torch/qat/test_qat_segmentation.py @@ -50,7 +50,7 @@ from nncf import NNCFConfig from nncf.common.compression import BaseCompressionAlgorithmController from nncf.torch.utils import is_main_process -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT CONFIGS = list((PROJECT_ROOT / Path("examples/torch/semantic_segmentation/configs")).glob("*")) diff --git a/tests/torch/quantization/extensions/isolated_cases.py b/tests/torch/quantization/extensions/isolated_cases.py index d70fda91b19..801538e8f3c 100644 --- a/tests/torch/quantization/extensions/isolated_cases.py +++ b/tests/torch/quantization/extensions/isolated_cases.py @@ -22,7 +22,7 @@ import torch import nncf -from tests.shared.isolation_runner import ISOLATION_RUN_ENV_VAR +from tests.cross_fw.shared.isolation_runner import ISOLATION_RUN_ENV_VAR @pytest.mark.skipif(ISOLATION_RUN_ENV_VAR not in os.environ, reason="Should be run via isolation proxy") diff --git a/tests/torch/quantization/extensions/test_extension_unavailable.py b/tests/torch/quantization/extensions/test_extension_unavailable.py index 0fcc3093799..4ae9c654cca 100644 --- a/tests/torch/quantization/extensions/test_extension_unavailable.py +++ b/tests/torch/quantization/extensions/test_extension_unavailable.py @@ -11,7 +11,7 @@ import pytest -from tests.shared.isolation_runner import run_pytest_case_function_in_separate_process +from tests.cross_fw.shared.isolation_runner import run_pytest_case_function_in_separate_process from tests.torch.quantization.extensions.isolated_cases import ( test_missing_cuda_compiler_fails_with_message_isolated_calledprocesserror, ) diff --git a/tests/torch/quantization/extensions/test_timeout_extension_loader.py b/tests/torch/quantization/extensions/test_timeout_extension_loader.py index b32505528e3..37f6cf63a97 100644 --- a/tests/torch/quantization/extensions/test_timeout_extension_loader.py +++ b/tests/torch/quantization/extensions/test_timeout_extension_loader.py @@ -19,8 +19,8 @@ from nncf.torch.extensions import ExtensionLoaderTimeoutException from nncf.torch.quantization.extensions import QuantizedFunctionsCPU from nncf.torch.quantization.extensions import QuantizedFunctionsCUDA -from tests.shared.isolation_runner import ISOLATION_RUN_ENV_VAR -from tests.shared.isolation_runner import run_pytest_case_function_in_separate_process +from tests.cross_fw.shared.isolation_runner import ISOLATION_RUN_ENV_VAR +from tests.cross_fw.shared.isolation_runner import run_pytest_case_function_in_separate_process @pytest.mark.skipif(ISOLATION_RUN_ENV_VAR not in os.environ, reason="Should be run via isolation proxy") diff --git a/tests/torch/quantization/test_adjust_padding.py b/tests/torch/quantization/test_adjust_padding.py index e2a2018b15f..d848caedbe2 100644 --- a/tests/torch/quantization/test_adjust_padding.py +++ b/tests/torch/quantization/test_adjust_padding.py @@ -15,7 +15,7 @@ import pytest import torch -from nncf.common.quantization.quantizer_propagation.solver import PropagationStrategy +from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationRule from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationSolver from nncf.torch.hardware.config import PTHWConfig from nncf.torch.layers import NNCFConv2d @@ -50,10 +50,10 @@ def __init__(self, name: str): } self._hw_config = False self.custom_hw_config_dict = None - self.propagation_strategy = PropagationStrategy.MERGE_WITH_SINGLE_FQ_RESULT + self.propagation_strategy = QuantizerPropagationRule.MERGE_ALL_IN_ONE def requant_prop_strategy(self): - self.propagation_strategy = PropagationStrategy.MERGE_WITH_POTENTIAL_REQUANTIZATION + self.propagation_strategy = QuantizerPropagationRule.MERGE_WITH_POTENTIAL_REQUANTIZATION return self @staticmethod diff --git a/tests/torch/quantization/test_hawq_precision_init.py b/tests/torch/quantization/test_hawq_precision_init.py index c056ba96216..ee7a52c77fd 100644 --- a/tests/torch/quantization/test_hawq_precision_init.py +++ b/tests/torch/quantization/test_hawq_precision_init.py @@ -59,8 +59,8 @@ from nncf.torch.utils import get_all_modules_by_type from nncf.torch.utils import get_model_device from nncf.torch.utils import safe_thread_call -from tests.shared.nx_graph import compare_nx_graph_with_reference -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import BasicConvTestModel from tests.torch.helpers import create_compressed_model_and_algo_for_test from tests.torch.helpers import create_conv diff --git a/tests/torch/quantization/test_manual_precision_init.py b/tests/torch/quantization/test_manual_precision_init.py index 39fea875fd0..fc9c102d3f7 100644 --- a/tests/torch/quantization/test_manual_precision_init.py +++ b/tests/torch/quantization/test_manual_precision_init.py @@ -21,8 +21,8 @@ from nncf.common.quantization.structs import WeightQuantizerId from nncf.torch import register_default_init_args from nncf.torch.quantization.algo import QuantizationController -from tests.shared.paths import EXAMPLES_DIR -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import EXAMPLES_DIR +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import BasicConvTestModel from tests.torch.helpers import create_compressed_model_and_algo_for_test from tests.torch.helpers import create_ones_mock_dataloader diff --git a/tests/torch/quantization/test_quantization_metric.py b/tests/torch/quantization/test_quantization_metric.py index a7fff0d395b..9e58cbb63e1 100644 --- a/tests/torch/quantization/test_quantization_metric.py +++ b/tests/torch/quantization/test_quantization_metric.py @@ -219,7 +219,7 @@ def test_memory_consumption_stats(data): weights={}, ignored_scopes=[], target_device="TRIAL", - expected={"quantized_edges_in_cfg": 176, "total_edges_in_cfg": 177}, + expected={"quantized_edges_in_cfg": 173, "total_edges_in_cfg": 177}, ), CaseStruct( initializers={}, diff --git a/tests/torch/quantization/test_sanity_sample.py b/tests/torch/quantization/test_sanity_sample.py index 4b9a5929fec..ab0f27a944d 100644 --- a/tests/torch/quantization/test_sanity_sample.py +++ b/tests/torch/quantization/test_sanity_sample.py @@ -20,7 +20,7 @@ from nncf import NNCFConfig from nncf.common.quantization.structs import QuantizerConfig from nncf.torch.quantization.algo import QuantizationController -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.sample_test_validator import SampleType from tests.torch.sample_test_validator import SanitySampleValidator from tests.torch.sample_test_validator import SanityTestCaseDescriptor diff --git a/tests/torch/quantization/test_serialize_to_json.py b/tests/torch/quantization/test_serialize_to_json.py index fdccb80e0af..b539712951f 100644 --- a/tests/torch/quantization/test_serialize_to_json.py +++ b/tests/torch/quantization/test_serialize_to_json.py @@ -21,7 +21,7 @@ from nncf.common.quantization.structs import QuantizerConfig from nncf.torch.dynamic_graph.context import Scope from nncf.torch.graph.transformations.commands import PTTargetPoint -from tests.shared.serialization import check_serialization +from tests.cross_fw.shared.serialization import check_serialization DUMMY_STR = "dummy" diff --git a/tests/torch/requirements.txt b/tests/torch/requirements.txt index be82652d65f..2765860b236 100644 --- a/tests/torch/requirements.txt +++ b/tests/torch/requirements.txt @@ -15,7 +15,7 @@ accelerate==0.28.0 transformers==4.38.2 # Required for movement_sparsity tests -datasets==2.14.7 +datasets==3.0.1 evaluate==0.3.0 openvino timm==0.9.2 diff --git a/tests/torch/sample_test_validator.py b/tests/torch/sample_test_validator.py index 1cafce603c0..5fb2bc3c602 100644 --- a/tests/torch/sample_test_validator.py +++ b/tests/torch/sample_test_validator.py @@ -24,10 +24,10 @@ from examples.torch.common.utils import get_run_name from nncf import NNCFConfig from nncf.common.utils.registry import Registry -from tests.shared.command import arg_list_from_arg_dict -from tests.shared.config_factory import ConfigFactory -from tests.shared.paths import EXAMPLES_DIR -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.command import arg_list_from_arg_dict +from tests.cross_fw.shared.config_factory import ConfigFactory +from tests.cross_fw.shared.paths import EXAMPLES_DIR +from tests.cross_fw.shared.paths import TEST_ROOT def create_command_line(args: Dict[str, Any], sample_type: str, main_filename: str = "main.py") -> str: diff --git a/tests/torch/sota_checkpoints_eval.json b/tests/torch/sota_checkpoints_eval.json index 5fb02a9a112..2efb9bd482a 100644 --- a/tests/torch/sota_checkpoints_eval.json +++ b/tests/torch/sota_checkpoints_eval.json @@ -124,7 +124,7 @@ "mobilenet_v2_imagenet_int8": { "config": "examples/torch/classification/configs/quantization/mobilenet_v2_imagenet_int8.json", "reference": "mobilenet_v2_imagenet", - "target_ov": 71.07, + "target_ov": 71.18, "target_pt": 71.3, "metric_type": "Acc@1", "resume": "mobilenet_v2_imagenet_int8.pth", @@ -138,7 +138,7 @@ "mobilenet_v2_imagenet_int8_per_tensor": { "config": "examples/torch/classification/configs/quantization/mobilenet_v2_imagenet_int8_per_tensor.json", "reference": "mobilenet_v2_imagenet", - "target_ov": 71.23, + "target_ov": 71.25, "target_pt": 71.28, "metric_type": "Acc@1", "resume": "mobilenet_v2_imagenet_int8_per_tensor.pth", @@ -187,7 +187,7 @@ "mobilenet_v3_small_imagenet_int8": { "config": "examples/torch/classification/configs/quantization/mobilenet_v3_small_imagenet_int8.json", "reference": "mobilenet_v3_small_imagenet", - "target_ov": 66.9, + "target_ov": 66.86, "target_pt": 66.87, "metric_type": "Acc@1", "resume": "mobilenet_v3_small_imagenet_int8.pth", @@ -208,7 +208,7 @@ "squeezenet1_1_imagenet_int8": { "config": "examples/torch/classification/configs/quantization/squeezenet1_1_imagenet_int8.json", "reference": "squeezenet1_1_imagenet", - "target_ov": 58.2, + "target_ov": 58.17, "target_pt": 58.28, "metric_type": "Acc@1", "resume": "squeezenet1_1_imagenet_int8.pth", diff --git a/tests/torch/sparsity/movement/helpers/utils.py b/tests/torch/sparsity/movement/helpers/utils.py index 51c4b744c6e..e63022e08ff 100644 --- a/tests/torch/sparsity/movement/helpers/utils.py +++ b/tests/torch/sparsity/movement/helpers/utils.py @@ -21,7 +21,7 @@ from nncf.common.graph.graph import NNCFNode from nncf.common.graph.layer_attributes import LinearLayerAttributes from nncf.experimental.torch.sparsity.movement.algo import MovementSparsifier -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT FACTOR_NAME_IN_MOVEMENT_STAT = "movement_sparsity/importance_regularization_factor" THRESHOLD_NAME_IN_MOVEMENT_STAT = "movement_sparsity/importance_threshold" diff --git a/tests/torch/sparsity/movement/test_training.py b/tests/torch/sparsity/movement/test_training.py index 7424013f1e6..407fbb62177 100644 --- a/tests/torch/sparsity/movement/test_training.py +++ b/tests/torch/sparsity/movement/test_training.py @@ -20,7 +20,7 @@ from packaging import version from pytest import approx -from tests.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import PROJECT_ROOT from tests.torch.helpers import Command from tests.torch.sample_test_validator import BaseSampleTestCaseDescriptor from tests.torch.sample_test_validator import BaseSampleValidator diff --git a/tests/torch/test_backward_compat.py b/tests/torch/test_backward_compat.py index 4b472826ba7..22eef38008e 100644 --- a/tests/torch/test_backward_compat.py +++ b/tests/torch/test_backward_compat.py @@ -28,9 +28,9 @@ from nncf.torch.quantization.algo import QUANTIZER_BUILDER_STATE_VERSION_SAVE_NAME from nncf.torch.quantization.algo import QuantizerBuilderStateVersion from nncf.torch.quantization.external_quantizer import EXTERNAL_QUANTIZERS_STORAGE_PREFIX -from tests.shared.helpers import get_cli_dict_args -from tests.shared.paths import ROOT_PYTHONPATH_ENV -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.helpers import get_cli_dict_args +from tests.cross_fw.shared.paths import ROOT_PYTHONPATH_ENV +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import Command from tests.torch.helpers import create_compressed_model_and_algo_for_test from tests.torch.helpers import create_ones_mock_dataloader diff --git a/tests/torch/test_compressed_graph.py b/tests/torch/test_compressed_graph.py index e49dbfcb6d0..64450224b36 100644 --- a/tests/torch/test_compressed_graph.py +++ b/tests/torch/test_compressed_graph.py @@ -43,8 +43,8 @@ from nncf.torch.quantization.algo import QuantizationBuilder from nncf.torch.utils import get_all_modules_by_type from nncf.torch.utils import get_model_device -from tests.shared.nx_graph import compare_nx_graph_with_reference -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch import test_models from tests.torch.helpers import create_compressed_model_and_algo_for_test from tests.torch.helpers import get_empty_config @@ -55,6 +55,7 @@ from tests.torch.test_models.synthetic import Baddbmm from tests.torch.test_models.synthetic import ConvBNLeakyReLU from tests.torch.test_models.synthetic import ConvGeluGetItem +from tests.torch.test_models.synthetic import ConvolutionWithMinModel from tests.torch.test_models.synthetic import ConvRelu6HSwishHSigmoid from tests.torch.test_models.synthetic import EmbeddingCatLinearModel from tests.torch.test_models.synthetic import EmbeddingSumModel @@ -109,7 +110,9 @@ def get_full_path_to_the_graph(path_to_dot: str, graph_dir: str) -> None: return path_to_dot -def check_graph(graph: PTNNCFGraph, path_to_dot: str, graph_dir: str, sort_dot_graph: bool = True) -> None: +def check_graph( + graph: PTNNCFGraph, path_to_dot: str, graph_dir: str, sort_dot_graph: bool = True, extended: bool = False +) -> None: """ Builds the nx.Digraph for the structural analysis from 'graph', gets the full path to the reference graph from 'path_to_dot' and 'graph_dir'. Then checks that the reference and the built graphs are identical. @@ -118,10 +121,11 @@ def check_graph(graph: PTNNCFGraph, path_to_dot: str, graph_dir: str, sort_dot_g :param path_to_dot: The filename of the reference graph file. :param graph_dir: The parent directory of .dot file. :param sort_dot_graph: If True the dumped graph will be sorted, if False - otherwise. + :param extended: If true the dumped graph will be extended with edge params and other meta. :return: None """ - nx_graph = graph.get_graph_for_structure_analysis() + nx_graph = graph.get_graph_for_structure_analysis(extended=extended) path_to_dot = get_full_path_to_the_graph(path_to_dot, graph_dir) compare_nx_graph_with_reference(nx_graph, path_to_dot, sort_dot_graph=sort_dot_graph) @@ -732,7 +736,7 @@ def forward(self, x): SingleLayerModelDesc(model_name="relu", layer=torch.relu), SingleLayerModelDesc(model_name="relu_", layer=torch.relu_), SingleLayerModelDesc(model_name="max", layer=torch.max), - SingleLayerModelDesc(model_name="min", layer=torch.min), + GeneralModelDesc(model_builder=ConvolutionWithMinModel, input_sample_sizes=([1, 1, 5, 5])), GeneralModelDesc(model_builder=ArangeModel), SingleLayerModelDesc(model_name="transpose", layer=partial(torch.transpose, dim0=0, dim1=0)), GeneralModelDesc(model_builder=TransposeModel, input_sample_sizes=([1])), diff --git a/tests/torch/test_compression_training.py b/tests/torch/test_compression_training.py index 265c9d5bc63..f3af94cac48 100644 --- a/tests/torch/test_compression_training.py +++ b/tests/torch/test_compression_training.py @@ -20,9 +20,9 @@ from pytest import approx from nncf import NNCFConfig -from tests.shared.helpers import get_cli_dict_args -from tests.shared.paths import PROJECT_ROOT -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.helpers import get_cli_dict_args +from tests.cross_fw.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import Command from tests.torch.sample_test_validator import BaseSampleTestCaseDescriptor from tests.torch.sample_test_validator import BaseSampleValidator diff --git a/tests/torch/test_dataset_generators.py b/tests/torch/test_dataset_generators.py new file mode 100644 index 00000000000..6e888232c5f --- /dev/null +++ b/tests/torch/test_dataset_generators.py @@ -0,0 +1,74 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from transformers import AutoModelForCausalLM +from transformers import AutoTokenizer + +import nncf +from nncf.data import generate_text_data +from tests.cross_fw.shared.helpers import load_json +from tests.cross_fw.shared.paths import TEST_ROOT +from tests.torch.helpers import set_torch_seed + +BASE_TEST_MODEL_ID = "hf-internal-testing/tiny-random-gpt2" +GENERATED_TEXT_REF = TEST_ROOT / "torch" / "data" / "ref_generated_data.json" + + +@pytest.mark.parametrize( + "model, tokenizer, usage_error", + [ + [None, None, True], + [AutoModelForCausalLM.from_pretrained(BASE_TEST_MODEL_ID), None, True], + [None, AutoTokenizer.from_pretrained(BASE_TEST_MODEL_ID), True], + [ + AutoModelForCausalLM.from_pretrained(BASE_TEST_MODEL_ID), + AutoTokenizer.from_pretrained(BASE_TEST_MODEL_ID), + False, + ], + ], +) +def test_generate_text_data_usage(model, tokenizer, usage_error): + try: + with set_torch_seed(0): + generate_text_data(model, tokenizer, seq_len=2, dataset_size=1) + except Exception as e: + if usage_error: + assert isinstance(e, nncf.ValidationError), "Expected exception." + + +def test_generate_text_data_functional(): + seq_len = 12 + max_seq_len = seq_len + seq_len // 2 + dataset_size = 6 + + model = AutoModelForCausalLM.from_pretrained(BASE_TEST_MODEL_ID) + tokenizer = AutoTokenizer.from_pretrained(BASE_TEST_MODEL_ID) + + with set_torch_seed(0): + generated_data = generate_text_data( + model, + tokenizer, + seq_len=seq_len, + dataset_size=dataset_size, + ) + + assert len(generated_data) == dataset_size + generated_data = [tokenizer.encode(d) for d in generated_data] + + # Uncomment lines below to generate reference for new models. + # from tests.shared.helpers import dump_to_json + # dump_to_json(GENERATED_TEXT_REF, generated_data) + + reference_data = load_json(GENERATED_TEXT_REF) + for ref_data, gen_data in zip(reference_data, generated_data): + assert len(gen_data) <= max_seq_len + assert ref_data == gen_data diff --git a/tests/torch/test_extensions_build.py b/tests/torch/test_extensions_build.py index d6879c9ff56..8b7a57f9aa0 100644 --- a/tests/torch/test_extensions_build.py +++ b/tests/torch/test_extensions_build.py @@ -18,10 +18,10 @@ import torch from nncf.common.utils.os import is_windows -from tests.shared.command import Command -from tests.shared.helpers import create_venv_with_nncf -from tests.shared.helpers import get_python_executable_with_venv -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.command import Command +from tests.cross_fw.shared.helpers import create_venv_with_nncf +from tests.cross_fw.shared.helpers import get_python_executable_with_venv +from tests.cross_fw.shared.paths import TEST_ROOT EXTENSIONS_BUILD_FILENAME = "extensions_build_checks.py" diff --git a/tests/torch/test_model_transformer.py b/tests/torch/test_model_transformer.py index 3eabe1f7e99..816671544c9 100644 --- a/tests/torch/test_model_transformer.py +++ b/tests/torch/test_model_transformer.py @@ -77,7 +77,7 @@ from tests.common.quantization.mock_graphs import get_mock_model_graph_with_no_mergeable_pattern from tests.common.quantization.mock_graphs import get_nncf_graph_from_mock_nx_graph from tests.common.quantization.mock_graphs import get_two_branch_mock_model_graph -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import HookChecker diff --git a/tests/torch/test_models/synthetic.py b/tests/torch/test_models/synthetic.py index 06a54579e49..2074f1a31f2 100644 --- a/tests/torch/test_models/synthetic.py +++ b/tests/torch/test_models/synthetic.py @@ -21,6 +21,7 @@ from nncf.torch import nncf_model_input from nncf.torch import register_module from nncf.torch.dynamic_graph.io_handling import wrap_nncf_model_outputs_with_objwalk +from tests.torch.helpers import create_bn from tests.torch.helpers import create_conv @@ -522,3 +523,89 @@ def __init__(self): def forward(self, x): w = self._conv_w + 10 return x + nn.functional.conv2d(self._conv_i, w) + + +class ConvolutionWithMinModel(torch.nn.Module): + def __init__(self): + super().__init__() + self._conv_w = nn.Parameter(torch.ones((1, 1, 1, 1))) + + def forward(self, x): + w = self._conv_w + 10 + t = nn.functional.conv2d(x, w) + return torch.minimum(t, torch.ones_like(t)) + + +class MultiBranchesConnectedModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv_a = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=1) + self.conv_b = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=1) + self.conv_c = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=1) + self.bias = torch.tensor([1]) + + def forward(self, x): + a = self.conv_a(x) + b = self.conv_b(a) + a += self.bias + b += self.bias + y = a + b + return self.conv_c(y) + self.bias + + +class LinearPTQParamsTestModel(nn.Module): + INPUT_SIZE = None + + def __init__(self): + super().__init__() + self.conv1 = create_conv(3, 3, 1) + self.bn1 = create_bn(3) + self.relu = nn.ReLU() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv2 = create_conv(3, 1, 1) + self.bn2 = create_bn(1) + + def forward(self, x): + x = self.relu(self.conv1(x)) + x = self.bn1(x) + x = self.avg_pool(x) + x = self.relu(self.conv2(x)) + x = self.bn2(x) + return x + + +class ConstantFoldingTestModel(nn.Module): + INPUT_SIZE = (1, 3, 3, 3) + + def __init__(self): + super().__init__() + self.linear_act = nn.Linear(3, 3) + self.linear_act.weight.data = 2 * torch.ones((3, 3)) + + self.linear_w = nn.Linear(3, 3) + self.linear_w.weight.data = 3 * torch.ones((3, 3)) + + self.param = nn.Parameter(4 * torch.ones((3, 3))) + + def forward(self, x): + y = self.linear_w(self.param) + y += 10 + x = self.linear_act(x) + return x + y + + +class ShortTransformer(torch.nn.Module): + def __init__(self, in_features, num_embeddings, share_weights=False): + super().__init__() + self.wte = torch.nn.Embedding(num_embeddings, in_features) + self.linear = torch.nn.Linear(in_features, in_features) + self.lm_head = torch.nn.Linear(in_features, num_embeddings) + + if share_weights: + self.lm_head.weight = self.wte.weight + + def forward(self, input_ids): + x = self.wte(input_ids) + x = self.linear(x) + res = self.lm_head(x) + return res diff --git a/tests/torch/test_pattern_manager.py b/tests/torch/test_pattern_manager.py index 4ec5996823a..bbf070f32f3 100644 --- a/tests/torch/test_pattern_manager.py +++ b/tests/torch/test_pattern_manager.py @@ -12,8 +12,8 @@ from nncf.common.graph.patterns import HWFusedPatternNames from nncf.common.graph.patterns import IgnoredPatternNames from nncf.common.utils.backend import BackendType -from tests.shared.patterns import check_hw_patterns -from tests.shared.patterns import check_ignored_patterns +from tests.cross_fw.shared.patterns import check_hw_patterns +from tests.cross_fw.shared.patterns import check_ignored_patterns IGNORING_HW_PATTERN_REASONS = { HWFusedPatternNames.ADD_SCALE_SHIFT_OUTPUT: "Not relevant for Torch.", diff --git a/tests/torch/test_pytorch_patch.py b/tests/torch/test_pytorch_patch.py index d66241551f2..1164d304ea4 100644 --- a/tests/torch/test_pytorch_patch.py +++ b/tests/torch/test_pytorch_patch.py @@ -10,6 +10,7 @@ # limitations under the License. import inspect +import os from typing import List import pytest @@ -29,7 +30,7 @@ from nncf.torch.dynamic_graph.trace_tensor import TensorMeta from nncf.torch.dynamic_graph.trace_tensor import TracedTensor from nncf.torch.graph.operator_metatypes import PT_OPERATOR_METATYPES -from tests.shared.isolation_runner import run_pytest_case_function_in_separate_process +from tests.cross_fw.shared.isolation_runner import run_pytest_case_function_in_separate_process from tests.torch.helpers import BasicConvTestModel from tests.torch.helpers import create_compressed_model_and_algo_for_test from tests.torch.helpers import register_bn_adaptation_init_args @@ -114,8 +115,10 @@ def test_jit_script_exception_preserves_patching(): @pytest.mark.xfail(is_windows(), reason="https://github.com/pytorch/pytorch/issues/122094") -def test_torch_compile(): +@pytest.mark.parametrize("compile_forward", [False, True]) +def test_torch_compile(compile_forward): # Run test case in a separate process to track patching of torch by NNCF + os.environ["COMPILE_FORWARD"] = f"{int(compile_forward)}" run_pytest_case_function_in_separate_process(test_compile) diff --git a/tests/torch/test_sanity_sample.py b/tests/torch/test_sanity_sample.py index b1b24240eca..71aa67260bd 100644 --- a/tests/torch/test_sanity_sample.py +++ b/tests/torch/test_sanity_sample.py @@ -32,12 +32,12 @@ from nncf.common.compression import BaseControllerStateNames from nncf.common.hardware.config import HWConfigType from nncf.config import NNCFConfig -from tests.shared.command import arg_list_from_arg_dict -from tests.shared.config_factory import ConfigFactory -from tests.shared.helpers import remove_line_breaks -from tests.shared.paths import ROOT_PYTHONPATH_ENV -from tests.shared.paths import TEST_ROOT -from tests.shared.paths import get_accuracy_aware_checkpoint_dir_path +from tests.cross_fw.shared.command import arg_list_from_arg_dict +from tests.cross_fw.shared.config_factory import ConfigFactory +from tests.cross_fw.shared.helpers import remove_line_breaks +from tests.cross_fw.shared.paths import ROOT_PYTHONPATH_ENV +from tests.cross_fw.shared.paths import TEST_ROOT +from tests.cross_fw.shared.paths import get_accuracy_aware_checkpoint_dir_path from tests.torch.helpers import Command from tests.torch.sample_test_validator import create_command_line diff --git a/tests/torch/test_sota_checkpoints.py b/tests/torch/test_sota_checkpoints.py index e9b9fe43584..eff03d3e2e8 100644 --- a/tests/torch/test_sota_checkpoints.py +++ b/tests/torch/test_sota_checkpoints.py @@ -23,11 +23,11 @@ from pytest import FixtureRequest import nncf -from tests.shared.metric_thresholds import DIFF_FP32_MAX_GLOBAL -from tests.shared.metric_thresholds import DIFF_FP32_MIN_GLOBAL -from tests.shared.paths import DATASET_DEFINITIONS_PATH -from tests.shared.paths import PROJECT_ROOT -from tests.shared.paths import TEST_ROOT +from tests.cross_fw.shared.metric_thresholds import DIFF_FP32_MAX_GLOBAL +from tests.cross_fw.shared.metric_thresholds import DIFF_FP32_MIN_GLOBAL +from tests.cross_fw.shared.paths import DATASET_DEFINITIONS_PATH +from tests.cross_fw.shared.paths import PROJECT_ROOT +from tests.cross_fw.shared.paths import TEST_ROOT from tests.torch.helpers import Command DIFF_TARGET_PT_MIN = -0.1 diff --git a/tests/torch/test_telemetry.py b/tests/torch/test_telemetry.py index dbb5e7e4c10..e41be269919 100644 --- a/tests/torch/test_telemetry.py +++ b/tests/torch/test_telemetry.py @@ -10,7 +10,7 @@ # limitations under the License. from nncf import NNCFConfig -from tests.shared.helpers import telemetry_send_event_test_driver +from tests.cross_fw.shared.helpers import telemetry_send_event_test_driver from tests.torch.helpers import TwoConvTestModel from tests.torch.helpers import create_compressed_model_and_algo_for_test diff --git a/tests/torch/test_tensor.py b/tests/torch/test_tensor.py index c473228559b..781f89c4ad0 100644 --- a/tests/torch/test_tensor.py +++ b/tests/torch/test_tensor.py @@ -15,7 +15,7 @@ from nncf.tensor import TensorDataType from nncf.tensor.definitions import TensorBackend from nncf.tensor.definitions import TensorDeviceType -from tests.shared.test_templates.template_test_nncf_tensor import TemplateTestNNCFTensorOperators +from tests.cross_fw.test_templates.template_test_nncf_tensor import TemplateTestNNCFTensorOperators def cast_to(x: torch.Tensor, dtype: TensorDataType) -> torch.Tensor: diff --git a/tests/torch2/__init__.py b/tests/torch2/__init__.py new file mode 100644 index 00000000000..2e49d63977d --- /dev/null +++ b/tests/torch2/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/torch2/conftest.py b/tests/torch2/conftest.py new file mode 100644 index 00000000000..9fc825a6d7f --- /dev/null +++ b/tests/torch2/conftest.py @@ -0,0 +1,107 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import os +import random + +import pytest +import torch +from pytest import Config +from pytest import FixtureRequest +from pytest import Parser + +from nncf import set_log_level + +pytest.register_assert_rewrite("tests.torch.helpers") + + +@pytest.fixture(scope="session", autouse=True) +def disable_tf32_precision(): + if torch: + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + + +def pytest_addoption(parser: Parser): + parser.addoption( + "--regen-ref-data", + action="store_true", + default=False, + help="If specified, the reference files will be regenerated using the current state of the repository.", + ) + parser.addoption( + "--nncf-debug", + action="store_true", + default=False, + help="Set debug level for nncf logger.", + ) + + +def pytest_configure(config: Config) -> None: + regen_dot = config.getoption("--regen-ref-data", False) + if regen_dot: + os.environ["NNCF_TEST_REGEN_DOT"] = "1" + + nncf_debug = config.getoption("--nncf-debug", False) + if nncf_debug: + set_log_level(logging.DEBUG) + + +@pytest.fixture +def regen_ref_data(request: FixtureRequest): + return request.config.getoption("--regen-ref-data", False) + + +@pytest.fixture(params=[pytest.param(True, marks=pytest.mark.cuda), False], ids=["cuda", "cpu"]) +def use_cuda(request: FixtureRequest): + return request.param + + +@pytest.fixture +def _seed(): + """ + Fixture to ensure deterministic randomness across tests. + """ + import numpy as np + from torch.backends import cudnn + + deterministic = cudnn.deterministic + benchmark = cudnn.benchmark + seed = torch.seed() + + cudnn.deterministic = True + cudnn.benchmark = False + torch.manual_seed(0) + np.random.seed(0) + random.seed(0) + + yield + + cudnn.deterministic = deterministic + cudnn.benchmark = benchmark + torch.manual_seed(seed) + + +@pytest.fixture +def _safe_deterministic_state(): + """ + This fixture sets both cuDNN and PyTorch deterministic algorithms to their + original states after each test to avoid unintended side effects + caused by modifying these settings globally. + """ + import torch + from torch.backends import cudnn + + cudnn_deterministic = cudnn.deterministic + torch_deterministic = torch.are_deterministic_algorithms_enabled() + yield + cudnn.deterministic = cudnn_deterministic + torch.use_deterministic_algorithms(torch_deterministic) diff --git a/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_disable.dot b/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_disable.dot new file mode 100644 index 00000000000..e225405e6d3 --- /dev/null +++ b/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_disable.dot @@ -0,0 +1,18 @@ +digraph { +rankdir=TB; +0 [label=x]; +1 [label="conv.weight"]; +2 [label="conv.bias"]; +3 [label="conv/conv2d/0"]; +4 [label="__nncf_hooks.post_hooks.conv/conv2d/0__0.0.w"]; +5 [label="conv/post_hook__conv-conv2d-0__0[0]/add/0"]; +6 [label="/relu/0"]; +7 [label=output]; +0 -> 3 [label="0 → 0"]; +1 -> 3 [label="0 → 1"]; +2 -> 3 [label="0 → 2"]; +3 -> 5 [label="0 → 0"]; +4 -> 5 [label="0 → 1"]; +5 -> 6 [label="0 → 0"]; +6 -> 7 [label="0 → 0"]; +} diff --git a/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_full.dot b/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_full.dot new file mode 100644 index 00000000000..96dda7bf691 --- /dev/null +++ b/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_full.dot @@ -0,0 +1,18 @@ +digraph { +rankdir=TB; +0 [fillcolor="#adadad", fontcolor="#000000", label="{type: input|name: x|dtype: torch.float32|shape: (1, 1, 3, 3)}", shape=record, style="filled,rounded"]; +1 [fillcolor="#ffffff", fontcolor="#000000", label="{type: const|name: conv.weight|dtype: torch.float32|shape: (1, 1, 1, 1)}", shape=record, style="filled,rounded"]; +2 [fillcolor="#ffffff", fontcolor="#000000", label="{type: const|name: conv.bias|dtype: torch.float32|shape: (1,)}", shape=record, style="filled,rounded"]; +3 [fillcolor="#ffd6a5", fontcolor="#000000", label="{type: function_call|op_name: conv/conv2d/0|fn_name: conv2d|args: [\nTensorMeta(dtype=torch.float32, shape=(1, 1, 3, 3), requires_grad=False),\nTensorMeta(dtype=torch.float32, shape=(1, 1, 1, 1), requires_grad=True),\nTensorMeta(dtype=torch.float32, shape=(1,), requires_grad=True),\n[1, 1],\n[0, 0],\n[1, 1],\n1,\n]|kwargs: \{\}}", shape=record, style="filled,rounded"]; +4 [fillcolor="#ffffff", fontcolor="#000000", label="{type: const|name: __nncf_hooks.post_hooks.conv/conv2d/0__0.0.w|dtype: torch.float32|shape: (1,)}", shape=record, style="filled,rounded"]; +5 [fillcolor="#caffbf", fontcolor="#000000", label="{type: function_call|op_name: conv/post_hook__conv-conv2d-0__0[0]/add/0|fn_name: add|args: [\nTensorMeta(dtype=torch.float32, shape=(1, 1, 3, 3), requires_grad=True),\nTensorMeta(dtype=torch.float32, shape=(1,), requires_grad=True),\n]|kwargs: \{\}}", shape=record, style="filled,rounded"]; +6 [fillcolor="#a0c4ff", fontcolor="#000000", label="{type: function_call|op_name: /relu/0|fn_name: relu|args: [\nTensorMeta(dtype=torch.float32, shape=(1, 1, 3, 3), requires_grad=True),\n]|kwargs: \{\}}", shape=record, style="filled,rounded"]; +7 [fillcolor="#adadad", fontcolor="#000000", label="{type: output|name: output|dtype: torch.float32|shape: (1, 1, 3, 3)}", shape=record, style="filled,rounded"]; +0 -> 3 [label="(1, 1, 3, 3)\n0 → 0"]; +1 -> 3 [label="(1, 1, 1, 1)\n0 → 1"]; +2 -> 3 [label="(1,)\n0 → 2"]; +3 -> 5 [label="(1, 1, 3, 3)\n0 → 0"]; +4 -> 5 [label="(1,)\n0 → 1"]; +5 -> 6 [label="(1, 1, 3, 3)\n0 → 0"]; +6 -> 7 [label="(1, 1, 3, 3)\n0 → 0"]; +} diff --git a/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_short.dot b/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_short.dot new file mode 100644 index 00000000000..92d68b7c840 --- /dev/null +++ b/tests/torch2/data/function_hook/graph_visualization/to_pydot_style_short.dot @@ -0,0 +1,18 @@ +digraph { +rankdir=TB; +0 [fillcolor="#adadad", fontcolor="#000000", label=x, shape=record, style="filled,rounded"]; +1 [fillcolor="#ffffff", fontcolor="#000000", label="conv.weight", shape=record, style="filled,rounded"]; +2 [fillcolor="#ffffff", fontcolor="#000000", label="conv.bias", shape=record, style="filled,rounded"]; +3 [fillcolor="#ffd6a5", fontcolor="#000000", label="conv/conv2d/0", shape=record, style="filled,rounded"]; +4 [fillcolor="#ffffff", fontcolor="#000000", label="__nncf_hooks.post_hooks.conv/conv2d/0__0.0.w", shape=record, style="filled,rounded"]; +5 [fillcolor="#caffbf", fontcolor="#000000", label="conv/post_hook__conv-conv2d-0__0[0]/add/0", shape=record, style="filled,rounded"]; +6 [fillcolor="#a0c4ff", fontcolor="#000000", label="/relu/0", shape=record, style="filled,rounded"]; +7 [fillcolor="#adadad", fontcolor="#000000", label=output, shape=record, style="filled,rounded"]; +0 -> 3 [label="(1, 1, 3, 3)\n0 → 0"]; +1 -> 3 [label="(1, 1, 1, 1)\n0 → 1"]; +2 -> 3 [label="(1,)\n0 → 2"]; +3 -> 5 [label="(1, 1, 3, 3)\n0 → 0"]; +4 -> 5 [label="(1,)\n0 → 1"]; +5 -> 6 [label="(1, 1, 3, 3)\n0 → 0"]; +6 -> 7 [label="(1, 1, 3, 3)\n0 → 0"]; +} diff --git a/tests/torch2/data/function_hook/handle_inner_functions/inner_functions_BatchNormModel.dot b/tests/torch2/data/function_hook/handle_inner_functions/inner_functions_BatchNormModel.dot new file mode 100644 index 00000000000..a3020107cbc --- /dev/null +++ b/tests/torch2/data/function_hook/handle_inner_functions/inner_functions_BatchNormModel.dot @@ -0,0 +1,16 @@ +digraph { +rankdir=TB; +0 [fillcolor="#adadad", fontcolor="#000000", label="{type: input|name: x|dtype: torch.float32|shape: (1, 1, 1)}", shape=record, style="filled,rounded"]; +1 [fillcolor="#ffffff", fontcolor="#000000", label="{type: const|name: bn.weight|dtype: torch.float32|shape: (1,)}", shape=record, style="filled,rounded"]; +2 [fillcolor="#ffffff", fontcolor="#000000", label="{type: const|name: bn.bias|dtype: torch.float32|shape: (1,)}", shape=record, style="filled,rounded"]; +3 [fillcolor="#ffffff", fontcolor="#000000", label="{type: const|name: bn.running_mean|dtype: torch.float32|shape: (1,)}", shape=record, style="filled,rounded"]; +4 [fillcolor="#ffffff", fontcolor="#000000", label="{type: const|name: bn.running_var|dtype: torch.float32|shape: (1,)}", shape=record, style="filled,rounded"]; +5 [fillcolor="#ffadad", fontcolor="#000000", label="{type: function_call|op_name: bn/batch_norm/0|fn_name: batch_norm|args: [\nTensorMeta(dtype=torch.float32, shape=(1, 1, 1), requires_grad=False),\nTensorMeta(dtype=torch.float32, shape=(1,), requires_grad=True),\nTensorMeta(dtype=torch.float32, shape=(1,), requires_grad=True),\nTensorMeta(dtype=torch.float32, shape=(1,), requires_grad=False),\nTensorMeta(dtype=torch.float32, shape=(1,), requires_grad=False),\nFalse,\n0.1,\n1e-05,\nTrue,\n]|kwargs: \{\}}", shape=record, style="filled,rounded"]; +6 [fillcolor="#adadad", fontcolor="#000000", label="{type: output|name: output|dtype: torch.float32|shape: (1, 1, 1)}", shape=record, style="filled,rounded"]; +0 -> 5 [label="(1, 1, 1)\n0 → 0"]; +1 -> 5 [label="(1,)\n0 → 1"]; +2 -> 5 [label="(1,)\n0 → 2"]; +3 -> 5 [label="(1,)\n0 → 3"]; +4 -> 5 [label="(1,)\n0 → 4"]; +5 -> 6 [label="(1, 1, 1)\n0 → 0"]; +} diff --git a/tests/torch2/data/function_hook/handle_inner_functions/inner_functions_MultiHeadAttention.dot b/tests/torch2/data/function_hook/handle_inner_functions/inner_functions_MultiHeadAttention.dot new file mode 100644 index 00000000000..1a1630fa69e --- /dev/null +++ b/tests/torch2/data/function_hook/handle_inner_functions/inner_functions_MultiHeadAttention.dot @@ -0,0 +1,72 @@ +digraph { +rankdir=TB; +0 [fillcolor="#adadad", fontcolor="#000000", label="{type: input|name: query|dtype: torch.float32|shape: (5, 2, 16)}", shape=record, style="filled,rounded"]; +1 [fillcolor="#adadad", fontcolor="#000000", label="{type: input|name: key|dtype: torch.float32|shape: (5, 2, 16)}", shape=record, style="filled,rounded"]; +2 [fillcolor="#adadad", fontcolor="#000000", label="{type: input|name: value|dtype: torch.float32|shape: (5, 2, 16)}", shape=record, style="filled,rounded"]; +3 [fillcolor="#a0c4ff", fontcolor="#000000", label="{type: function_call|op_name: /rand/0|fn_name: rand|args: [\n[48, 16],\n]|kwargs: \{\}}", shape=record, style="filled,rounded"]; +4 [fillcolor="#a0c4ff", fontcolor="#000000", label="{type: function_call|op_name: /rand/1|fn_name: rand|args: [\n48,\n]|kwargs: \{\}}", shape=record, style="filled,rounded"]; +5 [fillcolor="#a0c4ff", fontcolor="#000000", label="{type: function_call|op_name: /rand/2|fn_name: rand|args: [\n[16, 16],\n]|kwargs: \{\}}", 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style=solid]; +"704 Mixed_7b/cat/2" -> "771 Mixed_7c/avg_pool2d/0" [label="(1, 2048, 8, 8)", style=solid]; +"705 Mixed_7c.branch1x1.conv.weight" -> "706 Mixed_7c/branch1x1/conv/conv2d/0" [label="(320, 2048, 1, 1)", style=solid]; +"706 Mixed_7c/branch1x1/conv/conv2d/0" -> "711 Mixed_7c/branch1x1/bn/batch_norm/0" [label="(1, 320, 8, 8)", style=solid]; +"707 Mixed_7c.branch1x1.bn.weight" -> "711 Mixed_7c/branch1x1/bn/batch_norm/0" [label="(320,)", style=solid]; +"708 Mixed_7c.branch1x1.bn.bias" -> "711 Mixed_7c/branch1x1/bn/batch_norm/0" [label="(320,)", style=solid]; +"709 Mixed_7c.branch1x1.bn.running_mean" -> "711 Mixed_7c/branch1x1/bn/batch_norm/0" [label="(320,)", style=solid]; +"710 Mixed_7c.branch1x1.bn.running_var" -> "711 Mixed_7c/branch1x1/bn/batch_norm/0" [label="(320,)", style=solid]; +"711 Mixed_7c/branch1x1/bn/batch_norm/0" -> "712 Mixed_7c/branch1x1/relu_/0" [label="(1, 320, 8, 8)", style=solid]; +"712 Mixed_7c/branch1x1/relu_/0" -> "780 Mixed_7c/cat/2" [label="(1, 320, 8, 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[label="(1, 1000)", style=solid]; +} diff --git a/tests/torch2/data/function_hook/nncf_graph/model_graph_mobilenet_v2.dot b/tests/torch2/data/function_hook/nncf_graph/model_graph_mobilenet_v2.dot new file mode 100644 index 00000000000..988f40b9c84 --- /dev/null +++ b/tests/torch2/data/function_hook/nncf_graph/model_graph_mobilenet_v2.dot @@ -0,0 +1,845 @@ +strict digraph { +"0 x" [id=0, type=nncf_model_input]; +"1 features.0.0.weight" [id=1, type=nncf_model_const]; +"2 features/0/0/conv2d/0" [id=2, type=conv2d]; +"3 features.0.1.weight" [id=3, type=nncf_model_const]; +"4 features.0.1.bias" [id=4, type=nncf_model_const]; +"5 features.0.1.running_mean" [id=5, type=nncf_model_const]; +"6 features.0.1.running_var" [id=6, type=nncf_model_const]; +"7 features/0/1/batch_norm/0" [id=7, type=batch_norm]; +"8 features/0/2/hardtanh/0" [id=8, type=hardtanh]; +"9 features.1.conv.0.0.weight" [id=9, type=nncf_model_const]; +"10 features/1/conv/0/0/conv2d/0" [id=10, type=conv2d]; +"11 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layer2/0/conv1/conv2d/0" [label="(128, 64, 3, 3)", style=solid]; +"45 layer2/0/conv1/conv2d/0" -> "50 layer2/0/bn1/batch_norm/0" [label="(1, 128, 8, 8)", style=solid]; +"46 layer2.0.bn1.weight" -> "50 layer2/0/bn1/batch_norm/0" [label="(128,)", style=solid]; +"47 layer2.0.bn1.bias" -> "50 layer2/0/bn1/batch_norm/0" [label="(128,)", style=solid]; +"48 layer2.0.bn1.running_mean" -> "50 layer2/0/bn1/batch_norm/0" [label="(128,)", style=solid]; +"49 layer2.0.bn1.running_var" -> "50 layer2/0/bn1/batch_norm/0" [label="(128,)", style=solid]; +"50 layer2/0/bn1/batch_norm/0" -> "51 layer2/0/relu/relu_/0" [label="(1, 128, 8, 8)", style=solid]; +"51 layer2/0/relu/relu_/0" -> "53 layer2/0/conv2/conv2d/0" [label="(1, 128, 8, 8)", style=solid]; +"52 layer2.0.conv2.weight" -> "53 layer2/0/conv2/conv2d/0" [label="(128, 128, 3, 3)", style=solid]; +"53 layer2/0/conv2/conv2d/0" -> "58 layer2/0/bn2/batch_norm/0" [label="(1, 128, 8, 8)", style=solid]; +"54 layer2.0.bn2.weight" -> "58 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layer4/1/conv2/conv2d/0" [label="(1, 512, 2, 2)", style=solid]; +"158 layer4.1.conv2.weight" -> "159 layer4/1/conv2/conv2d/0" [label="(512, 512, 3, 3)", style=solid]; +"159 layer4/1/conv2/conv2d/0" -> "164 layer4/1/bn2/batch_norm/0" [label="(1, 512, 2, 2)", style=solid]; +"160 layer4.1.bn2.weight" -> "164 layer4/1/bn2/batch_norm/0" [label="(512,)", style=solid]; +"161 layer4.1.bn2.bias" -> "164 layer4/1/bn2/batch_norm/0" [label="(512,)", style=solid]; +"162 layer4.1.bn2.running_mean" -> "164 layer4/1/bn2/batch_norm/0" [label="(512,)", style=solid]; +"163 layer4.1.bn2.running_var" -> "164 layer4/1/bn2/batch_norm/0" [label="(512,)", style=solid]; +"164 layer4/1/bn2/batch_norm/0" -> "165 layer4/1/add_/0" [label="(1, 512, 2, 2)", style=solid]; +"165 layer4/1/add_/0" -> "166 layer4/1/relu/relu_/1" [label="(1, 512, 2, 2)", style=solid]; +"166 layer4/1/relu/relu_/1" -> "167 avgpool/adaptive_avg_pool2d/0" [label="(1, 512, 2, 2)", style=solid]; +"167 avgpool/adaptive_avg_pool2d/0" -> "168 /flatten/0" [label="(1, 512, 1, 1)", style=solid]; +"168 /flatten/0" -> "171 fc/linear/0" [label="(1, 512)", style=solid]; +"169 fc.weight" -> "171 fc/linear/0" [label="(1000, 512)", style=solid]; +"170 fc.bias" -> "171 fc/linear/0" [label="(1000,)", style=solid]; +"171 fc/linear/0" -> "172 output" [label="(1, 1000)", style=solid]; +} diff --git a/tests/torch2/data/function_hook/nncf_graph/model_graph_resnext50_32x4d.dot b/tests/torch2/data/function_hook/nncf_graph/model_graph_resnext50_32x4d.dot new file mode 100644 index 00000000000..8e643307fd3 --- /dev/null +++ b/tests/torch2/data/function_hook/nncf_graph/model_graph_resnext50_32x4d.dot @@ -0,0 +1,905 @@ +strict digraph { +"0 x" [id=0, type=nncf_model_input]; +"1 conv1.weight" [id=1, type=nncf_model_const]; +"2 conv1/conv2d/0" [id=2, type=conv2d]; +"3 bn1.weight" [id=3, type=nncf_model_const]; +"4 bn1.bias" [id=4, type=nncf_model_const]; +"5 bn1.running_mean" [id=5, type=nncf_model_const]; +"6 bn1.running_var" [id=6, 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layer3.4.bn3.running_mean" [id=326, type=nncf_model_const]; +"327 layer3.4.bn3.running_var" [id=327, type=nncf_model_const]; +"328 layer3/4/bn3/batch_norm/0" [id=328, type=batch_norm]; +"329 layer3/4/add_/0" [id=329, type=add_]; +"330 layer3/4/relu/relu_/2" [id=330, type=relu_]; +"331 layer3.5.conv1.weight" [id=331, type=nncf_model_const]; +"332 layer3/5/conv1/conv2d/0" [id=332, type=conv2d]; +"333 layer3.5.bn1.weight" [id=333, type=nncf_model_const]; +"334 layer3.5.bn1.bias" [id=334, type=nncf_model_const]; +"335 layer3.5.bn1.running_mean" [id=335, type=nncf_model_const]; +"336 layer3.5.bn1.running_var" [id=336, type=nncf_model_const]; +"337 layer3/5/bn1/batch_norm/0" [id=337, type=batch_norm]; +"338 layer3/5/relu/relu_/0" [id=338, type=relu_]; +"339 layer3.5.conv2.weight" [id=339, type=nncf_model_const]; +"340 layer3/5/conv2/conv2d/0" [id=340, type=conv2d]; +"341 layer3.5.bn2.weight" [id=341, type=nncf_model_const]; +"342 layer3.5.bn2.bias" [id=342, type=nncf_model_const]; +"343 layer3.5.bn2.running_mean" [id=343, type=nncf_model_const]; +"344 layer3.5.bn2.running_var" [id=344, type=nncf_model_const]; +"345 layer3/5/bn2/batch_norm/0" [id=345, type=batch_norm]; +"346 layer3/5/relu/relu_/1" [id=346, type=relu_]; +"347 layer3.5.conv3.weight" [id=347, type=nncf_model_const]; +"348 layer3/5/conv3/conv2d/0" [id=348, type=conv2d]; +"349 layer3.5.bn3.weight" [id=349, type=nncf_model_const]; +"350 layer3.5.bn3.bias" [id=350, type=nncf_model_const]; +"351 layer3.5.bn3.running_mean" [id=351, type=nncf_model_const]; +"352 layer3.5.bn3.running_var" [id=352, type=nncf_model_const]; +"353 layer3/5/bn3/batch_norm/0" [id=353, type=batch_norm]; +"354 layer3/5/add_/0" [id=354, type=add_]; +"355 layer3/5/relu/relu_/2" [id=355, type=relu_]; +"356 layer4.0.conv1.weight" [id=356, type=nncf_model_const]; +"357 layer4/0/conv1/conv2d/0" [id=357, type=conv2d]; +"358 layer4.0.bn1.weight" [id=358, type=nncf_model_const]; +"359 layer4.0.bn1.bias" [id=359, type=nncf_model_const]; +"360 layer4.0.bn1.running_mean" [id=360, type=nncf_model_const]; +"361 layer4.0.bn1.running_var" [id=361, type=nncf_model_const]; +"362 layer4/0/bn1/batch_norm/0" [id=362, type=batch_norm]; +"363 layer4/0/relu/relu_/0" [id=363, type=relu_]; +"364 layer4.0.conv2.weight" [id=364, type=nncf_model_const]; +"365 layer4/0/conv2/conv2d/0" [id=365, type=conv2d]; +"366 layer4.0.bn2.weight" [id=366, type=nncf_model_const]; +"367 layer4.0.bn2.bias" [id=367, type=nncf_model_const]; +"368 layer4.0.bn2.running_mean" [id=368, type=nncf_model_const]; +"369 layer4.0.bn2.running_var" [id=369, type=nncf_model_const]; +"370 layer4/0/bn2/batch_norm/0" [id=370, type=batch_norm]; +"371 layer4/0/relu/relu_/1" [id=371, type=relu_]; +"372 layer4.0.conv3.weight" [id=372, type=nncf_model_const]; +"373 layer4/0/conv3/conv2d/0" [id=373, type=conv2d]; +"374 layer4.0.bn3.weight" [id=374, type=nncf_model_const]; +"375 layer4.0.bn3.bias" [id=375, type=nncf_model_const]; +"376 layer4.0.bn3.running_mean" [id=376, type=nncf_model_const]; +"377 layer4.0.bn3.running_var" [id=377, type=nncf_model_const]; +"378 layer4/0/bn3/batch_norm/0" [id=378, type=batch_norm]; +"379 layer4.0.downsample.0.weight" [id=379, type=nncf_model_const]; +"380 layer4/0/downsample/0/conv2d/0" [id=380, type=conv2d]; +"381 layer4.0.downsample.1.weight" [id=381, type=nncf_model_const]; +"382 layer4.0.downsample.1.bias" [id=382, type=nncf_model_const]; +"383 layer4.0.downsample.1.running_mean" [id=383, type=nncf_model_const]; +"384 layer4.0.downsample.1.running_var" [id=384, type=nncf_model_const]; +"385 layer4/0/downsample/1/batch_norm/0" [id=385, type=batch_norm]; +"386 layer4/0/add_/0" [id=386, type=add_]; +"387 layer4/0/relu/relu_/2" [id=387, type=relu_]; +"388 layer4.1.conv1.weight" [id=388, type=nncf_model_const]; +"389 layer4/1/conv1/conv2d/0" [id=389, type=conv2d]; +"390 layer4.1.bn1.weight" [id=390, type=nncf_model_const]; +"391 layer4.1.bn1.bias" [id=391, type=nncf_model_const]; +"392 layer4.1.bn1.running_mean" [id=392, type=nncf_model_const]; +"393 layer4.1.bn1.running_var" [id=393, type=nncf_model_const]; +"394 layer4/1/bn1/batch_norm/0" [id=394, type=batch_norm]; +"395 layer4/1/relu/relu_/0" [id=395, type=relu_]; +"396 layer4.1.conv2.weight" [id=396, type=nncf_model_const]; +"397 layer4/1/conv2/conv2d/0" [id=397, type=conv2d]; +"398 layer4.1.bn2.weight" [id=398, type=nncf_model_const]; +"399 layer4.1.bn2.bias" [id=399, type=nncf_model_const]; +"400 layer4.1.bn2.running_mean" [id=400, type=nncf_model_const]; +"401 layer4.1.bn2.running_var" [id=401, type=nncf_model_const]; +"402 layer4/1/bn2/batch_norm/0" [id=402, type=batch_norm]; +"403 layer4/1/relu/relu_/1" [id=403, type=relu_]; +"404 layer4.1.conv3.weight" [id=404, type=nncf_model_const]; +"405 layer4/1/conv3/conv2d/0" [id=405, type=conv2d]; +"406 layer4.1.bn3.weight" [id=406, type=nncf_model_const]; +"407 layer4.1.bn3.bias" [id=407, type=nncf_model_const]; +"408 layer4.1.bn3.running_mean" [id=408, type=nncf_model_const]; +"409 layer4.1.bn3.running_var" [id=409, type=nncf_model_const]; +"410 layer4/1/bn3/batch_norm/0" [id=410, type=batch_norm]; +"411 layer4/1/add_/0" [id=411, type=add_]; +"412 layer4/1/relu/relu_/2" [id=412, type=relu_]; +"413 layer4.2.conv1.weight" [id=413, type=nncf_model_const]; +"414 layer4/2/conv1/conv2d/0" [id=414, type=conv2d]; +"415 layer4.2.bn1.weight" [id=415, type=nncf_model_const]; +"416 layer4.2.bn1.bias" [id=416, type=nncf_model_const]; +"417 layer4.2.bn1.running_mean" [id=417, type=nncf_model_const]; +"418 layer4.2.bn1.running_var" [id=418, type=nncf_model_const]; +"419 layer4/2/bn1/batch_norm/0" [id=419, type=batch_norm]; +"420 layer4/2/relu/relu_/0" [id=420, type=relu_]; +"421 layer4.2.conv2.weight" [id=421, type=nncf_model_const]; +"422 layer4/2/conv2/conv2d/0" [id=422, type=conv2d]; +"423 layer4.2.bn2.weight" [id=423, type=nncf_model_const]; +"424 layer4.2.bn2.bias" [id=424, type=nncf_model_const]; +"425 layer4.2.bn2.running_mean" [id=425, type=nncf_model_const]; +"426 layer4.2.bn2.running_var" [id=426, type=nncf_model_const]; +"427 layer4/2/bn2/batch_norm/0" [id=427, type=batch_norm]; +"428 layer4/2/relu/relu_/1" [id=428, type=relu_]; +"429 layer4.2.conv3.weight" [id=429, type=nncf_model_const]; +"430 layer4/2/conv3/conv2d/0" [id=430, type=conv2d]; +"431 layer4.2.bn3.weight" [id=431, type=nncf_model_const]; +"432 layer4.2.bn3.bias" [id=432, type=nncf_model_const]; +"433 layer4.2.bn3.running_mean" [id=433, type=nncf_model_const]; +"434 layer4.2.bn3.running_var" [id=434, type=nncf_model_const]; +"435 layer4/2/bn3/batch_norm/0" [id=435, type=batch_norm]; +"436 layer4/2/add_/0" [id=436, type=add_]; +"437 layer4/2/relu/relu_/2" [id=437, type=relu_]; +"438 avgpool/adaptive_avg_pool2d/0" [id=438, type=adaptive_avg_pool2d]; +"439 /flatten/0" [id=439, type=flatten]; +"440 fc.weight" [id=440, type=nncf_model_const]; +"441 fc.bias" [id=441, type=nncf_model_const]; +"442 fc/linear/0" [id=442, type=linear]; +"443 output" [id=443, type=nncf_model_output]; +"0 x" -> "2 conv1/conv2d/0" [label="(1, 3, 64, 64)", style=solid]; +"1 conv1.weight" -> "2 conv1/conv2d/0" [label="(64, 3, 7, 7)", style=solid]; +"2 conv1/conv2d/0" -> "7 bn1/batch_norm/0" [label="(1, 64, 32, 32)", style=solid]; +"3 bn1.weight" -> "7 bn1/batch_norm/0" [label="(64,)", style=solid]; +"4 bn1.bias" -> "7 bn1/batch_norm/0" [label="(64,)", style=solid]; +"5 bn1.running_mean" -> "7 bn1/batch_norm/0" [label="(64,)", style=solid]; +"6 bn1.running_var" -> "7 bn1/batch_norm/0" [label="(64,)", style=solid]; +"7 bn1/batch_norm/0" -> "8 relu/relu_/0" [label="(1, 64, 32, 32)", style=solid]; +"8 relu/relu_/0" -> "9 maxpool/max_pool2d/0" [label="(1, 64, 32, 32)", style=solid]; +"9 maxpool/max_pool2d/0" -> "11 layer1/0/conv1/conv2d/0" [label="(1, 64, 16, 16)", style=solid]; +"9 maxpool/max_pool2d/0" -> "34 layer1/0/downsample/0/conv2d/0" [label="(1, 64, 16, 16)", style=solid]; +"10 layer1.0.conv1.weight" -> "11 layer1/0/conv1/conv2d/0" [label="(128, 64, 1, 1)", style=solid]; +"11 layer1/0/conv1/conv2d/0" -> "16 layer1/0/bn1/batch_norm/0" [label="(1, 128, 16, 16)", style=solid]; +"12 layer1.0.bn1.weight" -> "16 layer1/0/bn1/batch_norm/0" [label="(128,)", style=solid]; +"13 layer1.0.bn1.bias" -> "16 layer1/0/bn1/batch_norm/0" [label="(128,)", style=solid]; +"14 layer1.0.bn1.running_mean" -> "16 layer1/0/bn1/batch_norm/0" [label="(128,)", style=solid]; +"15 layer1.0.bn1.running_var" -> "16 layer1/0/bn1/batch_norm/0" [label="(128,)", style=solid]; +"16 layer1/0/bn1/batch_norm/0" -> "17 layer1/0/relu/relu_/0" [label="(1, 128, 16, 16)", style=solid]; +"17 layer1/0/relu/relu_/0" -> "19 layer1/0/conv2/conv2d/0" [label="(1, 128, 16, 16)", style=solid]; +"18 layer1.0.conv2.weight" -> "19 layer1/0/conv2/conv2d/0" [label="(128, 4, 3, 3)", style=solid]; +"19 layer1/0/conv2/conv2d/0" -> "24 layer1/0/bn2/batch_norm/0" [label="(1, 128, 16, 16)", style=solid]; +"20 layer1.0.bn2.weight" -> "24 layer1/0/bn2/batch_norm/0" [label="(128,)", style=solid]; +"21 layer1.0.bn2.bias" -> "24 layer1/0/bn2/batch_norm/0" [label="(128,)", style=solid]; +"22 layer1.0.bn2.running_mean" -> "24 layer1/0/bn2/batch_norm/0" [label="(128,)", style=solid]; +"23 layer1.0.bn2.running_var" -> "24 layer1/0/bn2/batch_norm/0" [label="(128,)", style=solid]; +"24 layer1/0/bn2/batch_norm/0" -> "25 layer1/0/relu/relu_/1" [label="(1, 128, 16, 16)", style=solid]; +"25 layer1/0/relu/relu_/1" -> "27 layer1/0/conv3/conv2d/0" [label="(1, 128, 16, 16)", style=solid]; +"26 layer1.0.conv3.weight" -> "27 layer1/0/conv3/conv2d/0" [label="(256, 128, 1, 1)", style=solid]; +"27 layer1/0/conv3/conv2d/0" -> "32 layer1/0/bn3/batch_norm/0" [label="(1, 256, 16, 16)", style=solid]; +"28 layer1.0.bn3.weight" -> "32 layer1/0/bn3/batch_norm/0" [label="(256,)", style=solid]; +"29 layer1.0.bn3.bias" -> "32 layer1/0/bn3/batch_norm/0" [label="(256,)", style=solid]; +"30 layer1.0.bn3.running_mean" -> "32 layer1/0/bn3/batch_norm/0" [label="(256,)", style=solid]; +"31 layer1.0.bn3.running_var" -> "32 layer1/0/bn3/batch_norm/0" [label="(256,)", style=solid]; +"32 layer1/0/bn3/batch_norm/0" -> "40 layer1/0/add_/0" [label="(1, 256, 16, 16)", style=solid]; +"33 layer1.0.downsample.0.weight" -> "34 layer1/0/downsample/0/conv2d/0" [label="(256, 64, 1, 1)", style=solid]; +"34 layer1/0/downsample/0/conv2d/0" -> "39 layer1/0/downsample/1/batch_norm/0" [label="(1, 256, 16, 16)", style=solid]; +"35 layer1.0.downsample.1.weight" -> "39 layer1/0/downsample/1/batch_norm/0" [label="(256,)", style=solid]; +"36 layer1.0.downsample.1.bias" -> "39 layer1/0/downsample/1/batch_norm/0" [label="(256,)", style=solid]; +"37 layer1.0.downsample.1.running_mean" -> "39 layer1/0/downsample/1/batch_norm/0" [label="(256,)", style=solid]; +"38 layer1.0.downsample.1.running_var" -> "39 layer1/0/downsample/1/batch_norm/0" [label="(256,)", style=solid]; +"39 layer1/0/downsample/1/batch_norm/0" -> "40 layer1/0/add_/0" [label="(1, 256, 16, 16)", style=solid]; +"40 layer1/0/add_/0" -> "41 layer1/0/relu/relu_/2" [label="(1, 256, 16, 16)", style=solid]; +"41 layer1/0/relu/relu_/2" -> "43 layer1/1/conv1/conv2d/0" [label="(1, 256, 16, 16)", style=solid]; +"41 layer1/0/relu/relu_/2" -> "65 layer1/1/add_/0" [label="(1, 256, 16, 16)", style=solid]; +"42 layer1.1.conv1.weight" -> "43 layer1/1/conv1/conv2d/0" [label="(128, 256, 1, 1)", style=solid]; +"43 layer1/1/conv1/conv2d/0" -> "48 layer1/1/bn1/batch_norm/0" [label="(1, 128, 16, 16)", style=solid]; +"44 layer1.1.bn1.weight" -> "48 layer1/1/bn1/batch_norm/0" [label="(128,)", style=solid]; +"45 layer1.1.bn1.bias" -> "48 layer1/1/bn1/batch_norm/0" [label="(128,)", style=solid]; +"46 layer1.1.bn1.running_mean" -> "48 layer1/1/bn1/batch_norm/0" [label="(128,)", style=solid]; +"47 layer1.1.bn1.running_var" -> "48 layer1/1/bn1/batch_norm/0" [label="(128,)", style=solid]; +"48 layer1/1/bn1/batch_norm/0" -> "49 layer1/1/relu/relu_/0" [label="(1, 128, 16, 16)", style=solid]; +"49 layer1/1/relu/relu_/0" -> "51 layer1/1/conv2/conv2d/0" [label="(1, 128, 16, 16)", style=solid]; +"50 layer1.1.conv2.weight" -> "51 layer1/1/conv2/conv2d/0" [label="(128, 4, 3, 3)", style=solid]; +"51 layer1/1/conv2/conv2d/0" -> "56 layer1/1/bn2/batch_norm/0" [label="(1, 128, 16, 16)", style=solid]; +"52 layer1.1.bn2.weight" -> "56 layer1/1/bn2/batch_norm/0" [label="(128,)", style=solid]; +"53 layer1.1.bn2.bias" -> "56 layer1/1/bn2/batch_norm/0" [label="(128,)", style=solid]; +"54 layer1.1.bn2.running_mean" -> "56 layer1/1/bn2/batch_norm/0" [label="(128,)", style=solid]; +"55 layer1.1.bn2.running_var" -> "56 layer1/1/bn2/batch_norm/0" [label="(128,)", style=solid]; +"56 layer1/1/bn2/batch_norm/0" -> "57 layer1/1/relu/relu_/1" [label="(1, 128, 16, 16)", style=solid]; +"57 layer1/1/relu/relu_/1" -> "59 layer1/1/conv3/conv2d/0" [label="(1, 128, 16, 16)", style=solid]; +"58 layer1.1.conv3.weight" -> "59 layer1/1/conv3/conv2d/0" [label="(256, 128, 1, 1)", style=solid]; +"59 layer1/1/conv3/conv2d/0" -> "64 layer1/1/bn3/batch_norm/0" [label="(1, 256, 16, 16)", 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layer4/0/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"378 layer4/0/bn3/batch_norm/0" -> "386 layer4/0/add_/0" [label="(1, 2048, 2, 2)", style=solid]; +"379 layer4.0.downsample.0.weight" -> "380 layer4/0/downsample/0/conv2d/0" [label="(2048, 1024, 1, 1)", style=solid]; +"380 layer4/0/downsample/0/conv2d/0" -> "385 layer4/0/downsample/1/batch_norm/0" [label="(1, 2048, 2, 2)", style=solid]; +"381 layer4.0.downsample.1.weight" -> "385 layer4/0/downsample/1/batch_norm/0" [label="(2048,)", style=solid]; +"382 layer4.0.downsample.1.bias" -> "385 layer4/0/downsample/1/batch_norm/0" [label="(2048,)", style=solid]; +"383 layer4.0.downsample.1.running_mean" -> "385 layer4/0/downsample/1/batch_norm/0" [label="(2048,)", style=solid]; +"384 layer4.0.downsample.1.running_var" -> "385 layer4/0/downsample/1/batch_norm/0" [label="(2048,)", style=solid]; +"385 layer4/0/downsample/1/batch_norm/0" -> "386 layer4/0/add_/0" [label="(1, 2048, 2, 2)", style=solid]; +"386 layer4/0/add_/0" -> "387 layer4/0/relu/relu_/2" [label="(1, 2048, 2, 2)", style=solid]; +"387 layer4/0/relu/relu_/2" -> "389 layer4/1/conv1/conv2d/0" [label="(1, 2048, 2, 2)", style=solid]; +"387 layer4/0/relu/relu_/2" -> "411 layer4/1/add_/0" [label="(1, 2048, 2, 2)", style=solid]; +"388 layer4.1.conv1.weight" -> "389 layer4/1/conv1/conv2d/0" [label="(1024, 2048, 1, 1)", style=solid]; +"389 layer4/1/conv1/conv2d/0" -> "394 layer4/1/bn1/batch_norm/0" [label="(1, 1024, 2, 2)", style=solid]; +"390 layer4.1.bn1.weight" -> "394 layer4/1/bn1/batch_norm/0" [label="(1024,)", style=solid]; +"391 layer4.1.bn1.bias" -> "394 layer4/1/bn1/batch_norm/0" [label="(1024,)", style=solid]; +"392 layer4.1.bn1.running_mean" -> "394 layer4/1/bn1/batch_norm/0" [label="(1024,)", style=solid]; +"393 layer4.1.bn1.running_var" -> "394 layer4/1/bn1/batch_norm/0" [label="(1024,)", style=solid]; +"394 layer4/1/bn1/batch_norm/0" -> "395 layer4/1/relu/relu_/0" [label="(1, 1024, 2, 2)", style=solid]; +"395 layer4/1/relu/relu_/0" -> "397 layer4/1/conv2/conv2d/0" [label="(1, 1024, 2, 2)", style=solid]; +"396 layer4.1.conv2.weight" -> "397 layer4/1/conv2/conv2d/0" [label="(1024, 32, 3, 3)", style=solid]; +"397 layer4/1/conv2/conv2d/0" -> "402 layer4/1/bn2/batch_norm/0" [label="(1, 1024, 2, 2)", style=solid]; +"398 layer4.1.bn2.weight" -> "402 layer4/1/bn2/batch_norm/0" [label="(1024,)", style=solid]; +"399 layer4.1.bn2.bias" -> "402 layer4/1/bn2/batch_norm/0" [label="(1024,)", style=solid]; +"400 layer4.1.bn2.running_mean" -> "402 layer4/1/bn2/batch_norm/0" [label="(1024,)", style=solid]; +"401 layer4.1.bn2.running_var" -> "402 layer4/1/bn2/batch_norm/0" [label="(1024,)", style=solid]; +"402 layer4/1/bn2/batch_norm/0" -> "403 layer4/1/relu/relu_/1" [label="(1, 1024, 2, 2)", style=solid]; +"403 layer4/1/relu/relu_/1" -> "405 layer4/1/conv3/conv2d/0" [label="(1, 1024, 2, 2)", style=solid]; +"404 layer4.1.conv3.weight" -> "405 layer4/1/conv3/conv2d/0" [label="(2048, 1024, 1, 1)", style=solid]; +"405 layer4/1/conv3/conv2d/0" -> "410 layer4/1/bn3/batch_norm/0" [label="(1, 2048, 2, 2)", style=solid]; +"406 layer4.1.bn3.weight" -> "410 layer4/1/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"407 layer4.1.bn3.bias" -> "410 layer4/1/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"408 layer4.1.bn3.running_mean" -> "410 layer4/1/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"409 layer4.1.bn3.running_var" -> "410 layer4/1/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"410 layer4/1/bn3/batch_norm/0" -> "411 layer4/1/add_/0" [label="(1, 2048, 2, 2)", style=solid]; +"411 layer4/1/add_/0" -> "412 layer4/1/relu/relu_/2" [label="(1, 2048, 2, 2)", style=solid]; +"412 layer4/1/relu/relu_/2" -> "414 layer4/2/conv1/conv2d/0" [label="(1, 2048, 2, 2)", style=solid]; +"412 layer4/1/relu/relu_/2" -> "436 layer4/2/add_/0" [label="(1, 2048, 2, 2)", style=solid]; +"413 layer4.2.conv1.weight" -> "414 layer4/2/conv1/conv2d/0" [label="(1024, 2048, 1, 1)", style=solid]; +"414 layer4/2/conv1/conv2d/0" -> "419 layer4/2/bn1/batch_norm/0" [label="(1, 1024, 2, 2)", style=solid]; +"415 layer4.2.bn1.weight" -> "419 layer4/2/bn1/batch_norm/0" [label="(1024,)", style=solid]; +"416 layer4.2.bn1.bias" -> "419 layer4/2/bn1/batch_norm/0" [label="(1024,)", style=solid]; +"417 layer4.2.bn1.running_mean" -> "419 layer4/2/bn1/batch_norm/0" [label="(1024,)", style=solid]; +"418 layer4.2.bn1.running_var" -> "419 layer4/2/bn1/batch_norm/0" [label="(1024,)", style=solid]; +"419 layer4/2/bn1/batch_norm/0" -> "420 layer4/2/relu/relu_/0" [label="(1, 1024, 2, 2)", style=solid]; +"420 layer4/2/relu/relu_/0" -> "422 layer4/2/conv2/conv2d/0" [label="(1, 1024, 2, 2)", style=solid]; +"421 layer4.2.conv2.weight" -> "422 layer4/2/conv2/conv2d/0" [label="(1024, 32, 3, 3)", style=solid]; +"422 layer4/2/conv2/conv2d/0" -> "427 layer4/2/bn2/batch_norm/0" [label="(1, 1024, 2, 2)", style=solid]; +"423 layer4.2.bn2.weight" -> "427 layer4/2/bn2/batch_norm/0" [label="(1024,)", style=solid]; +"424 layer4.2.bn2.bias" -> "427 layer4/2/bn2/batch_norm/0" [label="(1024,)", style=solid]; +"425 layer4.2.bn2.running_mean" -> "427 layer4/2/bn2/batch_norm/0" [label="(1024,)", style=solid]; +"426 layer4.2.bn2.running_var" -> "427 layer4/2/bn2/batch_norm/0" [label="(1024,)", style=solid]; +"427 layer4/2/bn2/batch_norm/0" -> "428 layer4/2/relu/relu_/1" [label="(1, 1024, 2, 2)", style=solid]; +"428 layer4/2/relu/relu_/1" -> "430 layer4/2/conv3/conv2d/0" [label="(1, 1024, 2, 2)", style=solid]; +"429 layer4.2.conv3.weight" -> "430 layer4/2/conv3/conv2d/0" [label="(2048, 1024, 1, 1)", style=solid]; +"430 layer4/2/conv3/conv2d/0" -> "435 layer4/2/bn3/batch_norm/0" [label="(1, 2048, 2, 2)", style=solid]; +"431 layer4.2.bn3.weight" -> "435 layer4/2/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"432 layer4.2.bn3.bias" -> "435 layer4/2/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"433 layer4.2.bn3.running_mean" -> "435 layer4/2/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"434 layer4.2.bn3.running_var" -> "435 layer4/2/bn3/batch_norm/0" [label="(2048,)", style=solid]; +"435 layer4/2/bn3/batch_norm/0" -> "436 layer4/2/add_/0" [label="(1, 2048, 2, 2)", style=solid]; +"436 layer4/2/add_/0" -> "437 layer4/2/relu/relu_/2" [label="(1, 2048, 2, 2)", style=solid]; +"437 layer4/2/relu/relu_/2" -> "438 avgpool/adaptive_avg_pool2d/0" [label="(1, 2048, 2, 2)", style=solid]; +"438 avgpool/adaptive_avg_pool2d/0" -> "439 /flatten/0" [label="(1, 2048, 1, 1)", style=solid]; +"439 /flatten/0" -> "442 fc/linear/0" [label="(1, 2048)", style=solid]; +"440 fc.weight" -> "442 fc/linear/0" [label="(1000, 2048)", style=solid]; +"441 fc.bias" -> "442 fc/linear/0" [label="(1000,)", style=solid]; +"442 fc/linear/0" -> "443 output" [label="(1, 1000)", style=solid]; +} diff --git a/tests/torch2/data/function_hook/nncf_graph/model_graph_shufflenet_v2_x0_5.dot b/tests/torch2/data/function_hook/nncf_graph/model_graph_shufflenet_v2_x0_5.dot new file mode 100644 index 00000000000..b56e153157d --- /dev/null +++ b/tests/torch2/data/function_hook/nncf_graph/model_graph_shufflenet_v2_x0_5.dot @@ -0,0 +1,1075 @@ +strict digraph { +"0 x" [id=0, type=nncf_model_input]; +"1 conv1.0.weight" [id=1, type=nncf_model_const]; +"2 conv1/0/conv2d/0" [id=2, type=conv2d]; +"3 conv1.1.weight" [id=3, type=nncf_model_const]; +"4 conv1.1.bias" [id=4, type=nncf_model_const]; +"5 conv1.1.running_mean" [id=5, type=nncf_model_const]; +"6 conv1.1.running_var" [id=6, type=nncf_model_const]; +"7 conv1/1/batch_norm/0" [id=7, type=batch_norm]; +"8 conv1/2/relu_/0" [id=8, type=relu_]; +"9 maxpool/max_pool2d/0" [id=9, type=max_pool2d]; +"10 stage2.0.branch1.0.weight" [id=10, type=nncf_model_const]; +"11 stage2/0/branch1/0/conv2d/0" [id=11, type=conv2d]; +"12 stage2.0.branch1.1.weight" [id=12, type=nncf_model_const]; +"13 stage2.0.branch1.1.bias" [id=13, type=nncf_model_const]; +"14 stage2.0.branch1.1.running_mean" [id=14, type=nncf_model_const]; +"15 stage2.0.branch1.1.running_var" [id=15, type=nncf_model_const]; +"16 stage2/0/branch1/1/batch_norm/0" [id=16, type=batch_norm]; +"17 stage2.0.branch1.2.weight" [id=17, type=nncf_model_const]; +"18 stage2/0/branch1/2/conv2d/0" [id=18, type=conv2d]; +"19 stage2.0.branch1.3.weight" [id=19, type=nncf_model_const]; +"20 stage2.0.branch1.3.bias" [id=20, type=nncf_model_const]; +"21 stage2.0.branch1.3.running_mean" [id=21, type=nncf_model_const]; +"22 stage2.0.branch1.3.running_var" [id=22, type=nncf_model_const]; +"23 stage2/0/branch1/3/batch_norm/0" [id=23, type=batch_norm]; +"24 stage2/0/branch1/4/relu_/0" [id=24, type=relu_]; +"25 stage2.0.branch2.0.weight" [id=25, type=nncf_model_const]; +"26 stage2/0/branch2/0/conv2d/0" [id=26, type=conv2d]; +"27 stage2.0.branch2.1.weight" [id=27, type=nncf_model_const]; +"28 stage2.0.branch2.1.bias" [id=28, type=nncf_model_const]; +"29 stage2.0.branch2.1.running_mean" [id=29, type=nncf_model_const]; +"30 stage2.0.branch2.1.running_var" [id=30, type=nncf_model_const]; +"31 stage2/0/branch2/1/batch_norm/0" [id=31, type=batch_norm]; +"32 stage2/0/branch2/2/relu_/0" [id=32, type=relu_]; +"33 stage2.0.branch2.3.weight" [id=33, type=nncf_model_const]; +"34 stage2/0/branch2/3/conv2d/0" [id=34, type=conv2d]; +"35 stage2.0.branch2.4.weight" [id=35, type=nncf_model_const]; +"36 stage2.0.branch2.4.bias" [id=36, type=nncf_model_const]; +"37 stage2.0.branch2.4.running_mean" [id=37, type=nncf_model_const]; +"38 stage2.0.branch2.4.running_var" [id=38, type=nncf_model_const]; +"39 stage2/0/branch2/4/batch_norm/0" [id=39, type=batch_norm]; +"40 stage2.0.branch2.5.weight" [id=40, type=nncf_model_const]; +"41 stage2/0/branch2/5/conv2d/0" [id=41, type=conv2d]; +"42 stage2.0.branch2.6.weight" [id=42, type=nncf_model_const]; +"43 stage2.0.branch2.6.bias" [id=43, type=nncf_model_const]; +"44 stage2.0.branch2.6.running_mean" [id=44, type=nncf_model_const]; +"45 stage2.0.branch2.6.running_var" [id=45, type=nncf_model_const]; +"46 stage2/0/branch2/6/batch_norm/0" [id=46, type=batch_norm]; +"47 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stage2.1.branch2.4.running_var" -> "68 stage2/1/branch2/4/batch_norm/0" [label="(24,)", style=solid]; +"68 stage2/1/branch2/4/batch_norm/0" -> "70 stage2/1/branch2/5/conv2d/0" [label="(1, 24, 28, 28)", style=solid]; +"69 stage2.1.branch2.5.weight" -> "70 stage2/1/branch2/5/conv2d/0" [label="(24, 24, 1, 1)", style=solid]; +"70 stage2/1/branch2/5/conv2d/0" -> "75 stage2/1/branch2/6/batch_norm/0" [label="(1, 24, 28, 28)", style=solid]; +"71 stage2.1.branch2.6.weight" -> "75 stage2/1/branch2/6/batch_norm/0" [label="(24,)", style=solid]; +"72 stage2.1.branch2.6.bias" -> "75 stage2/1/branch2/6/batch_norm/0" [label="(24,)", style=solid]; +"73 stage2.1.branch2.6.running_mean" -> "75 stage2/1/branch2/6/batch_norm/0" [label="(24,)", style=solid]; +"74 stage2.1.branch2.6.running_var" -> "75 stage2/1/branch2/6/batch_norm/0" [label="(24,)", style=solid]; +"75 stage2/1/branch2/6/batch_norm/0" -> "76 stage2/1/branch2/7/relu_/0" [label="(1, 24, 28, 28)", style=solid]; +"76 stage2/1/branch2/7/relu_/0" 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[label="(24,)", style=solid]; +"95 stage2.2.branch2.4.running_mean" -> "97 stage2/2/branch2/4/batch_norm/0" [label="(24,)", style=solid]; +"96 stage2.2.branch2.4.running_var" -> "97 stage2/2/branch2/4/batch_norm/0" [label="(24,)", style=solid]; +"97 stage2/2/branch2/4/batch_norm/0" -> "99 stage2/2/branch2/5/conv2d/0" [label="(1, 24, 28, 28)", style=solid]; +"98 stage2.2.branch2.5.weight" -> "99 stage2/2/branch2/5/conv2d/0" [label="(24, 24, 1, 1)", style=solid]; +"99 stage2/2/branch2/5/conv2d/0" -> "104 stage2/2/branch2/6/batch_norm/0" [label="(1, 24, 28, 28)", style=solid]; +"100 stage2.2.branch2.6.weight" -> "104 stage2/2/branch2/6/batch_norm/0" [label="(24,)", style=solid]; +"101 stage2.2.branch2.6.bias" -> "104 stage2/2/branch2/6/batch_norm/0" [label="(24,)", style=solid]; +"102 stage2.2.branch2.6.running_mean" -> "104 stage2/2/branch2/6/batch_norm/0" [label="(24,)", style=solid]; +"103 stage2.2.branch2.6.running_var" -> "104 stage2/2/branch2/6/batch_norm/0" [label="(24,)", 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type=nncf_model_const]; +"72 classifier/6/linear/0" [id=72, type=linear]; +"73 output" [id=73, type=nncf_model_output]; +"0 x" -> "3 features/0/conv2d/0" [label="(1, 3, 32, 32)", style=solid]; +"1 features.0.weight" -> "3 features/0/conv2d/0" [label="(64, 3, 3, 3)", style=solid]; +"2 features.0.bias" -> "3 features/0/conv2d/0" [label="(64,)", style=solid]; +"3 features/0/conv2d/0" -> "4 features/1/relu_/0" [label="(1, 64, 32, 32)", style=solid]; +"4 features/1/relu_/0" -> "7 features/2/conv2d/0" [label="(1, 64, 32, 32)", style=solid]; +"5 features.2.weight" -> "7 features/2/conv2d/0" [label="(64, 64, 3, 3)", style=solid]; +"6 features.2.bias" -> "7 features/2/conv2d/0" [label="(64,)", style=solid]; +"7 features/2/conv2d/0" -> "8 features/3/relu_/0" [label="(1, 64, 32, 32)", style=solid]; +"8 features/3/relu_/0" -> "9 features/4/max_pool2d/0" [label="(1, 64, 32, 32)", style=solid]; +"9 features/4/max_pool2d/0" -> "12 features/5/conv2d/0" [label="(1, 64, 16, 16)", style=solid]; +"10 features.5.weight" -> "12 features/5/conv2d/0" [label="(128, 64, 3, 3)", style=solid]; +"11 features.5.bias" -> "12 features/5/conv2d/0" [label="(128,)", style=solid]; +"12 features/5/conv2d/0" -> "13 features/6/relu_/0" [label="(1, 128, 16, 16)", style=solid]; +"13 features/6/relu_/0" -> "16 features/7/conv2d/0" [label="(1, 128, 16, 16)", style=solid]; +"14 features.7.weight" -> "16 features/7/conv2d/0" [label="(128, 128, 3, 3)", style=solid]; +"15 features.7.bias" -> "16 features/7/conv2d/0" [label="(128,)", style=solid]; +"16 features/7/conv2d/0" -> "17 features/8/relu_/0" [label="(1, 128, 16, 16)", style=solid]; +"17 features/8/relu_/0" -> "18 features/9/max_pool2d/0" [label="(1, 128, 16, 16)", style=solid]; +"18 features/9/max_pool2d/0" -> "21 features/10/conv2d/0" [label="(1, 128, 8, 8)", style=solid]; +"19 features.10.weight" -> "21 features/10/conv2d/0" [label="(256, 128, 3, 3)", style=solid]; +"20 features.10.bias" -> "21 features/10/conv2d/0" [label="(256,)", style=solid]; +"21 features/10/conv2d/0" -> "22 features/11/relu_/0" [label="(1, 256, 8, 8)", style=solid]; +"22 features/11/relu_/0" -> "25 features/12/conv2d/0" [label="(1, 256, 8, 8)", style=solid]; +"23 features.12.weight" -> "25 features/12/conv2d/0" [label="(256, 256, 3, 3)", style=solid]; +"24 features.12.bias" -> "25 features/12/conv2d/0" [label="(256,)", style=solid]; +"25 features/12/conv2d/0" -> "26 features/13/relu_/0" [label="(1, 256, 8, 8)", style=solid]; +"26 features/13/relu_/0" -> "29 features/14/conv2d/0" [label="(1, 256, 8, 8)", style=solid]; +"27 features.14.weight" -> "29 features/14/conv2d/0" [label="(256, 256, 3, 3)", style=solid]; +"28 features.14.bias" -> "29 features/14/conv2d/0" [label="(256,)", style=solid]; +"29 features/14/conv2d/0" -> "30 features/15/relu_/0" [label="(1, 256, 8, 8)", style=solid]; +"30 features/15/relu_/0" -> "31 features/16/max_pool2d/0" [label="(1, 256, 8, 8)", style=solid]; +"31 features/16/max_pool2d/0" -> "34 features/17/conv2d/0" [label="(1, 256, 4, 4)", style=solid]; +"32 features.17.weight" -> "34 features/17/conv2d/0" [label="(512, 256, 3, 3)", style=solid]; +"33 features.17.bias" -> "34 features/17/conv2d/0" [label="(512,)", style=solid]; +"34 features/17/conv2d/0" -> "35 features/18/relu_/0" [label="(1, 512, 4, 4)", style=solid]; +"35 features/18/relu_/0" -> "38 features/19/conv2d/0" [label="(1, 512, 4, 4)", style=solid]; +"36 features.19.weight" -> "38 features/19/conv2d/0" [label="(512, 512, 3, 3)", style=solid]; +"37 features.19.bias" -> "38 features/19/conv2d/0" [label="(512,)", style=solid]; +"38 features/19/conv2d/0" -> "39 features/20/relu_/0" [label="(1, 512, 4, 4)", style=solid]; +"39 features/20/relu_/0" -> "42 features/21/conv2d/0" [label="(1, 512, 4, 4)", style=solid]; +"40 features.21.weight" -> "42 features/21/conv2d/0" [label="(512, 512, 3, 3)", style=solid]; +"41 features.21.bias" -> "42 features/21/conv2d/0" [label="(512,)", style=solid]; +"42 features/21/conv2d/0" -> "43 features/22/relu_/0" [label="(1, 512, 4, 4)", style=solid]; +"43 features/22/relu_/0" -> "44 features/23/max_pool2d/0" [label="(1, 512, 4, 4)", style=solid]; +"44 features/23/max_pool2d/0" -> "47 features/24/conv2d/0" [label="(1, 512, 2, 2)", style=solid]; +"45 features.24.weight" -> "47 features/24/conv2d/0" [label="(512, 512, 3, 3)", style=solid]; +"46 features.24.bias" -> "47 features/24/conv2d/0" [label="(512,)", style=solid]; +"47 features/24/conv2d/0" -> "48 features/25/relu_/0" [label="(1, 512, 2, 2)", style=solid]; +"48 features/25/relu_/0" -> "51 features/26/conv2d/0" [label="(1, 512, 2, 2)", style=solid]; +"49 features.26.weight" -> "51 features/26/conv2d/0" [label="(512, 512, 3, 3)", style=solid]; +"50 features.26.bias" -> "51 features/26/conv2d/0" [label="(512,)", style=solid]; +"51 features/26/conv2d/0" -> "52 features/27/relu_/0" [label="(1, 512, 2, 2)", style=solid]; +"52 features/27/relu_/0" -> "55 features/28/conv2d/0" [label="(1, 512, 2, 2)", style=solid]; +"53 features.28.weight" -> "55 features/28/conv2d/0" [label="(512, 512, 3, 3)", style=solid]; +"54 features.28.bias" -> "55 features/28/conv2d/0" [label="(512,)", style=solid]; +"55 features/28/conv2d/0" -> "56 features/29/relu_/0" [label="(1, 512, 2, 2)", style=solid]; +"56 features/29/relu_/0" -> "57 features/30/max_pool2d/0" [label="(1, 512, 2, 2)", style=solid]; +"57 features/30/max_pool2d/0" -> "58 avgpool/adaptive_avg_pool2d/0" [label="(1, 512, 1, 1)", style=solid]; +"58 avgpool/adaptive_avg_pool2d/0" -> "59 /flatten/0" [label="(1, 512, 7, 7)", style=solid]; +"59 /flatten/0" -> "62 classifier/0/linear/0" [label="(1, 25088)", style=solid]; +"60 classifier.0.weight" -> "62 classifier/0/linear/0" [label="(4096, 25088)", style=solid]; +"61 classifier.0.bias" -> "62 classifier/0/linear/0" [label="(4096,)", style=solid]; +"62 classifier/0/linear/0" -> "63 classifier/1/relu_/0" [label="(1, 4096)", style=solid]; +"63 classifier/1/relu_/0" -> "64 classifier/2/dropout/0" [label="(1, 4096)", style=solid]; +"64 classifier/2/dropout/0" -> "67 classifier/3/linear/0" [label="(1, 4096)", style=solid]; +"65 classifier.3.weight" -> "67 classifier/3/linear/0" [label="(4096, 4096)", style=solid]; +"66 classifier.3.bias" -> "67 classifier/3/linear/0" [label="(4096,)", style=solid]; +"67 classifier/3/linear/0" -> "68 classifier/4/relu_/0" [label="(1, 4096)", style=solid]; +"68 classifier/4/relu_/0" -> "69 classifier/5/dropout/0" [label="(1, 4096)", style=solid]; +"69 classifier/5/dropout/0" -> "72 classifier/6/linear/0" [label="(1, 4096)", style=solid]; +"70 classifier.6.weight" -> "72 classifier/6/linear/0" [label="(1000, 4096)", style=solid]; +"71 classifier.6.bias" -> "72 classifier/6/linear/0" [label="(1000,)", style=solid]; +"72 classifier/6/linear/0" -> "73 output" [label="(1, 1000)", style=solid]; +} diff --git a/tests/torch2/function_hook/graph/test_build_graph_mode.py b/tests/torch2/function_hook/graph/test_build_graph_mode.py new file mode 100644 index 00000000000..e2ef6047ca7 --- /dev/null +++ b/tests/torch2/function_hook/graph/test_build_graph_mode.py @@ -0,0 +1,263 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Union + +import pytest +import torch +from pytest import FixtureRequest +from torch import nn + +from nncf.experimental.torch2.function_hook.graph.build_graph_mode import GraphBuilderMode +from nncf.experimental.torch2.function_hook.graph.build_graph_mode import build_graph +from nncf.experimental.torch2.function_hook.graph.graph_utils import ConstMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import EdgeMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import FunctionMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import InOutMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import NodeType +from nncf.experimental.torch2.function_hook.graph.graph_utils import TensorInfo +from nncf.experimental.torch2.function_hook.graph.graph_utils import TensorMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import TensorSource +from nncf.experimental.torch2.function_hook.hook_executor_mode import OpMeta +from nncf.experimental.torch2.function_hook.hook_storage import HookStorage +from nncf.experimental.torch2.function_hook.wrapper import get_hook_storage +from nncf.experimental.torch2.function_hook.wrapper import wrap_model +from tests.torch2.function_hook import helpers + + +def test_register_new_node_id(): + ctx = GraphBuilderMode(nn.Identity(), HookStorage()) + assert ctx.next_node_id == 0 + + id = ctx.register_new_node_id() + assert id == 0 + assert ctx.next_node_id == 1 + + id = ctx.register_new_node_id() + assert id == 1 + assert ctx.next_node_id == 2 + + +def test_execute_hooks_for_model_input(): + ctx = GraphBuilderMode(nn.Identity(), HookStorage()) + + tensor = torch.ones(1) + ctx.execute_hooks_for_model_input("x", tensor) + + assert ctx.graph.nodes[0]["type"] == NodeType.input + assert ctx.graph.nodes[0]["meta"] == InOutMeta(tensor.dtype, tuple(tensor.shape), "x") + + +def test_execute_hooks_for_model_output(): + ctx = GraphBuilderMode(nn.Identity(), HookStorage()) + + tensor = torch.ones(1) + node_id = ctx.register_new_node_id() + ctx.tensor_info[tensor] = TensorInfo( + NodeType.const, + tuple(tensor.shape), + tensor.dtype, + 0, + node_id, + None, + ) + ctx.execute_hooks_for_model_output("output", tensor) + + assert ctx.graph.nodes[1]["type"] == NodeType.output + assert ctx.graph.nodes[1]["meta"] == InOutMeta(tensor.dtype, tuple(tensor.shape), "output") + + assert ctx.graph.edges[0, 1, 0]["meta"] == EdgeMeta(dtype=torch.float32, shape=(1,), input_port=0, output_port=0) + + +def test_execute_pre_hooks(): + model = helpers.get_wrapped_simple_model_with_hook() + ctx = GraphBuilderMode(model, get_hook_storage(model)) + + ctx.execute_pre_hooks((torch.ones(1), model.conv.weight), {"b": model.conv.bias}, OpMeta("/relu/0", torch.relu)) + + nodes = list(ctx.graph.nodes(data=True)) + assert nodes[0][0] == 0 + assert nodes[0][1] == { + "type": NodeType.const, + "meta": ConstMeta(dtype=torch.float32, shape=(1, 1, 1, 1), name_in_model="conv.weight"), + } + + assert nodes[1][0] == 1 + assert nodes[1][1] == { + "type": NodeType.const, + "meta": ConstMeta(dtype=torch.float32, shape=(1,), name_in_model="conv.bias"), + } + + assert nodes[2][0] == 2 + assert nodes[2][1] == { + "type": NodeType.fn_call, + "meta": FunctionMeta( + op_name="/relu/0", + fn_name="relu", + args=( + TensorMeta(dtype=torch.float32, shape=(1,), requires_grad=False), + TensorMeta(dtype=torch.float32, shape=(1, 1, 1, 1), requires_grad=True), + ), + kwargs={"b": TensorMeta(dtype=torch.float32, shape=(1,), requires_grad=True)}, + ), + } + assert len(nodes) == 3 + + edges = list(ctx.graph.edges(data=True)) + assert edges[0][0] == 0 + assert edges[0][1] == 2 + assert edges[0][2] == {"meta": EdgeMeta(dtype=torch.float32, shape=(1, 1, 1, 1), input_port=1, output_port=0)} + + assert edges[1][0] == 1 + assert edges[1][1] == 2 + assert edges[1][2] == {"meta": EdgeMeta(dtype=torch.float32, shape=(1,), input_port=2, output_port=0)} + assert len(edges) == 2 + + +@pytest.fixture(params=["tensor", "list", "torch_return_type"]) +def example_outputs(request: FixtureRequest) -> Union[torch.Tensor, List[torch.Tensor], torch.return_types.max]: + return { + "tensor": torch.tensor(1.0), + "list": [torch.tensor(1), torch.tensor([2])], + "torch_return_type": torch.return_types.max((torch.tensor(1.0), torch.tensor([2]))), + }.get(request.param) + + +def test_execute_post_hooks(example_outputs: Union[torch.Tensor, List[torch.Tensor], torch.return_types.max]): + ctx = GraphBuilderMode(nn.Identity(), HookStorage()) + op_meta = OpMeta("/relu/0", torch.relu, {"node_id": 0}) + ctx.execute_post_hooks(example_outputs, op_meta) + + if isinstance(example_outputs, torch.Tensor): + assert ctx.tensor_info[example_outputs] == TensorInfo(TensorSource.function, (), torch.float32, 0, 0, None) + + if isinstance(example_outputs, list): + assert ctx.tensor_info[example_outputs[0]] == TensorInfo(TensorSource.function, (), torch.int64, 0, 0, None) + assert ctx.tensor_info[example_outputs[1]] == TensorInfo(TensorSource.function, (1,), torch.int64, 1, 0, None) + + if isinstance(example_outputs, torch.return_types.max): + assert ctx.tensor_info[example_outputs.values] == TensorInfo( + TensorSource.function, (), torch.float32, 0, 0, None + ) + assert ctx.tensor_info[example_outputs.indices] == TensorInfo( + TensorSource.function, (1,), torch.int64, 1, 0, None + ) + + +def test_build_graph(): + model = helpers.get_wrapped_simple_model_with_hook() + graph = build_graph(model, model.get_example_inputs()) + + nodes = list(graph.nodes(data=True)) + assert len(nodes) == 8 + assert len(list(filter(lambda x: x[1]["type"] == NodeType.input, nodes))) == 1 + assert len(list(filter(lambda x: x[1]["type"] == NodeType.output, nodes))) == 1 + assert len(list(filter(lambda x: x[1]["type"] == NodeType.const, nodes))) == 3 + assert len(list(filter(lambda x: x[1]["type"] == NodeType.fn_call, nodes))) == 3 + + edges = list(graph.edges(data=True)) + assert len(edges) == 7 + assert len(list(filter(lambda x: isinstance(x[2]["meta"], EdgeMeta), edges))) == 7 + + +class ModelTensorAttribute(nn.Module): + def __init__(self, attribute: str) -> None: + super().__init__() + self.attr = attribute + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if self.attr == ".T": + return x.T + if self.attr == ".mT": + return x.mT + raise ValueError(f"Unexpected attribute: {self.attr}") + + +@pytest.mark.parametrize("attr", [".T", ".mT"]) +def test_tensor_attributes(attr): + model = ModelTensorAttribute(attribute=attr) + model = wrap_model(model) + graph = build_graph(model, torch.ones(2, 3, requires_grad=True)) + + if attr == ".T": + ref_meta = FunctionMeta( + op_name="/__get__/0", + fn_name="permute", + args=(TensorMeta(dtype=torch.float32, shape=(2, 3), requires_grad=True),), + kwargs={"dims": (1, 0)}, + ) + else: + ref_meta = FunctionMeta( + op_name="/__get__/0", + fn_name="transpose", + args=(TensorMeta(dtype=torch.float32, shape=(2, 3), requires_grad=True),), + kwargs={"dim0": -2, "dim1": -1}, + ) + assert graph.nodes[1]["type"] == NodeType.fn_call + assert graph.nodes[1]["meta"] == ref_meta + + +class ModelTensorAttribute2(nn.Module): + def __init__( + self, + ) -> None: + super().__init__() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x + 1 + _ = x.shape + x = x.T + x = x + 1 + _ = x.shape + return x + + +class HookWithAttribute(nn.Module): + def __init__( + self, + ) -> None: + super().__init__() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + _ = x.ndim + x = x - 1 + return x + + +def test_remove_get_node_with_no_tensor_output(): + model = ModelTensorAttribute2() + model = wrap_model(model) + hook_storage = get_hook_storage(model) + hook_storage.register_pre_function_hook("/add/1", 0, HookWithAttribute()) + + # Set requires_grad=True to get grad_fn + graph = build_graph(model, torch.ones(2, 3, requires_grad=True)) + + nodes = list(graph.nodes(data=True)) + get_op_nodes = list( + filter(lambda x: isinstance(x[1]["meta"], FunctionMeta) and "__get__" in x[1]["meta"].op_name, nodes) + ) + assert len(get_op_nodes) == 1 + assert get_op_nodes[0][1]["meta"].op_name == "/__get__/1" + + +def test_multi_edges(): + model = helpers.ModelMultiEdge() + model = wrap_model(model) + graph = build_graph(model, torch.ones(1, 1)) + + edges = list(graph.edges(data=True)) + ref_edges = [ + (0, 1, {"meta": EdgeMeta(dtype=torch.float32, shape=(1, 1), input_port=0, output_port=0)}), + (0, 1, {"meta": EdgeMeta(dtype=torch.float32, shape=(1, 1), input_port=1, output_port=0)}), + (1, 2, {"meta": EdgeMeta(dtype=torch.float32, shape=(1, 1), input_port=0, output_port=0)}), + ] + assert edges == ref_edges diff --git a/tests/torch2/function_hook/graph/test_graph_visualisation.py b/tests/torch2/function_hook/graph/test_graph_visualisation.py new file mode 100644 index 00000000000..b398b138330 --- /dev/null +++ b/tests/torch2/function_hook/graph/test_graph_visualisation.py @@ -0,0 +1,33 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytest + +from nncf.experimental.torch2.function_hook.graph.build_graph_mode import build_graph +from nncf.experimental.torch2.function_hook.graph.graph_visualization import PydotStyleTemplate +from nncf.experimental.torch2.function_hook.graph.graph_visualization import to_pydot +from tests.cross_fw.shared.paths import TEST_ROOT +from tests.torch2.function_hook import helpers +from tests.torch2.utils import compare_with_reference_file + +REF_DIR = TEST_ROOT / "torch2" / "data" / "function_hook" / "graph_visualization" + + +@pytest.mark.parametrize("style", PydotStyleTemplate) +def test_to_pydot(style, regen_ref_data: bool): + model = helpers.get_wrapped_simple_model_with_hook() + graph = build_graph(model, model.get_example_inputs()) + + dot_graph = to_pydot(graph, style_template=style) + ref_file = REF_DIR / f"to_pydot_style_{style}.dot" + + compare_with_reference_file(str(dot_graph), ref_file, regen_ref_data) diff --git a/tests/torch2/function_hook/helpers.py b/tests/torch2/function_hook/helpers.py new file mode 100644 index 00000000000..89ae33cc8e6 --- /dev/null +++ b/tests/torch2/function_hook/helpers.py @@ -0,0 +1,105 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn + +from nncf.experimental.torch2.function_hook.wrapper import register_post_function_hook +from nncf.experimental.torch2.function_hook.wrapper import wrap_model + + +class CallCount(torch.nn.Module): + def __init__(self): + super().__init__() + self.call_count = 0 + + def forward(self, x): + self.call_count += 1 + return x + + +class AddModule(torch.nn.Module): + def __init__(self, value): + super().__init__() + self.w = torch.nn.Parameter(torch.tensor(value, dtype=torch.float32)) + + def forward(self, x): + return x + self.w + + +class ConvModel(nn.Module): + @staticmethod + def get_example_inputs(): + return torch.ones([1, 1, 3, 3]) + + def __init__(self) -> None: + super().__init__() + self.conv = nn.Conv2d(1, 1, 1) + + def forward(self, x: torch.Tensor): + x = self.conv(x) + x = torch.relu(x) + return x + + +@torch.jit.script +def jit_norm(x: torch.Tensor) -> torch.Tensor: + return torch.norm(x) + + +class ConvJitNormModel(nn.Module): + @staticmethod + def get_example_inputs(): + return torch.ones([1, 1, 3, 3]) + + def __init__(self) -> None: + super().__init__() + self.conv = nn.Conv2d(1, 1, 1) + + def forward(self, x: torch.Tensor): + x = self.conv(x) + x = jit_norm(x) + return x + + +class SimpleModel(nn.Module): + + @staticmethod + def get_example_inputs(): + return torch.ones([1, 1, 3, 3]) + + def __init__(self) -> None: + super().__init__() + self.conv1 = nn.Conv2d(1, 1, 1) + self.simple = ConvModel() + + def forward(self, x): + x = x + 1 + x += 1 + x = torch.add(input=x, other=1) + return self.simple(self.conv1(x)) + 1, x + + +def get_wrapped_simple_model_with_hook() -> ConvModel: + model = ConvModel() + wrapped = wrap_model(model) + register_post_function_hook(wrapped, "conv/conv2d/0", 0, AddModule([2.0])) + wrapped.eval() + return wrapped + + +class ModelMultiEdge(nn.Module): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x + x + return x diff --git a/tests/torch2/function_hook/nncf_graph/test_nncf_graph.py b/tests/torch2/function_hook/nncf_graph/test_nncf_graph.py new file mode 100644 index 00000000000..2d2237ad3f2 --- /dev/null +++ b/tests/torch2/function_hook/nncf_graph/test_nncf_graph.py @@ -0,0 +1,148 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from functools import partial +from typing import List, Tuple, Union + +import networkx as nx +import pytest +import torch +import torchvision.models as models + +from nncf.common.graph.layer_attributes import Dtype +from nncf.experimental.torch2.function_hook.graph.build_graph_mode import build_graph +from nncf.experimental.torch2.function_hook.graph.graph_utils import ConstMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import FunctionMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import InOutMeta +from nncf.experimental.torch2.function_hook.graph.graph_utils import NodeType +from nncf.experimental.torch2.function_hook.nncf_graph.nncf_graph_builder import build_nncf_graph +from nncf.experimental.torch2.function_hook.nncf_graph.nncf_graph_builder import convert_to_nncf_graph +from nncf.experimental.torch2.function_hook.nncf_graph.nncf_graph_builder import get_dtype +from nncf.experimental.torch2.function_hook.nncf_graph.nncf_graph_builder import get_name_of_node +from nncf.experimental.torch2.function_hook.nncf_graph.nncf_graph_builder import get_node_type +from nncf.experimental.torch2.function_hook.wrapper import wrap_model +from tests.cross_fw.shared.paths import TEST_ROOT +from tests.torch2.function_hook import helpers +from tests.torch2.utils import compare_with_reference_file + +REF_DIR = TEST_ROOT / "torch2" / "data" / "function_hook" / "nncf_graph" + + +@pytest.mark.parametrize( + "node_type, meta, ref", + [ + [NodeType.input, InOutMeta(torch.float32, (1), "input"), "nncf_model_input"], + [NodeType.output, InOutMeta(torch.float32, (1), "output"), "nncf_model_output"], + [NodeType.output, FunctionMeta("op", "fn_name_ref", [], {}), "fn_name_ref"], + [NodeType.output, ConstMeta(torch.float32, (1), "model.bias"), "nncf_model_const"], + ], +) +def test_get_node_type(node_type: NodeType, meta: Union[ConstMeta, FunctionMeta, InOutMeta], ref: str): + assert get_node_type(node_type, meta) == ref + + +@pytest.mark.parametrize( + "meta, ref", + [ + [InOutMeta(torch.float32, (1), "input"), "input"], + [InOutMeta(torch.float32, (1), "output"), "output"], + [FunctionMeta("op_name", "fn_name", [], {}), "op_name"], + [ConstMeta(torch.float32, (1), "model.bias"), "model.bias"], + ], +) +def test_get_name_of_node( + meta: list[InOutMeta | str] | list[FunctionMeta | str] | list[ConstMeta | str], + ref: list[InOutMeta | str] | list[FunctionMeta | str] | list[ConstMeta | str], +): + assert get_name_of_node(meta) == ref + + +@pytest.mark.parametrize( + "dtype, ref", + [ + [torch.float, Dtype.FLOAT], + [torch.float32, Dtype.FLOAT], + [torch.float64, Dtype.FLOAT], + [torch.bfloat16, Dtype.FLOAT], + [torch.int, Dtype.INTEGER], + [torch.int8, Dtype.INTEGER], + [torch.int16, Dtype.INTEGER], + [torch.int32, Dtype.INTEGER], + [torch.int64, Dtype.INTEGER], + ], +) +def test_get_dtype(dtype: torch.dtype, ref: Dtype): + assert get_dtype(dtype) == ref + + +def test_convert_to_nncf_graph(regen_ref_data: bool): + model = helpers.get_wrapped_simple_model_with_hook() + nx_graph = build_graph(model, model.get_example_inputs()) + + nncf_graph = convert_to_nncf_graph(nx_graph) + graph = nncf_graph.get_graph_for_structure_analysis(extended=True) + nx_nncf_graph = nx.nx_pydot.to_pydot(graph) + + ref_file = REF_DIR / "convert_to_nncf_graph.dot" + + compare_with_reference_file(str(nx_nncf_graph), ref_file, regen_ref_data) + + +def test_convert_to_nncf_graph_multi_edges(regen_ref_data: bool): + model = helpers.ModelMultiEdge() + model = wrap_model(model) + nx_graph = build_graph(model, torch.ones(1, 1)) + nncf_graph = convert_to_nncf_graph(nx_graph) + graph = nncf_graph.get_graph_for_structure_analysis(extended=True) + nx_nncf_graph = nx.nx_pydot.to_pydot(graph) + ref_file = REF_DIR / "convert_to_nncf_graph_multi_edges.dot" + + compare_with_reference_file(str(nx_nncf_graph), ref_file, regen_ref_data) + + +@dataclass +class ModelDesc: + model_name: str + model_builder: callable + inputs_info: Union[List[List[int]], Tuple[List[int], ...]] + + def __str__(self): + return self.model_name + + +TEST_MODELS_DESC = [ + ModelDesc("convnext_small", partial(models.convnext_small, weights=None), [1, 3, 64, 64]), + ModelDesc("densenet121", partial(models.densenet121, weights=None), [1, 3, 64, 64]), + ModelDesc("efficientnet_b0", partial(models.efficientnet_b0, weights=None), [1, 3, 64, 64]), + ModelDesc("inception_v3", partial(models.inception_v3, weights=None), [1, 3, 300, 300]), + ModelDesc("mobilenet_v2", partial(models.mobilenet_v2, weights=None), [1, 3, 64, 64]), + ModelDesc("mobilenet_v3_small", partial(models.mobilenet_v3_small, weights=None), [1, 3, 64, 64]), + ModelDesc("resnet18", partial(models.resnet18, weights=None), [1, 3, 64, 64]), + ModelDesc("resnext50_32x4d", partial(models.resnext50_32x4d, weights=None), [1, 3, 64, 64]), + ModelDesc("shufflenet_v2_x0_5", partial(models.shufflenet_v2_x0_5, weights=None), [1, 3, 224, 224]), + ModelDesc("squeezenet1_0", partial(models.squeezenet1_0, weights=None), [1, 3, 64, 64]), + ModelDesc("swin_v2_b", partial(models.swin_v2_b, weights=None), [1, 3, 64, 64]), + ModelDesc("vgg16", partial(models.vgg16, weights=None), [1, 3, 32, 32]), +] + + +@pytest.mark.parametrize("desc", TEST_MODELS_DESC, ids=str) +def test_model_graph(desc: ModelDesc, regen_ref_data: bool): + model: torch.nn.Module = desc.model_builder() + model = model.eval() + inputs = [torch.randn(desc.inputs_info)] + model = wrap_model(model) + nncf_graph = build_nncf_graph(model, *inputs) + graph = nncf_graph.get_graph_for_structure_analysis(extended=True) + nx_nncf_graph = nx.nx_pydot.to_pydot(graph) + ref_file = REF_DIR / f"model_graph_{desc}.dot" + compare_with_reference_file(str(nx_nncf_graph), ref_file, regen_ref_data) diff --git a/tests/torch2/function_hook/test_function_hook_mode.py b/tests/torch2/function_hook/test_function_hook_mode.py new file mode 100644 index 00000000000..b0975a26b10 --- /dev/null +++ b/tests/torch2/function_hook/test_function_hook_mode.py @@ -0,0 +1,116 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from dataclasses import dataclass +from typing import Any, List, Optional, Union + +import pytest +import torch +from pytest import FixtureRequest +from torch import nn + +from nncf.experimental.torch2.function_hook.hook_executor_mode import FunctionHookMode +from nncf.experimental.torch2.function_hook.hook_executor_mode import OpMeta +from nncf.experimental.torch2.function_hook.hook_executor_mode import generate_normalized_op_name +from nncf.experimental.torch2.function_hook.hook_storage import HookStorage +from nncf.experimental.torch2.function_hook.wrapper import get_hook_storage +from tests.torch2.function_hook import helpers +from tests.torch2.function_hook.helpers import CallCount + + +@dataclass +class NormalizedOpNameTestCase: + model_name: str + fn_name: str + call_id: Optional[str] + ref: str + + +def idfn(value: Any): + if isinstance(value, NormalizedOpNameTestCase): + return value.ref + return None + + +@pytest.mark.parametrize( + "param", + ( + NormalizedOpNameTestCase("module", "foo", None, "module/foo"), + NormalizedOpNameTestCase("module", "foo", 0, "module/foo/0"), + NormalizedOpNameTestCase("module", "foo", 1, "module/foo/1"), + ), + ids=idfn, +) +def test_generate_normalized_op_name(param: NormalizedOpNameTestCase): + op_name = generate_normalized_op_name(module_name=param.model_name, fn_name=param.fn_name, call_id=param.call_id) + assert op_name == param.ref + + +def test_current_relative_name(): + model = helpers.get_wrapped_simple_model_with_hook() + hook_storage = get_hook_storage(model) + hook_executor_mode = FunctionHookMode(model, hook_storage) + hook_executor_mode.push_module_call_stack(model) + # assert hook_executor_mode.get_current_relative_name() == "" + + hook_executor_mode.push_module_call_stack(model.conv) + # assert hook_executor_mode.get_current_relative_name() == "conv" + + hook_executor_mode.push_module_call_stack(hook_storage.post_hooks["conv/conv2d/0__0"]["0"]) + assert hook_executor_mode.get_current_relative_name() == "conv/post_hook__conv-conv2d-0__0[0]" + + +def test_get_current_executed_op_name(): + model = helpers.get_wrapped_simple_model_with_hook() + hook_storage = get_hook_storage(model) + hook_executor_mode = FunctionHookMode(model, hook_storage) + + hook_executor_mode.push_module_call_stack(model) + assert hook_executor_mode.get_current_executed_op_name("foo") == "/foo/0" + hook_executor_mode.register_op("foo") + assert hook_executor_mode.get_current_executed_op_name("foo") == "/foo/1" + + hook_executor_mode.push_module_call_stack(model.conv) + assert hook_executor_mode.get_current_executed_op_name("foo") == "conv/foo/0" + hook_executor_mode.register_op("foo") + assert hook_executor_mode.get_current_executed_op_name("foo") == "conv/foo/1" + + hook_executor_mode.push_module_call_stack(hook_storage.post_hooks["conv/conv2d/0__0"]["0"]) + assert hook_executor_mode.get_current_executed_op_name("foo") == "conv/post_hook__conv-conv2d-0__0[0]/foo/0" + + +@pytest.fixture(params=["tensor", "list", "torch_return_type"]) +def example_outputs(request: FixtureRequest) -> Union[torch.Tensor, List[torch.Tensor], torch.return_types.max]: + return { + "tensor": torch.tensor(1), + "list": [torch.tensor(1), torch.tensor([2])], + "torch_return_type": torch.return_types.max((torch.tensor(1), torch.tensor([2]))), + }.get(request.param) + + +def test_execute_post_hooks(example_outputs: Union[torch.Tensor, List[torch.Tensor], torch.return_types.max]): + op_name = "/relu/0" + hook_storage = HookStorage() + hook_port_0 = CallCount() + hook_port_1 = CallCount() + hook_storage.register_post_function_hook(op_name, 0, hook_port_0) + hook_storage.register_post_function_hook(op_name, 1, hook_port_1) + ctx = FunctionHookMode(nn.Identity(), hook_storage) + op_meta = OpMeta("/relu/0", torch.relu) + ret_val = ctx.execute_post_hooks(example_outputs, op_meta) + assert type(example_outputs) == type(ret_val) + + assert hook_port_0.call_count == 1 + if isinstance(example_outputs, torch.Tensor): + assert hook_port_1.call_count == 0 + else: + assert hook_port_1.call_count == 1 diff --git a/tests/torch2/function_hook/test_handle_inner_functions.py b/tests/torch2/function_hook/test_handle_inner_functions.py new file mode 100644 index 00000000000..d24cb73d63f --- /dev/null +++ b/tests/torch2/function_hook/test_handle_inner_functions.py @@ -0,0 +1,169 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import ast +import inspect +from typing import Tuple + +import pytest +import torch +import torch.nn.functional as F +from torch import nn + +from nncf.experimental.torch2.function_hook.graph.build_graph_mode import build_graph +from nncf.experimental.torch2.function_hook.graph.graph_visualization import to_pydot +from nncf.experimental.torch2.function_hook.handle_inner_functions import MAP_HANDLER_TO_INNER_FUNCTION +from nncf.experimental.torch2.function_hook.wrapper import wrap_model +from tests.cross_fw.shared.paths import TEST_ROOT +from tests.torch2.utils import compare_with_reference_file + +REF_DIR = TEST_ROOT / "torch2" / "data" / "function_hook" / "handle_inner_functions" + + +class ReluModel(nn.Module): + + @staticmethod + def example_input() -> Tuple[torch.Tensor, ...]: + return (torch.rand(1, 1, 1),) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = torch.relu(x) + x = torch.relu_(x) + x = F.relu(x, inplace=False) + x = F.relu(x, inplace=True) + return x + + +class BatchNormModel(nn.Module): + @staticmethod + def example_input() -> Tuple[torch.Tensor, ...]: + return (torch.rand(1, 1, 1),) + + def __init__(self): + super().__init__() + self.bn = nn.BatchNorm1d(1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.bn(x) + return x + + +class MultiHeadAttention(nn.Module): + + batch_size = 2 + seq_length = 5 + embed_dim = 16 + num_heads = 4 + + @classmethod + def example_input(cls) -> Tuple[torch.Tensor, ...]: + query = torch.rand((cls.seq_length, cls.batch_size, cls.embed_dim)) + key = torch.rand((cls.seq_length, cls.batch_size, cls.embed_dim)) + value = torch.rand((cls.seq_length, cls.batch_size, cls.embed_dim)) + return (query, key, value) + + def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor: + in_proj_weight = torch.rand((3 * self.embed_dim, self.embed_dim)) + in_proj_bias = torch.rand(3 * self.embed_dim) + bias_k = None + bias_v = None + add_zero_attn = False + dropout_p = 0.1 + out_proj_weight = torch.rand((self.embed_dim, self.embed_dim)) + out_proj_bias = torch.rand(self.embed_dim) + + return F.multi_head_attention_forward( + query=query, + key=key, + value=value, + embed_dim_to_check=self.embed_dim, + num_heads=self.num_heads, + in_proj_weight=in_proj_weight, + in_proj_bias=in_proj_bias, + bias_k=bias_k, + bias_v=bias_v, + add_zero_attn=add_zero_attn, + dropout_p=dropout_p, + out_proj_weight=out_proj_weight, + out_proj_bias=out_proj_bias, + use_separate_proj_weight=False, + training=True, + need_weights=True, + ) + + +@pytest.mark.parametrize("model_cls", (ReluModel, BatchNormModel, MultiHeadAttention)) +def test_inner_functions(model_cls: nn.Module, regen_ref_data: bool): + model = wrap_model(model_cls()).eval() + graph = build_graph(model, *model_cls.example_input()) + + dot_graph = to_pydot(graph) + ref_file = REF_DIR / f"inner_functions_{model_cls.__name__}.dot" + compare_with_reference_file(str(dot_graph), ref_file, regen_ref_data) + + +class TypeHintRemover(ast.NodeTransformer): + def visit_FunctionDef(self, node): + # Remove type hints from arguments + for arg in node.args.args: + arg.annotation = None + # Remove return type annotation + node.returns = None + + # Remove docstring + if node.body and isinstance(node.body[0], ast.Expr) and isinstance(node.body[0].value, (ast.Str, ast.Constant)): + node.body.pop(0) + + # Process inner nodes + self.generic_visit(node) + return node + + +def _get_clean_source_code(func) -> str: + source = inspect.getsource(func) + tree = ast.parse(source) + + transformer = TypeHintRemover() + tree = transformer.visit(tree) + + # Convert the AST back to source code + cleaned_source = ast.unparse(tree) + + # Remove comments and empty strings + cleaned_lines = [] + for line in cleaned_source.splitlines(): + stripped_line = line.strip() + if stripped_line and not stripped_line.startswith("#"): + cleaned_line = stripped_line.split("#")[0].strip() + cleaned_lines.append(cleaned_line) + + return "\n".join(cleaned_lines) + + +@pytest.mark.parametrize("torch_func", MAP_HANDLER_TO_INNER_FUNCTION) +def test_compare_torch_function_with_handle_inner_function(torch_func): + func = MAP_HANDLER_TO_INNER_FUNCTION[torch_func] + + torch_func_code = _get_clean_source_code(torch_func) + + # Filter handle_function from code + filtered_torch_func_code = [] + func_begin = False + for line in torch_func_code.splitlines(): + if "handle_torch_function" in line: + func_begin = True + continue + if func_begin or line.startswith("def"): + filtered_torch_func_code.append(line) + filtered_torch_func_code = "\n".join(filtered_torch_func_code) + func_code = _get_clean_source_code(func) + + assert filtered_torch_func_code == func_code diff --git a/tests/torch2/function_hook/test_hook_storage.py b/tests/torch2/function_hook/test_hook_storage.py new file mode 100644 index 00000000000..322512e436b --- /dev/null +++ b/tests/torch2/function_hook/test_hook_storage.py @@ -0,0 +1,97 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List + +import pytest +import torch +from torch import nn + +from nncf.experimental.torch2.function_hook.hook_storage import HookStorage +from tests.torch2.function_hook.helpers import CallCount + + +@pytest.fixture(params=["pre_hook", "post_hook"]) +def hook_type(request: pytest.FixtureRequest) -> str: + return request.param + + +def test_insert(): + hook_storage = HookStorage() + hook = nn.Identity() + hook_storage.register_pre_function_hook("foo", 0, hook) + assert hook_storage.pre_hooks["foo__0"]["0"] is hook + + hook_storage.register_post_function_hook("foo", 0, hook) + assert hook_storage.post_hooks["foo__0"]["0"] is hook + + +def test_execute(): + hook_storage = HookStorage() + + pre_hook = CallCount() + hook_storage.register_pre_function_hook("foo", 0, pre_hook) + hook_storage.execute_pre_function_hooks("foo", 0, None) + assert pre_hook.call_count == 1 + + post_hook = CallCount() + hook_storage.register_post_function_hook("foo", 0, post_hook) + hook_storage.execute_post_function_hooks("foo", 0, None) + assert post_hook.call_count == 1 + + +def test_remove_handle(): + hook_storage = HookStorage() + + handle1 = hook_storage.register_pre_function_hook("foo", 0, nn.Identity()) + handle2 = hook_storage.register_pre_function_hook("foo", 0, nn.Identity()) + assert len(hook_storage.pre_hooks["foo__0"]) == 2 + + handle1.remove() + assert len(hook_storage.pre_hooks["foo__0"]) == 1 + + handle2.remove() + assert "foo__0" not in hook_storage.pre_hooks + + +class CheckPriority(nn.Module): + def __init__(self, storage: List, name: str): + super().__init__() + self.storage = storage + self.name = name + + def forward(self, x: torch.Tensor) -> torch.Tensor: + self.storage.append(self.name) + return x + + +def test_excitation_priority(): + hook_storage = HookStorage() + call_stack = [] + handles = [] + + for group_name in ["1", "2", "3"]: + hook = CheckPriority(call_stack, group_name) + h = hook_storage.register_pre_function_hook("foo", 0, hook) + handles.append(h) + + hook_storage.execute_pre_function_hooks("foo", 0, None) + assert call_stack == ["1", "2", "3"] + call_stack.clear() + + handles[1].remove() + hook_storage.execute_pre_function_hooks("foo", 0, None) + assert call_stack == ["1", "3"] + call_stack.clear() + + hook_storage.register_pre_function_hook("foo", 0, CheckPriority(call_stack, "4")) + hook_storage.execute_pre_function_hooks("foo", 0, None) + assert call_stack == ["1", "3", "4"] diff --git a/tests/torch2/function_hook/test_train.py b/tests/torch2/function_hook/test_train.py new file mode 100644 index 00000000000..35c015d16fa --- /dev/null +++ b/tests/torch2/function_hook/test_train.py @@ -0,0 +1,82 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +import torch.utils +from torch import nn +from torch.nn.parallel import DistributedDataParallel as DDP + +from tests.torch2.function_hook.helpers import get_wrapped_simple_model_with_hook + + +def run_one_epoch(model: nn.Module, optimizer: torch.optim.Optimizer, use_cuda: bool): + tensor = torch.ones((10, 1, 3, 3)) + if use_cuda: + tensor = tensor.cuda() + pred: torch.Tensor = model(tensor) + loss = torch.tensor(100) - pred.sum() + optimizer.zero_grad() + loss.backward() + optimizer.step() + + +def test_train_simple(use_cuda): + wrapped_model = get_wrapped_simple_model_with_hook() + if use_cuda: + wrapped_model = wrapped_model.cuda() + optimizer = torch.optim.Adam(wrapped_model.parameters(), lr=0.1) + run_one_epoch(wrapped_model, optimizer, use_cuda) + assert all(p.grad is not None for p in wrapped_model.parameters()) + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="requires 2xGPUs") +@pytest.mark.cuda +def test_train_data_parallel(): + wrapped_model = get_wrapped_simple_model_with_hook().cuda() + optimizer = torch.optim.Adam(wrapped_model.parameters(), lr=0.1) + parallel_model = torch.nn.DataParallel(wrapped_model) + parallel_model.to("cuda") + run_one_epoch(parallel_model, optimizer, use_cuda=True) + assert all(p.grad is not None for p in wrapped_model.parameters()) + + +def _train_ddp(rank, world_size): + dist.init_process_group("gloo", rank=rank, world_size=world_size) + + device = torch.device(f"cuda:{rank}") + model = get_wrapped_simple_model_with_hook().to(device) + model = DDP(model, device_ids=[rank]) + model.train() + optimizer = torch.optim.Adam(model.parameters(), lr=0.1) + + run_one_epoch(model, optimizer, use_cuda=True) + dist.destroy_process_group() + assert all(p.grad is not None for p in model.parameters()) + + +@pytest.fixture +def _ddp_env(): + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "12345" + yield + os.environ.pop("MASTER_ADDR") + os.environ.pop("MASTER_PORT") + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="requires 2xGPUs") +@pytest.mark.cuda +def test_train_distributed_data_parallel(_ddp_env): + world_size = torch.cuda.device_count() + mp.spawn(_train_ddp, args=(world_size,), nprocs=world_size, join=True) diff --git a/tests/torch2/function_hook/test_weak_map.py b/tests/torch2/function_hook/test_weak_map.py new file mode 100644 index 00000000000..4d9d8734729 --- /dev/null +++ b/tests/torch2/function_hook/test_weak_map.py @@ -0,0 +1,52 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc + +import torch + +from nncf.experimental.torch2.function_hook.weak_map import WeakUnhashableKeyMap + + +def test_set_get(): + map = WeakUnhashableKeyMap() + obj = torch.tensor([1]) + map[obj] = 1 + assert map[obj] == 1 + + +def test_on_del(): + map = WeakUnhashableKeyMap() + obj1 = torch.tensor([1]) + obj2 = torch.tensor([2]) + map[obj1] = 1 + map[obj2] = 2 + assert len(map._data) == 2 + + del obj1 + gc.collect() + assert len(map._data) == 1 + assert map[obj2] == 2 + + del obj2 + gc.collect() + assert len(map._data) == 0 + + +def test_set_same(): + map = WeakUnhashableKeyMap() + obj1 = torch.tensor([1]) + map[obj1] = 1 + map[obj1] = 2 + assert len(map._data) == 1 + del obj1 + gc.collect() + assert len(map._data) == 0 diff --git a/tests/torch2/function_hook/test_wrapper.py b/tests/torch2/function_hook/test_wrapper.py new file mode 100644 index 00000000000..7244c72812c --- /dev/null +++ b/tests/torch2/function_hook/test_wrapper.py @@ -0,0 +1,176 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from copy import deepcopy +from pathlib import Path + +import onnxruntime as ort +import pytest +import torch + +from nncf.experimental.torch2.function_hook.wrapper import is_wrapped +from nncf.experimental.torch2.function_hook.wrapper import register_post_function_hook +from nncf.experimental.torch2.function_hook.wrapper import register_pre_function_hook +from nncf.experimental.torch2.function_hook.wrapper import wrap_model +from tests.torch2.function_hook import helpers + +ADD_VALUE = 2.0 + + +def test_wrapper(): + example_input = helpers.ConvModel.get_example_inputs() + model = helpers.ConvModel() + model.eval() + ret = model(example_input) + wrapped = wrap_model(model) + wrapped_ret = wrapped(example_input) + torch.testing.assert_close(ret, wrapped_ret) + + +def test_export_strict_false(): + example_input = helpers.ConvModel.get_example_inputs() + + model = helpers.ConvModel() + return_origin = model(example_input) + + wrapped = wrap_model(model) + register_post_function_hook(wrapped, "/relu/0", 0, helpers.AddModule(ADD_VALUE)) + reference = wrapped(example_input) + + m_traced = torch.export.export(wrapped, args=(example_input,), strict=False) + actual = m_traced.module()(example_input) + + torch.testing.assert_close(actual, reference) + torch.testing.assert_close(actual, return_origin + ADD_VALUE) + + +def test_jit_trace(): + example_input = helpers.ConvModel.get_example_inputs() + + model = helpers.ConvModel() + return_origin = model(example_input) + + wrapped = wrap_model(model) + register_post_function_hook(wrapped, "/relu/0", 0, helpers.AddModule(ADD_VALUE)) + reference = wrapped(example_input) + + m_traced = torch.jit.trace(wrapped, example_inputs=(example_input,), strict=False) + actual = m_traced(example_input) + + torch.testing.assert_close(actual, reference) + torch.testing.assert_close(actual, return_origin + ADD_VALUE) + + +def test_compile_via_trace(): + example_input = helpers.ConvModel.get_example_inputs() + + model = helpers.ConvModel() + return_origin = model(example_input) + wrapped = wrap_model(model) + register_post_function_hook(wrapped, "/relu/0", 0, helpers.AddModule(ADD_VALUE)) + reference = wrapped(example_input) + m_traced = torch.jit.trace(wrapped, example_inputs=(example_input,), strict=False) + m_compiled = torch.compile(m_traced) + actual = m_compiled(example_input) + + torch.testing.assert_close(actual, reference) + torch.testing.assert_close(actual, return_origin + ADD_VALUE) + + +def test_export_onnx(tmp_path: Path): + example_input = helpers.ConvModel.get_example_inputs() + + model = helpers.ConvModel() + return_origin = model(example_input) + + wrapped = wrap_model(model) + register_post_function_hook(wrapped, "/relu/0", 0, helpers.AddModule(ADD_VALUE)) + reference = wrapped(example_input) + + onnx_file = tmp_path / "model.onnx" + torch.onnx.export(wrapped, (example_input,), onnx_file.as_posix()) + session = ort.InferenceSession(onnx_file) + + actual = session.run(None, {"input": example_input.numpy()})[0] + torch.testing.assert_close(torch.tensor(actual), reference) + torch.testing.assert_close(torch.tensor(actual), return_origin + ADD_VALUE) + + +def test_deepcopy(): + example_input = helpers.ConvModel.get_example_inputs() + model = helpers.get_wrapped_simple_model_with_hook() + ref = model(example_input) + copy = deepcopy(model) + del model + import gc + + gc.collect() + act = copy(example_input) + torch.testing.assert_close(act, ref) + + +def test_torch_save_load(tmp_path: Path): + example_input = helpers.ConvModel.get_example_inputs() + model = helpers.get_wrapped_simple_model_with_hook() + ref = model(example_input) + path = tmp_path / "model.pt" + torch.save(model, path) + del model + import gc + + gc.collect() + loaded = torch.load(path, weights_only=False) + act = loaded(example_input) + torch.testing.assert_close(act, ref) + + +@pytest.mark.parametrize( + "hook_type, target_name", + ( + ("post_hook", "x"), + ("pre_hook", "/relu/0"), + ("post_hook", "/relu/0"), + ("pre_hook", "output"), + ("post_hook", "conv:weight"), + ), +) +def test_insert_hook(hook_type, target_name): + example_input = helpers.ConvModel.get_example_inputs() + model = helpers.ConvModel() + wrapped = wrap_model(model) + assert is_wrapped(wrapped) + + hook = helpers.CallCount() + if hook_type == "pre_hook": + register_pre_function_hook(wrapped, target_name, 0, hook) + else: + register_post_function_hook(wrapped, target_name, 0, hook) + + wrapped(example_input) + assert hook.call_count == 1 + + +@pytest.mark.parametrize("hook_type", ["pre_hook", "post_hook"]) +def test_insert_nested_hook(hook_type: str): + example_input = helpers.ConvModel.get_example_inputs() + model = helpers.ConvModel() + wrapped = wrap_model(model) + + hook = helpers.CallCount() + if hook_type == "pre_hook": + register_pre_function_hook(wrapped, "/relu/0", 0, helpers.AddModule(2.0)) + register_pre_function_hook(wrapped, "pre_hook__-relu-0__0[0]/add/0", 0, hook) + else: + register_post_function_hook(wrapped, "/relu/0", 0, helpers.AddModule(2.0)) + register_post_function_hook(wrapped, "post_hook__-relu-0__0[0]/add/0", 0, hook) + wrapped(example_input) + + assert hook.call_count == 1 diff --git a/tests/torch2/pytest.ini b/tests/torch2/pytest.ini new file mode 100644 index 00000000000..b953dce4160 --- /dev/null +++ b/tests/torch2/pytest.ini @@ -0,0 +1,5 @@ +[pytest] +markers = + cuda +python_files = test_* +xfail_strict = true diff --git a/tests/torch2/requirements.txt b/tests/torch2/requirements.txt new file mode 100644 index 00000000000..a7ad0bba674 --- /dev/null +++ b/tests/torch2/requirements.txt @@ -0,0 +1,11 @@ +-c ../../constraints.txt +onnx +onnxruntime +openvino +pytest +pytest-cov +pytest-dependency +pytest-mock +pytest-xdist +torch +torchvision diff --git a/tests/torch2/utils.py b/tests/torch2/utils.py new file mode 100644 index 00000000000..9c4fdb47fa1 --- /dev/null +++ b/tests/torch2/utils.py @@ -0,0 +1,39 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from pathlib import Path + + +def compare_with_reference_file(text_data: str, ref_path: Path, regen_ref_data: bool): + """ + Compares the given text data with the contents of a reference file. + Optionally, the reference file can be regenerated with the provided text data. + + :param text_data: The actual data to compare against the reference file. + :param ref_path: The path to the reference file that contains the expected data. + :param regen_ref_data: If True, the reference file will be overwritten with the current text_data. + """ + if regen_ref_data: + ref_path.parent.mkdir(parents=True, exist_ok=True) + ref_path.write_text(text_data) + return + + assert ref_path.exists(), f"Reference file does not exist: {ref_path}" + ref_text_data = ref_path.read_text(encoding="utf-8") + + # Split lines for comparison without keeping newlines + act = text_data.splitlines(keepends=False) + ref = ref_text_data.splitlines(keepends=False) + + assert act == ref, ( + f"Data mismatch between actual data and reference file: {ref_path}\n" + f"Actual data and reference data differ. Please review the file contents." + ) diff --git a/tools/README.md b/tools/README.md index 440d6f21ebd..efa0a4aeb8a 100644 --- a/tools/README.md +++ b/tools/README.md @@ -108,3 +108,48 @@ def allocate_memory(): max_memory_usage: float = mmc.memory_data[MemoryType.SYSTEM] ``` + +## Visualization of Weight Compression results + +The [visualize_compression_results.py](visualize_compression_results.py) script is a useful tool for visualizing the results of weight compression. +The result of the script is a .md file with a table: + +| mode | %int4 | %int8 | lora
rank | average
relative
error | compression
rate | +|:------------------------------------------|:--------|:--------|:---------------|:-------------------------------|:----------------------| +| fp32 | 0% | 0% | | 0.0% | 1.0x | +| int8 | 0% | 100% | | 1.0% | 4.0x | +| int4 + scale estimation + lora correction | 100% | 0% | 256.0 | 3.9% | 6.0x | +| int4 + scale estimation | 40% | 60% | | 4.1% | 4.8x | +| int4 + scale estimation | 60% | 40% | | 4.3% | 5.4x | +| int4 + scale estimation + lora correction | 100% | 0% | 128.0 | 4.6% | 6.5x | +| int4 + scale estimation | 80% | 20% | | 5.7% | 6.1x | +| int4 + scale estimation + lora correction | 100% | 0% | 8.0 | 5.8% | 7.1x | +| int4 + scale estimation + gptq | 100% | 0% | | 6.1% | 7.1x | +| int4 + scale estimation | 100% | 0% | | 7.5% | 7.1x | +| int4 | 100% | 0% | | 11.9% | 7.1x | + +Also it plots a trade-off between accuracy and footprint by processing a CSV file in a specific format. +The resulting images are employed for [the relevant section](/docs/usage/post_training_compression/weights_compression/Usage.md#accuracyfootprint-trade-off) in the Weight Compression documentation: + +![alt text](/docs/usage/post_training_compression/weights_compression/phi3_asym.png) + +### CSV-file format + +The input file should contain the following columns: + +- `mode` - The string indicating the compression method used for the model. The 'fp32' mode corresponds to the uncompressed version. To calculate the accuracy-footprint trade-off, the following words must be present in at least one row: "gptq", "int4", "fp32", "int8". +- `%int4` - The ratio of int4 layers. +- `%int8` - The ratio of int8 layers. +- `lora rank` - The rank of the adapters used in Lora Correction algorithm. +- `plot name` - Short names for annotation in the plot. +- `model size, Gb` - The size of the corresponding model in Gb. +- `wikitext, word perplexity` - Word perplexity on the Wikitext dataset, measured using rolling loglikelihoods in the [lm_eval tool](https://github.com/EleutherAI/lm-evaluation-harness). +- `lambada-openai, acc` - Accuracy on the Lambada-OpenAI dataset, measured using [lm_eval tool](https://github.com/EleutherAI/lm-evaluation-harness). +- `lambada-openai, perplexity` - Perplexity on the Lambada-OpenAI dataset, measured using the [lm_eval tool](https://github.com/EleutherAI/lm-evaluation-harness). +- `WWB, similarity` - Similarity, measured using the [WWB tool](https://github.com/openvinotoolkit/openvino.genai/tree/master/llm_bench/python/). + +### Example of script usage + +```shell +python visualize_compression_results.py --input-file data/llama2_asym.csv --output-dir output_dir +``` diff --git a/tools/visualize_compression_results.py b/tools/visualize_compression_results.py new file mode 100644 index 00000000000..dea1af4a50a --- /dev/null +++ b/tools/visualize_compression_results.py @@ -0,0 +1,180 @@ +# Copyright (c) 2024 Intel Corporation +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys +from argparse import ArgumentParser +from pathlib import Path + +import matplotlib.pyplot as plt +import pandas as pd +from tabulate import tabulate + +WIKI_PPL = "wikitext, word perplexity" +LAMBADA_ACC = "lambada-openai, acc" +LAMBADA_PPL = "lambada-openai, perplexity" +WWB_SIM = "WWB, similarity" +MODEL_SIZE = "model size, Gb" +INT4_RATIO = "%int4" +INT8_RATIO = "%int8" +MODE = "mode" +LORA_RANK = "lora rank" +PLOT_NAME = "plot name" + + +EXPECTED_COLUMNS = [ + MODE, + INT4_RATIO, + INT8_RATIO, + LORA_RANK, + PLOT_NAME, + MODEL_SIZE, + WIKI_PPL, + LAMBADA_ACC, + LAMBADA_PPL, + WWB_SIM, +] + +GPTQ = "gptq" +INT4 = "int4" +FP32 = "fp32" +INT8 = "int8" + +EXPECTED_IN_MODE_COLUMN = [GPTQ, INT4, FP32] # , INT8] + + +COMPRESSION_RATE = "compression rate" +AVG_REL_ERROR = "average relative error" + + +def check_format(df): + missing_columns = [col for col in EXPECTED_COLUMNS if col not in df.columns] + if missing_columns: + raise RuntimeError(f"The following columns are missing: {missing_columns}") + + missing_in_mode = [item for item in EXPECTED_IN_MODE_COLUMN if not any(df[MODE].str.contains(item))] + if missing_in_mode: + raise RuntimeError( + f"The `{MODE}` column must have at least one entry that includes the following words: {missing_in_mode}" + ) + + +def add_relative_metrics(df): + df.sort_values(by=[MODEL_SIZE], ascending=False, inplace=True) + + df[AVG_REL_ERROR] = ( + (df[LAMBADA_ACC].iloc[0] - df[LAMBADA_ACC]) / df[LAMBADA_ACC].iloc[0] + + (df[LAMBADA_PPL] - df[LAMBADA_PPL].iloc[0]) / df[LAMBADA_PPL].iloc[0] + + (1 - df[WWB_SIM]) + + (df[WIKI_PPL] - df[WIKI_PPL].iloc[0]) / df[WIKI_PPL].iloc[0] + ) / 4 + df[COMPRESSION_RATE] = df[MODEL_SIZE].iloc[0] / df[MODEL_SIZE] + df.sort_values(by=[AVG_REL_ERROR], inplace=True) + return df + + +def to_markdown(df_, output_file): + md_columns = [ + MODE, + INT4_RATIO, + INT8_RATIO, + LORA_RANK, + AVG_REL_ERROR, + COMPRESSION_RATE, + ] + df = df_[md_columns].fillna("") + prct_fmt = lambda x: f"{x * 100:.0f}%" + err_fmt = lambda x: f"{x * 100:.1f}%" + rate_fmt = lambda x: f"{x:.1f}x" + df[INT4_RATIO] = df[INT4_RATIO].apply(prct_fmt) + df[INT8_RATIO] = df[INT8_RATIO].apply(prct_fmt) + df[COMPRESSION_RATE] = df[COMPRESSION_RATE].apply(rate_fmt) + df[AVG_REL_ERROR] = df[AVG_REL_ERROR].apply(err_fmt) + + def wrap_text(text): + return "
".join(text.split(" ")) + + wrapped_headers = [wrap_text(header) for header in df.columns] + + markdown_table = tabulate(df, headers=wrapped_headers, tablefmt="pipe", showindex=False) + with open(output_file, "w") as f: + f.write(markdown_table) + + +def to_plot(df, output_file): + df[AVG_REL_ERROR] = df[AVG_REL_ERROR] * 100 + lora = df[LORA_RANK].notnull() + data_free = df[MODE].isin([INT4]) + gptq = df[MODE].str.contains(GPTQ) + mixed = ~(gptq | lora | data_free | df[MODE].isin([FP32, INT8])) + + BASELINE_ALGO = df[mixed][MODE].iloc[0].split(f"{INT4} + ")[1] + MODEL_NAME = df[df.columns[0]].iloc[0] + + colors = ["r", "green", "gray", "b"] + labels = [ + f"100% int4, lora correction + {BASELINE_ALGO}", + f"100% int4, gptq + {BASELINE_ALGO}", + "100% int4, data-free", + f"mixed-precision, {BASELINE_ALGO}", + ] + locs = [(10, -10), (10, 2), (0, -10), (10, -10)] + dfs = list(map(lambda x: df[x], [lora, gptq, data_free, mixed])) + + for color, label, data in zip(colors, labels, dfs): + plt.plot(data[AVG_REL_ERROR], data[COMPRESSION_RATE], "o-", color=color, label=label) + + for data, loc in zip(dfs, locs): + for i, row in data.iterrows(): + plt.annotate( + row[PLOT_NAME], + (row[AVG_REL_ERROR], row[COMPRESSION_RATE]), + textcoords="offset points", + xytext=loc, + ha="center", + ) + + plt.xlabel("Relative error to fp32 model on 3 tasks, %") + plt.ylabel("Compression rate relative to fp32 model") + plt.title(f"Footprint/Accuracy tradeoff for {MODEL_NAME}") + plt.legend() + + print("Saving image to :", output_file) + plt.savefig(output_file) + plt.close() + + +def visualize(input_file: str, output_dir: str): + input_file = Path(input_file) + if not output_dir: + output_dir = input_file.parent + output_dir = Path(output_dir) + + df = pd.read_csv(input_file) + check_format(df) + df = add_relative_metrics(df) + + output_file = output_dir / (input_file.stem + ".md") + to_markdown(df, output_file) + + to_plot(df, output_file.with_suffix(".png")) + + +def main(argv): + parser = ArgumentParser() + parser.add_argument("-i", "--input_file", help="Input .csv file", default="phi3_asym.csv") + parser.add_argument("-o", "--output_dir", help="Directory for output files (.csv, .md and .png)", default="") + args = parser.parse_args(args=argv) + + visualize(args.input_file, args.output_dir) + + +if __name__ == "__main__": + main(sys.argv[1:])